Abstract
With the advent of Minimally Invasive Surgery (MIS), intra-operative imaging has become crucial for surgery and therapy guidance, allowing to partially compensate for the lack of information typical of MIS. This paper reviews the advancements in both classical (i.e. ultrasounds, X-ray, optical coherence tomography and magnetic resonance imaging) and more recent (i.e. multispectral, photoacoustic and Raman imaging) intra-operative imaging modalities. Each imaging modality was analyzed, focusing on benefits and disadvantages in terms of compatibility with the operating room, costs, acquisition time and image characteristics. Tables are included to summarize this information. New generation of hybrid surgical room and algorithms for real time/in room image processing were also investigated. Each imaging modality has its own (site- and procedure-specific) peculiarities in terms of spatial and temporal resolution, field of view and contrasted tissues. Besides the benefits that each technique offers for guidance, considerations about operators and patient risk, costs, and extra time required for surgical procedures have to be considered. The current trend is to equip surgical rooms with multimodal imaging systems, so as to integrate multiple information for real-time data extraction and computer-assisted processing. The future of surgery is to enhance surgeons eye to minimize intra- and after-surgery adverse events and provide surgeons with all possible support to objectify and optimize the care-delivery process.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Introduction
With the advent of Minimally Invasive Surgery (MIS), intraoperative imaging started to play a crucial role in different fields, such as neurosurgery,121 urology81 and nephrectomy,35 to access hidden targets, allow intraoperative optical biopsy, guide navigation and, in general, to guarantee minimal invasiveness and maximal safety. In the last decades, several advancements have been done in the field of intraoperative imaging, leading to real-time (or quasi-real time) systems with higher resolution, efficiency, lower costs and able to execute complex data analyses.116
Intra-Operative UltraSound (iOUS), X-ray, Optical Coherence Tomography (OCT), intra-operative Magnetic Resonance Imaging (iMRI), Nuclear Medicine (NM), endo/laparoscopy, PhotoAcoustic (PA), and Raman imaging are among the most rapidly evolving modalities, even if with different levels of diffusion in clinics. In Fig. 1, exemplary intra-operative images are shown. These imaging modalities are commonly exploited for different surgical tasks and in different surgical phases, according to their specifications. Tables 1 and 2 summarize this information, while Table 3 highlights the main clinical applications for each modality.
Considering how fast the field of intra-operative imaging is evolving, the motivations behind reviewing such a topic resides in the fact that, by analyzing the relevant state of art, we found that the majority of published reviews are either focused on technical aspects (e.g. AR,203 anatomy segmentation,126 deep-learning processing111) or limited to a specific imaging modality (e.g. OCT,31 Endo/laparoscopy,113 iMRI,14 Raman.160)
The closer work to ours is the one presented in Ref. 5, which, however, only surveys emerging imaging modalities (i.e. fluorescence, PA, Raman and nuclear imaging). As a result, considering such information, the importance of intraoperative imaging in the surgery of the future will come to light. The goal of the review is, instead, to provide a compact and updated source of information for young researchers who are approaching the wide field on intra-operative imaging, and a reference overview document for those already working in the field.
This review article discusses the basic principles and development directions of intra-operative imaging modalities and is not intended to be a comprehensive review of intra-operative imaging applications. Eight imaging modalities are surveyed: iOUS (section “Intra-operative Ultrasound (iOUS) Imaging”), X-ray (section “X-ray”), OCT (section “Optical Coherence Tomography (OCT)”), (section “Intra-operative Magnetic Resonance Imaging (iMRI)”), Endo/laparoscopy (section “Endo/laparoscopy”), PA imaging (section “Photoacoustic Imaging”), Nuclear medicine (section “Nuclear Medicine”), Raman spectroscopy (section “Raman Spectroscopy”). To conclude this review, an overview of integrated surgical rooms, as well as a survey of real-time/quasi real-time image processing techniques for intraoperative applications, is presented (section “Hybrid Surgical Rooms and Real-Time/Quasi Real-Time Image Processing”). This way, we aim at providing the reader with useful information about the forthcoming trend to install ad-hoc operating rooms (ORs).
To limit the overlap with previous survey papers, we selected the articles according to the following criteria:
-
Papers about clinical application, mainly reported in Tables 4 and 5, had to be published from 2010 onward; no restriction for papers introducing general concepts about imaging physical principles;
-
Papers not strictly discussing intra-operative applications (such as diagnosis and follow-up and clinical trials) were not considered.
The 40% of the cited articles discuss the technical aspects of the investigated modalities, the remaining 60% are about clinical applications.
Intra-operative Ultrasound (iOUS) Imaging
UltraSounds (US) are a succession of rarefactions and compressions transmitted due to elastic forces between adjacent particles. Most diagnostic US has frequencies in the range 2–20 MHz.2 The way elastic waves are reflected provides information about internal tissues.
US imaging techniques have been introduced as intra-operative imaging modalities (iOUS) thanks to their real-time acquisition, reduced OR encumbrance and limited costs, which allow full in-room compatibility.
Technological Advancements
Recent technological advancements of iOUS are related to:
-
Probe miniaturization, down to few mm in diameter, which allows the probe insertion in hollow cavities, such vessels in vascular or cardiac procedures and in the patient abdomen trough the trochar port during minimal invasive surgery. This led to Intra-Cardiac Echocardiography (ICE), TransEsophageal Echocardiography (TEE), TransRectal US (TRUS) and IntraVascular US (IVUS). On this regard, an interesting comparative study IVUS vs OCT has been recently published.151
-
Probe navigation and 3D probes realization, which allows the visualization of a volumetric dataset, rather than a planar slice.
-
Signal processing capabilities, which allow for real-time visualization of inner anatomical structures and surgical tools.
-
High focused US implementation, for precise targeting of therapy (see paragraph 5.2).
Volumetric US imaging is surely among the most impacting advancements of iOUS systems in the actual clinical practice. A review on real-time 3D US imaging technology has been recently published.86
Volumetric US imaging can be achieved using 3D probes17 and spatial localizing the probe with external measuring devices and properly calibrated.52 Alternatively, US volumetric probes can be rigidly attached to robot end-effectors and provide intra-operative guidance of surgical interventions.119
Deep learning has been recently employed to reconstruct the 3D volume without any external tracking device.149 3D volume reconstruction can be achieved with frame rates up to 120 frames/s.33
The technological pharmacological combination of capsule endoscopy with US-mediated Targeted Drug Delivery (UmTDD) carries new potential for treatment of diseases throughout the gastro-intestinal tract. Finally Contrast-Enhanced US (CEUS) are used during robotic-assisted kidney surgery6 to enhance the visualization of macro and microvasculature of the kidneys.
Limitations and Open Issues
IOUS based devices are portable and low-cost systems for obtaining intra-operative information. Some open issues are still limiting their adoption in some clinical procedures, such as:
-
Poor tissue contrast due to low Signal-to-Noise Ratio (SNR), despite the adoption of contrast media (e.g. such as microbubbles). This is particularly limiting neurosurgical navigation, since the planning phase is currently done on pre-operative CT or MRI sequences.
-
Limited spatial resolution and FoV (inverse relationship, both are function of the excitation frequency of the transducer).
X-ray
X-ray based imaging techniques take advantage of the capability of high-energy photons to penetrate the matter. Radiations are artificially generated by means of X-ray tubes and it is possible to adjust the beam energy depending on patient size and desired tissue contrast. Information about the internal anatomy of the subject are revealed by photon attenuation through the matter.179 Projective (2D) or tomographic (3D) images can be generated depending on the device configuration. Both type of images can be acquired over time, although only 2D digital radiography (i.e. fluoroscopy) offers true real time imaging. High contrast is obtained for bone and air; soft tissues can be enhanced by injecting a radiocontrast agent. The use of X-rays is limited by the maximum patient’s radiation exposure stated by radiation protection limits.
Technological Advancements
X-rays for intraoperative use were introduced in the ’50, when Philips developed the first flexible and portable device known as C-arm. Nowadays, digital flat panel detectors replaced traditional image intensifiers, since they offer higher transducer efficiency with lower dose, higher spatial and radiometric resolution (100–200 \(\mu\)m and 14–16 bits respectively), fast sample rate (25–40Hz), larger FoV, and lower image degradation over the period of use. C-arms are traditionally used for static and cine 2D acquisition. However, since the arm can revolve 360 °C around the patient, Cone Beam Computed Tomography (CBCT) can be acquired for 3D volume reconstruction. In addition, in room CT devices are also available:
-
1.
On rail intraoperative CT (iCT), which are normal diagnostic CT scanners that can be moved into the room through ceiling rails. In some cases, the scanner is fixed and the surgical couch can be moved inside the CT device;
-
2.
Small and portable CT scanners which can be moved in and out from the surgical room.
The last technological advancements are now directed towards the possibility to enhance soft tissue contrast without injecting contrast mean. This can be obtained by means of: (1) dual energy X-ray sources; (2) use of different ionizing radiations, such as proton or carbon ion beams, to obtain proton radiography and tomography.166, 175 Even if such cutting edge technologies are not currently used for intraoperative applications, it is very likely they will be the next frontier of this in room modality.
Technological advancements about X-rays for guidance are related also to radiotherapy applications where X-ray beams are used not only for treatment, but also for guidance. To this purpose, stereoscopic radiographs for 3D reconstruction and CBCT are widely employed to verify patient’s position and localize the tumor.23, 177 In some centers, on rail CT and iCT are also employed for performing optimal adaptive radiotherapy treatments with the same quality of planning CT.141 Linear accelerator have been integrated with CT scanner as for TomoTherapy® and robotic X-ray arms as for CyberKnife™ for high precision radiosurgery treatments.162 Another promising technique relies in exploiting Cherenkov emission during irradiation in order to visualize, in real time, surface dose on the patient skin. This method has been proved for breast radiotherapy93 and total skin electron therapy,10 demonstrating the improvement of the irradiation quality assessment.
Limitations and Open Issues
The principal limitation of intraoperative X-ray based imaging is the invasiveness of ionizing radiation for biological tissues. In the last years, particular attention has been payed to reduce X-ray dose delivered both to patient and staff.1, 64 Especially for pediatric patients, other imaging techniques are preferred, when possible, to minimize the radiation exposure. From the image quality point of view, an important issue is represented by the presence of metal inserts which generate artifacts, especially for high density material.178 Many efforts are also made to improve the quality of CBCT reconstruction. In fact, due to the conic aperture of the beam, photon scatter represents a serious issue for image degradation. Many scatter correction algorithms have been proposed in literature.135, 222 However, standard practical solutions still remain inadequate.
Optical Coherence Tomography (OCT)
Optical coherence tomography85 is an imaging technique able to provide 1D (also named A-scan), 2D (B-scan) and 3D representations of biological tissue. It takes advantage of the optical reflection of light to obtain spatial information of the sample structure. By exploiting this physical propriety, it is possible to acquire high resolution images (axial resolution in the range of \(\mu\)m) without any tissue damage nor ionizing radiation dose delivered. Tissue details are revealed by time of flight of transmitted/reflected light signal, that is related to sample structure and composition. Ultrashort laser pulse, as well as low-coherence light, can be used as energy source. 2D images over time can be acquired and directly shown, meanwhile 4D representation (volumes over time) has been recently introduced.
Real-time OCT imaging has been made possible by Graphics Processing Units (GPU) computational power31 and spectral-domain paradigm.
OCT has full in-room compatibility, since no risks exist for patients and operators. Anyway, as discussed in Ref. 57, metallic surgical tools can affect OCT image quality (e.g. introducing shadow). For this reason, in order to allow a real-time intraoperative OCT, instead of a “stop and scan“ approach, instruments made of alternative materials (such as plastics and silicone) can be used.
Technological Advancements
Since the presentation of this new modality, reducing acquisition time and improving image quality were the most important challenges to deal with. The introduction of spectral-domain OCT as an alternative to time-domain OCT, allowed to reduce scanning time, making easier the investigation of bigger volume sample210, 218 and facilitating a real intraoperative usage. In addition to the spectral-domain strategy, another important improvement was the possibility to join the probe with microscopes, surgical instruments (such as needles), and laser modules.31
Limitations and Open Issues
Currently, main OCT limitations are due to the narrow FoV (including reduced depth of penetration) achievable by means of this modality. However, in Ref. 174, a possible methodology to overcome this limitation has been successfully tested, enabling an acquisition of a FoV up to \(20\times 20\) cm. If compared with IVUS, OCT has a smaller depth of penetration, that in turn affects the FoV.
Intra-operative Magnetic Resonance Imaging (iMRI)
MRI is based on the interaction of H+ proton spins immersed in a magnetic field and stimulated by Radio Frequency waves (RF pulse).
Tissues containing mobile protons, such soft tissues, present very high contrast in MRI. The contrast can be even modified in a process called pulse sequence, where a certain number of RF pulses and magnetic field gradients is set and combined to obtain an image with anatomical or functional appearance, such as for Perfusion MRI (Pe-MRI), MR Angiography (MRA), MR Venography (MRV), Diffusion Weighted Imaging (DWI), functional MRI (fMRI).80
Due to the high combination of parameter setting, MRI is a very versatile technique. It provides high image quality in terms of spatial and contrast resolution, it combines morphological and physiological information, it features multiplanar 2D acquisition in any direction and orientation, as well as 3D isotropic voxel acquisition. Moreover, it does not involve ionizing radiations, thus being less invasive than X-ray based imaging. On the other end, MRI is prone to several artifacts, most important being motion and magnetic field distortion and it can be dangerous for the patient in presence of metal implants and active implantable medical devices.
Technological Advancements
From the equipment perspective, the current possible configurations for iMRI can be grouped into 2 classes,83 whose main characteristics are reported in Table 6:
-
Low field scanners: with a static magnetic field \(\le\)1T, they are small and portable devices90 with a gap to allow access to the patient during the surgical procedures.
-
High field scanners: with a static magnetic field \(\ge\) 1.5T (closed bore). They are introduced to the OR by means of ceiling rails (or the patient is moved inside the scanner by means of a movable operative table.94) The main advantage is the higher image quality and the possibility to acquire non-anatomical images (DWI, Pe-MRI, MRA and fMRI).
Specific pulse sequences allowing rapid imaging have been developed for real-time or quasi real-time imaging (10–20 frames/s4,36).
Technological advancements led iMRI to be used as guide during radiotherapy and US based treatments. Image guidance in radiotherapy plays a crucial role for correct patient positioning, organ and tumor motion assessment during radiation delivery. By now, the scene has been dominated by US, optical tracking systems and X-ray based techniques, both for photon91 and proton based treatments.167 However, very recently, LINear ACcelerators (LINAC) have been integrated with MRI, giving birth to the first LINAC-MRI systems.
The great advantage provided by MRI guidance is the possibility to clearly contrast the cancerous tissue without use of any implanted or external surrogate point. Due to the promising results, the current trend in radio/particle therapy is to move toward MRI based treatments.109,147
US energy, finally, can be used to heat, store the heat and then release the heat over time into the tissue to be treated.159 The focal point can be localized using pre-operative MRI (MR-guided focused US or MRgFUS). Intra-operatively,76 introduced focal spot localization using Harmonic Motion Imaging (HMI). The motion of the organs can be compensated using robotic end-effectors.55
Limitations and Open Issues
iMRI systems, especially in the high field configuration, are still very expensive and require a re-arrangement or a complete new installation of the OR and the use of specific MRI-safe devices.
In many cases, the time required for operations increases compared to the standard navigation and the involved personnel need a specific training to work in presence of magnetic field. These issues limit the spread of iMRI to specialized clinical institutions or very big hospitals.
It is possible to foresee in the future a higher presence of iMRI in the OR, especially with the new trend of multi-modal operative rooms.
Endo/Laparoscopy
With the spread of MIS procedures, endo/laparoscopic imaging has become one of the most popular intraoperative imaging modality. Laparoscopic imaging is an optical, non-invasive and non-ionizing technology that provides surgeons with 2D images, with three (e.g., in case of RGB) or more channels, of the surgical scene. With respect to other imaging modalities (such as MRI and X-ray), endo/laparoscopic imaging is also fully compatible with standard OR instrumentation.182
Besides standard RGB imaging, powerful solutions include barrow band imaging, which is an optical technique where a filtered light enhances the visualization of epithelial and subepithelial microvascular patterns.125 This technique exploits the physical property that the depth of penetration of light is dependent on its wavelength. Narrow-Band Imaging (NBI) filters select the blue and green light with wavelengths of 415 and 540 nm, respectively, that correspond to the peaks of absorption of hemoglobin. These filtered wavelengths penetrate, respectively, the epithelium, thus highlighting the capillary network and the deeper levels, enhancing the subepithelial vessels.
Technological Advancements
Within this context, Multi-HyperSpectral Imaging (MHSI) has drawn the attention of the medical-imaging community, even if its use inside the OR is still limited.41 MHSI enables to capture both spatial and spectral information on structures. MHSI provides images that generally have dozens (multispectral) or hundred (hyperspectral) of channels, each corresponding to the reflection of light within a certain wavelength band.107 Multispectral bands are usually optimized to encode the informative content which is relevant for a specific application.212 Similarly to NBI systems, the measured reflectance spectrum is influenced by the optical properties of tissues, including the concentration of absorbers, such as hemoglobin, and scatterers, such as cells or structural connective tissues. However, MHSI allows higher resolution than NBI and often guarantees more accurate tissue analysis.107, 129
As a natural evolution of MHSI, Multi-HyperSpectral Fluorescence Imaging (MHSFI) is also becoming more and more spread.100,117,211 By combining MHSI and fluorescence molecular techniques (mostly based on fluorescein/fluorescein isothiocyanate or indocyanine green molecules), MHSFI is particularly suitable when dealing with tissues with multiple fluorescent labels that, however, have similar color and texture appearance (according to the human eye) and are localized in spatially overlapping areas.
Recently, fluorescence spectroscopy provided by 5-aminolevulinic acid (5-ALA) is showing promising results in assisting neurosurgeons during tumor resection. On this regard, studies were conducted to compare 5-ALA and iMRI, and the impact of a combined usage of these techniques.44,70
Large interest is today given to the development of near-infrared fluorescent probes for tumor margin assessment intraoperatively.122,191,193,204 Fluorescent probes may allow to detect lesions at an early stage, where conventional imaging may fail, lowering patients morbidity and mortality.
Label-free fluorescence lifetime imaging (FLIm) is a novel surgical-guidance technique, which relies only on tissue autofluorescence, without requiring exogenous contrast agents. By exploiting time-resolved measurements, FLIm overcomes the limitations of steady-state fluorescence, where non-uniform tissue illumination, and variable presence of endogenous absorbers may interfere with the fluorescence signal of interests. Preliminary results are already available for applications in surgery.7,72,196,209
Limitations and Open Issues
With advances in high-energy pulsed lasers, hardware cameras, image analysis methods, and computational power, many exciting applications in the medical field have been proposed in the endo/laparo-scopic fields.
MHSI and MHSFI offer a straightforward measurement of tissue characteristics (e.g. texture and perfusion), as long as the visualized tissue is close to the surface. This actually limits the use of MHSI/MHSFI when deeper tissues need to be investigated.
When dealing with steady-state fluorescence imaging (i.e., FLIm is excluded here), a further potential issue is represented by tissue autofluorescence, which is present in many living, non-cancerous cells. The autofluorescence causes non-specific background fluorescence, which may interact with the true cancer-specific fluorescent signal, and limit the imaging quality. With FLIm, this issue is not present. Open issues here deal with tissue motion and acquisition setup preparation, which may still require heavy time-consuming manual correction.
Selecting the most MHSI and MHSFI informative spectral bands and the most discriminative fluorescence molecules is crucial to allow the best visualization of structure of interests.100,212 MHSI/MHSFI systems could cover ultraviolet (200 to 400 nm), visible (400 to 780 nm), near (780 to 2500 nm) and mid infrared (2500 to 25000), depending on applications. However, visible and near infrared are the most widely used spectral ranges.113
A further issue is related to real-time data acquisition. Depending on hardware set-up and number of recorded spectral channels, acquisition time can range from a few seconds to several minutes. This could lead to misalignment in the multispectral stacks, resulting in noisy and blurred multispectral images, where the same pixel measured at different band could correspond to different tissues. Lens distortion and noisy image borders should also be considered when visualizing and processing multispectral data. Considering the high number of image channels, computational-time issues arise also when processing MHSI/MHSFI data, e.g. for segmentation purposes.
The translation of MHSI/MHSFI into the actual clinical practice is still limited by costs, even if now cheaper and cheaper sensors are becoming available. Moreover, the general lack of surgical guidelines and training could explain the slow introduction of MHSI/MHSFI in the OR.
Photoacoustic Imaging
PA imaging is emerging as a new biomedical imaging modality based on the photoacoustic effect. In photoacoustic imaging, non-ionizing laser pulses are delivered into biological tissues (when radio frequency pulses are used, the technology is referred to as thermoacoustic imaging). Some of the delivered energy will be absorbed and converted into heat, leading to transient thermoelastic expansion and thus wideband (i.e. MHz) ultrasonic emission. The generated ultrasonic waves are detected by ultrasonic transducers and then analyzed to produce images.
Technological Advancements
Thus, PA is naturally a 3D imaging modality. To lower costs and acquisition time associated to volumetric US detectors, other strategies can be used, such as using 2D US detectors focused on a plane or spherically-focused US detectors for sampling one point in the FoV at a time.185
Technological advancements in parallel detection and fast tuning of optical parametric oscillators allowed real-time multispectral PA, pushing its use in the clinical practice.99,184,216
Limitation and Open Issues
PA imaging is evolving fast but, although many exciting applications have been proposed in the medical field, large clinical trials are still lacking. One relevant issue is the PA signal attenuation, which prevents using this technology for imaging small and deep tissues. Hard tissue imaging (e.g. human brain imaging) is also prevented due to aberration processes of US wave-fronts.
Nuclear Medicine
Nuclear medicine based imaging provides information about the metabolism and functionality of tissues and organs, rather than anatomical details. It exploits the possibility to mark with a radioactive substances a given molecule involved into a physiological/pathological process. The obtained compound (also named radiopharmaceutical) is administered to the patient and then, by directly tracking the signal emitted by the radioactive element, functional details of the tissues can be revealed (both in 2D and in 3D).
As well as other imaging modalities, also nuclear medicine has been used to provide intraoperative information to the surgeon.75 In such a scenario, however, in room devices could significantly differ from diagnostic scanners. In fact, for intraoperative applications, 2D images are usually obtained by a hand-held probe. In addition, by combining localization system and 2D hand probe, it is possible to extract intraoperative volumetric representation of the radiopharmaceutical distribution.138
Technological Advancements
Over the years, the main technological advancements in this field were about the detector (commonly called “gamma camera”). Similarly to the diagnostic scanners, also the hand probe devices relies on detectors that can be classified as belonging to two different classes: scintillators (such as NaI(Tl) and bismuth germinate (BGO)) and semiconductors (Cadmium-Zinc-Telluride (CZT)). Both solutions offer advantages and disadvantages and both have been commercially used.82
Limitations and Open Issues
Although nuclear medicine probes can provide intra-operative information about tissues metabolism and lead to more accurate surgery procedure, some drawbacks still remains. Such limitations are mainly related to the physical working principle behind this modality. In particular, the main disadvantages are:
-
Patient and operators are exposed to ionizing radiation.
-
The system generates images with limited spatial and temporal resolution, low SNR and small FoV.
Raman Spectroscopy
Raman spectroscopy has emerged as a potential tool for detecting biochemical differences between cancerous and healthy tissue, improving the accuracy of tumor surgery since it is fast, non-destructive and non-invasive.77,160
In this modality, a laser light interacts with tissue sample and, due to the Raman effect, a portion of this light undergoes to an energy shift. The amount of the energy shift is informative about molecular composition of the tissue, resulting into a full characterization of the sample.
Raman spectroscopy does not require any special tissue preparation and staining or labelling, thus being cheap and fast. Moreover, the biochemical interpretation of the biological samples assists in the objective and quantitative evaluation about the tissue, overcoming the issues of the more subjective histopathological diagnosis performed by a single or panel of pathologists.
Technological Advancements
Recent advancements in the field include Surface-Enhanced Raman Spectroscopy (SERS)39 and Raman-Encoded Molecular Imaging (REMI)206 that, by exploiting nanoparticles delivered to the sample, allow both to amplify Raman signal (by a factor of ~ 10 orders of magnitude), and to speed up the acquisition process. On this regard, the design of ad-hoc nanoparticles, able to provide improved signal intensity, can further help Raman spetroscopy to better detect different types of tumor.79 Finally, an unique triple-modality MRI/PA/Raman has been developed and tested.98
Limitation and Open Issues
The main limitation and open issue of intraoperative Raman spetroscopy is about the safety in evaluating not excised patient’s tissue. In fact, since both SERS and REMI require nanoparticles tags directly applied on the tissue to analyze, the toxicity of this procedure should be carefully investigated.
Hybrid Surgical Rooms and Real-Time/Quasi Real-Time Image Processing
With the increasing need of image guidance in surgery and therapy, most of the modern surgical rooms are equipped with multimodal imaging systems. These are referred as hybrid surgical (or operating) rooms (or theatres). The most advanced present a multi-room layout to allow the presence of high field iMRI and CT or Positron Emission Tomography (PET)/CT scanner. Hybrid surgical rooms offer the advantage of performing different procedures in the same place. This is also a safety benefit from the patient side: if something goes wrong during a planned intervention the lay-out can easily converted to a more complicated surgical procedure. From the surgeon and medical staff side, these rooms offer the state of the art advancements in terms of imaging integration, real time data extraction and, in some case, voice and hand gesture control.
A representative example is the Advanced Multimodal Image-Guided Operating (AMIGO) suite (see Fig. 2), at Brigham Women Hospital in Boston (USA), which was launched in 2011. AMIGO consists of three adjacent rooms. The central room is the OR and it is equipped with MRI-compatible anesthesia delivery and monitoring systems; a surgical microscope with near-infrared capability; surgical navigation systems that track handheld tools; a ceiling-mounted C-Arm X-Ray system and 3D ultrasound devices. The side rooms include a high field (3T) iMRI scanner and a PET/CT scanner respectively. The iMRI can be moved into the OR by ceiling rails. The PET/CT is fixed and the patient is transferred from the OR through a shuttle system. Since its launch, more than 2000 (by January 2019) MIS procedures have been performed in AMIGO, mostly being neurosurgeries, ablations and biopsies.186
The trend has pushed companies like Siemens Health CareFootnote 1 (Erlangen, Germany) and IMRISFootnote 2 (Winnipeg, Canada) to invest on hybrid surgical rooms for different applications. Besides the advantages that a hybrid OR offers, its cost is still very high, ranging from 1 million to 4 million dollars, and it often requires re-restructuring the existing space. Moreover, with the fast technological advancement, these suites have to flexible to rapid changes and renovations. So, we can say that the future of OR is going to be hybrid, but still some year is required to have them as clinical practice.
On the other hand, taking advantage of image-processing algorithms, intraoperative images can be enriched by (i) computing and showing supplementary information extracted from the image itself (ii) merging different and complementary acquisitions of the same anatomical district.
A straightforward solution to achieve these goals is using augmented and virtual reality.21
However, the low image quality of some intraoperative images and the real-time or quasi real-time processing to be guaranteed pose technological challenges. Intraoperative processing algorithms can be grouped as:
-
Structure segmentation, identification and tracking:
Anatomical structures, as well as surgical tools, can be automatically identified (segmented) or tracked over time to provide surgeons with decision support and context awareness.
Exemplary applications include vertebrae156 segmentation on fluoroscopy images, tissues and surgical tools tracking132 in 3D US, vessel segmentation,123 organ segmentation and tumor margin assessment in laparoscopic imaging,104,124,128 surgical tool detection in video laparoscopy,45 cancerous tissue219 and organs at risk,29,152,183 panorama stitching to enlarge the field of view,127 surface reconstruction in plastic surgery,142 identification in planning radiotherapy CT, brachitherapy220 and biopsy120 needles segmentation in iMRI, and pyramidal tract reconstruction.24
-
Physiological parameter estimation: medical images have been used also to esteem some physical and physiological parameters not directly measurable. Examples include iOUS-based flow estimation78 and assessment of right ventricular function,192 oxygenation level assessment on MHSI.213
-
Workflow analysis: automatic methodologies, strongly relying on OR video images and able to recognize and to analyze each phase of the operation, could promptly and automatically detect possible incidents and/or document the whole procedure.140,181
Finally, in the last years, algorithms for converting one image modality to another one have been developed, in particular for radiotherapy application (e.g. MRI to CT, and CBCT to CT).146,176,190,195
Discussion and Conclusion
Nowadays, several image modalities are available, each of which offers different characteristics (resolution, invasiveness, surgical compatibility, cost) and different contrast among tissues. The best modality to use for the specific use case is decided by the surgeon by considering and evaluating all the specific peculiarities of each of them. Depending on the chosen modality, adopting some preventive measure to guarantee the safety of both operators and patient could be necessary. This has also to be considered in robotic-assisted surgery scenarios. An increasing number of clinics have started to increment the type of imaging devices usable by physicians into the OR, especially in large hospital centers. Meanwhile, the last frontier of science in the field is represented by real-time processing of the acquired images to provide the surgeon with additional information. However, the majority of the developed technology is still for research purpose only, without any Food and Drug Administration and/or European Conformity approval.
The aim of this review was to provide the reader with an updated overview about currently available imaging modalities for intraoperative guidance (iOUS, X-ray, OCT, iMRI, video-endoscopy, NM, PA, and Raman spectroscopy). For each modality, physical working principle, technological advancements, and relevant pros and cons were reported and discussed, highlighting sample applications in several surgical scenarios. In view of such information, supported also by a survey about pioneering hybrid surgical rooms and real time image processing algorithms, the importance of image guided surgery for achieving better therapy come to light.
To conclude, we drew a path for helping students, scientist and health care worker, to guess, design and choose the surgical room of the future.
References
Abul-Kasim, K., M. Söderberg, E. Selariu, M. Gunnarsson, M. Kherad, and A. Ohlin. Optimization of radiation exposure and image quality of the cone-beam o-arm intraoperative imaging system in spinal surgery. Clin. Spine Surg. 25(1):52–58, 2012.
Abu-Zidan, F. M., A. F. Hefny, and P. Corr. Clinical ultrasound physics. J. Emerg. Trauma Shock 4(4):501, 2011.
Ahmadi, S.-A., F. Milletari, N. Navab, M. Schuberth, A. Plate, and K. Bötzel. 3D transcranial ultrasound as a novel intra-operative imaging technique for DBS surgery: a feasibility study. Int. J. Comput. Assist. Radiol. Surg. 10:891–900, 2015.
Ahrar, K., S. H. Sabir, S. M. Yevich, R. A. Sheth, J. U. Ahrar, A. L. Tam, and J. R. Stafford. MRI-guided interventions in musculoskeletal system. Top. Magn. Reson. Imaging 27(3):129–139, 2018.
Alam, I. S., I. Steinberg, O. Vermesh, N. S. van den Berg, E. L. Rosenthal, G. M. van Dam, V. Ntziachristos, S. S. Gambhir, S. Hernot, and S. Rogalla. Emerging intraoperative imaging modalities to improve surgical precision. Mol. Imaging Biol. 20:705–715, 2018.
Alenezi, A. N. and O. Karim. Role of intra-operative contrast-enhanced ultrasound (CEUS) in robotic-assisted nephron-sparing surgery. J. Robot. Surg. 9(1):1–10, 2015.
Alfonso-Garcia, A., J. Bec, S. Sridharan Weaver, B. Hartl, J. Unger, M. Bobinski, M. Lechpammer, F. Girgis, J. Boggan, and L. Marcu. Real-time augmented reality for delineation of surgical margins during neurosurgery using autofluorescence lifetime contrast. J. Biophotonics 13(1):e201900108, 2020.
Alfonso, F., M. Paulo, N. Gonzalo, J. Dutary, P. Jimenez-Quevedo, V. Lennie, J. Escaned, C. Bañuelos, R. Hernandez, and C. Macaya. Diagnosis of spontaneous coronary artery dissection by optical coherence tomography. J. Am. Coll. Cardiol. 59(12):1073–1079, 2012.
Allard, M., J. Shubert, and M. A. L. Bell. Feasibility of photoacoustic-guided teleoperated hysterectomies. J. Med. Imaging 5(2):021213, 2018.
Andreozzi, J. M., R. Zhang, D. J. Gladstone, B. B. Williams, A. K. Glaser, B. W. Pogue, and L. A. Jarvis. Cherenkov imaging method for rapid optimization of clinical treatment geometry in total skin electron beam therapy. Med. Phys. 43(2):993–1002, 2016.
Antico, M., F. Sasazawa, L. Wu, A. Jaiprakash, J. Roberts, R. Crawford, A. K. Pandey, and D. Fontanarosa. Ultrasound guidance in minimally invasive robotic procedures. Med. Image Anal. 54:149, 2019.
Ashour, R., S. Reintjes, M. S. Park, S. Sivakanthan, H. van Loveren, and S. Agazzi. Intraoperative magnetic resonance imaging in skull base surgery: a review of 71 consecutive cases. World Neurosurg. 93:183–190, 2016.
Ayala, L., S. Wirkert, M. Herrera, A. Hernández-Aguilera, A. Vermuri, E. Santos, and L. Maier-Hein. Multispectral imaging enables visualization of spreading depolarizations in gyrencephalic brain. In: Bildverarbeitung für die Medizin 2019 edited by H. Handels, T. M. Deserno, A. Maier, K. H. Maier-Hein, C. Palm. Cham: Springer, 2019, pp. 244–244.
Barkhausen, J., T. Kahn, G. A. Krombach, C. K. Kuhl, J. Lotz, D. Maintz, J. Ricke, S. O. Schoenberg, T. J. Vogl, and F. K. Wacker. White paper: Interventional MRI: Current status and potential for development considering economic perspectives, part 1: General application. Natl. Libraray Med. 189:611–623, 2017
Barsa, P., R. Frőhlich, V. Beneš, and P. Suchomel. Intraoperative portable CT-scanner based spinal navigation-a feasibility and safety study. Acta Neurochir. 156(9):1807–1812, 2014.
Barsa, P., R. Frőhlich, M. Šercl, P. Buchvald, and P. Suchomel. The intraoperative portable ct scanner-based spinal navigation: a viable option for instrumentation in the region of cervico-thoracic junction. Eur. Spine J. 25(6):1643–1650, 2016.
Becker, D., T. Wray, and J. Hart. Ultrasonic intracavity probe for 3D imaging. US Patent 9,808,221, 2017.
Bell, R. B. Computer planning and intraoperative navigation in orthognathic surgery. J. Oral Maxillofac. Surg. 69(3):592–605, 2011.
Bell, M. A. L., X. Guo, D. Y. Song, and E. M. Boctor. Transurethral light delivery for prostate photoacoustic imaging. J. Biomed. Opt. 20(3):036002, 2015.
Bell, M. A. L., A. K. Ostrowski, K. Li, P. Kazanzides, and E. M. Boctor. Localization of transcranial targets for photoacoustic-guided endonasal surgeries. Photoacoustics 3(2):78–87, 2015.
Bernhardt, S., S. A. Nicolau, L. Soler, and C. Doignon. The status of augmented reality in laparoscopic surgery as of 2016. Med. Image Anal. 37:66–90, 2017.
Bluemel, C., K. Herrmann, A. Kübler, A. K. Buck, E. Geissinger, V. Wild, S. Hartmann, C. Lapa, C. Linz, and U. Müller-Richter. Intraoperative 3-d imaging improves sentinel lymph node biopsy in oral cancer. Eur. J. Nucl. Med. Mol. Imaging 41(12):2257–2264, 2014.
Boda-Heggemann, J., J. Fleckenstein, F. Lohr, H. Wertz, M. Nachit, M. Blessing, D. Stsepankou, I. Lob, B. Kupper, A. Kavanagh, V. N. Hansen, M. Brada, F. Wenz, and H. McNair. Multiple breath-hold CBCT for online image guided radiotherapy of lung tumors: simulation with a dynamic phantom and first patient data. Radiother. Oncol. 98(3):309–316, 2011.
Bozzao, A., A. Romano, A. Angelini, G. D’Andrea, L. F. Calabria, V. Coppola, L. Mastronardi, L. M. Fantozzi, and L. Ferrante. Identification of the pyramidal tract by neuronavigation based on intraoperative magnetic resonance tractography: correlation with subcortical stimulation. Eur. Radiol. 20(10):2475–2481, 2010.
Brattain, L. J., P. M. Loschak, C. M. Tschabrunn, E. Anter, and R. D. Howe. Instrument tracking and visualization for ultrasound catheter guided procedures,” in: Workshop on Augmented Environments for Computer-Assisted Interventions:41–50, Springer, Cham (2014)
Buchfelder, M. and S.-M. Schlaffer. Intraoperative magnetic resonance imaging for pituitary adenomas,” in Buchfelder, M., Guaraldi, F. (eds) Imaging in Endocrine Disorders 45:121–132, Karger Publishers, Basel (2016).
Burchiel, K. J., S. McCartney, A. Lee, and A. M. Raslan. Accuracy of deep brain stimulation electrode placement using intraoperative computed tomography without microelectrode recording. J. Neurosurg. 119(2):301–306, 2013.
Bus, M. T., B. G. Muller, D. M. de Bruin, D. J. Faber, G. M. Kamphuis, T. G. van Leeuwen, T. M. de Reijke, and J. J. de la Rosette. Volumetric in vivo visualization of upper urinary tract tumors using optical coherence tomography: a pilot study. J. Urol. 190(6):2236–2242, 2013.
Cardenas, C. E., J. Yang, B. M. Anderson, L. E. Court, and K. B. Brock. Advances in auto-segmentation,” in Semin. Radiat. Oncol., 29, 185–197, 2019.
Carrasco-Zevallos, O., B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. Kuo, C. A. Toth, and J. A. Izatt. Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography. Sci. Rep. 6:31689, 2016.
Carrasco-Zevallos, O. M., C. Viehland, B. Keller, M. Draelos, A. N. Kuo, C. A. Toth, and J. A. Izatt. Review of intraoperative optical coherence tomography: technology and applications. Biomed. Opt. Express 8(3):1607–1637, 2017.
Chakraborty, S., S. Zavarella, S. Salas, and M. Schulder. Intraoperative mri for resection of intracranial meningiomas.” J. Exp. Therap. Oncol., 12(2):157162, 2017.
Chen, Z. and Q. Huang. Real-time freehand 3d ultrasound imaging. Comput. Methods Biomech. Biomed. Eng. 6(1):74–83, 2018.
Chevrier, M.-C., J. David, M. El Khoury, L. Lalonde, M. Labelle, and I. Trop. Breast biopsies under magnetic resonance imaging guidance: challenges of an essential but imperfect technique. Curr. Probl. Diagn. Radiol. 45(3):193–204, 2016.
Chopra, S., A. M. Bove, and I. S. Gill. Robotic partial nephrectomy: Advanced techniques and use of intraoperative imaging,” in Su LM (ed) Atlas of Robotic Urologic Surgery:93–101, Springer, Cham (2017)
Chopra, S., J. Rump, S. Schmidt, F. Streitparth, C. Seebauer, G. Schumacher, I. Van der Voort, and U. Teichgräber. Imaging sequences for intraoperative MR-guided laparoscopic liver resection in 1.0-T high field open MRI. Eur. Radiol. 19(9):2191–2196, 2009.
Choudhri, A. F., A. Siddiqui, P. Klimo, and F. A. Boop. Intraoperative mri in pediatric brain tumors. Pediatr. Radiol. 45:397–405, 2015.
Chu, C. R., A. Williams, D. Tolliver, C. K. Kwoh, S. Bruno III, and J. J. Irrgang. Clinical optical coherence tomography of early articular cartilage degeneration in patients with degenerative meniscal tears. Arthritis Rheumatism 62(5):1412–1420, 2010.
Cialla-May, D., X.-S. Zheng, K. Weber, and J. Popp. Recent progress in surface-enhanced Raman spectroscopy for biological and biomedical applications: from cells to clinics. Chem. Soc. Rev. 46(13):3945–3961, 2017.
Clancy, N. T., S. Arya, D. Stoyanov, M. Singh, G. B. Hanna, and D. S. Elson. Intraoperative measurement of bowel oxygen saturation using a multispectral imaging laparoscope. Biomed. Opt. Express 6(10):4179–4190, 2015.
Clancy, N. T., G. Jones, L. Maier-Hein, D. S. Elson, and D. Stoyanov. Surgical spectral imaging. Med. Image Anal. 63:101699, 2020.
Clancy, N. T., S. Saso, D. Stoyanov, V. Sauvage, D. J. Corless, M. Boyd, D. E. Noakes, M.-Y. Thum, S. Ghaem-Maghami, J. R. Smith, and D. S. Elson. Multispectral imaging of organ viability during uterine transplantation surgery in rabbits and sheep. J. Biomed. Opt. 21(10):106006, 2016.
Coburger, J., A. Merkel, M. Scherer, F. Schwartz, F. Gessler, C. Roder, A. Pala, R. König, L. Bullinger, G. Nagel, C. Jungk, S. Bisdas, A. Nabavi, O. Ganslandt, V. Seifert, M. Tatagiba, C. Senft, M. Mehdorn, A. W. Unterberg, K. Rossler, and C. Rainer Wirtz. Low-grade glioma surgery in intraoperative magnetic resonance imaging: results of a multicenter retrospective assessment of the german study group for intraoperative magnetic resonance imaging. Neurosurgery 78(6):775–786, 2015.
Coburger, J. and C. R. Wirtz. Fluorescence guided surgery by 5-ala and intraoperative mri in high grade glioma: a systematic review. J. Neurooncol. 141(3):533–546, 2019.
Colleoni, E., S. Moccia, X. Du, E. De Momi, and D. Stoyanov. Deep learning based robotic tool detection and articulation estimation with spatio-temporal layers. IEEE Robot. Autom. Lett. 4(3):2714–2721, 2019.
Cooke, D. L., M. Levitt, L. Kim, D. Hallam, and B. Ghodke. Transcranial access using fluoroscopic flat panel detector ct navigation. Am. J. Neuroradiol. 32(4):E69–E70, 2011.
Coste, C., Y. Asloum, P. Marcheix, P. Dijoux, J. Charissoux, and C. Mabit. Percutaneous iliosacral screw fixation in unstable pelvic ring lesions: the interest of O-ARM CT-guided navigation. Orthopaed. Traumatol. 99(4):S273–S278, 2013.
Crane, L. M., G. Themelis, R. G. Pleijhuis, N. J. Harlaar, A. Sarantopoulos, H. J. Arts, A. G. van der Zee, N. Vasilis, and G. M. van Dam. Intraoperative multispectral fluorescence imaging for the detection of the sentinel lymph node in cervical cancer: a novel concept. Mol. Imaging Biol. 13(5):1043–1049, 2011.
Cui, Z., L. Pan, H. Song, X. Xu, B. Xu, X. Yu, and Z. Ling. Intraoperative mri for optimizing electrode placement for deep brain stimulation of the subthalamic nucleus in parkinson disease. J. Neurosurg. 124(1):62–69, 2016.
Cunningham, B., K. Jackson, and G. Ortega. Intraoperative CT in the assessment of posterior wall acetabular fracture stability. Orthopedics 37(4):e328–e331, 2014.
Das, S., M. K. Kummelil, V. Kharbanda, V. Arora, S. Nagappa, R. Shetty, and B. K. Shetty. Microscope integrated intraoperative spectral domain optical coherence tomography for cataract surgery: uses and applications. Curr. Eye Res. 41(5):643–652, 2016.
De Lorenzo, D., A. Vaccarella, G. Khreis, H. Moennich, G. Ferrigno, and E. De Momi. Accurate calibration method for 3D freehand ultrasound probe using virtual plane. Med. Phys. 38(12):6710–6720, 2011.
Dima, A., J. Gateau, J. Claussen, D. Wilhelm, and V. Ntziachristos. Optoacoustic imaging of blood perfusion: techniques for intraoperative tissue viability assessment. J. Biophotonics 6(6-7):485–492, 2013.
Dinesh, S. K., R. Tiruchelvarayan, and I. Ng. A prospective study on the use of intraoperative computed tomography (iCT) for image-guided placement of thoracic pedicle screws. Br. J. Neurosurg. 26(6):838–844, 2012.
Diodato, A., A. Cafarelli, A. Schiappacasse, S. Tognarelli, G. Ciuti, and A. Menciassi. Motion compensation with skin contact control for high intensity focused ultrasound surgery in moving organs. Phys. Med. Biol. 63(3):035017, 2018.
Diot, G., S. Metz, A. Noske, E. Liapis, B. Schroeder, S. V. Ovsepian, R. Meier, E. J. Rummeny, and V. Ntziachristos. Multi-spectral optoacoustic tomography (msot) of human breast cancer. Clin. Cancer Res., 23, 6912-6922, 2017.
Ehlers, J. P., A. Uchida, and S. K. Srivastava. Intraoperative optical coherence tomography-compatible surgical instruments for real-time image-guided ophthalmic surgery. Br. J. Ophthalmol., 101): 1306-1308, 2017.
Eitel, C., G. Hindricks, M. Grothoff, M. Gutberlet, and P. Sommer. Catheter ablation guided by real-time MRI. Curr. Cardiol. Rep. 16(8):511, 2014.
Ermolayev, V., X. L. Dean-Ben, S. Mandal, V. Ntziachristos, and D. Razansky. Simultaneous visualization of tumour oxygenation, neovascularization and contrast agent perfusion by real-time three-dimensional optoacoustic tomography. Eur. Radiol. 26(6):1843–1851, 2016.
Fabelo, H., S. Ortega, R. Lazcano, D. Madroñal, G. M Callicó, E. Juárez, R. Salvador, D. Bulters, H. Bulstrode, A. Szolna, J. F. Pineiro, C. Sosa, A. J. O’Shanahan, S. Bisshopp, M. Hernandez, J. Morera, D. Ravi, R. Kiran, A. Vega, A. Baez-Quevedo, G.-Z. Yang, B. Stanciulescu, and R. Sarmiento. An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation. Sensors 18(2):430, 2018.
Falkner-Radler, C. I., C. Glittenberg, S. Hagen, T. Benesch, and S. Binder. Spectral-domain optical coherence tomography for monitoring epiretinal membrane surgery. Ophthalmology 117(4):798–805, 2010.
Farnia, P., A. Ahmadian, T. Shabanian, N. D. Serej, and J. Alirezaie. Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity. Int. J. Comput. Assist. Radiol. Surg. 10:555–562, 2015.
Ferrante, G., P. Presbitero, R. Whitbourn, and P. Barlis. Current applications of optical coherence tomography for coronary intervention. Int. J. Cardiol. 165(1):7–16, 2013.
Fetterly, K. A., V. Mathew, R. Lennon, M. R. Bell, D. R. Holmes Jr, and C. S. Rihal. Radiation dose reduction in the invasive cardiovascular laboratory: implementing a culture and philosophy of radiation safety. JACC 5(8):866–873, 2012.
Fitts, J., P. Lee, P. Hofmaster, D. Malenka, et al. Fluoroscopy-guided femoral artery puncture reduces the risk of pci-related vascular complications. J. Interv. Cardiol. 21(3):273–278, 2008
Garai, E., S. Sensarn, C. L. Zavaleta, N. O. Loewke, S. Rogalla, M. J. Mandella, S. A. Felt, S. Friedland, J. T. Liu, S. S. Gambhir, and C. H. Contag. A real-time clinical endoscopic system for intraluminal, multiplexed imaging of surface-enhanced Raman scattering nanoparticles. PLoS ONE 10(4):e0123185, 2015.
Ghosh, D., N. V. Michalopoulos, T. Davidson, F. Wickham, N. R. Williams, and M. R. Keshtgar. Sentinel node detection in early breast cancer with intraoperative portable gamma camera: UK experience. Breast 32:53–59, 2017.
Gieroba, T. J., G. I. Bain, and P. J. Cundy. Review of the clinical use of fluoroscopy in hand surgery. Hand Surg. 20(02):228–236, 2015.
Ginat, D. T., B. Swearingen, W. Curry, D. Cahill, J. Madsen, and P. W. Schaefer. 3 tesla intraoperative mri for brain tumor surgery. J. Magn. Reson. Imaging 39(6):1357–1365, 2014.
Golub, D., J. Hyde, S. Dogra, J. Nicholson, K. A. Kirkwood, P. Gohel, S. Loftus, and T. H. Schwartz. Intraoperative mri versus 5-ala in high-grade glioma resection: a network meta-analysis. J. Neurosurg., 1, 1–15, 2020.
Gonzalo, N., J. Escaned, F. Alfonso, C. Nolte, V. Rodriguez, P. Jimenez-Quevedo, C. Bañuelos, A. Fernández-Ortiz, E. Garcia, R. Hernandez-Antolin, and C. Macaya. Morphometric assessment of coronary stenosis relevance with optical coherence tomography: a comparison with fractional flow reserve and intravascular ultrasound. J. Am. Coll. Cardiol. 59(12):1080–1089, 2012.
Gorpas, D., J. Phipps, J. Bec, D. Ma, S. Dochow, D. Yankelevich, J. Sorger, J. Popp, A. Bewley, R. Gandour-Edwards, et al.. Autofluorescence lifetime augmented reality as a means for real-time robotic surgery guidance in human patients. Sci. Rep. 9(1):1–9, 2019.
Guo, Z., M. C.-W. Leong, H. Su, K.-W. Kwok, D. T.-M. Chan, and W.-S. Poon. Techniques for stereotactic neurosurgery: Beyond the frame, toward the intraoperative magnetic resonance imaging–guided and robot-assisted approaches. World Neurosurg. 116:77–87, 2018.
Hahn, P., J. Migacz, R. O’Connell, R. S. Maldonado, J. A. Izatt, and C. A. Toth. The use of optical coherence tomography in intraoperative ophthalmic imaging. Ophthal. Surg. Lasers Imaging Retina 42(4):S85–S94, 2011.
Hall, N. C., S. P. Povoski, J. Zhang, M. V. Knopp, and E. W. Martin Jr. Use of intraoperative nuclear medicine imaging technology: strategy for improved patient management. Expert Rev. Med. Devices 10(2):149–152, 2013.
Han, Y., G. Y. Hou, S. Wang, and E. Konofagou. High intensity focused ultrasound (HIFU) focal spot localization using harmonic motion imaging (HMI). Phys. Med. Biol. 60(15):5911, 2015.
Hanlon, E., R. Manoharan, T. Koo, K. Shafer, J. Motz, M. Fitzmaurice, J. Kramer, I. Itzkan, R. Dasari, and M. Feld. Prospects for in vivo raman spectroscopy. Phys. Med. Biol. 45(2):R1, 2000.
Hansen, K. L., M. M. Pedersen, H. Møller-Sørensen, J. Kjaergaard, J. C. Nilsson, J. T. Lund, J. A. Jensen, and M. B. Nielsen. Intraoperative cardiac ultrasound examination using vector flow imaging. Ultrason. Imaging 35(4):318–332, 2013.
Harmsen, S., R. Huang, M. A. Wall, H. Karabeber, J. M. Samii, M. Spaliviero, J. R. White, S. Monette, R. O’Connor, K. L. Pitter, S. W. Lowe, R. G. Blasberg, and M. F. Kircher. Surface-enhanced resonance Raman scattering nanostars for high-precision cancer imaging. Sci. Transl. Med. 7(271):271ra7–271ra7, 2015.
Hashemi, R. H., W. G. Bradley, and C. J. Lisanti, MRI: The Basics. Lippincott Williams & Wilkins, 2012.
Hekman, M. C., M. Rijpkema, J. F. Langenhuijsen, O. C. Boerman, E. Oosterwijk, and P. F. Mulders. Intraoperative imaging techniques to support complete tumor resection in partial nephrectomy. Eur. Urol. Focus, 2017; 4, 960–968.
Heller, S. and P. Zanzonico. Nuclear probes and intraoperative gamma cameras,” in Semin. Nucl. Med., 41, 166–181, 2011.
Hlavac, M., C. R. Wirtz, and M.-E. Halatsch. Intraoperative magnetic resonance imaging. HNO 65:25–29, 2017.
Holzer, M. S., S. L. Best, N. Jackson, A. Thapa, G. V. Raj, J. A. Cadeddu, and K. J. Zuzak. Assessment of renal oxygenation during partial nephrectomy using hyperspectral imaging. J. Urol. 186(2):400–404, 2011.
Huang, D., E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito. Optical coherence tomography. Science 254(5035):1178–1181, 1991.
Huang, Q. and Z. Zeng. A review on real-time 3D ultrasound imaging technology. BioMed Res. Int. 17:6027029, 2017.
Imbault, M., D. Chauvet, J.-L. Gennisson, L. Capelle, and M. Tanter. Intraoperative functional ultrasound imaging of human brain activity. Sci. Rep. 7(1):7304, 2017.
Imola, F., M. T. Mallus, V. Ramazzotti, A. Manzoli, A. Pappalardo, A. Di Giorgio, M. Albertucci, and F. Prati. Safety and feasibility of frequency domain optical coherence tomography to guide decision making in percutaneous coronary intervention. EuroIntervention 6(5):575–581, 2010.
Ing, F. “Delivery of stents to target lesions: Techniques of intraoperative stent implantation and intraoperative angiograms. Pediatr. Cardiol. 26:260–266, 2005.
Iturri-Clavero, F., L. Galbarriatu-Gutierrez, A. Gonzalez-Uriarte, G. Tamayo-Medel, K. de Orte, A. Martinez-Ruiz, K. Castellon-Larios, and S. Bergese. “low-field” intraoperative MRI: a new scenario, a new adaptation. Clin. Radiol. 71(11):1193–1198, 2016.
Jaffray, D. A. “Image-guided radiotherapy: from current concept to future perspectives. Nat. Rev. Clin. Oncol. 9(12):688, 2012.
Jakobs, M., E. Krasniqi, M. Kloß, J.-O. Neumann, B. Campos, A. W. Unterberg, and K. L. Kiening. Intraoperative stereotactic magnetic resonance imaging for deep brain stimulation electrode planning in patients with movement disorders. World Neurosurg. 119:e801–e808, 2018.
Jarvis, L. A., R. Zhang, D. J. Gladstone, S. Jiang, W. Hitchcock, O. D. Friedman, A. K. Glaser, M. Jermyn, and B. W. Pogue. Cherenkov video imaging allows for the first visualization of radiation therapy in real time. Int. J. Radiat. Oncol. Biol. Phys. 89(3):615–622, 2014.
Jolesz, F. A. Intraoperative imaging in neurosurgery: where will the future take us? Intraoper. Imaging 109:21–25, 2011.
Kapur, T., J. Egger, A. Damato, E. J. Schmidt, and A. N. Viswanathan. 3-T MR-guided brachytherapy for gynecologic malignancies. Magn. Reson. Imaging 30(9):1279–1290, 2012.
Kenngott, H. G., M. Wagner, M. Gondan, F. Nickel, M. Nolden, A. Fetzer, J. Weitz, L. Fischer, S. Speidel, H.-P. Meinzer, D. Bockler, M. W. Buchler, and B. P. Muller-Stich. Real-time image guidance in laparoscopic liver surgery: first clinical experience with a guidance system based on intraoperative ct imaging. Surg. Endosc. 28(3):933–940, 2014.
King, D. R.,W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher. Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging. Burns 41(7):1478–1487, 2015.
Kircher, M. F., A. De La Zerda, J. V. Jokerst, C. L. Zavaleta, P. J. Kempen, E. Mittra, K. Pitter, R. Huang, C. Campos, F. Habte, R. Sinclair, M. I. K. Brennan, Cameron W and, E. C. Holland, and S. S Gambhir. A brain tumor molecular imaging strategy using a new triple-modality MRI-photoacoustic-Raman nanoparticle. Nat. Med. 18(5):829, 2012.
Kirchner, T., F. Sattler, J. Gröhl, and L. Maier-Hein. Signed real-time delay multiply and sum beamforming for multispectral photoacoustic imaging. J. Imaging 4(10):121, 2018.
Koch, M. and V. Ntziachristos. Advancing surgical vision with fluorescence imaging. Annu. Rev. Med. 67:153–164, 2016.
Kubo, T., Y. Ino, T. Tanimoto, H. Kitabata, A. Tanaka, and T. Akasaka. Optical coherence tomography imaging in acute coronary syndromes. Cardiol. Res. Pract. 2011:312978, 2011
Kumashiro, R., K. Konishi, T. Chiba, T. Akahoshi, S. Nakamura, M. Murata, M. Tomikawa, T. Matsumoto, Y. Maehara, and M. Hashizume. Integrated endoscopic system based on optical imaging and hyperspectral data analysis for colorectal cancer detection. Anticancer Res. 36(8):3925–3932, 2016.
Labadie, R. F., R. Balachandran, J. H. Noble, G. S. Blachon, J. E. Mitchell, F. A. Reda, B. M. Dawant, and J. M. Fitzpatrick. Minimally invasive image-guided cochlear implantation surgery: First report of clinical implementation. The Laryngoscope 124(8):1915–1922, 2014.
Leclerc, P., C. Ray, L. Mahieu-Williame, L. Alston, C. Frindel, P.-F. Brevet, D. Meyronet, J. Guyotat, B. Montcel, and D. Rousseau. Machine learning-based prediction of glioma margin from 5-ala induced ppix fluorescence spectroscopy. Sci. Rep. 10(1):1–9, 2020.
Lee, L. J., A. L. Damato, and A. N. Viswanathan. Clinical outcomes of high-dose-rate interstitial gynecologic brachytherapy using real-time CT guidance. Brachytherapy 12(4):303–310, 2013.
Lee, L. B. and S. K. Srivastava. Intraoperative spectral-domain optical coherence tomography during complex retinal detachment repair. Ophthal. Surg. Lasers Imaging Retina 42:71, 2011.
Li, Q., X. He, Y. Wang, H. Liu, D. Xu, and F. Guo. Review of spectral imaging technology in biomedical engineering: achievements and challenges. J. Biomed. Opt. 18(10):100901, 2013.
Lin, J., N. T. Clancy, Y. Hu, J. Qi, T. Tatla, D. Stoyanov, L. Maier-Hein, and D. S. Elson. Endoscopic depth measurement and super-spectral-resolution imaging,” in International Conference on Medical Image Computing and Computer-Assisted Intervention:39–47, Springer, Cham (2017)
Liney, G. P., B. Whelan, B. Oborn, M. Barton, and P. Keall. MRI-linear accelerator radiotherapy systems. Clin. Oncol. 30(11):686–691, 2018.
Li, P., R. Qian, C. Niu, and X. Fu. Impact of intraoperative mri-guided resection on resection and survival in patient with gliomas: a meta-analysis. Curr. Med. Res. Opin. 33(4):621–630, 2017.
Litjens, G., T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sánchez. A survey on deep learning in medical image analysis. Med. Image Anal. 42:60–88, 2017.
Li, R., P. Wang, L. Lan, F. P. Lloyd, C. J. Goergen, S. Chen, and J.-X. Cheng. Assessing breast tumor margin by multispectral photoacoustic tomography. Biomed. Opt. Express 6(4):1273–1281, 2015.
Lu, G. and B. Fei. Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1):010901, 2014.
Lu, G., L. Halig, D. Wang, Z. G. Chen, and B. Fei. Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images. Int. Soc. Opt. Photon. 9036:90360S, 2014.
Lu, G., D. Wang, X. Qin, L. Halig, S. Muller, H. Zhang, A. Chen, B. W. Pogue, Z. G. Chen, and B. Fei. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. J. Biomed. Opt. 20(12):126012, 2015.
Maier-Hein, L., S. S. Vedula, S. Speidel, N. Navab, R. Kikinis, A. Park, M. Eisenmann, H. Feussner, G. Forestier, S. Giannarou, M. Hashizume, D. Katic, H. Kenngott, M. Kranzfelder, A. Malpani, K. Marz, T. Neumuth, N. Padoy, C. Pugh, N. Schoch, S. Danail, R. Taylor, M. Wagner, G. D. Hager, and P. Jannin. Surgical data science for next-generation interventions. Nat. Biomed. Eng. 1(9):691, 2017.
Majlesara, A., M. Golriz, M. Hafezi, A. Saffari, E. Stenau, L. Maier-Hein, B. P. Müller-Stich, and A. Mehrabi. Indocyanine green fluorescence imaging in hepatobiliary surgery. Photodiagn. Photodyn. Ther. 17:208–215, 2017.
Mascharak, S., B. J. Baird, and F. C. Holsinger. Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning. Laryngoscope 128:2514, 2018.
Mathiassen, K., J. E. Fjellin, K. Glette, P. K. Hol, and O. J. Elle. An ultrasound robotic system using the commercial robot ur5. Front. Robot. AI 3:1, 2016.
Mehrtash, A., M. Ghafoorian, G. Pernelle, A. Ziaei, F. G. Heslinga, K. Tuncali, A. Fedorov, R. Kikinis, C. M. Tempany, W. M. Wells, P. Abolmaesumi, and T. Kapur. Automatic needle segmentation and localization in MRI with 3D convolutional neural networks: Application to MRI-targeted prostate biopsy. IEEE Trans. Med. Imaging 38:1026–1036, 2018.
Meola, A., F. Cutolo, M. Carbone, F. Cagnazzo, M. Ferrari, and V. Ferrari. Augmented reality in neurosurgery: a systematic review. Neurosurg. Rev. 40(4):537–548, 2017.
Miller, S. E., W. S. Tummers, N. Teraphongphom, N. S. van den Berg, A. Hasan, R. D. Ertsey, S. Nagpal, L. D. Recht, E. D. Plowey, H. Vogel, et al.. First-in-human intraoperative near-infrared fluorescence imaging of glioblastoma using cetuximab-IRDye800. J. Neurooncol. 139(1):135–143, 2018.
Moccia, S., S. Foti, A. Routray, F. Prudente, A. Perin, R. F. Sekula, L. S. Mattos, J. R. Balzer, W. Fellows-Mayle, E. De Momi, and C. Riviere. Toward improving safety in neurosurgery with an active handheld instrument. Ann. Biomed. Eng. 46(10):1450–1464, 2018.
Moccia, S., L. S. Mattos, I. Patrini, M. Ruperti, N. Poté, F. Dondero, F. Cauchy, A. Sepulveda, O. Soubrane, E. De Momi, et al.. Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int. J. Comput. Assist. Radiol. Surg. 13(9):1357–1367, 2018.
Moccia, S., E. De Momi, M. Guarnaschelli, M. Savazzi, A. Laborai, L. Guastini, G. Peretti, and L. S. Mattos. Confident texture-based laryngeal tissue classification for early stage diagnosis support. J. Med. Imaging 4(3):034502, 2017.
Moccia, S., E. De Momi, S. El Hadji, and L. S. Mattos. Blood vessel segmentation algorithms–review of methods, datasets and evaluation metrics. Comput. Methods Programs Biomed. 158:71–91, 2018.
Moccia, S., V. Penza, G. O. Vanone, E. De Momi, and L. S. Mattos. Automatic workflow for narrow-band laryngeal video stitching,” in 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society:1188–1191, IEEE, New York, 2016.
Moccia, S., G. O. Vanone, E. De Momi, A. Laborai, L. Guastini, G. Peretti, and L. S. Mattos. Learning-based classification of informative laryngoscopic frames. Comput. Methods Programs Biomed. 158:21–30, 2018.
Moccia, S., S. J. Wirkert, H. Kenngott, A. S. Vemuri, M. Apitz, B. Mayer, E. De Momi, L. S. Mattos, and L. Maier-Hein. Uncertainty-aware organ classification for surgical data science applications in laparoscopy. IEEE Trans. Biomed. Eng. 65:2649–2659, 2018
Mohyeldin, A. and J. B. Elder. Stereotactic biopsy platforms with intraoperative imaging guidance. Neurosurg. Clin. 28(4):465–475, 2017.
Mura, M., S. Parrini, G. Ciuti, V. Ferrari, C. Freschi, M. Ferrari, P. Dario, and A. Menciassi. A computer-assisted robotic platform for vascular procedures exploiting 3D US-based tracking. Comput. Assisted Surg. 21(1):63–79, 2016.
Nadeau, C., H. Ren, A. Krupa, and P. Dupont. Intensity-based visual servoing for instrument and tissue tracking in 3d ultrasound volumes. IEEE Trans. Autom. Sci. Eng. 12(1):367–371, 2015.
Nandy, S., A. Mostafa, P. D. Kumavor, M. Sanders, M. Brewer, and Q. Zhu. Characterizing optical properties and spatial heterogeneity of human ovarian tissue using spatial frequency domain imaging. J. Biomed. Opt. 21(10):101402, 2016.
Nguyen, F. T., A. M. Zysk, E. J. Chaney, S. G. Adie, J. G. Kotynek, U. J. Oliphant, F. J. Bellafiore, K. M. Rowland, P. A. Johnson, and S. A. Boppart. Optical coherence tomography: the intraoperative assessment of lymph nodes in breast cancer. IEEE Eng. Med. Biol. Mag. 29(2):63–70, 2010.
Ning, R., X. Tang, and D. Conover. X-ray scatter correction algorithm for cone beam ct imaging. Med. Phys. 31(5):1195–1202, 2004.
Nolan, R. M., S. G. Adie, M. Marjanovic, E. J. Chaney, F. A. South, G. L. Monroy, N. D. Shemonski, S. J. Erickson-Bhatt, R. L. Shelton, A. J. Bower, D. G. Simpson, K. A. Cradock, Z. G. Liu, P. S. Ray, and S. A. Boppart. Intraoperative optical coherence tomography for assessing human lymph nodes for metastatic cancer. BMC Cancer 16(1):144, 2016.
Ohayon, S., A. Caravaca-Aguirre, R. Piestun, and J. J. DiCarlo. Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging. Biomed. Opt. Express 9(4):1492–1509, 2018.
Olmos, R. A. V., S. Vidal-Sicart, and O. E. Nieweg. Technological innovation in the sentinel node procedure: towards 3-d intraoperative imaging. Eur. J. Nucl. Med. Mol. Imaging 37(8):1449–1451, 2010.
Ozkan, E. and A. Eroglu. The utility of intraoperative handheld gamma camera for detection of sentinel lymph nodes in melanoma. Nucl. Med. Mol. Imaging 49(4):318–320, 2015.
Padoy, N., T. Blum, S.-A. Ahmadi, H. Feussner, M.-O. Berger, and N. Navab. Statistical modeling and recognition of surgical workflow. Med. Image Anal. 16(3):632–641, 2012.
Papalazarou, C., G. J. Klop, M. T. Milder, J. P. Marijnissen, V. Gupta, B. J. Heijmen, J. J. Nuyttens, and M. S. Hoogeman. Cyberknife with integrated ct-on-rails: System description and first clinical application for pancreas sbrt. Med. Phys. 44(9):4816–4827, 2017.
Patete, P., M. Riboldi, M. F. Spadea, G. Catanuto, A. Spano, M. Nava, and G. Baroni. Motion compensation in hand-held laser scanning for surface modeling in plastic and reconstructive surgery. Ann. Biomed. Eng. 37(9):1877–1885, 2009.
Pediconi, F., F. Marzocca, B. Cavallo Marincola, and A. Napoli. MRI-guided treatment in the breast. J. Magn. Reson. Imaging 48(6):1479–1488, 2018.
Petrover, D., and P. Richette. Treatment of carpal tunnel syndrome: from ultrasonography to ultrasound guided carpal tunnel release. Joint Bone Spine 85(5):545–552, 2018.
Pike, R., G. Lu, D. Wang, Z. G. Chen, and B. Fei. A minimum spanning forest-based method for noninvasive cancer detection with hyperspectral imaging.. IEEE Trans. Biomed. Eng. 63(3):653–663, 2016.
Pileggi, G., C. Speier, G. C. Sharp, D. Izquierdo Garcia, C. Catana, J. Pursley, F. Amato, J. Seco, and M. F. Spadea. Proton range shift analysis on brain pseudo-ct generated from t1 and t2 mr. Acta Oncol. 57(11):1521–1531, 2018.
Pollard, J. M., Z. Wen, R. Sadagopan, J. Wang, and G. S. Ibbott. The future of image-guided radiotherapy will be MR guided. Br. J. Radiol. 90(1073):20160667, 2017.
Prati, F., L. DiVito, G. Biondi-Zoccai, M. Occhipinti, A. LaManna, C. Tamburino, F. Burzotta. Angiography alone versus angiography plus optical coherence tomography to guide decision-making during percutaneous coronary intervention: the centro per la lotta contro l’infarto-optimisation of percutaneous coronary intervention (CLI-OPCI) study. EuroIntervention 8:823–829, 2012.
Prevost, R., M. Salehi, S. Jagoda, N. Kumar, J. Sprung, A. Ladikos, R. Bauer, O. Zettinig, and W. Wein. 3D freehand ultrasound without external tracking using deep learning. Med. Image Anal. 48:187 – 202, 2018.
Rabie, A., A. M. Ibrahim, B. T. Lee, and S. J. Lin. Use of intraoperative computed tomography in complex facial fracture reduction and fixation. J. Craniofac. Surg. 22(4):1466–1467, 2011.
Rahim, H. M., E. Shlofmitz, A. Gore, E. Hakemi, G. S. Mintz, A. Maehara, A. Jeremias, O. Ben-Yehuda, G. W. Stone, R. A. Shlofmitz, and Z. A. Ali. Ivus- versus oct-guided coronary stent implantation: a comparison of intravascular imaging for stent optimization. Curr. Cardiovasc. Imaging Rep. 11:34, 2018.
Raudaschl, P. F., P. Zaffino, G. C. Sharp, M. F. Spadea, A. Chen, B. M. Dawant, T. Albrecht, T. Gass, C. Langguth, M. Lüthi, F. Jung, O. Knapp, S. Wesarg, R. Mannion-Haworth, M. Bowes, A. Ashman, G. Guillard, A. Brett, G. Vincent, M. Orbes-Arteaga, D. Cardenas-Pena, G. Castellanos-Dominguez, N. Aghdasi, Y. Li, A. Berens, K. Moe, B. Hannaford, R. Schubert, and K. D. Fritscher. Evaluation of segmentation methods on head and neck ct: auto-segmentation challenge 2015. Med. Phys. 44(5):2020–2036, 2017.
Ray, R., D. E. Barañano, J. A. Fortun, B. J. Schwent, B. E. Cribbs, C. S. Bergstrom, G. B. Hubbard III, and S. K. Srivastava. Intraoperative microscope-mounted spectral domain optical coherence tomography for evaluation of retinal anatomy during macular surgery. Ophthalmology 118(11):2212–2217, 2011.
Ray, A., X. Wang, Y.-E. K. Lee, H. J. Hah, G. Kim, T. Chen, D. A. Orringer, O. Sagher, X. Liu, and R. Kopelman. Targeted blue nanoparticles as photoacoustic contrast agent for brain tumor delineation. Nano Research 4(11):1163–1173, 2011.
Reder, N. P., S. Kang, A. K. Glaser, Q. Yang, M. A. Wall, S. H. Javid, S. M. Dintzis, and J. T. Liu. Raman-encoded molecular imaging with topically applied SERS nanoparticles for intraoperative guidance of lumpectomy. Cancer Res. 77(16):4506–4516, 2017.
Reiml, S., T. Kurzendorfer, D. Toth, P. Mountney, S. Steidl, A. Brost, and A. Maier. Automatic vertebrae segmentation in fluoroscopic images for electrophysiology,” in 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), Springer, Cham 2017.
Riva, M., C. Hennersperger, F. Milletari, A. Katouzian, F. Pessina, B. Gutierrez-Becker, A. Castellano, N. Navab, and L. Bello. 3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation. Int. J. Comput. Assist. Radiol. Surg. 12:1711–1725, 2017.
Roessler, K., A. Hofmann, B. Sommer, P. Grummich, R. Coras, B. S. Kasper, H. M. Hamer, I. Blumcke, H. Stefan, C. Nimsky, and M. Buchfelder. Resective surgery for medically refractory epilepsy using intraoperative MRI and functional neuronavigation: the erlangen experience of 415 patients. Neurosurg. Focus 40(3):E15, 2016.
Sanghvi, N. T., R. Bihrle, and F. J. Fry. Focussed ultrasound tissue treatment method. US Patent 5,676,692, 1997
Santos, I. P., E. M. Barroso, T. C. B. Schut, P. J. Caspers, C. G. van Lanschot, D.-H. Choi, M. F. van der Kamp, R. W. Smits, R. van Doorn, R. M. Verdijk, V. Noordhoek Hegt, J. von der Thüsen, C. H. M. van Deurzen, L. B. Koppert, J. L. H. van Leenders, P. C. Ewing-Graham, H. C. van Doorn, C. M. F. Dirven, M. B. Busstra, J. Hardillo, A. Sewnaik, I. ten Hove, H. Mast, D. A. Monserez, C. Meeuwis, T. Nijsten, E. B. Wolvius, R. J. Baatenburg de Jong, G. J. Puppels, and S. Koljenovic. Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics. Analyst 142(17):3025–3047, 2017.
Saso, S., N. T. Clancy, B. P. Jones, T. Bracewell-Milnes, M. Al-Memar, E. M. Cannon, S. Ahluwalia, J. Yazbek, M.-Y. Thum, T. Bourne, D. S. Elson, J. R. Smith, and S. Ghaem-Maghami. Use of biomedical photonics in gynecological surgery: a uterine transplantation model. Fut. Sci. 4(4):FSO286, 2018.
Saw, C. B., C. Gillette, C. A. Peters, and L. Koutcher. Clinical implementation of radiosurgery using the helical tomotherapy unit. Med. Dosim. 43(3):284–290, 2018.
Schafer, S., Y. Otake, A. Uneri, D. J. Mirota, S. Nithiananthan, J. W. Stayman, W. Zbijewski, G. Kleinszig, R. Graumann, M. Sussman, and J. H. Siewerdsen. High-performance C-arm cone-beam CT guidance of thoracic surgery. Int. Soc. Opt. Photon. 8316:831611 (2012)
Schichor, C., N. Terpolilli, J. Thorsteinsdottir, and J.-C. Tonn. Intraoperative computed tomography in cranial neurosurgery. Neurosurg. Clin. 28(4):595–602, 2017.
Schwartz, J. G., A. M. Neubauer, T. E. Fagan, N. J. Noordhoek, M. Grass, and J. D. Carroll. Potential role of three-dimensional rotational angiography and c-arm ct for valvular repair and implantation. Int. J. Cardiovasc. Imaging 27(8):1205–1222, 2011.
Seco, J., M. Oumano, N. Depauw, M. F. Dias, R. P. Teixeira, and M. F. Spadea. Characterizing the modulation transfer function (mtf) of proton/carbon radiography using Monte Carlo simulations. Med. Phys. 40(9):91717, 2013
Seco, J. and M. F. Spadea. Imaging in particle therapy: state of the art and future perspective. Acta Oncol. 54(9):1254–1258, 2015.
Sequeiros, R. B., J.-J. Sinikumpu, R. Ojala, J. Järvinen, and J. Fritz. Pediatric musculoskeletal interventional mri. Top. Magn. Reson. Imaging 27(1):39–44, 2018.
Sharma, M. and M. Deogaonkar. Accuracy and safety of targeting using intraoperative “O-arm” during placement of deep brain stimulation electrodes without electrophysiological recordings. J. Clin. Neurosci. 27:80–86, 2016.
Shaye, D. A., T. T. Tollefson, and E. B. Strong. Use of intraoperative computed tomography for maxillofacial reconstructive surgery. JAMA Facial Plast. Surg. 17(2):113–119, 2015.
Siebelmann, S., C. Cursiefen, A. Lappas, and T. Dietlein. Intraoperative optical coherence tomography enables noncontact imaging during canaloplasty. J. Glaucoma 25(2):236–238, 2016.
Simpfendörfer, T., C. Gasch, G. Hatiboglu, M. Müller, L. Maier-Hein, M. Hohenfellner, and D. Teber. Intraoperative computed tomography imaging for navigated laparoscopic renal surgery: first clinical experience. J. Endourol. 30(10):1105–1111, 2016.
Sommerey, S. , N. Al Arabi, R. Ladurner, C. Chiapponi, H. Stepp, K. K. Hallfeldt, and J. K. Gallwas. Intraoperative optical coherence tomography imaging to identify parathyroid glands. Surg. Endosc. 29(9):2698–2704, 2015.
Song, S., J. Xu, and R. K. Wang. Long-range and wide field of view optical coherence tomography for in vivo 3D imaging of large volume object based on akinetic programmable swept source. Biomed. Opt. Express 7(11):4734–4748, 2016.
Spadea, M. F., A. Fassi, P. Zaffino, M. Riboldi, G. Baroni, N. Depauw, and J. Seco. Contrast-enhanced proton radiography for patient set-up by using x-ray ct prior knowledge. Int. J. Radiat. Oncol. Biol. Phys. 90(3):628–636, 2014.
Spadea, M. F., G. Pileggi, P. Zaffino, P. Salome, C. Catana, D. Izquierdo-Garcia, F. Amato, and J. Seco. Deep convolution neural network (dcnn) multiplane approach to synthetic ct generation from mr images–application in brain proton therapy. Int. J. Radiat. Oncol. Biol. Phys. 105(3):495–503, 2019.
Spadea, M. F., B. Tagaste, M. Riboldi, E. Preve, D. Alterio, G. Piperno, C. Garibaldi, R. Orecchia, A. Pedotti, and G. Baroni. Intra-fraction setup variability: IR optical localization vs. X-ray imaging in a hypofractionated patient population. Radiat. Oncol. 6(1):38, 2011.
Spadea, M. F., J. Verburg, G. Baroni, and J. Seco. Dosimetric assessment of a novel metal artifact reduction method in ct images. J. Appl. Clin. Med. Phys. 14(1):299–304, 2013.
Suetens, P. Fundamentals of Medical Imaging. Cambridge: Cambridge University Press, 2002.
Sullivan, J. P., B. A. Warme, and B. R. Wolf. Use of an o-arm intraoperative computed tomography scanner for closed reduction of posterior sternoclavicular dislocations. J. Shoulder Elbow Surg. 21(3):e17–e20, 2012.
Suzuki, T., Y. Sakurai, K. Yoshimitsu, K. Nambu, Y. Muragaki, and H. Iseki. Intraoperative multichannel audio-visual information recording and automatic surgical phase and incident detection,” in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE:1190–1193, IEEE, New Yok, 2010.
Tamadazte, B., A. Agustinos, P. Cinquin, G. Fiard, and S. Voros. Multi-view vision system for laparoscopy surgery. Int. J. Comput. Assist. Radiol. Surg. 10(2):195–203, 2015.
Tappeiner, E., S. Pröll, M. Hönig, P. F. Raudaschl, P. Zaffino, M. F. Spadea, G. C. Sharp, R. Schubert, and K. Fritscher. Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach. Int. J. Comput. Assist. Radiol. Surg. 14(5):745–754, 2019.
Taruttis, A., E. Herzog, D. Razansky, and V. Ntziachristos. Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography. Opt. Express 18(19):19592–19602, 2010.
Taruttis, A. and V. Ntziachristos. Advances in real-time multispectral optoacoustic imaging and its applications. Nat. Photonics 9(4):219, 2015.
Tempany, C. M., J. Jayender, T. Kapur, R. Bueno, A. Golby, N. Agar, and F. A. Jolesz. Multimodal imaging for improved diagnosis and treatment of cancers. Cancer 121(6):817–827, 2015.
Thatcher, J. E., W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, J. A. Martinez-Lorenzo, and J. M. DiMaio. Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision. J. Burn Care Res. 37(1):38–52, 2016.
Thatcher, J. E., J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio. Imaging techniques for clinical burn assessment with a focus on multispectral imaging. Adv. Wound Care 5(8):360–378, 2016.
Thomas, G., T.-Q. Nguyen, I. Pence, B. Caldwell, M. O’Connor, J. Giltnane, M. Sanders, A. Grau, I. Meszoely, M. Hooks, M. C. Kelley, and A. Mahadevan-Jansen. Evaluating feasibility of an automated 3-dimensional scanner using Raman spectroscopy for intraoperative breast margin assessment. Sci. Rep. 7(1):13548, 2017.
Thummerer, A., P. Zaffino, A. Meijers, G. G. Marmitt, J. Seco, R. J. Steenbakkers, J. A. Langendijk, S. Both, M. F. Spadea, and A.-C. Knopf. Comparison of cbct based synthetic ct methods suitable for proton dose calculations in adaptive proton therapy. Phys. Med. Biol. 65(9):095002, 2020.
Tipirneni, K., E. Rosenthal, L. Moore, A. Haskins, N. Udayakumar, A. Jani, W. Carroll, A. Morlandt, M. Bogyo, J. Rao, et al.. Fluorescence imaging for cancer screening and surveillance. Mol. Imag. Biol. 19(5):645–655, 2017.
Tousignant, C., M. Desmet, R. Bowry, A. M. Harrington, J. D. Cruz, and C. D. Mazer. Speckle tracking for the intraoperative assessment of right ventricular function: a feasibility study. J. Cardiothorac. Vasc. Anesth. 24(2):275–279, 2010.
Tummers, W. S., S. E. Miller, N. T. Teraphongphom, A. Gomez, I. Steinberg, D. M. Huland, S. Hong, S.-R. Kothapalli, A. Hasan, R. Ertsey, et al.. Intraoperative pancreatic cancer detection using tumor-specific multimodality molecular imaging. Ann. Surg. Oncol. 25(7):1880–1888, 2018.
Tzifa, A., T. Schaeffter, and R. Razavi. MR imaging-guided cardiovascular interventions in young children. Magn. Reson. Imaging Clin. 20(1):117–128, 2012.
Uh, J., T. E. Merchant, Y. Li, X. Li, and C. Hua. Mri-based treatment planning with pseudo ct generated through atlas registration. Med. Phys. 41(5):051711, 2014.
Unger, J., C. Hebisch, J. E. Phipps, J. L. Lagarto, H. Kim, M. A. Darrow, R. J. Bold, and L. Marcu. Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning. Biomed. Opt. Express 11(3):1216, 2020.
Van Dam, G. M. , G. Themelis, L. M. Crane, N. J. Harlaar, R. G. Pleijhuis, W. Kelder, A. Sarantopoulos, J. S. De Jong, H. J. Arts, A. G. Van Der Zee, J. Bart, P. S. Low, and V. Ntziachristos. Intraoperative tumor-specific fluorescence imaging in ovarian cancer by folate receptor-\(\alpha\) targeting: first in-human results. Nat. Med. 17(10):1315, 2011.
van den Berg, N. S., T. Buckle, G. H. KleinJan, H. G. van der Poel, and F. W. van Leeuwen. Multispectral fluorescence imaging during robot-assisted laparoscopic sentinel node biopsy: a first step towards a fluorescence–based anatomic roadmap. Eur. Urol. 72(1):110–117, 2017.
van den Berg, P., K. Daoudi, and W. Steenbergen. Review of photoacoustic flow imaging: its current state and its promises. Photoacoustics 3(3):89–99, 2015.
Vermeeren, L., W. Meinhardt, A. Bex, H. G. van der Poel, W. V. Vogel, C. A. Hoefnagel, S. Horenblas, and R. A. V. Olmos. Paraaortic sentinel lymph nodes: toward optimal detection and intraoperative localization using spect/ct and intraoperative real-time imaging. J. Nucl. Med. 51(3):376–382, 2010.
Vermeeren, L., R. A. V. Olmos, W. M. C. Klop, A. J. Balm, and M. W. van den Brekel. A portable \(\gamma\)-camera for intraoperative detection of sentinel nodes in the head and neck region. J. Nucl. Med. 51(5):700–703, 2010.
Vermeeren, L., R. A. V. Olmos, W. Meinhardt, and S. Horenblas. Intraoperative imaging for sentinel node identification in prostate carcinoma: its use in combination with other techniques. J. Nucl. Med. 52(5):741–744, 2011.
Viergever, M. A., J. A. Maintz, S. Klein, K. Murphy, M. Staring, and J. P. Pluim, A survey of medical image registration. Med. Image Anal. 2:1–36, 2016.
Walsh, E. M., D. Cole, K. E. Tipirneni, K. I. Bland, N. Udayakumar, B. B. Kasten, S. L. Bevans, B. M. McGrew, J. J. Kain, Q. T. Nguyen, et al.. Fluorescence imaging of nerves during surgery. Ann. Surg. 270(1):69–76, 2019.
Wang, Y. W., S. Kang, A. Khan, P. Q. Bao, and J. T. Liu. In vivo multiplexed molecular imaging of esophageal cancer via spectral endoscopy of topically applied SERS nanoparticles. Biomed. Opt. Express 6(10):3714–3723, 2015.
Wang, Y., S. Kang, A. Khan, G. Ruttner, S. Y. Leigh, M. Murray, S. Abeytunge, G. Peterson, M. Rajadhyaksha, S. Dintzis, S. Javid, and J. T. Liu. Quantitative molecular phenotyping with topically applied sers nanoparticles for intraoperative guidance of breast cancer lumpectomy. Sci. Rep. 6:21242, 2016.
Warsi, N. M., O. Lasry, A. Farah, C. Saint-Martin, J. L. Montes, J. Atkinson, J.-P. Farmer, and R. W. Dudley. 3-T intraoperative MRI (iMRI) for pediatric epilepsy surgery. Child’s Nervous Syst. 32(12):2415–2422, 2016.
Wegelin, O., H. H. van Melick, L. Hooft, J. R. Bosch, H. B. Reitsma, J. O. Barentsz, and D. M. Somford. Comparing three different techniques for magnetic resonance imaging-targeted prostate biopsies: a systematic review of in-bore versus magnetic resonance imaging-transrectal ultrasound fusion versus cognitive registration. is there a preferred technique?. Eur. Urol. 71(4):517–531, 2017.
Weyers, B. W., M. Marsden, T. Sun, J. Bec, A. F. Bewley, R. F. Gandour-Edwards, M. G. Moore, D. G. Farwell, and L. Marcu. Fluorescence lifetime imaging for intraoperative cancer delineation in transoral robotic surgery. Transl. Biophoton. 1(1–2):e201900017, 2019.
Wieser, W. , B. R. Biedermann, T. Klein, C. M. Eigenwillig, and R. Huber. Multi-megahertz OCT: High quality 3D imaging at 20 million A-scans and 4.5 GVoxel per second. Opt. Express 18(14):14685–14704, 2010.
Wild, E., D. Teber, D. Schmid, T. Simpfendörfer, M. Müller, A.-C. Baranski, H. Kenngott, K. Kopka, and L. Maier-Hein. Robust augmented reality guidance with fluorescent markers in laparoscopic surgery. Int. J. Comput. Assist. Radiol. Surg. 11(6):899–907, 2016.
Wirkert, S. J., N. T. Clancy, D. Stoyanov, S. Arya, G. B. Hanna, H.-P. Schlemmer, P. Sauer, D. S. Elson, and L. Maier-Hein. Endoscopic sheffield index for unsupervised in vivo spectral band selection,” in: S. J. Wirkert, N. T. Clancy (eds) International Workshop on Computer-Assisted and Robotic Endoscopy:110–120, Springer, Cham (2014)
Wirkert, S. J., H. Kenngott, B. Mayer, P. Mietkowski, M. Wagner, P. Sauer, N. T. Clancy, D. S. Elson, and L. Maier-Hein. Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression. Int. J. Comput. Assist. Radiol. Surg. 11(6):909–917, 2016.
Wirkert, S. J., A. S. Vemuri, H. G. Kenngott, S. Moccia, M. Götz, B. F. Mayer, K. H. Maier-Hein, D. S. Elson, and L. Maier-Hein. Physiological parameter estimation from multispectral images unleashed,” in International Conference on Medical Image Computing and Computer-Assisted Intervention:134–141, Springer, Cham (2017).
Wong, W. K., Y. Matsuwaki, K. Omura, and H. Moriyama. Role of intraoperative ct-updates during image-guided endoscopic sinus surgery for sinonasal fibro-osseous lesions. Auris Nasus Larynx 38(5):628–631, 2011.
Yang, J.-M., K. Maslov, H.-C. Yang, Q. Zhou, K. K. Shung, and L. V. Wang. Photoacoustic endoscopy. Opt. Lett. 34(10):1591–1593, 2009.
Yao, J. and L. V. Wang. Photoacoustic brain imaging: from microscopic to macroscopic scales. Neurophotonics 1(1):011003, 2014.
Yun, S.-H., G. J. Tearney, J. F. de Boer, N. Iftimia, and B. E. Bouma. High-speed optical frequency-domain imaging. Opt. express 11(22):2953–2963, 2003.
Zaffino, P., D. Ciardo, G. Piperno, L. Travaini, S. Comi, A. Ferrari, D. Alterio, B. Jereczek-Fossa, R. Orecchia, G. Baroni, and M. F. Spadea. Radiotherapy of Hodgkin and non-Hodgkin lymphoma: A nonrigid image-based registration method for automatic localization of prechemotherapy gross tumor volume. Technol. Cancer Res. Treat. 15(2):355–364, 2016.
Zaffino, P., G. Pernelle, A. Mastmeyer, A. Mehrtash, H. Zhang, R. Kikinis, T. Kapur, and M. F. Spadea. Fully automatic catheter segmentation in mri with 3d convolutional neural networks: application to mri-guided gynecologic brachytherapy. Phys. Med. Biol. 64(16):165008, 2019.
Zelefsky, M. J., M. Worman, G. N. Cohen, X. Pei, M. Kollmeier, J. Yamada, B. Cox, Z. Zhang, E. Bieniek, L. Dauer, and M. Zaider. Real-time intraoperative computed tomography assessment of quality of permanent interstitial seed implantation for prostate cancer. Urology 76(5):1138–1142, 2010.
Zhu, L., Y. Xie, J. Wang, and L. Xing. Scatter correction for cone-beam ct in radiation therapy. Med. Phys. 36(6Part1):2258–2268, 2009.
Zuzak, K. J., R. P. Francis, E. F. Wehner, M. Litorja, J. A. Cadeddu, and E. H. Livingston. Active DLP hyperspectral illumination: a noninvasive, in vivo, system characterization visualizing tissue oxygenation at near video rates. Anal. Chem. 83(19):7424–7430, 2011.
Conflict of interest
All authors declare that they have no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed Consent
Informed consent was obtained from all individuals for whom identifying information is included in this article.
Author information
Authors and Affiliations
Corresponding author
Additional information
Associate Editor Stefan M. Duma oversaw the review of this article
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zaffino, P., Moccia, S., De Momi, E. et al. A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future. Ann Biomed Eng 48, 2171–2191 (2020). https://doi.org/10.1007/s10439-020-02553-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-020-02553-6