Introduction

In recent years, iterative reconstruction (IR) techniques have been provided by all major manufacturers of clinical computed tomography (CT) scanners as an alternative image reconstruction algorithm. IR can improve the image quality by reducing image noise and some artefacts, and it has the potential to reduce radiation dose compared with filtered back projection (FBP), which has been used as a standard reconstruction algorithm for several decades. Adaptive iterative dose reduction 3-dimensional (AIDR 3-D, Toshiba Medical Systems, Otawara, Japan)　is one of the hybrid IR algorithms, which involves blending with FBP to keep the noise characteristics and image textures. AIDR 3-D requires relatively short reconstruction time and is easy to introduce into daily practice. Some previous studies showed the usefulness of this algorithm in reduced-dose abdominal or pelvic CT [1–9].

Although many reports have been published regarding the utility of IR algorithms in reduced-dose CT, only a few reports investigated the appropriate dose for reduced-dose abdominal CT that would not impair lesion detectability. Maintaining diagnostic performance, not reducing objective image noise, would be the most important issue when we reduce the radiation dose of CT using the IR algorithm. In order to analyze the diagnostic performance precisely, it would be desirable to use a free-response methodology, such as the jackknife free-response receiver-operating characteristic (JAFROC) analysis, using phantoms with randomly placed lesions [10–12]. To the best of our knowledge, no report has investigated the diagnostic performance of reduced-dose abdominal CT with hybrid IR using JAFROC analysis. Moreover, the diagnostic performance of reduced-dose CT with hybrid IR for the detection of hypervascular lesions of the liver has not been well discussed. The purpose of this study was to investigate the diagnostic performance of reduced-dose CT with AIDR 3-D for the detection of hypervascular liver lesions in comparison with control-dose CT with FBP using JAFROC methodology.

Materials and methods

Phantom

Custom-made spherical simulated lesions made from polyurethane and hydroxyapatite with two different diameters (5 and 10 mm) and two different CT numbers (approximately +120 and +140 Hounsfield units [HU]) were prepared. Lesions with CT numbers of +120 and +140 HU were used to simulate low-contrast and high-contrast hypervascular liver lesions (LCLs and HCLs), respectively (Fig. 1). Simulated liver parenchyma was made from diluted nonionic contrast material (Iomeron, Eisai, Tokyo, Japan) jellified with 1% agar in cylindrical containers with a diameter of 170 mm and made with polyethylene. The height of simulated liver parenchyma was approximately 110 mm. The concentration of the contrast material was adjusted so that the simulated liver parenchyma had a CT number of approximately +100 HU (3.2 mg iodine per mL). Simulated liver lesions were randomly placed within the simulated liver parenchyma in the process of jellifying, and the locations of the lesions were recorded. The number of lesions in each phantom was randomly selected from zero, one, or two for each of four different types of lesions (5 mm LCL, 10 mm LCL, 5 mm HCL, and 10 mm HCL). Thus, the total numbers of lesions per phantom ranged from zero to eight. Thirty such phantoms were prepared, of which 28 contained two-eight lesions (mean, 4.7 lesions), whereas two contained no lesions.

Fig. 1
figure 1

An example 5-mm-thick image of a phantom scanned with a control-dose setting and reconstructed with FBP. High-contrast lesions (arrows) and low-contrast lesions (arrowheads) are placed within the simulated liver parenchyma

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CT examination

All phantoms were scanned with a 320-slice scanner (AquilionONE, Toshiba Medical Systems) in the helical scanning mode with beam collimation of 64 × 0.5 mm with three different tube current settings. The tube current of the control-dose (CD) scan was set at 80 mA, which yielded a standard deviation (SD) of approximately ten for the CT number of simulated liver parenchyma on 5-mm-thick images in the preliminary study. As reduced-dose scans, 75% and 50% dose (60 and 40 mA, respectively) scans were used. Other settings were consistent for all scans. Acquisition and reconstruction protocols are summarized in Table 1. The scan length in the direction of the z-axis was 130 mm, which could cover the entire simulated liver parenchyma. Images of the CD scan were reconstructed with FBP, and images of the 75% and 50% scans were reconstructed with FBP and AIDR 3D Mild. Both 5-mm and 2-mm-thick images were reconstructed because they were routinely used for abdominal CT in daily practice in our institution. A quantum denoising filter (QDS), which was applied to 2-mm-thick FBP images in our institution, was used for 2-mm-thick images reconstructed with FBP.

Table 1 Acquisition and reconstruction parameters of CT examination
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Quantitative analysis

For quantitative analysis, five image sets (CD FBP images, 75% dose FBP images, 75% AIDR 3-D images, 50% dose FBP images, and 50% AIDR 3-D images) of 5-mm-thick images were prepared. A radiologist placed a circular region of interest (ROI) of approximately 100 mm2 on the simulated liver parenchyma, and the average and SD of the CT number were recorded. The CT numbers of HCLs with a diameter of 10 mm were also measured. The ROI placement was repeated three times, and the value was averaged. The SD of the CT number of simulated liver parenchyma was used as the index of image noise. The lesion to liver contrast-to-noise ratio (CNR) was calculated using the following equation:

$$ \mathrm{C}\mathrm{N}\mathrm{R} = \left({\mathrm{CT}}_{\mathrm{HCL}}\hbox{--}\ {\mathrm{CT}}_{\mathrm{Liver}}\right)/{\mathrm{SD}}_{\mathrm{Liver},} $$

where CTHCL and CTLiver represent the CT numbers of simulated high-contrast lesions and simulated liver parenchyma, respectively. SDLiver represents the SD of CTLiver. Small lesions (5 mm in diameter) were not included in the evaluation of CNR to avoid the partial volume effect.

Qualitative analysis

For the evaluation of diagnostic performance, five image sets (CD FBP images, 75% dose FBP images, 75% AIDR 3-D images, 50% dose FBP images, and 50% AIDR 3-D images) of 2-mm-thick images were prepared. In the present study, 2-mm-thick images rather than 5-mm-thick images were used for qualitative analysis because small lesions (5 mm in diameter) would be hard to detect on 5-mm-thick images when the lesions were placed at the boundary of two adjacent slices. Each image set contained 66 images per phantom. Two blinded radiologists familiar with abdominal radiology (8 and 4 years of experience, respectively) independently assessed these image sets on a commercially available workstation (Ziostation2 version 2.4.0.3, Ziosoft, Tokyo, Japan), and they classified all detected lesions using the following 4-point confidence score scale: 1, probably no lesion present; 2, indefinite presence of lesion; 3, lesion probably present; and 4, lesion definitely present. The readers were informed that there might be some hyperdensity lesions within the phantom, and the lesions would be at least 5 mm in diameter, but they were blinded to the number and location of lesions, scanning protocols, and reconstruction algorithms. A confidence score of 3 or 4 was considered a positive diagnosis. The assessments of each image set were separated by at least 2 weeks, and the orders of the 30 phantoms were randomized for every image set to minimize recall bias.

Statistical analysis

Objective image noise and CNR were compared among five image sets using Scheffe’s F test. To compare diagnostic performance, JAFROC analysis was performed using JAFROC software (JAFROC Version 4.2.1, www.devchakraborty.com). The software computes a figure of merit (FOM), which is defined as the probability that a lesion is rated higher than the highest rated non-lesion on a normal image [12]. In this study, the JAFROC1 method was used because JARFOC1 rather than the JAFROC or JAFROC2 method is recommended due to its high statistical power for human observers [10, 13]. JAFROC1 FOMs were compared among five image sets. Sensitivity was also compared among five images using the chi-squared test with Bonferroni correction (n = 10). A P value less than .05 for quantitative analysis and JAFROC1 analysis, and a P value less than .005 for sensitivity were considered significant.

Results

Quantitative analysis

The results of quantitative analysis for objective image noise and CNR are summarized in Figs. 2 and 3, respectively. AIDR 3-D images of 75% and 50% dose yielded significantly lower noise and higher CNR than FBP images of corresponding doses (P < .01). AIDR 3-D achieved about 32% and 39% image noise reduction and 44% and 60% CNR increase in 75% and 50% dose scans, respectively. Even 50% dose AIDR 3-D images yielded significantly lower noise and higher CNR than CD FBP images (P < .05).

Fig. 2
figure 2

Box and whisker plots show the results of objective image noise for each image set. The plots indicate the median (centre line), 75th percentile (top of box), 25th percentile (bottom of box), the smallest and largest nonoutlier values (whisker), and outliers (circle)

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Fig. 3
figure 3

Box and whisker plots show the CNR results for each image set. The plots indicate the median (centre line), 75th percentile (top of box), 25th percentile (bottom of box), the smallest and largest nonoutlier values (whisker), and outliers (circle)

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Qualitative analysis

The results of JAFROC1 analysis are summarized in Table 2. With both readers, FOM tended to be lower for images with a lower radiation dose (Fig 4). For 75% dose scans, a significant difference compared to CD FBP images was observed only in FBP images with Reader 1 (P < .01). For 50% dose scans, FOMs of both FBP and AIDR 3-D image sets were significantly lower than that of CD FBP images with both readers (P < .01). For both 50% and 75% scans, AIDR 3-D images and FBP images of corresponding doses were not significantly different in FOMs with both readers.

Table 2 JAFROC1 Figure of merit
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Fig 4
figure 4

JAFROC figure of merit for each image set. Error bars represent the 95% confidence intervals

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Sensitivity for detection of simulated liver lesions is summarized in Table 3. Sensitivity tended to be lower for images with lower radiation dose with both readers (Fig. 5). Improvement of sensitivity was not observed in AIDR 3-D images compared to FBP images of corresponding doses with Reader 1 for 75% dose scans and with both readers for 50% dose scans. Decreased sensitivity in lower dose images was prominent in the detection of LCLs or small lesions compared to that of HCLs or large lesions. The number of detected HCLs decreased by 3-6% in 50% dose images compared to CD FBP images, whereas that of detected LCLs decreased by 19-35%. The number of detected large lesions decreased by 3-6% in 50% dose images compared to CD FBP images, whereas that of detected small lesions decreased by 17-31%. For all lesions, LCLs and small lesions, the sensitivity of 50% images (both FBP and AIDR 3D) was significantly lower than that of CD FBP images with Reader 2 (P < .005).

Table 3 Sensitivity for detection of simulated liver lesions
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Fig. 5
figure 5

Sensitivity for detection of simulated liver lesions. Columns show the mean sensitivity for each image set. Error bars represent the 95% confidence intervals

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Discussion

The present results demonstrated that AIDR 3D could significantly reduce the objective image noise and increase the CNR. Even 50% dose images with AIDR 3D Mild yielded significantly lower image noise and higher CNR compared with CD FBP images. Thus, at least 50% dose reduction would be accepted from the perspective of objective image quality. These results were concordant with some previous reports [1, 3, 6–8].

However, on JAFROC1 analysis, diagnostic performance of reduced-dose CT was not improved by using AIDR 3-D, and the FOM of 50% dose images was significantly lower than that of CD FBP images, regardless of the reconstruction algorithm. Moreover, the diagnostic performance of 75% dose AIDR 3-D images was also inferior to that of CD FBP images, even though the difference was not significant. The sensitivity of reduced-dose CT images was also inferior to that of CD FBP images, and improvement was not observed by AIDR 3-D. LCLs and small lesions especially tended to be hard to detect in lower-dose images (Fig. 6), and up to 35% of LCLs and 31% of small lesions could be missed in 50% dose images even with the use of AIDR 3-D. These results indicated that an aggressive reduction of radiation dose would decrease the detectability of low-contrast lesions, although objective image noise would be maintained by AIDR 3-D.

Fig. 6
figure 6

An example of small LCLs scanned with three different tube current settings. In the CD FBP image, a small lesion can be recognized (arrow). In the 50% FBP image, this lesion is obscured by increased image noise and is hard to recognize. The conspicuity of this lesion is not improved by AIDR 3-D

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A number of previous studies reported the usefulness of the IR algorithm in reduced-dose CT based on the results of quantitative analyses and the assessment of subjective image quality. Only a few studies have investigated the diagnostic performance of reduced-dose CT with IR in a blind fashion, and some previous phantom studies have shown that low-contrast detectability would not be preserved by hybrid IR when the radiation dose was reduced [14–17]. Phantom studies conducted by Schindera et al. reported that AIDR 3-D images could not preserve low-contrast detectability as the radiation dose decreased [15, 16]. Jensen et al. showed that some hybrid IR algorithms including AIDR 3-D did not improve lesion detection [17]. On the other hand, Joemai et al. reported that AIDR 3-D provided better low-contrast detectability than FBP [18], although a model observer, not human observers was used. To the best of our knowledge, no studies have demonstrated an improvement of lesion detectability by AIDR 3-D in reduced-dose CT on the basis of blind reading by human observers. The present study would strongly support these previous reports. Thus, we believe that we should not reduce the radiation of CT with AIDR 3-D without careful consideration even if the objective image noise can be preserved.

QDS, which is one type of edge-preserving adaptive filter, was applied to 2-mm-thick images reconstructed with FBP in this study because it was used for thin-slice images with FBP in daily practice in our institution. Low-contrast detectability could be improved by this filter [19], and the results might differ if FBP images without QDS were used for the evaluation of lesion detectability. We think, however, that it would be necessary to maintain comparable diagnostic performance to the CD FBP images with QDS in order to apply the reduced-dose scan with hybrid IR to daily practice.

One of the reasons why the diagnostic performance was not preserved in lower-dose settings with AIDR 3-D might be the difference in the noise power spectrum (NPS). A previous report evaluating the noise characteristics of model-based iterative reconstruction (MBIR [Veo®, GE Healthcare, Waukesha, WI]) showed that the shapes of the NPS of MBIR were dose-dependent, and lower doses led to NPSs with lower mean and peak frequencies [20]. The detectability of low-contrast lesions is mainly affected by the low spatial frequency components of the noise rather than the overall amount of the noise [21]. Thus, the diagnostic performance would not be substantially improved unless the low-frequency components were reduced, even if the overall amount of noise could be reduced by IR algorithms. Although the present study did not investigate the NPS of AIDR 3-D, a previous study indicated the possibility of a small change in the shape of the NPS with AIDR 3-D [18]. Further study will be needed to confirm the relationship between the shape of the NPS of IR algorithms and low-contrast detectability.

Recently, some manufacturers have implemented new IR algorithms, such as MBIR, iterative model reconstruction (IMR, Philips Healthcare, Best, The Netherlands), advanced modelled iterative reconstruction (ADMIRE, Siemens Medical Solutions, Forchheim, Germany), and forward projected model-based iterative reconstruction solution (FIRST, Toshiba Medical Systems) [20–38]. These are forward projection-based IR algorithms and can yield substantially higher noise reduction than hybrid IR algorithms [23]. Some previous phantom studies demonstrated that these newly developed IR algorithms improved the low-contrast detectability of reduced-dose CT [17, 27, 28]. Thus, forward projection-based IR is a promising technique for the reduction of radiation dose without impairing low-contrast detectability. Further phantom or clinical studies using free-response methodology are needed to confirm the improvement of lesion detectability by forward projection-based IR and determine the appropriate radiation dose for reduced-dose scans.

This study had several limitations. First, the phantoms contained only simulated liver parenchyma and liver lesions, and other structures such as vessels, bones, and subcutaneous fat were not simulated. Although beam hardening artefacts caused by ribs would be one of the major factors that affect lesion conspicuity in reduced-dose images, the effect of the reduction of this artefact by AIDR 3-D on the diagnostic performance could not be investigated in the present study. Second, only hypervascular lesions were simulated in this study, and hypovascular lesions were not included. However, the detection of hypervascular lesions is one of the major tasks of dynamic contrast-enhanced CT. Thus, preserving the detectability of hypervascular lesions would be essential for reduced-dose liver CT. Third, only AIDR 3-D Mild, which is one of 4 IR strengths of AIDR 3-D (Weak [available only in Japan], Mild, Standard, and Strong) was evaluated. AIDR 3-D Strong would reduce the objective image noise more strongly in lower dose settings. However, improvement of diagnostic performance would not be expected even by stronger IR presets, as a previous study indicated [17].

In conclusion, AIDR 3-D would not improve diagnostic performance for detecting hypervascular liver lesions. We have to recognize that an aggressive reduction of the radiation dose would impair diagnostic performance, although a dose reduction of more than 50% could be achieved using AIDR 3-D in terms of objective image noise and CNR.