Low-dose Abdominal CT Research Articles

DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment

No-reference image quality assessment (IQA) is a critical step in medical image analysis, with the objective of predicting perceptual image quality without the need for a pristine reference image. The application of no-reference IQA to CT scans is valuable in providing an automated and objective approach to assessing scan quality, optimizing radiation dose, and improving overall healthcare efficiency. In this paper, we introduce DistilIQA, a novel distilled Vision Transformer network designed for no-reference CT image quality assessment. DistilIQA integrates convolutional operations and multi-head self-attention mechanisms by incorporating a powerful convolutional stem at the beginning of the traditional ViT network. Additionally, we present a two-step distillation methodology aimed at improving network performance and efficiency. In the initial step, a ”teacher ensemble network” is constructed by training five vision Transformer networks using a five-fold division schema. In the second step, a ”student network”, comprising of a single Vision Transformer, is trained using the original labeled dataset and the predictions generated by the teacher network as new labels. DistilIQA is evaluated in the task of quality score prediction from low-dose chest CT scans obtained from the LDCT and Projection data of the Cancer Imaging Archive, along with low-dose abdominal CT images from the LDCTIQAC2023 Grand Challenge. Our results demonstrate DistilIQA‘s remarkable performance in both benchmarks, surpassing the capabilities of various CNNs and Transformer architectures. Moreover, our comprehensive experimental analysis demonstrates the effectiveness of incorporating convolutional operations within the ViT architecture and highlights the advantages of our distillation methodology.

Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial. To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V). This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality. In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared. The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30. DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

A novel protocol for abdominal low-dose CT scans adapted with a model-based iterative reconstruction method.

This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images. Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared. The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol. With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis.

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.

Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction.

The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT. This retrospective study of 9066 asymptomatic adults (mean age, 57.1 ± 7.8 [SD] years; 4020 men, 5046 women) undergoing unenhanced low-dose abdominal CT for colorectal cancer screening. A previously validated artificial intelligence (AI) tool was used to assess cross-sectional visceral and subcutaneous adipose tissue areas (SAT and VAT), as well as their ratio (VSR) at the L1 and L3 levels. Post-CT survival prediction was compared using area under the ROC curve (ROC AUC) and hazard ratios (HRs). Median clinical follow-up interval after CT was 8.8years (interquartile range, 5.2-11.6years), during which 5.9% died (532/9066). No significant difference (p > 0.05) for mortality was observed between L1 and L3 VAT and SAT at 10-year ROC AUC. However, L3 measures were significantly better for VSR at 10-year AUC (p < 0.001). HRs comparing worst-to-best quartiles for mortality at L1 vs. L3 were 2.12 (95% CI, 1.65-2.72) and 2.22 (1.74-2.83) for VAT; 1.20 (0.95-1.52) and 1.16 (0.92-1.46) for SAT; and 2.26 (1.7-2.93) and 3.05 (2.32-4.01) for VSR. In women, the corresponding HRs for VSR were 2.58 (1.80-3.69) (L1) and 4.49 (2.98-6.78) (L3). Automated CT-based measures of visceral fat (VAT and VSR) at L1 are predictive of survival, although overall measures of adiposity at L1 level are somewhat inferior to the standard L3-level measures. Utilizing predictive L1-level fat measures could expand opportunistic screening to chest CT imaging.

Low-Dose Abdominal CT for Evaluating Suspected Appendicitis: Recommendations for CT Imaging Techniques and Practical Issues

A vast disparity exists between science and practice for CT radiation dose. Despite high-level evidence supporting the use of low-dose CT (LDCT) in diagnosing appendicitis, a recent survey showed that many care providers were still concerned that the low image quality of LDCT may lead to incorrect diagnoses. For successful implementation of LDCT practice, it is important to inform and educate the care providers not only of the scientific discoveries but also of concrete guidelines on how to overcome more practical matters. Here, we discuss CT imaging techniques and other practical issues for implementing LDCT practice.

Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction

PurposeTo evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).Materials and methodsTwo patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups.ResultsThe background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001).ConclusionsDLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

Low-Dose Abdominal CT for Evaluating Suspected Appendicitis in Adolescents and Young Adults: Review of Evidence.

Due to its excellent diagnostic performance, CT is the mainstay of diagnostic test in adults with suspected acute appendicitis in many countries. Although debatable, extensive epidemiological studies have suggested that CT radiation is carcinogenic, at least in children and adolescents. Setting aside the debate over the carcinogenic risk of CT radiation, the value of judicious use of CT radiation cannot be overstated for the diagnosis of appendicitis, considering that appendicitis is a very common disease, and that the vast majority of patients with suspected acute appendicitis are adolescents and young adults with average life expectancies. Given the accumulated evidence justifying the use of low-dose CT (LDCT) of only 2 mSv, there is no reasonable basis to insist on using radiation dose of multi-purpose abdominal CT for the diagnosis of appendicitis, particularly in adolescents and young adults. Published data strongly suggest that LDCT is comparable to conventional dose CT in terms of clinical outcomes and diagnostic performance. In this narrative review, we will discuss such evidence for reducing CT radiation in adolescents and young adults with suspected appendicitis.

Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.

Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict human observer performance in clinical CT tasks that involve realistic anatomical background. Deep-learning-based model observers (DL-MO) have recently been developed, but have not been validated for challenging low contrast diagnostic tasks in abdominal CT. We consequently sought to validate a DL-MO for a low-contrast hepatic metastases localization task. We adapted our recently developed DL-MO framework for the liver metastases localization task. Our previously-validated projection-domain lesion-/noise-insertion techniques were used to synthesize realistic positive and low-dose abdominal CT exams, using the archived patient projection data. Ten experimental conditions were generated, which involved different lesion sizes/contrasts, radiation dose levels, and image reconstruction types. Each condition included 100 trials generated from a patient cohort of 7 cases. Each trial was presented as liver image patches (160×160×5 voxels). The DL-MO performance was calculated for each condition and was compared with human observer performance, which was obtained by three sub-specialized radiologists in an observer study. The performance of DL-MO and radiologists was gauged by the area under localization receiver-operating-characteristic curves. The generalization performance of the DL-MO was estimated with the repeated twofold cross-validation method over the same set of trials used in the human observer study. A multi-slice Channelized Hoteling Observers (CHO) was compared with the DL-MO across the same experimental conditions. The performance of DL-MO was highly correlated to that of radiologists (Pearson's correlation coefficient: 0.987; 95% CI: [0.942, 0.997]). The performance level of DL-MO was comparable to that of the grouped radiologists, that is, the mean performance difference was -3.3%. The CHO performance was poorer than the grouped radiologist performance, before internal noise could be added. The correlation between CHO and radiologists was weaker (Pearson's correlation coefficient: 0.812, and 95% CI: [0.378, 0.955]), and the corresponding performance bias (-29.5%) was statistically significant. The presented study demonstrated the potential of using the DL-MO for image quality assessment in patient abdominal CT tasks.

Cone beam computed tomography for detecting residual stones in percutaneous nephrolithotomy, a randomized controlled trial (CAPTURE) protocol

IntroductionPercutaneous nephrolithotomy (PCNL) is the standard surgical treatment method for large kidney stones. Its aim is to achieve a stone-free status, since any residual fragments (RFs) after PCNL are likely to cause additional morbidity or stone growth. Enhancing intraoperative detectability of RFs could lead to increased stone-free rates and decreased re-intervention rates. Cone beam computed tomography (CBCT) has recently been introduced in urology as a feasible method for intraoperatively imaging RFs. The aim of this trial is to determine the added value of CBCT in percutaneous nephrolithotomy, by measuring differences in stone-related morbidity for patients with procedures in which a CBCT is used versus patients with procedures without the use of CBCT.MethodsThe CAPTURE trial is an investigator-initiated single-center, randomized controlled trial (RCT) in adult patients who have an indication for percutaneous nephrolithotomy. A contemporary percutaneous nephrolithotomy is performed. Once the surgeon is convinced of a stone-free status by means of fluoroscopy and nephroscopy, randomization allocates patients to either the study group in whom an intraoperative CBCT scan is performed or to the control group in whom no intraoperative CBCT scan is performed. The main endpoint is the stone-free status as assessed four weeks postoperatively by low-dose non-contrast abdominal CT, as a standard follow-up procedure. Secondary endpoints include the number of PCNL procedures required and the number of stone-related events (SREs) registered. The total study population will consist of 320 patients that undergo PCNL and are eligible for randomization for an intraoperative CBCT scan.DiscussionWe deem a randomized controlled trial to be the most effective and reliable method to assess the efficacy of CBCT in PCNL. Though some bias may occur due to the impossibility of blinding the urologist at randomization, we estimate that the pragmatic nature of the study, standardized circumstances, and follow-up methods with pre-defined outcome measures will result in a high level of evidence.Trial registrationNetherlands Trial Register (NTR) NL8168, ABR NL70728.042.19. Registered on 15 October 2019. Prospectively registered.

Abdominal CT radiation dose reduction at Siriraj Hospital (Phase III)

OBJECTIVE: To compare the image quality and the radiation dose between fixed tube current (FTC) low dose abdominal CT currently performed at our hospital and new automatic tube current modulation (ATCM) low dose abdominal CT. MATERIALS AND METHODS: We prospectively performed ATCM low dose abdominal CT in 88 participants who had prior FTC low dose CT for comparison. Four experienced abdominal radiologists independently and blindly assessed the quality of FTC and ATCM low dose CT images by using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality). Each reader selected the preferred image set between FTC and ATCM low dose techniques for each participant. The image noise of the liver and the aorta in both techniques was measured. The volume CT dose index (CTDIvol) of both techniques was compared. RESULTS: The mean satisfaction scores (SD) for FTC and ATCM low dose CT were 4.38 (0.66) and 4.38 (0.64), respectively with the ranges of 3 to 5 in both techniques, which were all acceptable for CT interpretation. The preferred image set between FTC and ATCM low dose techniques of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on FTC and ATCM low dose CT accounted for 34.75 and 36.46, respectively, while the mean image noise of the liver was 28.86 and 29.81, respectively. The mean CTDIvol (SD) of FTC and ATCM low dose CT were 8.42 (0.32) and 8.12 (0.43) mGy, respectively. CONCLUSION: FTC and ATCM low dose abdominal CT provided comparable acceptable image quality and showed no clinical significance in radiation dose optimization.

A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm.

To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm. Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175-545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp for patients with BMI > 24 kg/m2) and reconstructed with DLIR at medium setting (DLIR-M) and high setting (DLIR-H), ASIR-V at 0% (FBP), 40% and 80% strength. Both the quantitative measurement and qualitative analysis of the five types of reconstruction methods were compared. In addition, radiation dose and image quality between the early-arterial phase ASIR-V images using standard-dose and the late-arterial phase DLIR images using low-dose were compared. For the late-arterial phase, all five reconstructions had similar CT value (P > 0.05). DLIR-H, DLIR-M and ASIR-V80% images significantly reduced the image noise and improved the image contrast noise ratio, compared with the standard ASIR-V40% images (P < 0.05). ASIR-V80% images had undesirable image characteristics with obvious "waxy" artifacts, while DLIR-H images maintained high spatial resolution and had the highest subjective image quality. Compared with the early-arterial scans, the late-arterial phase scans significantly reduced the radiation dose (P < 0.05), while the DLIR-H images exhibited lower image noise and good display of the specific image details of lesions. DLIR algorithm improves image quality under low-dose scan condition and may be used to reduce the radiation dose without adversely affecting the image quality.

Abdominal CT radiation dose reduction at Siriraj Hospital (Phase II)

Objective: To compare radiation dose, radiologists’ satisfaction, and image noise between the standard dose abdominal CT currently performed at our hospital and the new automatic tube current modulation (ATCM) low dose abdominal CT, using various parameters (0%, 10%, 20%, and 30%) of the Adaptive Statistical Iterative Reconstruction (ASiR). Materials and Methods: We prospectively performed the ATCM low dose abdominal CT in 111 participants who had prior standard dose CT for comparison. The ATCM low dose CT images were post processed with 4 parameters (0%, 10%, 20% and 30%) of ASiR on a CT workstation. The volume CT dose index (CTDIvol) of the ATCM low dose and the standard dose CT were compared. Four experienced abdominal radiologists independently assessed the quality of the ATCM low dose CT with the aforementioned ASiR parameters using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality) by using the prior standard dose CT as a reference of an excellent image quality (5). Each reader selected the preferred ASiR parameter for each participant. The image noise of the liver and the aorta in all 5 techniques (1 prior standard dose and 4 current ATCM low dose techniques) was measured. The correlation between the image quality vs the participants’ body mass index (BMI) and waist circumferences were analyzed. Results: The mean CTDIvol of the ATCM low dose CT was significantly lower than of the standard dose CT (7.29 ± 0.20 vs 11.28 ± 0.23 mGy, p&lt;0.001). The mean satisfaction score for the ATCM low dose CT with 0%, 10%, 20% and 30% ASiR were 4.14, 4.16, 4.17, and 4.26, respectively with the ranges of 3 to 5 in all techniques. The preferred ASiR parameters of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on the standard dose CT and the ATCM low dose CT with 0%, 10%, 20%, and 30% ASiR was 30.69, 36.60, 34.05, 31.43, and 29.09, respectively, while the mean image noise of the liver was 24.96, 29.90, 27.86, 25.66, and 23.68, respectively. There was a correlation between the image quality (satisfaction score and image noise) vs the participants’ BMI and waist circumferences. Conclusion: The ATCM low dose CT received acceptable radiologists’ satisfaction with significant radiation dose reduction. The increment of ASiR was helpful in reducing the image noise and had a tendency to increase the radiologists’ satisfaction score.

Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography.

Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information.The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

Abdominal CT radiation dose optimization at Siriraj Hospital

OBJECTIVE: To compare radiation dose and image quality between standard dose abdominal CT currently performed at our hospital and new low dose abdominal CT using various percentages (0%, 10%, 20%, and 30%) of Adaptive Statistical Iterative Reconstruction (ASiR). MATERIALS AND METHODS: We prospectively performed low dose abdominal CT (30% reduction of standard tube current) in 119 participants. The low dose CT images were post processed with four parameters (0%, 10%, 20% and 30%) of ASiR. The volume CT dose index (CTDIvol) of standard and low dose CT were compared. Four experienced abdominal radiologists independently assessed the quality of low dose CT with aforementioned ASiR parameters using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality) by using prior standard dose CT as a reference of excellent image quality (5). Each reader selected the preference ASiR parameter for each participant. The image noise of the liver and the aorta in all 5 (1 prior standard dose and 4 current low dose) image sets was measured. RESULTS: The mean CTDIvol of low dose CT was significantly lower than of standard dose CT (7.17 ± 0.08 vs 12.02 ±1.61 mGy, p&lt;0.001). The mean satisfaction scores for low dose CT with 0%, 10%, 20% and 30% ASiR were 3.95, 3.99, 3.91 and 3.87, respectively with the ranges of 3 to 5 in all techniques. The preferred ASiR parameters of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on standard dose CT and low dose CT with 0%, 10%, 20%, and 30% ASiR was 29.07, 36.97, 33.92, 31.49, and 29.11, respectively, while the mean image noise of the liver was 24.60, 30.21, 28.33, 26.25, and 24.32, respectively. CONCLUSION: Low dose CT with 30% reduction of standard mA had acceptable image quality with significantly reduced radiation dose. The increment of ASiR was helpful in reducing image noise.

Abdominal CT radiation dose optimization at Siriraj Hospital

OBJECTIVE: To compare radiation dose and image quality between standard dose abdominal CT currently performed at our hospital and new low dose abdominal CT using various percentages (0%, 10%, 20%, and 30%) of Adaptive Statistical Iterative Reconstruction (ASiR). MATERIALS AND METHODS: We prospectively performed low dose abdominal CT (30% reduction of standard tube current) in 119 participants. The low dose CT images were post processed with four parameters (0%, 10%, 20% and 30%) of ASiR. The volume CT dose index (CTDIvol) of standard and low dose CT were compared. Four experienced abdominal radiologists independently assessed the quality of low dose CT with aforementioned ASiR parameters using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality) by using prior standard dose CT as a reference of excellent image quality (5). Each reader selected the preference ASiR parameter for each participant. The image noise of the liver and the aorta in all 5 (1 prior standard dose and 4 current low dose) image sets was measured. RESULTS: The mean CTDIvol of low dose CT was significantly lower than of standard dose CT (7.17 ± 0.08 vs 12.02 ±1.61 mGy, p&lt;0.001). The mean satisfaction scores for low dose CT with 0%, 10%, 20% and 30% ASiR were 3.95, 3.99, 3.91 and 3.87, respectively with the ranges of 3 to 5 in all techniques. The preferred ASiR parameters of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on standard dose CT and low dose CT with 0%, 10%, 20%, and 30% ASiR was 29.07, 36.97, 33.92, 31.49, and 29.11, respectively, while the mean image noise of the liver was 24.60, 30.21, 28.33, 26.25, and 24.32, respectively. CONCLUSION: Low dose CT with 30% reduction of standard mA had acceptable image quality with significantly reduced radiation dose. The increment of ASiR was helpful in reducing image noise.

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults.

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.

Establishment of Submillisievert Abdominal CT Protocols With an In Vivo Swine Model and an Anthropomorphic Phantom

OBJECTIVE. The purpose of this study was to evaluate radiation dose, effective dose, and image quality of different low-dose abdominal CT protocols in a swine model and an anthropomorphic phantom using a third-generation dual-source CT scanner. MATERIALS AND METHODS. Four different abdominal low-dose protocols were established using a swine model and were regarded as diagnostic by two experienced radiologists on the basis of clarity and sharpness of anatomic structures. General image conditions such as noise and spatial resolution as well as diagnostic acceptability and artifacts were evaluated. Objective image quality was determined by measuring signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in different anatomic locations. To evaluate the effective dose, thermoluminescent dosimeter (TLD) measurements were repeated in a phantom. RESULTS. Diagnostic acceptability, spatial resolution, and noise were rated as optimal in all four protocols, which were therefore regarded as diagnostic. We found no statistically significant differences in SNR or CNR for the four low-dose protocols. Effective dose determined from the phantom measurements did not exceed 0.33 mSv for any protocol. Overall evaluation of the 86 TLD measurements for the four low-dose protocols revealed a statistically significant difference in radiation dose (p < 0.0001), showing that the dual-source protocol had the lowest radiation dose. CONCLUSION. Submillisievert abdominal CT is feasible with good image quality and doses even lower than conventional abdominal radiography. Our dual-source protocol achieved the lowest dose, which further shows that dual-source imaging is possible in the submillisievert range without additional dose.

Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader's confidence.

BackgroundIterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).PurposeTo examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.Material and MethodsThe LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.ResultsThe image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).ConclusionAn ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

A2225 The potential association of spine bone mineral density with metabolism syndromes

Objectives: To evaluate the associations between bone mineral density (BMD) and metabolism syndromes (MS) among Chinese adults Methods: 46,285 Chinese adults were recruited in a prospective urban and rural epidemiological study in China (PURE-China). During the 6-to-9-year follow-up at 2013–2017, 3,457 participants from 7 centers (Beijing, Jiangsu, Shanxi, Shaanxi, Liaoning, Jiangxi, and Sichuan) underwent a low dose abdominal CT scan with QCT capacity for BMD measurement of L1–3. MS is diagnosed if at least three of the following criteria are met: 1) waist circumference (WC) > 101 cm in males and > 89 cm in female; 2) triglycerid≥1.70mmol/L or had corresponding treatment; 3) high density lipoprotein-cholesterol < 1.0 mmol/L for males and < 1.3 mmol/L for females or had corresponding treatment; 4) systolic/diastolic blood pressure≥130/85mmHg or self-reported hypertension or had blood pressure medication; 5) fasting glucose≥5.6 mmol/L or self-reported diabetes or had anti-diabetic medication. Results: 3341 participants with complete data available were included for analysis. 54.7% from urban areas, more females were enrolled (62.7%) and mean age was 61.3 ± 9.0 years old (ranged from 40 to 81) when QCT were conducted. The mean of spine BMD was 107.6 ± 37.8 mg/cm3, and 39.8% was diagnozed as osteopenia (BMD ranged at 80–120 mg/cm3) and 24.7% as osteoporosis (BMD < 80 mg/cm3). The positive association between osteopenia or osteoporosis risk and MS was observed only among females (Odds ratio [OR], 1.67; 95% confidence interval [CI], 1.34–2.10) and older adults (OR, 1.32; 95% CI, 1.01–1.71), but not males and middle-aged adults. Conclusion: Conclusively, the result of regression analysis between MS and bone density reducation risk may be different in men and women, as well as older and middle-aged adults.