Optimizing twin-beam dual-energy CT reconstruction: Quantitative consistency and stability assessment in reference to 120 kV: An observational study.

The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.

Active learning using adaptable task-based prioritisation.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for a new kidney segmentation task, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale.

OBJECTIVES: The aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans. METHODS: Abdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC). RESULTS: Twenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396-0.814) to 0.785 (95%CI 0.676-0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034-0.500) to 0.562 (95%CI 0.337-0.729). Intraobserver agreement increased for four out of six observers. CONCLUSION: The PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement. CLINICAL RELEVANCE STATEMENT: This study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms. KEY POINTS: • Subjective assessment of diagnostic image quality via Likert scale has limited reliability. • A pairwise comparison method improves the inter- and intraobserver agreement. • The pairwise comparison method is more reliable for CT optimization studies.

Clinical evaluation of a novel multibolus contrast agent injection protocol for thoraco-abdominal CT angiography: Assessment of homogeneity of arterial contrast enhancement.

PURPOSE: To evaluate the in vivo feasibility of a multibolus contrast agent (CA) injection protocol with a reduced CA volume for thoraco-abdominal CT angiography (CTA) and to compare it to a single-bolus CA injection protocol. METHOD: 63 patients who underwent CTA with the multibolus protocol (60 ml CA) were divided in two groups either without (group 1, n = 48) or with (group 2, n = 15) aortic dissection. The aortic contrast enhancement was measured in group 1 using manual ROI analysis (10 segments), as well as semi-automated linear attenuation profiles. A subgroup (n = 18) of group 1, who also underwent imaging with the single-bolus protocol (94 ml CA), was used to compare both protocols. In group 2, differences in attenuation of the true and the false lumen for both the single- and the multibolus protocol were assessed with ROI attenuation measurements in both lumina. Comparisons were made using Wilcoxon test. RESULTS: Average attenuation was above 200 HU for 98 % of cases using the multibolus protocol. There was superior contrast homogeneity for the multibolus protocol with a lower standard deviation of attenuation values along the length of the scan (p = 0.003), while average attenuation was higher for the single-bolus protocol (p = 0.002). Prolonged enhancement plateau lead to a more uniform opacification of the true and the false lumen in patients with aortic dissection using the multibolus protocol (p = 0.012). CONCLUSIONS: The multibolus protocol in thoraco-abdominal CTA is feasible in patients. It shows consistently high arterial enhancement with superior contrast homogeneity compared to a single-bolus protocol in patients with and without aortic dissection.

Method of determining geometric patient size surrogates using localizer images in CT.

PURPOSE: Size-specific dose estimates (SSDE) requires accurate estimates of patient size surrogates. AAPM Report 204 shows that the SSDE is the product of CTDIvol and a scaling factor, the normalized dose coefficient (NDC) which depends on patient size surrogates for CT axial images. However, SSDE can be determined from CT localizer prior to CT scanning. AAPM Report 220 charges that a magnification correction is needed for geometric patient size-surrogates. In this study, we demonstrate a novel "model-based" magnification correction on patient data. METHODS: 573 patient scans obtained from a clinical CT system including 229 adult abdomen, 284 adult chest, 48 pediatric abdomen, and 12 pediatric chest exams. LAT and AP dimensions were extracted from CT localizers using a threshold extraction method (the ACR DIR). The model-based magnification correction was applied to the AP and LAT dimensions extracted using the ACR DIR. NDC was calculated using the effective diameter for the ACR DIR only, the model-based localizer-based and axial-based approaches. The LAT and AP dimensions were extracted from the "gold" standard CT axial scans. Outliers are defined as points outside the 95% confidence intervals and were analyzed. RESULTS: NDC estimates for the localizer-based model-based approach had an excellent correlation (R2  = 0.92) with the gold standard approach. The effective diameter for ACR DIR and model-based approaches are 8.0% and 1.0% greater than the gold standard respectively. Outliers were determined to be primarily patient truncation, with arms down or with devices. ACR DIR size extraction method fails for bariatric patients where the threshold is too high and some of their anatomy was included in the CT couch, and small patients due to the CT couch being included in the size measurement. CONCLUSION: The model-based magnification method gives an accurate estimate of patient size surrogates extracted from CT localizers that are needed for calculating NDC to achieve accurate SSDE.

Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment.

OBJECTIVE: To investigate a fully automated abdominal CT-based muscle tool in a large adult screening population. METHODS: A fully automated validated muscle segmentation algorithm was applied to 9310 non-contrast CT scans, including a primary screening cohort of 8037 consecutive asymptomatic adults (mean age, 57.1±7.8 years; 3555M/4482F). Sequential follow-up scans were available in a subset of 1171 individuals (mean interval, 5.1 years). Muscle tissue cross-sectional area and attenuation (Hounsfield unit, HU) at the L3 level were assessed, including change over time. RESULTS: Mean values were significantly higher in males for both muscle area (190.6±33.6 vs 133.3±24.1 cm2, p<0.001) and density (34.3±11.1 HU vs 27.3±11.7 HU, p<0.001). Age-related losses were observed, with mean muscle area reduction of -1.5 cm2/year and attenuation reduction of -1.5 HU/year. Overall age-related muscle density (attenuation) loss was steeper than for muscle area for both sexes up to the age of 70 years. Between ages 50 and 70, relative muscle attenuation decreased significantly more in females (-30.6% vs -18.0%, p<0.001), whereas relative rates of muscle area loss were similar (-8%). Between ages 70 and 90, males lost more density (-22.4% vs -7.5%) and area (-13.4% vs -6.9%, p<0.001). Of the 1171 patients with longitudinal follow-up, 1013 (86.5%) showed a decrease in muscle attenuation, 739 (63.1%) showed a decrease in area, and 1119 (95.6%) showed a decrease in at least one of these measures. CONCLUSION: This fully automated CT muscle tool allows for both individualized and population-based assessment. Such data could be automatically derived at abdominal CT regardless of study indication, allowing for opportunistic sarcopenia detection. ADVANCES IN KNOWLEDGE: This fully automated tool can be applied to routine abdominal CT scans for prospective or retrospective opportunistic sarcopenia assessment, regardless of the original clinical indication. Mean values were significantly higher in males for both muscle area and muscle density. Overall age-related muscle density (attenuation) loss was steeper than for muscle area for both sexes, and therefore may be a more valuable predictor of adverse outcomes.

Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks.

OBJECTIVES: The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated construction of patient-specific computational phantoms based on actual patient CT images to enable accurate estimation of conceptus dose. METHODS: We developed a 3D deep convolutional network algorithm for automated segmentation of CT images to build realistic computational phantoms. The neural network architecture consists of analysis and synthesis paths with four resolution levels each, trained on manually labeled CT scans of six identified anatomical structures. Thirty-two CT exams were augmented to 128 datasets and randomly split into 80%/20% for training/testing. The absorbed doses for six segmented organs/tissues from abdominal CT scans were estimated using Monte Carlo calculations. The resulting radiation doses were then compared between the computational models generated using automated segmentation and manual segmentation, serving as reference. RESULTS: The Dice similarity coefficient for identified internal organs between manual segmentation and automated segmentation results varies from 0.92 to 0.98 while the mean Hausdorff distance for the uterus is 16.1 mm. The mean absorbed dose for the uterus is 2.9 mGy whereas the mean organ dose differences between manual and automated segmentation techniques are 0.07%, - 0.45%, - 1.55%, - 0.48%, - 0.12%, and 0.28% for the kidney, liver, lung, skeleton, uterus, and total body, respectively. CONCLUSION: The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures. KEY POINTS: • The conceptus dose during diagnostic radiology and nuclear medicine imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. • The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures. • The dosimetric results can be used for the risk-benefit analysis of radiation hazards to conceptus from diagnostic imaging procedures, thus guiding the decision-making process.

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p < 0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80-0.94), performed statistically significantly better (p < .001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52-0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p < 0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions.

Understanding the Constipation Conundrum: Predictors of Obtaining an Abdominal Radiograph During the Emergency Department Evaluation of Pediatric Constipation.

OBJECTIVES: Many children with constipation who are evaluated in emergency departments (EDs) receive an abdominal radiograph (AR) despite evidence-based guidelines discouraging imaging. The objectives of this study were to identify predictors associated with obtaining an AR and to determine if ARs were associated with a longer length of stay (LOS) among children with constipation evaluated in the ED. METHODS: A review of billing and electronic health records was conducted in an academic pediatric ED for children ages 0 to 17 years who had a primary discharge diagnosis of constipation from July 2013 to June 2014. Logistic regression was used to identify predictors for obtaining an AR. Differences in mean LOS were analyzed using linear regression. RESULTS: In total, 326 children met inclusion criteria, and 60% of the children received an AR. In logistic regression, significant predictors included age (odds ratio [OR] = 1.1/year of age, P = 0.004), presenting with abdominal pain as chief complaint compared with constipation (OR = 4.4, P < 0.0001), and history of emesis (OR = 2.8, P = 0.001) after controlling for provider type and previous constipation medication use. In linear regression, the adjusted mean LOS for those with an AR was 163 minutes compared with 117 minutes for those without after controlling for age, provider type, and history of constipation medication use (P < 0.0001). CONCLUSIONS: Abdominal radiographs were used frequently in the ED diagnosis and management of constipation, particularly in older children and those with abdominal pain and emesis. Abdominal radiographs were associated with increased LOS.

Opportunistic Measurement of Skeletal Muscle Size and Muscle Attenuation on Computed Tomography Predicts 1-Year Mortality in Medicare Patients.

BACKGROUND: Opportunistic assessment of sarcopenia on CT examinations is becoming increasingly common. This study aimed to determine relationships between CT-measured skeletal muscle size and attenuation with 1-year risk of mortality in older adults enrolled in a Medicare Shared Savings Program (MSSP). METHODS: Relationships between skeletal muscle metrics and all-cause mortality were determined in 436 participants (52% women, mean age 75 years) who had abdominopelvic CT examinations. On CT images, skeletal muscles were segmented at the level of L3 using two methods: (a) all muscles with a threshold of -29 to +150 Hounsfield units (HU), using a dedicated segmentation software, (b) left psoas muscle using a free-hand region of interest tool on a clinical workstation. Muscle cross-sectional area (CSA) and muscle attenuation were measured. Cox regression models were fit to determine the associations between muscle metrics and mortality, adjusting for age, sex, race, smoking status, cancer diagnosis, and Charlson comorbidity index. RESULTS: Within 1 year of follow-up, 20.6% (90/436) participants died. In the fully-adjusted model, higher muscle index and muscle attenuation were associated with lower risk of mortality. A one-unit standard deviation (SD) increase was associated with a HR = 0.69 (95% CI = 0.49, 0.96; p = .03) for total muscle index, HR = 0.67 (95% CI = 0.49, 0.90; p < .01) for psoas muscle index, HR = 0.54 (95% CI = 0.40, 0.74; p < .01) for total muscle attenuation, and HR = 0.79 (95% CI = 0.66, 0.95; p = .01) for psoas muscle attenuation. CONCLUSION: In older adults, higher skeletal muscle index and muscle attenuation on abdominopelvic CT examinations were associated with better survival, after adjusting for multiple risk factors.

Diabetes risk assessment with imaging: a radiomics study of abdominal CT.

OBJECTIVES: To identify CT markers for screening of early type 2 diabetes and assessment of the risk of incident diabetes using a radiomics method. METHODS: The medical records of 26,947 inpatients were reviewed. A total of 690 patients were selected and allocated to a primary cohort, a validation cohort, and a prediction cohort and used to build prediction models for diabetes. Three radiomics signatures were constructed using CT image features extracted from three regions of interest, i.e., in the pancreas, liver, and psoas major muscle. By incorporating radiomics signatures and other markers, we built a radiomics nomogram that could be used to screen for early diabetes and predict future diabetes. RESULTS: Of the three abdominal organs for which radiomics signature were constructed, that of the pancreas showed the best discriminatory power for early diabetes screening and prediction (C-statistics of 0.833, 0.846, and 0.899 for the primary cohort, validation cohort, and prediction cohort, respectively). The sensitivity and specificity of the nomogram for prediction of 3-year incident diabetes were 0.827 and 0.807, respectively. CONCLUSIONS: This study presents alternative radiomics markers that have potential for use in screening for undiagnosed type 2 diabetes and prediction of 3-year incident diabetes. KEY POINTS: • CT images may provide useful information to evaluate the risk of developing diabetes. • Radiomics score for diabetes prediction is based on subtle changes of abdominal organs detected by CT. • The radiomics signature of pancreas, a combination of five features of CT images, is efficient for early diabetes screening and prediction of future diabetes (AUC > 0.8).

Image quality assessment with dose reduction using high kVp and additional filtration for abdominal digital radiography.

PURPOSE: Dose reduction using additional filters with high kilovoltage peak (kVp) for abdominal digital radiography has received much attention recently. We evaluated image quality with dose reduction in abdominal digital radiography by using high kVp and additional copper filters at a tertiary hospital. METHODS: Between June 2016 and July 2016, 82 patients underwent abdominal digital radiography using 80 kVp in X-ray room 1 and 82 were imaged using 92 kVp with 0.1-mm copper filtration in X-ray room 2. The effective dose was calculated using a PC-based Monte Carlo program. Image quality of the abdominal radiography acquired in the two rooms was evaluated using a five-point ordinal scale, as well as the signal-to-noise and contrast-to-noise ratios. RESULTS: The mean effective dose decreased by 25.8% and 25.7% for the supine and standing positions, respectively, when abdominal digital radiography using 92 kVp with 0.1-mm copper filtration was performed. In the 20 patients who performed abdominal digital radiography twice in each room, visual grading scores for visualisation of psoas outlines and kidney outlines are higher in room 1. However, there was no statistical significant difference of visual grading scores among the 124 patients who underwent only one abdominal radiography in the room 1 or 2 (P > 0.05). CONCLUSIONS: Dose reduction for abdominal digital radiography can be achieved with comparable image quality by performing abdominal digital radiography using 92 kVp with 0.1-mm copper filtration, despite the higher AEC dose.

Application of 80-kVp scan and raw data-based iterative reconstruction for reduced iodine load abdominal-pelvic CT in patients at risk of contrast-induced nephropathy referred for oncological assessment: effects on radiation dose, image quality and renal function.

OBJECTIVE: To evaluate the image quality, radiation dose, and renal safety of contrast medium (CM)-reduced abdominal-pelvic CT combining 80-kVp and sinogram-affirmed iterative reconstruction (SAFIRE) in patients with renal dysfunction for oncological assessment. METHODS: We included 45 patients with renal dysfunction (estimated glomerular filtration rate  <45 ml per min per 1.73 m2) who underwent reduced-CM abdominal-pelvic CT (360 mgI kg-1, 80-kVp, SAFIRE) for oncological assessment. Another 45 patients without renal dysfunction (estimated glomerular filtration rate >60 ml per lmin per 1.73 m2) who underwent standard oncological abdominal-pelvic CT (600 mgI kg-1, 120-kVp, filtered-back projection) were included as controls. CT attenuation, image noise, and contrast-to-noise ratio (CNR) were compared. Two observers performed subjective image analysis on a 4-point scale. Size-specific dose estimate and renal function 1-3 months after CT were measured. RESULTS: The size-specific dose estimate and iodine load of 80-kVp protocol were 32 and 41%,, respectively, lower than of 120-kVp protocol (p < 0.01). CT attenuation and contrast-to-noise ratio of parenchymal organs and vessels in 80-kVp images were significantly better than those of 120-kVp images (p < 0.05). There were no significant differences in quantitative or qualitative image noise or subjective overall quality (p > 0.05). No significant kidney injury associated with CM administration was observed. CONCLUSION: 80-kVp abdominal-pelvic CT with SAFIRE yields diagnostic image quality in oncology patients with renal dysfunction under substantially reduced iodine and radiation dose without renal safety concerns. Advances in knowledge: Using 80-kVp and SAFIRE allows for 40% iodine load and 32% radiation dose reduction for abdominal-pelvic CT without compromising image quality and renal function in oncology patients at risk of contrast-induced nephropathy.

Comparing Dose-Length Product-Based and Monte Carlo Simulation Organ-Based Calculations of Effective Dose in 16- and 64-MDCT Examinations Using Automatic Tube Current Modulation.

OBJECTIVE: The purpose of this study is to compare dose-length product (DLP)-based calculation of effective dose (EDDLP) with Monte Carlo simulation organ-based calculation of effective dose (EDMCO) in 16- and 64-MDCT examinations, with the use of clinical examinations with automatic tube current modulation. MATERIALS AND METHODS: Dose data were obtained from 50 consecutive unenhanced head CT examinations, unenhanced chest CT examinations, and unenhanced and contrast-enhanced abdominopelvic CT examinations performed using 16- and 64-MDCT scanners, as well as from 50 pulmonary CT angiography (CTA) examinations performed using a 64-MDCT scanner and 31 pulmonary CTA examinations performed using a 16-MDCT scanner. The EDMCO and the mean patient effective diameter were calculated using commercially available software. The EDDLP was also calculated. Both the mean difference and percentage difference between EDDLP and EDMCO were calculated, and they were statistically compared according to patient sex, type of examination performed, and type of scanner used. RESULTS: EDDLP significantly underestimated the EDMCO by 0.3 mSv (19%) for men who underwent unenhanced head CT, 0.5 mSv (29%) for women who underwent unenhanced head CT, 0.9-1.4 mSv (9-13%) for men who underwent chest CT, and 4.7-4.8 mSv (39%) for women who underwent chest CT (p < 0.001). The EDDLP overestimated the EDMCO by 1.9-2.0 mSv (12-14%) for men who underwent abdominopelvic CT (p < 0.001), with no significant difference noted for women who underwent abdominopelvic CT's. No significant difference was noted in the percentage difference in ED between the 16- and 64-MDCT scanners (p ≥ 0.13). CONCLUSION: EDDLP underestimates EDMCO, the reference standard for dose calculation, by 19-39% in unenhanced head CT examinations and, among women, in chest CT examinations. EDDLP deviates from EDMCO by less than 15% for chest CT examinations of men and for abdominopelvic CT. These differences can be attributed to variable patient body habitus, automatic tube current modulation, and sex-neutral k-coefficients, and they should be considered when calculating ED, particularly in women.

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction.

PURPOSE: To estimate potential dose reduction in abdominal CT by visually comparing images reconstructed with filtered back projection (FBP) and strengths of 3 and 5 of a specific MBIR. MATERIAL AND METHODS: A dual-source scanner was used to obtain three data sets each for 50 recruited patients with 30, 70 and 100% tube loads (mean CTDIvol 1.9, 3.4 and 6.2 mGy). Six image criteria were assessed independently by five radiologists. Potential dose reduction was estimated with Visual Grading Regression (VGR). RESULTS: Comparing 30 and 70% tube load, improved image quality was observed as a significant strong effect of log tube load and reconstruction method with potential dose reduction relative to FBP of 22-47% for MBIR strength 3 (p < 0.001). For MBIR strength 5 no dose reduction was possible for image criteria 1 (liver parenchyma), but dose reduction between 34 and 74% was achieved for other criteria. Interobserver reliability showed agreement of 71-76% (κw 0.201-0.286) and intra-observer reliability of 82-96% (κw 0.525-0.783). CONCLUSION: MBIR showed improved image quality compared to FBP with positive correlation between MBIR strength and increasing potential dose reduction for all but one image criterion. KEY POINTS: • MBIR's main advantage is its de-noising properties, which facilitates dose reduction. • MBIR allows for potential dose reduction in relation to FBP. • Visual Grading Regression (VGR) produces direct numerical estimates of potential dose reduction. • MBIR strengths 3 and 5 dose reductions were 22-34 and 34-74%. • MBIR strength 5 demonstrates inferior performance for liver parenchyma.

Assessment of CT dose to the fetus and pregnant female patient using patient-specific computational models.

PURPOSE: This work provides detailed estimates of the foetal dose from diagnostic CT imaging of pregnant patients to enable the assessment of the diagnostic benefits considering the associated radiation risks. MATERIALS AND METHODS: To produce realistic biological and physical representations of pregnant patients and the embedded foetus, we developed a methodology for construction of patient-specific voxel-based computational phantoms based on existing standardised hybrid computational pregnant female phantoms. We estimated the maternal absorbed dose and foetal organ dose for 30 pregnant patients referred to the emergency unit of Geneva University Hospital for abdominal CT scans. RESULTS: The effective dose to the mother varied from 1.1 mSv to 2.0 mSv with an average of 1.6 mSv, while commercial dose-tracking software reported an average effective dose of 1.9 mSv (range 1.7-2.3 mSv). The foetal dose normalised to CTDIvol varies between 0.85 and 1.63 with an average of 1.17. CONCLUSION: The methodology for construction of personalised computational models can be exploited to estimate the patient-specific radiation dose from CT imaging procedures. Likewise, the dosimetric data can be used for assessment of the radiation risks to pregnant patients and the foetus from various CT scanning protocols, thus guiding the decision-making process. KEY POINTS: • In CT examinations, the absorbed dose is non-uniformly distributed within foetal organs. • This work reports, for the first time, estimates of foetal organ-level dose. • The foetal brain and skeleton doses present significant correlation with gestational age. • The conceptus dose normalised to CTDI vol varies between 0.85 and 1.63. • The developed methodology is adequate for patient-specific CT radiation dosimetry.

Automatic calculation of patient size metrics in computed tomography: What level of computational accuracy do we need?

OBJECTIVES: To compare the effectiveness of two different patient size metrics based on water equivalent diameter (Dw ), the mid-scan water equivalent diameter Dw_c , and the mean (average) water equivalent diameter in the imaged region, Dw_ave , for automatic detection of accidental changes in computed tomography (CT) acquisition protocols. METHODS: Patient biometric data (height and weight) were available from a previous survey for 80 adult chest examinations, and 119 adult single-acquisition chest-abdomen-pelvis (CAP) examinations for two 16 slice scanners (GE LightSpeed and Toshiba Aquilion RXL) equipped with automatic tube current modulation (ATCM). Dw_c and Dw_ave were calculated from the archived CT images. Size-specific dose estimates (SSDE) were obtained from volume CT dose index (CTDIvol ), using the conversion factors for a patient diameter of Dw_c . RESULTS: CTDIvol and SSDE correlate better with Dw_ave than with Dw_c . R-squared values of linear fits to CTDIvol of CAP examinations were 0.81-0.89 for Dw_c and 0.93-0.94 for Dw_ave (SSDE: 0.69-080 for Dw_c , 0.87-0.92 for Dw_ave ). Percentage differences between Dw_c and Dw_ave were -4 ± 4% for chest and +5 ± 4% for CAP examinations (in % of Dw_ave ). However, small Dw variations translated as larger variations in CTDIvol for these ATCM systems (e.g., a 24% increase in Dw doubled CTDIvol ). The dependence of CTDIvol on Dw_ave was similar for chest and CAP examinations performed with similar ATCM parameters, while use of Dw_c resulted in a clear separation of the same data according to examination type. Maximum Dw variation in the imaged region was 5.6 ± 1.6 cm for chest and 6.5 ± 1.4 cm for CAP examinations. CONCLUSIONS: Dw_ave is a better metric than Dw_c for binning similar-sized patients in dose comparison studies, despite the additional computational effort required for its calculation Therefore, when implementing automatic determination of Dw for SSDE calculations, automatic calculation of Dw_ave should be considered.

Evaluation of AAPM Reports 204 and 220: Estimation of effective diameter, water-equivalent diameter, and ellipticity ratios for chest, abdomen, pelvis, and head CT scans.

PURPOSE: To confirm AAPM Reports 204/220 and provide data for the future expansion of these reports by: (a) presenting the first large-scale confirmation of the reports using clinical data, (b) providing the community with size surrogate data for the head region which was not provided in the original reports, and additionally providing the measurements of patient ellipticity ratio for different body regions. METHOD: A total of 884 routine scans were included in our analysis including data from the head, thorax, abdomen, and pelvis for adults and pediatrics. We calculated the ellipticity ratio and all of the size surrogates presented in AAPM Reports 204/220. We correlated the purely geometric-based metrics with the "gold standard" water-equivalent diameter (DW ). RESULTS: Our results and AAPM Reports 204/220 agree within our data's 95% confidence intervals. Outliers to the AAPM reports' methods were caused by excess gas in the GI tract, exceptionally low BMI, and cranial metaphyseal dysplasia. For the head, we show lower correlation (R2 = 0.812) between effective diameter and DW relative to other body regions. The ellipticity ratio of the shoulder region was the highest at 2.28 ± 0.22 and the head the smallest at 0.85 ± 0.08. The abdomen pelvis, chest, thorax, and abdomen regions all had ellipticity values near 1.5. CONCLUSION: We confirmed AAPM reports 204/220 using clinical data and identified patient conditions causing discrepancies. We presented new size surrogate data for the head region and for the first time presented ellipticity data for all regions. Future automatic exposure control characterization should include ellipticity information.

Improved assessment of mediastinal and pulmonary pathologies in combined staging CT examinations using a fast-speed acquisition dual-source CT protocol.

OBJECTIVES: To demonstrate the feasibility of fast Dual-Source CT (DSCT) and to evaluate the clinical utility in chest/abdomen/pelvis staging CT studies. METHODS: 45 cancer patients with two follow-up combined chest/abdomen/pelvis staging CT examinations (maximally ±10 kV difference in tube potential) were included. The first scan had to be performed with our standard protocol (fixed pitch 0.6), the second one using a novel fast-speed DSCT protocol (fixed pitch 1.55). Effective doses (ED) were calculated, noise measurements performed. Scan times were compared, motion artefacts and the diagnostic confidence rated in consensus reading. RESULTS: ED for the standard and fast-speed scans was 9.1 (7.0-11.1) mSv and 9.2 (7.4-12.8) mSv, respectively (P = 0.075). Image noise was comparable (abdomen; all P > 0.05) or reduced for fast-speed CTs (trachea, P = 0.001; ascending aorta, P < 0.001). Motion artefacts of the heart/the ascending aorta (all P < 0.001) and breathing artefacts (P < 0.031) were reduced in fast DSCT. The diagnostic confidence for the evaluation of mediastinal (P < 0.001) and pulmonary (P = 0.008) pathologies was improved for fast DSCT. CONCLUSIONS: Fast DSCT for chest/abdomen/pelvis staging CT examinations is performed within 2 seconds scan time and eliminates relevant intrathoracic motion/breathing artefacts. Mediastinal/pulmonary pathologies can thus be assessed with high diagnostic confidence. Abdominal image quality remains excellent. KEY POINTS: • Fast dual-source CT provides chest/abdomen/pelvis staging examinations within 2 seconds scan time. • The sevenfold scan time reduction eliminates relevant intrathoracic motion/breathing artefacts. • Mediastinal/pulmonary pathologies can now be assessed with high diagnostic confidence. • The coverage of the peripheral soft tissues is comparable to single-source CT. • Fast and large-volume oncologic DSCT can be performed with 9 mSv effective dose.