Enhancing diagnostic precision for acute chest syndrome in sickle cell disease: insights from dual-energy CT lung perfusion mapping.

PURPOSE: Acute chest syndrome (ACS) is secondary to occlusion of the pulmonary vasculature and a potentially life-threatening complication of sickle cell disease (SCD). Dual-energy CT (DECT) iodine perfusion map reconstructions can provide a method to visualize and quantify the extent of pulmonary microthrombi. METHODS: A total of 102 patients with sickle cell disease who underwent DECT CTPA with perfusion were retrospectively identified. The presence or absence of airspace opacities, segmental perfusion defects, and acute or chronic pulmonary emboli was noted. The number of segmental perfusion defects between patients with and without acute chest syndrome was compared. Sub-analyses were performed to investigate robustness. RESULTS: Of the 102 patients, 68 were clinically determined to not have ACS and 34 were determined to have ACS by clinical criteria. Of the patients with ACS, 82.4% were found to have perfusion defects with a median of 2 perfusion defects per patient. The presence of any or new perfusion defects was significantly associated with the diagnosis of ACS (P = 0.005 and < 0.001, respectively). Excluding patients with pulmonary embolism, 79% of patients with ACS had old or new perfusion defects, and the specificity for new perfusion defects was 87%, higher than consolidation/ground glass opacities (80%). CONCLUSION: DECT iodine map has the capability to depict microthrombi as perfusion defects. The presence of segmental perfusion defects on dual-energy CT maps was found to be associated with ACS with potential for improved specificity and reclassification.

Optimisation of virtual monoenergetic reconstructions for the diagnosis of pulmonary embolism using photon-counting detector computed tomography angiography.

Purpose: Computed tomography (CT) pulmonary angiography is considered the gold standard for pulmonary embolism (PE) diagnosis, relying on the discrimination between contrast and embolus. Photon-counting detector CT (PCD-CT) generates monoenergetic reconstructions through energy-resolved detection. Virtual monoenergetic images (VMI) at low keV can be used to improve pulmonary artery opacification. While studies have assessed VMI for PE diagnosis on dual-energy CT (DECT), there is a lack of literature on optimal settings for PCD-CT-PE reconstructions, warranting further investigation. Material and methods: Twenty-five sequential patients who underwent PCD-CT pulmonary angiography for suspicion of acute PE were retrospectively included in this study. Quantitative metrics including signal-to-noise ratio (SNR) and contrast-to-noise (CNR) ratio were calculated for 4 VMI values (40, 60, 80, and 100 keV). Qualitative measures of diagnostic quality were obtained for proximal to distal pulmonary artery branches by 2 cardiothoracic radiologists using a 5-point modified Likert scale. Results: SNR and CNR were highest for the 40 keV VMI (49.3 ± 22.2 and 48.2 ± 22.1, respectively) and were inversely related to monoenergetic keV. Qualitatively, 40 and 60 keV both exhibited excellent diagnostic quality (mean main pulmonary artery: 5.0 ± 0 and 5.0 ± 0; subsegmental pulmonary arteries 4.9 ± 0.1 and 4.9 ± 0.1, respectively) while distal segments at high (80-100) keVs had worse quality. Conclusions: 40 keV was the best individual VMI for the detection of pulmonary embolism by quantitative metrics. Qualitatively, 40-60 keV reconstructions may be used without a significant decrease in subjective quality. VMIs at higher keV lead to reduced opacification of the distal pulmonary arteries, resulting in decreased image quality.

Non-contrast computed tomography findings for identification of chronically occluded coronary artery bypass grafts.

BACKGROUND: Detecting occlusions of coronary artery bypass grafts using non-contrast computed tomography (CT) series is understudied and underestimated. PURPOSE: To evaluate morphological findings for the diagnosis of chronic coronary artery bypass graft occlusion on non-contrast CT and investigate performance statistics for potential use cases. MATERIAL AND METHODS: Seventy-three patients with coronary artery bypass grafts who had CT angiography of the chest (non-contrast and arterial phases) were retrospectively included. Two readers applied pre-set morphologic findings to assess the patency of a bypass graft on non-contrast series. These findings included vessel shape (linear-band like), collapsed lumen and surgical graft marker without a visible vessel. Performance was tested using the simultaneously acquired arterial phase series as the ground truth. RESULTS: The per-patient diagnostic accuracy for occlusion was 0.890 (95% confidence interval = 0.795-0.951). Venous grafts overall had an 88% accuracy. None of the left internal mammary artery to left anterior descending artery arterial graft occlusions were detected. The negative likelihood ratio for an occluded graft that is truly patent was 0.121, demonstrating a true post-test probability of 97% for identifying a patent graft as truly patent given a prevalence of 20% occlusion at a median 8.4 years post-surgery. Neither years post-surgery, nor number of vessels was associated with a significant decrease in reader accuracy. CONCLUSION: Evaluation of coronary bypass grafts for chronic occlusion on non-contrast CT based off vessel morphology is feasible and accurate for venous grafts. Potential use cases include low-intermediate risk patients with chest pain or shortness of breath for whom non-contrast CT was ordered, or administration of iodine-based contrast is contraindicated.

Left atrial calcification on chest CT: atrial ablation replaces rheumatic heart disease as the most identified etiology.

Purpose: Left atrial calcification (LAC), a primarily radiologic diagnosis, has been associated with rheumatic heart disease (RHD) and rheumatic fever (RF). However, left atrial calcification continues to be observed despite a significant decrease in the prevalence of rheumatic heart disease. The purpose of this study was to investigate other possible etiologies of left atrial calcification. Material and methods: This retrospective, observational single-center study included patients from 2017 to 2022 identified as having left atrial calcification as well as age- and sex-matched controls. The prevalence of rheumatic heart disease, atrial ablation, and mitral valve disease was compared, and odds ratios were calculated for each independent variable. Results: Sixty-two patients with left atrial calcifications were included and compared with 62 controls. 87.1% of patients in the left atrial calcifications cohort had a history of atrial fibrillation compared with 21% in the control cohort (p < 0.001). 16.1% of patients in the calcifications cohort presented a history of rheumatic fever compared with zero in the control cohort (p = 0.004). 66.1% of the left atrial calcifications cohort had a history of atrial ablation compared with 6.5% of the control group (p < 0.001). The odds ratio for left atrial calcification was 19.0 vs. 4.8 for rheumatic fever (comparative odds = 4.0 for atrial ablation vs. rheumatic fever). Multivariable log model found atrial ablation to explain 79.8% of left atrial calcifications identified. Conclusions: Our study found a 4-fold higher association between history of atrial ablation and left atrial calcification compared with rheumatic heart disease, suggesting a potential shift in etiology.

Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes.

Background The role of CT angiography-derived fractional flow reserve (CT-FFR) in pre-transcatheter aortic valve replacement (TAVR) assessment is uncertain. Purpose To evaluate the predictive value of on-site machine learning-based CT-FFR for adverse clinical outcomes in candidates for TAVR. Materials and Methods This observational retrospective study included patients with severe aortic stenosis referred to TAVR after coronary CT angiography (CCTA) between September 2014 and December 2019. Clinical end points comprised major adverse cardiac events (MACE) (nonfatal myocardial infarction, unstable angina, cardiac death, or heart failure admission) and all-cause mortality. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm. The ability of CT-FFR (abnormal if ≤0.75) to predict outcomes and improve the predictive value of the current noninvasive work-up was assessed. Survival analysis was performed, and the C-index was used to assess the performance of each predictive model. To compare nested models, the likelihood ratio χ2 test was performed. Results A total of 196 patients (mean age ± standard deviation, 75 years ± 11; 110 women [56%]) were included; the median time of follow-up was 18 months. MACE occurred in 16% (31 of 196 patients) and all-cause mortality in 19% (38 of 196 patients). Univariable analysis revealed CT-FFR was predictive of MACE (hazard ratio [HR], 4.1; 95% CI: 1.6, 10.8; P = .01) but not all-cause mortality (HR, 1.2; 95% CI: 0.6, 2.2; P = .63). CT-FFR was independently associated with MACE (HR, 4.0; 95% CI: 1.5, 10.5; P = .01) when adjusting for potential confounders. Adding CT-FFR as a predictor to models that include CCTA and clinical data improved their predictive value for MACE (P = .002) but not all-cause mortality (P = .67), and it showed good discriminative ability for MACE (C-index, 0.71). Conclusion CT angiography-derived fractional flow reserve was associated with major adverse cardiac events in candidates for transcatheter aortic valve replacement and improved the predictive value of coronary CT angiography assessment. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Choe in this issue.

Diagnostic accuracy and performance of artificial intelligence in measuring left atrial volumes and function on multiphasic CT in patients with atrial fibrillation.

OBJECTIVES: To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation. METHODS: We included 79 patients (mean age 63 ± 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients. RESULTS: The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p < 0.001) than controls (all ICCs ≥ 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by ≤ 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62). CONCLUSION: The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients. KEY POINTS: • There was good-to-excellent agreement between manual and automated methods for left atrial volume quantification. • The AI comparably estimated LA volumes in AF patients, but overestimated volumes by clinically negligible amounts in controls. • The AI's ability to distinguish AF patients from controls was similar to the manual methods.

Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study.

BACKGROUND. Deep learning-based convolutional neural networks have enabled major advances in development of artificial intelligence (AI) software applications. Modern AI applications offer comprehensive multiorgan evaluation. OBJECTIVE. The purpose of this article was to evaluate the impact of an automated AI platform integrated into clinical workflow for chest CT interpretation on radiologists' interpretation times when evaluated in a real-world clinical setting. METHODS. In this prospective single-center study, a commercial AI software solution was integrated into clinical workflow for chest CT interpretation. The software provided automated analysis of cardiac, pulmonary, and musculoskeletal findings, including labeling, segmenting, and measuring normal structures as well as detecting, labeling, and measuring abnormalities. AI-annotated images and autogenerated summary results were stored in the PACS and available to interpreting radiologists. A total of 390 patients (204 women, 186 men; mean age, 62.8 ± 13.3 [SD] years) who underwent out-patient chest CT between January 19, 2021, and January 28, 2021, were included. Scans were randomized using 1:1 allocation between AI-assisted and non-AI-assisted arms and were clinically interpreted by one of three cardiothoracic radiologists (65 scans per arm per radiologist; total of 195 scans per arm) who recorded interpretation times using a stopwatch. Findings were categorized according to review of report impressions. Interpretation times were compared between arms. RESULTS. Mean interpretation times were significantly shorter in the AI-assisted than in the non-AI-assisted arm for all three readers (289 ± 89 vs 344 ± 129 seconds, p < .001; 449 ± 110 vs 649 ± 82 seconds, p < .001; 281 ± 114 vs 348 ± 93 seconds, p = .01) and for readers combined (328 ± 122 vs 421 ± 175 seconds, p < .001). For readers combined, the mean difference was 93 seconds (95% CI, 63-123 seconds), corresponding with a 22.1% reduction in the AI-assisted arm. Mean interpretation time was also shorter in the AI-assisted arm compared with the non-AI-assisted arm for contrast-enhanced scans (83 seconds), noncontrast scans (104 seconds), negative scans (84 seconds), positive scans without significant new findings (117 seconds), and positive scans with significant new findings (92 seconds). CONCLUSION. Cardiothoracic radiologists exhibited a 22.1% reduction in chest CT interpretations times when they had access to results from an automated AI support platform during real-world clinical practice. CLINICAL IMPACT. Integration of the AI support platform into clinical workflow improved radiologist efficiency.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. METHODS: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. RESULTS: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). CONCLUSION: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Fast Brain Magnetic Resonance Imaging With Half-Fourier Acquisition With Single-Shot Turbo Spin Echo Sequence in Detection of Intracranial Hemorrhage and Skull Fracture in General Pediatric Patients: Preliminary Results.

OBJECTIVE: The objective of this study was to determine the accuracy of fast brain magnetic resonance imaging (MRI) in the detection of intra- and extra-axial intracranial hemorrhage compared with standard-of-care computed tomography (CT) or MRI in pediatric patients. Unlike previous studies, we did not focus exclusively on patients with head trauma. We evaluated the fast brain MRI findings in a general pediatric population referred for indications other than evaluation of ventricular size. METHODS: We retrospectively reviewed 48 pediatric patients with indications other than hydrocephalus and shunt follow-up, who underwent a standard head CT or standard MRI within 15 days of the fast brain MRI. All fast brain MRI scans included half-Fourier acquisition with single-shot turbo spin echo (HASTE) sequences in the axial, coronal, and sagittal plane. Two neuroradiologists blinded to patient information and study indications reviewed the fast brain MRI studies independently and then concurrently. RESULTS: A total of 48 patients met the inclusion and exclusion criteria. The median and mean time interval between the standard and fast imaging were 2 and 3.9 days, respectively. The sensitivity and specificity of fast brain MRI to detect intraparenchymal hemorrhage were 100% and 97%, respectively. The sensitivity and specificity of fast brain MRI in the detection of extra-axial hemorrhage (subdural and/or epidural) were 86% and 96%, respectively. The sensitivity and specificity of fast brain MRI were, respectively, 10% and 100% for subarachnoid hemorrhage, 50% and 100% for intraventricular hemorrhage, and 47% and 97% for skull fracture, respectively. CONCLUSIONS: Our results show that fast brain MRI with HASTE sequence is as sensitive as CT and standard MRI in the detection of intra-axial hemorrhage and has moderate sensitivity in the detection of extra-axial hemorrhage. Our preliminary results show that T2-weighted HASTE imaging may be suitable for the follow-up of intraparenchymal and extra-axial (subdural and/or epidural) hemorrhages.

Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI.

OBJECTIVE. The purpose of this study was to evaluate agreement among radiologists in detecting and assessing prostate cancer at multiparametric MRI using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2). MATERIALS AND METHODS. Treatment-naïve patients underwent 3-T multipara-metric MRI between April 2012 and June 2015. Among the 163 patients evaluated, 110 underwent prostatectomy after MRI and 53 had normal MRI findings and transrectal ultrasound-guided biopsy results. Nine radiologists participated (three each with high, intermediate, and low levels of experience). Readers interpreted images of 58 patients on average (range, 56-60) using PI-RADSv2. Prostatectomy specimens registered to MRI were ground truth. Interob-server agreement was evaluated with the index of specific agreement for lesion detection and kappa and proportion of agreement for PI-RADS category assignment. RESULTS. The radiologists detected 336 lesions. Sensitivity for index lesions was 80.9% (95% CI, 75.1-85.9%), comparable across reader experience (p = 0.392). Patient-level specificity was experience dependent; highly experienced readers had 84.0% specificity versus 55.2% for all others (p < 0.001). Interobserver agreement was excellent for detecting index lesions (index of specific agreement, 0.871; 95% CI, 0.798-0.923). Agreement on PI-RADSv2 category assignment of index lesions was moderate (κ = 0.419; 95% CI, 0.238-0.595). For individual category assignments, proportion of agreement was slight for PI-RADS category 3 (0.208; 95% CI, 0.086-0.284) but substantial for PI-RADS category 4 (0.674; 95% CI, 0.540-0.776). However, proportion of agreement for T2-weighted PI-RADS 4 in the transition zone was 0.250 (95% CI, 0.108-0.372). Proportion of agreement for category assignment of index lesions on dynamic contrast-enhanced MR images was 0.822 (95% CI, 0.728-0.903), on T2-weighted MR images was 0.515 (95% CI, 0.430-0623), and on DW images was 0.586 (95% CI, 0.495-0.682). Proportion of agreement for dominant lesion was excellent (0.828; 95% CI, 0.742-0.913). CONCLUSION. Radiologists across experience levels had excellent agreement for detecting index lesions and moderate agreement for category assignment of lesions using PI-RADS. Future iterations of PI-RADS should clarify PI-RADS 3 and PI-RADS 4 in the transition zone.

All over the map: An interobserver agreement study of tumor location based on the PI-RADSv2 sector map.

BACKGROUND: Prostate imaging reporting and data system version 2 (PI-RADSv2) recommends a sector map for reporting findings of prostate cancer mulitparametric MRI (mpMRI). Anecdotally, radiologists may demonstrate inconsistent reproducibility with this map. PURPOSE: To evaluate interobserver agreement in defining prostate tumor location on mpMRI using the PI-RADSv2 sector map. STUDY TYPE: Retrospective. POPULATION: Thirty consecutive patients who underwent mpMRI between October, 2013 and March, 2015 and who subsequently underwent prostatectomy with whole-mount processing. FIELD STRENGTH: 3T mpMRI with T2 W, diffusion-weighted imaging (DWI) (apparent diffusion coefficient [ADC] and b-2000), dynamic contrast-enhanced (DCE). ASSESSMENT: Six radiologists (two high, two intermediate, and two low experience) from six institutions participated. Readers were blinded to lesion location and detected up to four lesions as per PI-RADSv2 guidelines. Readers marked the long-axis of lesions, saved screen-shots of each lesion, and then marked the lesion location on the PI-RADSv2 sector map. Whole-mount prostatectomy specimens registered to the MRI served as ground truth. Index lesions were defined as the highest grade lesion or largest lesion if grades were equivalent. STATISTICAL TEST: Agreement was calculated for the exact, overlap, and proportion of agreement. RESULTS: Readers detected an average of 1.9 lesions per patient (range 1.6-2.3). 96.3% (335/348) of all lesions for all readers were scored PI-RADS ≥3. Readers defined a median of 2 (range 1-18) sectors per lesion. Agreement for detecting index lesions by screen shots was 83.7% (76.1%-89.9%) vs. 71.0% (63.1-78.3%) overlap agreement on the PI-RADS sector map (P < 0.001). Exact agreement for defining sectors of detected index lesions was only 21.2% (95% confidence interval [CI]: 14.4-27.7%) and rose to 49.0% (42.4-55.3%) when overlap was considered. Agreement on defining the same level of disease (ie, apex, mid, base) was 61.4% (95% CI 50.2-71.8%). DATA CONCLUSION: Readers are highly likely to detect the same index lesion on mpMRI, but exhibit poor reproducibility when attempting to define tumor location on the PI-RADSv2 sector map. The poor agreement of the PI-RADSv2 sector map raises concerns its utility in clinical practice. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:482-490.

Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study.

OBJECTIVES: To evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists. METHODS: Nine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone-peripheral (PZ) and transition (TZ). RESULTS: Index lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001). CONCLUSIONS: CAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience. KEY POINTS: • Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI. • CAD assistance improves agreement between radiologists in detecting prostate cancer lesions. • However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone. • CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence.

Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2.

Purpose To validate the dominant pulse sequence paradigm and limited role of dynamic contrast material-enhanced magnetic resonance (MR) imaging in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 for prostate multiparametric MR imaging by using data from a multireader study. Materials and Methods This HIPAA-compliant retrospective interpretation of prospectively acquired data was approved by the local ethics committee. Patients were treatment-naïve with endorectal coil 3-T multiparametric MR imaging. A total of 163 patients were evaluated, 110 with prostatectomy after multiparametric MR imaging and 53 with negative multiparametric MR imaging and systematic biopsy findings. Nine radiologists participated in this study and interpreted images in 58 patients, on average (range, 56-60 patients). Lesions were detected with PI-RADS version 2 and were compared with whole-mount prostatectomy findings. Probability of cancer detection for overall, T2-weighted, and diffusion-weighted (DW) imaging PI-RADS scores was calculated in the peripheral zone (PZ) and transition zone (TZ) by using generalized estimating equations. To determine dominant pulse sequence and benefit of dynamic contrast-enhanced (DCE) imaging, odds ratios (ORs) were calculated as the ratio of odds of cancer of two consecutive scores by logistic regression. Results A total of 654 lesions (420 in the PZ) were detected. The probability of cancer detection for PI-RADS category 2, 3, 4, and 5 lesions was 15.7%, 33.1%, 70.5%, and 90.7%, respectively. DW imaging outperformed T2-weighted imaging in the PZ (OR, 3.49 vs 2.45; P = .008). T2-weighted imaging performed better but did not clearly outperform DW imaging in the TZ (OR, 4.79 vs 3.77; P = .494). Lesions classified as PI-RADS category 3 at DW MR imaging and as positive at DCE imaging in the PZ showed a higher probability of cancer detection than did DCE-negative PI-RADS category 3 lesions (67.8% vs 40.0%, P = .02). The addition of DCE imaging to DW imaging in the PZ was beneficial (OR, 2.0; P = .027), with an increase in the probability of cancer detection of 15.7%, 16.0%, and 9.2% for PI-RADS category 2, 3, and 4 lesions, respectively. Conclusion DW imaging outperforms T2-weighted imaging in the PZ; T2-weighted imaging did not show a significant difference when compared with DW imaging in the TZ by PI-RADS version 2 criteria. The addition of DCE imaging to DW imaging scores in the PZ yields meaningful improvements in probability of cancer detection. © RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on July 27, 2017. Online supplemental material is available for this article.

Does Abstinence From Ejaculation Before Prostate MRI Improve Evaluation of the Seminal Vesicles?

OBJECTIVE: The purpose of the present study is to determine whether abstinence from ejaculation before undergoing multiparametric prostate MRI increases seminal vesicle (SV) volume and therefore improves diagnostic interpretation of the SVs. MATERIALS AND METHODS: This retrospective study included 238 patients who underwent 3-T MRI of the prostate over a 4-month period. Patients were requested to complete a questionnaire that asked how long it had been since their last ejaculation (i.e., < 3 days vs ≥ 3 days). Forty-two patients (mean patient age, 62.0 years) indicated that it had been less than 3 days since their last ejaculation and were designated as group 1, whereas the remainder indicated an interval of 3 days or more since their last ejaculation. A group of 42 age-matched subjects (mean patient age, 62.1 years) were randomly selected from the remaining 196 patients and were designated as group 2. SV volumes were measured manually. Two radiologists who were blinded to group assignment and patient characteristics scored the right and left SVs separately to determine diagnostic interpretability, which was scored on a 3-point scale as follows: a score of 1 denoted that the SVs were not dilated and the score was nondiagnostic, a score of 2 indicated that the SVs were not dilated but the score was diagnostic, and a score of 3 denoted that the SVs were dilated and the score was diagnostic. Volume differences and interpretability scores were analyzed using a t test. Interobserver agreement was analyzed using the Cohen kappa statistic. A separate analysis was performed to evaluate differences in diagnostic interpretability for patients 60 years and younger versus patients older than 60 years, by use of the chi-square test and relative risk ratio analysis. RESULTS: The right, left, and total SV volumes for group 1 were 3.1 mL, 2.9 mL, and 6.0 mL, respectively, whereas those for group 2 were 4.7 mL, 4.1 mL, and 8.8 mL, respectively (p = 0.011). The mean interpretability scores for group 1 and group 2 were 2.0 and 2.5, respectively. For group 1, reader 1 and reader 2 assigned a nondiagnostic score for 10 and 13 patients, respectively, whereas for group 2, they assigned a nondiagnostic score for two and five patients, respectively (p = 0.01, for reader 1; and p = 0.03, for reader 2). For men in group 1 who were older than 60 years, reader 1 and reader 2 gave a nondiagnostic score for nine and 11 patients, respectively; whereas for men in group 2 who were older than 60 years, the readers gave a nondiagnostic score for two and five patients, respectively (p = 0.01, for reader 1; and p = 0.05, for reader 2). CONCLUSION: For men older than 60 years, abstinence from ejaculation for 3 or more days before undergoing MRI examination resulted in larger SV volumes and lower rates of nondiagnostic evaluation and therefore might improve evaluation of SV invasion on multi-parametric MRI. The difference is less striking in men 60 years and younger.

Improved Detection of Small and Low-Density Plaques in Virtual Noncontrast Imaging-based Calcium Scoring at Photon-Counting Detector CT.

Purpose To investigate the impact of plaque size and density on virtual noncontrast (VNC)-based coronary artery calcium scoring (CACS) using photon-counting detector CT and to provide safety net reconstructions for improved detection of subtle plaques in patients whose VNC-based CACS would otherwise be erroneously zero when compared with true noncontrast (TNC)-based CACS. Materials and Methods In this prospective study, CACS was evaluated in a phantom containing calcifications with different diameters (5, 3, and 1 mm) and densities (800, 400, and 200 mg/cm3) and in participants who underwent TNC and contrast-enhanced cardiac photon-counting detector CT (July 2021-March 2022). VNC images were reconstructed at different virtual monoenergetic imaging (55-80 keV) and quantum iterative reconstruction (QIR) levels (QIR,1-4). TNC scans at 70 keV with QIR off served as the reference standard. In vitro CACS was analyzed using standard settings (3.0-mm sections, kernel Qr36, 130-HU threshold). Calcification detectability and CACS of small and low-density plaques were also evaluated using 1.0-mm sections, kernel Qr44, and 120- or 110-HU thresholds. Safety net reconstructions were defined based on background Agatston scores and evaluated in vivo in TNC plaques initially nondetectable using standard VNC reconstructions. Results The in vivo cohort included 63 participants (57.8 years ± 15.5 [SD]; 37 [59%] male, 26 [41%] female). Correlation and agreement between standard CACSVNC and CACSTNC were higher in large- and medium-sized and high- and medium-density than in low-density plaques (in vitro: intraclass correlation coefficient [ICC] ≥ 0.90; r > 0.9 vs ICC = 0.20-0.48; r = 0.5-0.6). Small plaques were not detectable using standard VNC reconstructions. Calcification detectability was highest using 1.0-mm sections, kernel Qr44, 120- and 110-HU thresholds, and QIR level of 2 or less VNC reconstructions. Compared with standard VNC, using safety net reconstructions (55 keV, QIR 2, 110-HU threshold) for in vivo subtle plaque detection led to higher detection (increased by 89% [50 of 56]) and improved correlation and agreement of CACSVNC with CACSTNC (in vivo: ICC = 0.51-0.61; r = 0.6). Conclusion Compared with TNC-based calcium scoring, VNC-based calcium scoring was limited for small and low-density plaques but improved using safety net reconstructions, which may be particularly useful in patients with low calcium scores who would otherwise be treated based on potentially false-negative results. Keywords: Coronary Artery Calcium CT, Photon-Counting Detector CT, Virtual Noncontrast, Plaque Size, Plaque Density Supplemental material is available for this article. © RSNA, 2024.

Photon Counting Detector CT-Based Virtual Noniodine Reconstruction Algorithm for In Vitro and In Vivo Coronary Artery Calcium Scoring: Impact of Virtual Monoenergetic and Quantum Iterative Reconstructions.

OBJECTIVES: The aim of this study was to evaluate the impact of virtual monoenergetic imaging (VMI) and quantum iterative reconstruction (QIR) on the accuracy of coronary artery calcium scoring (CACS) using a virtual noniodine (VNI) reconstruction algorithm on a first-generation, clinical, photon counting detector computed tomography system. MATERIALS AND METHODS: Coronary artery calcium scoring was evaluated in an anthropomorphic chest phantom simulating 3 different patient sizes by using 2 extension rings (small: 300 × 200 mm, medium: 350 × 250 mm, large: 400 × 300 mm) and in patients (n = 61; final analyses only in patients with coronary calcifications [n = 34; 65.4 ± 10.0 years; 73.5% male]), who underwent nonenhanced and contrast-enhanced, electrocardiogram-gated, cardiac computed tomography on a photon counting detector system. Phantom and patient data were reconstructed using a VNI reconstruction algorithm at different VMI (55-80 keV) and QIR (strength 1-4) levels (CACS VNI ). True noncontrast (TNC) scans at 70 keV and QIR "off" were used as reference for phantom and patient studies (CACS TNC ). RESULTS: In vitro and in vivo CACS VNI showed strong correlation ( r > 0.9, P < 0.001 for all) and excellent agreement (intraclass correlation coefficient > 0.9 for all) with CACS TNC at all investigated VMI and QIR levels. Phantom and patient CACS VNI significantly increased with decreasing keV levels (in vitro: from 475.2 ± 26.3 at 80 keV up to 652.5 ± 42.2 at 55 keV; in vivo: from 142.5 [7.4/737.7] at 80 keV up to 248.1 [31.2/1144] at 55 keV; P < 0.001 for all), resulting in an overestimation of CACS VNI at 55 keV compared with CACS TNC at 70 keV in some cases (in vitro: 625.8 ± 24.4; in vivo: 225.4 [35.1/959.7]). In vitro CACS increased with rising QIR at low keV. In vivo scores were significantly higher at QIR 1 compared with QIR 4 only at 60 and 80 keV (60 keV: 220.3 [29.6-1060] vs 219.5 [23.7/1048]; 80 keV: 152.0 [12.0/735.6] vs 142.5 [7.4/737.7]; P < 0.001). CACS VNI was closest to CACS TNC at 60 keV, QIR 2 (+0.1%) in the small; 55 keV, QIR 1 (±0%) in the medium; 55 keV, QIR 4 (-0.1%) in the large phantom; and at 60 keV, QIR 1 (-2.3%) in patients. CONCLUSIONS: Virtual monoenergetic imaging reconstructions have a significant impact on CACS VNI . The effects of different QIR levels are less consistent and seem to depend on several individual conditions, which should be further investigated.

Application of an artificial intelligence ensemble for detection of important secondary findings on lung ventilation and perfusion SPECT-CT.

RATIONALE: Single-photon-emission-computerized-tomography/computed-tomography(SPECT/CT) is commonly used for pulmonary disease. Scant work has been done to determine ability of AI for secondary findings using low-dose-CT(LDCT) attenuation correction series of SPECT/CT. METHODS: 120 patients with ventilation-perfusion-SPECT/CT from 9/1/21-5/1/22 were included in this retrospective study. AI-RAD companion(VA10A,Siemens-Healthineers, Erlangen, Germany), an ensemble of deep-convolutional-neural-networks was evaluated for the detection of pulmonary nodules, coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss. Accuracy, sensitivity, specificity was measured for the outcomes. Inter-rater reliability were measured. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC) by comparing the number of nodules identified by the AI to radiologist. RESULTS: Overall per-nodule accuracy, sensitivity, and specificity for detection of lung nodules were 0.678(95%CI 0.615-0.732), 0.956(95%CI 0.900-0.985), and 0.456(95%CI 0.376-0.543), respectively, with an intraclass correlation coefficient (ICC) between AI and radiologist of 0.78(95%CI 0.71-0.83). Overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.939(95%CI 0.878-0.975), 0.974(95%CI 0.925-0.995), and 0.857(95%CI 0.781-0.915), respectively. Sensitivity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.898(95%CI 0.778-0.966), 1 (95%CI 0.958-1), and 1 (95%CI 0.961-1), respectively. Specificity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.969(95% CI 0.893-0.996), 0.897 (95% CI 0.726-0.978), and 0.346 (95% CI 0.172-0.557), respectively. CONCLUSION: AI ensemble was accurate for coronary artery calcium and aortic ectasia/aneurysm, while sensitive for aortic ectasia/aneurysm, lung nodules and vertebral height loss on LDCT attenuation correction series of SPECT/CT.

Ultra-high-resolution photon-counting detector computed tomography of the lungs: Phantom and clinical assessment of radiation dose and image quality.

PURPOSE: Photon-counting-detector computed tomography (PCD-CT) offers enhanced noise reduction, spatial resolution, and image quality in comparison to energy-integrated-detectors CT (EID-CT). These hypothesized improvements were compared using PCD-CT ultra-high (UHR) and standard-resolution (SR) scan-modes. METHODS: Phantom scans were obtained with both EID-CT and PCD-CT (UHR, SR) on an adult body-phantom. Radiation dose was measured and noise levels were compared at a minimum achievable slice thickness of 0.5 mm for EID-CT, 0.2 mm for PCD-CT-UHR and 0.4 mm for PCD-CT-SR. Signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were calculated for five tissue densities. Additionally, data from 25 patients who had PCD-CT of chest were reconstructed at 1 mm and 0.2 mm (UHR) slice-thickness and compared quantitatively (SNR) and qualitatively (noise, quality, sharpness, bone details). RESULTS: Phantom PCD-CT-UHR and PCD-CT-SR scans had similar measured radiation dose (16.0mGy vs 15.8 mGy). Phantom PCD-CT-SR (0.4 mm) had lower noise level in comparison to EID-CT (0.5 mm) (9.0HU vs 9.6HU). PCD-CT-UHR (0.2 mm) had slightly higher noise level (11.1HU). Phantom PCD-CT-SR (0.4 mm) had higher SNR in comparison to EID-CT (0.5 mm) while achieving higher resolution (Bone 115 vs 96, Acrylic 14 vs 14, Polyethylene 11 vs 10). SNR was slightly lower across all densities for PCD-CT UHR (0.2 mm). Interestingly, CNR was highest in the 0.2 mm PCD-CT group; PCD-CT CNR was 2.45 and 2.88 times the CNR for 0.5 mm EID-CT for acrylic and poly densities. Clinical comparison of SNR showed predictably higher SNR for 1 mm (30.3 ± 10.7 vs 14.2 ± 7, p = 0.02). Median subjective ratings were higher for 0.2 mm UHR vs 1 mm PCD-CT for nodule contour (4.6 ± 0.3 vs 3.6 ± 0.1, p = 0.02), bone detail (5 ± 0 vs 4 ± 0.1, p = 0.001), image quality (5 ± 0.1 vs 4.6 ± 0.4, p = 0.001), and sharpness (5 ± 0.1 vs 4 ± 0.2). CONCLUSION: Both UHR and SR PCD-CT result in similar radiation dose levels. PCD-CT can achieve higher resolution with lower noise level in comparison to EID-CT.

Quantitative myocardial T2 mapping adds value to Japanese circulation society diagnostic criteria for active cardiac sarcoidosis.

Noninvasive identification of active myocardial inflammation in patients with cardiac sarcoidosis plays a key role in management but remains elusive. T2 mapping is a proposed solution, but the added value of quantitative myocardial T2 mapping for active cardiac sarcoidosis is unknown. Retrospective cohort analysis of 56 sequential patients with biopsy-confirmed extracardiac sarcoidosis who underwent cardiac MRI for myocardial T2 mapping. The presence or absence of active myocardial inflammation in patients with CS was defined using a modified Japanese circulation society criteria within one month of MRI. Myocardial T2 values were obtained for the 16 standard American Heart Association left ventricular segments. The best model was selected using logistic regression. Receiver operating characteristic curves and dominance analysis were used to evaluate the diagnostic performance and variable importance. Of the 56 sarcoidosis patients included, 14 met criteria for active myocardial inflammation. Mean basal T2 value was the best performing model for the diagnosis of active myocardial inflammation in CS patients (pR2 = 0.493, AUC = 0.918, 95% CI 0.835-1). Mean basal T2 value > 50.8 ms was the most accurate threshold (accuracy = 0.911). Mean basal T2 value + JCS criteria was significantly more accurate than JCS criteria alone (AUC = 0.981 vs. 0.887, p = 0.017). Quantitative regional T2 values are independent predictors of active myocardial inflammation in CS and may add additional discriminatory capability to JCS criteria for active disease.