Iodine quantification of renal lesions: Preliminary results using spectral-based material extraction on photon-counting CT.

Objectives: To assess the range of quantitative iodine values in renal cysts (RC) (with a few renal neoplasms [RNs] as a comparison) to develop an expected range of values for RC that can be used in future studies for their differentiation. Material and Methods: Consecutive patients (n = 140) with renal lesions who had undergone abdominal examination on a clinical photon-counting computed tomography (PCCT) were retrospectively included. Automated iodine quantification maps were reconstructed, and region of interest (ROI) measurements of iodine concentration (IC) (mg/cm3) were performed on whole renal lesions. In addition, for heterogeneous lesions, a secondary ROI was placed on the area most suspicious for malignancy. The discriminatory values of minimum, maximum, mean, and standard deviation for IC were compared using simple logistic regression and receiver operating characteristic curves (area under the curve [AUC]). Results: A total of 259 renal lesions (243 RC and 16 RN) were analyzed. There were significant differences between RC and RN for all IC measures with the best-performing metrics being mean and maximum IC of the entire lesion ROI (AUC 0.912 and 0.917, respectively) but also mean and minimum IC of the most suspicious area in heterogeneous lesions (AUC 0.983 and 0.992, respectively). Most RC fell within a range of low measured iodine values although a few had higher values. Conclusion: Automated iodine quantification maps reconstructed from clinical PCCT have a high diagnostic ability to differentiate RCs and neoplasms. The data from this pilot study can be used to help establish quantitative values for clinical differentiation of renal lesions.

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.

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.

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.

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.

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.

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.

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.

A Case Report of Brachiocephalic Vein Spasm Secondary to Peripherally Inserted Central Catheter.

Vascular spasm is well known and studied in the arterial system. There are only a few cases reported related to central venous spasms. We present the case of a 63-year-old male with an extensive medical history, including deep vein thrombosis (DVT), who underwent peripheral insertion of a central catheter in his left upper extremity with subsequent development of left upper extremity edema. The central catheter was removed before the patient underwent a contrast-enhanced computed tomography of the chest which revealed severe narrowing of the left brachiocephalic vein, consistent with venospasm in the clinical setting. Nitroglycerin might be useful to prevent vasospasm, or it might also be used for treatment. In our case, the catheter was removed, and no subsequent treatment was necessary.

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.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia. MATERIALS AND METHODS: A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes. RESULTS: Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively). CONCLUSION: The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning.

RATIONALE AND OBJECTIVES: To date, no clinically useful classification system has been developed for reliably differentiating mucinous cystic neoplasm (MCN) from a benign hepatic cyst (BHC) in the liver. The objective was to use machine learning and a multi-center study design to develop and assess the performance of a novel classification system for predicting whether a hepatic cystic lesion represents MCN or BHC. MATERIALS AND METHODS: A multi-center cohort study identified 154 surgically resected hepatic cystic lesions in 154 subjects which were pathologic confirmed as MCN (43) or BHC (111). Readers at each institution recorded seven pre-determined imaging features previously identified as potential differentiating features from prior publications. The contribution of each of these features to differentiating MCN from BHC was assessed by machine learning to develop an optimal classification system. RESULTS: Although several of the assessed imaging features demonstrated statistical significance, only 3 imaging features were found by machine learning to significantly contribute to a potential classification system: (1) solid enhancing nodule (2) all septations arising from an external macro-lobulation (3) whether the lesion was solitary or one of multiple cystic liver lesions. The optimal classification system had only four categories and correctly identified 144/154 lesion (93.5%). CONCLUSION: This multi-center follow-up study was able to use machine learning to develop a highly accurate classification system for differentiation of hepatic MCN from BHC, which could be readily applied to clinical practice.

Improving CT-Derived Fractional Flow Reserve Analysis: A Quality Improvement Initiative.

Objectives The aim of this study was to identify factors and quality improvement strategies to improve coronary computed tomography angiography (CCTA) studies referred for fractional flow reserve derived from CT angiography (FFRCT) analysis. Methods Thirty randomly selected CCTAs were analyzed for quality control. A uniform CCTA protocol was implemented by an in-house steering committee, emphasizing the importance of adequate heart rate control and nitroglycerine usage. Sixty additional randomly selected CCTAs were evaluated for quality at multiple time points during intervention, and FFRCT acceptance rate was analyzed at the conclusion. Results Prior to the implementation of this quality improvement program, our overall institution-specific percent acceptance rate was 76.1% for FFRCT compared to the national average of >95%. Post-intervention, this was improved to an average acceptance rate of 90% for FFRCT analysis. Conclusions Establishment and strict adherence to CCTA imaging protocols with appropriate training and adequate buy-in of CT technologists and nurses is a viable way of improving the quality of imaging and subsequent patient care.