Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome.

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

Precision and Test-Retest Repeatability of Stiffness Measurement with MR Elastography: A Multicenter Phantom Study.

Background MR elastography (MRE) has been shown to have excellent performance for noninvasive liver fibrosis staging. However, there is limited knowledge regarding the precision and test-retest repeatability of stiffness measurement with MRE in the multicenter setting. Purpose To determine the precision and test-retest repeatability of stiffness measurement with MRE across multiple centers using the same phantoms. Materials and Methods In this study, three cylindrical phantoms made of polyvinyl chloride gel mimicking different degrees of liver stiffness in humans (phantoms 1-3: soft, medium, and hard stiffness, respectively) were evaluated. Between January 2021 and January 2022, phantoms were circulated between five different centers and scanned with 10 MRE-equipped clinical 1.5-T and 3-T systems from three major vendors, using two-dimensional (2D) gradient-recalled echo (GRE) imaging and/or 2D spin-echo (SE) echo-planar imaging (EPI). Similar MRE acquisition parameters, hardware, and reconstruction algorithms were used at each center. Mean stiffness was measured by a single observer for each phantom and acquisition on a single section. Stiffness measurement precision and same-session test-retest repeatability were assessed using the coefficient of variation (CV) and the repeatability coefficient (RC), respectively. Results The mean precision represented by the CV was 5.8% (95% CI: 3.8, 7.7) for all phantoms and both sequences combined. For all phantoms, 2D GRE achieved a CV of 4.5% (95% CI: 3.3, 5.7) whereas 2D SE EPI achieved a CV of 7.8% (95% CI: 3.1, 12.6). The mean RC of stiffness measurement was 5.8% (95% CI: 3.7, 7.8) for all phantoms and both sequences combined, 4.9% (95% CI: 2.7, 7.0) for 2D GRE, and 7.0% (95% CI: 2.9, 11.2) for 2D SE EPI (all phantoms). Conclusion MRE had excellent in vitro precision and same-session test-retest repeatability in the multicenter setting when similar imaging protocols, hardware, and reconstruction algorithms were used. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tang in this issue.

Proposal of an Abbreviated Noncontrast MR Enterography Protocol for Patients With Crohn Disease.

MR enterography (MRE) protocols used in patients with Crohn disease are burdened by long acquisition time, high cost, and suboptimal patient experience. For several indications, highly diagnostic MRE can be performed in five or fewer sequences, without IV contrast material or antiperistaltic medication and with an examination room time of less than 12 minutes. As such, MRE could be more patient friendly, more frequently performed, and require fewer health care resources.

Associations of Liver Stiffness Measured by Ultrasound Shear-Wave Elastography With Portal Hypertension and Circulatory Failure in Individuals With Fontan Circulation.

BACKGROUND. The Fontan operation palliates single-ventricle congenital heart disease but causes hepatic congestion with associated progressive hepatic fibrosis. OBJECTIVE. The purpose of this study was to evaluate associations between liver stiffness measured using ultrasound (US) shear-wave elastography (SWE) in patients with Fontan palliation and the occurrence of portal hypertension and Fontan circulatory failure during follow-up. METHODS. This retrospective study included 119 individuals 10 years old or older (median age, 19.1 years; 61 female patients, 58 male patients) with Fontan circulation who underwent liver US with 2D SWE from January 1, 2015, to January 1, 2022, and had 1 year or more of clinical follow-up (unless experiencing earlier outcome-related events). Median liver stiffness from the initial US examination was documented. Varices, ascites, splenomegaly, and thrombocytopenia (VAST) scores (range, 0-4) were determined as a marker of portal hypertension on initial US examination and 1 year or more of follow-up imaging (US, CT, or MRI). Composite clinical outcome for Fontan circulatory failure (death, mechanical circulatory support, cardiac transplant, or unexpected Fontan circulation-related hospitalization) was assessed. Analysis included the Wilcoxon rank sum test, logistic regression analysis with stepwise variable selection, and ROC analysis. RESULTS. Median initial liver stiffness was 2.22 m/s. Median initial VAST score was 0 (IQR, 0-1); median follow-up VAST score was 1 (IQR, 0-2) (p = .004). Fontan circulatory failure occurred in 37 of 119 (31%) patients (median follow-up, 3.4 years). Initial liver stiffness was higher in patients with a follow-up VAST score of 1 or greater (2.37 m/s) than in those with a follow-up VAST score of 0 (2.08 m/s) (p = .005), and initial liver stiffness was higher in patients with (2.43 m/s) than without (2.10 m/s) Fontan circulatory failure during follow-up (p < .001). Initial liver stiffness was the only significant independent predictor of Fontan circulatory failure (OR = 3.76; p < .001); age, sex, Fontan operation type, dominant ventricular morphology, and initial VAST score were not independent predictors. Initial liver stiffness had an AUC of 0.70 (sensitivity, 79%; specificity, 57%; threshold, > 2.11 m/s) for predicting a follow-up VAST score of 1 or greater and an AUC of 0.74 (sensitivity, 84%; specificity, 52%; threshold, > 2.12 m/s) for predicting Fontan circulatory failure. CONCLUSION. In patients with Fontan circulation, increased initial liver stiffness was associated with portal hypertension and circulatory failure during follow-up, although it had moderate performance in predicting these outcomes. CLINICAL IMPACT. US SWE may play a role in post-Fontan surveillance, supporting tailored medical and surgical care.

Performance Analysis in Children of Traditional and Deep Learning CT Lung Nodule Computer-Aided Detection Systems Trained on Adults.

BACKGROUND. Although primary lung cancer is rare in children, chest CT is commonly performed to assess for lung metastases in children with cancer. Lung nodule computer-aided detection (CAD) systems have been designed and studied primarily using adult training data, and the efficacy of such systems when applied to pediatric patients is poorly understood. OBJECTIVE. The purpose of this study was to evaluate in children the diagnostic performance of traditional and deep learning CAD systems trained with adult data for the detection of lung nodules on chest CT scans and to compare the ability of such systems to generalize to children versus to other adults. METHODS. This retrospective study included pediatric and adult chest CT test sets. The pediatric test set comprised 59 CT scans in 59 patients (30 boys, 29 girls; mean age, 13.1 years; age range, 4-17 years), which were obtained from November 30, 2018, to August 31, 2020; lung nodules were annotated by fellowship-trained pediatric radiologists as the reference standard. The adult test set was the publicly available adult Lung Nodule Analysis (LUNA) 2016 subset 0, which contained 89 deidentified scans with previously annotated nodules. The test sets were processed through the traditional FlyerScan (github.com/rhardie1/FlyerScanCT) and deep learning Medical Open Network for Artificial Intelligence (MONAI; github.com/Project-MONAI/model-zoo/releases) lung nodule CAD systems, which had been trained on separate sets of CT scans in adults. Sensitivity and false-positive (FP) frequency were calculated for nodules measuring 3-30 mm; nonoverlapping 95% CIs indicated significant differences. RESULTS. Operating at two FPs per scan, on pediatric testing data FlyerScan and MONAI showed significantly lower detection sensitivities of 68.4% (197/288; 95% CI, 65.1-73.0%) and 53.1% (153/288; 95% CI, 46.7-58.4%), respectively, than on adult LUNA 2016 subset 0 testing data (83.9% [94/112; 95% CI, 79.1-88.0%] and 95.5% [107/112; 95% CI, 90.0-98.4%], respectively). Mean nodule size was smaller (p < .001) in the pediatric testing data (5.4 ± 3.1 [SD] mm) than in the adult LUNA 2016 subset 0 testing data (11.0 ± 6.2 mm). CONCLUSION. Adult-trained traditional and deep learning-based lung nodule CAD systems had significantly lower sensitivity for detection on pediatric data than on adult data at a matching FP frequency. The performance difference may relate to the smaller size of pediatric lung nodules. CLINICAL IMPACT. The results indicate a need for pediatric-specific lung nodule CAD systems trained on data specific to pediatric patients.

Implementation of a Program for Appendicitis MRI in a Pediatric Hospital.

MRI is increasingly used as an alternate to CT for the evaluation of suspected appendicitis in pediatric patients presenting to the emergency department (ED) with abdominal pain, when further imaging is needed after an initial ultrasound examination. The available literature shows a similar diagnostic performance of MRI and CT in this setting. At the authors' institution, to evaluate for appendicitis in children in the ED, MRI is performed using a rapid three-sequence free-breathing protocol without IV contrast media. Implementation of an MRI program for appendicitis in children involves multiple steps, including determination of imaging resource availability, collaboration with other services to develop imaging pathways, widespread educational efforts, and regular quality review. Such programs can face numerous practice-specific challenges, such as those involving scanner capacity, costs, and buy-in of impacted groups. Nonetheless, through careful consideration of these factors, MRI can be used to positively impact the care of children presenting to the ED with suspected appendicitis. This Clinical Perspective aims to provide guidance on the development of a program for appendicitis MRI in children, drawing on one institution's experience while highlighting the advantages of MRI and practical strategies for overcoming potential barriers.

Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data.

BACKGROUND. Radiologists have variable diagnostic performance and considerable interreader variability when interpreting MR enterography (MRE) examinations for suspected Crohn disease (CD). OBJECTIVE. The purposes of this study were to develop a machine learning method for predicting ileal CD by use of radiomic features of ileal wall and mesenteric fat from noncontrast T2-weighted MRI and to compare the performance of the method with that of expert radiologists. METHODS. This single-institution study included retrospectively identified patients who underwent MRE for suspected ileal CD from January 1, 2020, to January 31, 2021, and prospectively enrolled participants (patients with newly diagnosed ileal CD or healthy control participants) from December 2018 to October 2021. Using axial T2-weighted SSFSE images, a radiologist selected two slices showing greatest terminal ileal wall thickening. Four ROIs were segmented, and radiomic features were extracted from each ROI. After feature selection, support-vector machine models were trained to classify the presence of ileal CD. Three fellowship-trained pediatric abdominal radiologists independently classified the presence of ileal CD on SSFSE images. The reference standard was clinical diagnosis of ileal CD based on endoscopy and biopsy results. Radiomic-only, clinical-only, and radiomic-clinical ensemble models were trained and evaluated by nested cross-validation. RESULTS. The study included 135 participants (67 female, 68 male; mean age, 15.2 ± 3.2 years); 70 were diagnosed with ileal CD. The three radiologists had accuracies of 83.7% (113/135), 88.1% (119/135), and 86.7% (117/135) for diagnosing CD; consensus accuracy was 88.1%. Interradiologist agreement was substantial (κ = 0.78). The best-performing ROI was bowel core (AUC, 0.95; accuracy, 89.6%); other ROIs had worse performance (whole-bowel AUC, 0.86; fat-core AUC, 0.70; whole-fat AUC, 0.73). For the clinical-only model, AUC was 0.85 and accuracy was 80.0%. The ensemble model combining bowel-core radiomic and clinical models had AUC of 0.98 and accuracy of 93.5%. The bowel-core radiomic-only model had significantly greater accuracy than radiologist 1 (p = .009) and radiologist 2 (p = .02) but not radiologist 3 (p > .99) or the radiologists in consensus (p = .05). The ensemble model had greater accuracy than the radiologists in consensus (p = .02). CONCLUSION. A radiomic machine learning model predicted CD diagnosis with better performance than two of three expert radiologists. Model performance improved when radiomic data were ensembled with clinical data. CLINICAL IMPACT. Deployment of a radiomic-based model including T2-weighted MRI data could decrease interradiologist variability and increase diagnostic accuracy for pediatric CD.

Deep-Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets.

Background: Deep-learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. Objective: To develop and validate deep-learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. Methods: This retrospective study developed and validated deep-learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age ≤18) datasets (n=483) and three public datasets comprising pediatric and adult examinations with various pathologies (n=1248). Three deep-learning model architectures (SegResNet, DynUNet, and SwinUNETR) from the Medical Open Network for AI (MONAI) framework underwent training using native training (NT), relying solely on institutional datasets, and transfer learning (TL), incorporating pre-training on public datasets. For comparison, TotalSegmentator (TS), a publicly available segmentation model, was applied to test data without further training. Segmentation performance was evaluated using mean Dice similarity coefficient (DSC), with manual segmentations as reference. Results: For internal pediatric data, DSC for normal liver was 0.953 (TS), 0.964-0.965 (NT models), and 0.965-0.966 (TL models); normal spleen, 0.914 (TS), 0.942-0.945 (NT models), and 0.937-0.945 (TL models); normal pancreas, 0.733 (TS), 0.774-0.785 (NT models), and 0.775-0.786 (TL models); pancreas with pancreatitis, 0.703 (TS), 0.590-0.640 (NT models), and 0.667-0.711 (TL models). For public pediatric data, DSC for liver was 0.952 (TS), 0.876-0.908 (NT models), and 0.941-0.946 (TL models); spleen, 0.905 (TS), 0.771-0.827 (NT models), and 0.897-0.926 (TL models); pancreas, 0.700 (TS), 0.577-0.648 (NT models), and 0.693-0.736 (TL models). For public primarily adult data, DSC for liver was 0.991 (TS), 0.633-0.750 (NT models), and 0.926-0.952 (TL models); spleen, 0.983 (TS), 0.569-0.604 (NT models), and 0.923-0.947 (TL models); pancreas, 0.909 (TS), 0.148-0.241 (NT models), and 0.699-0.775 (TL models). DynUNet-TL was selected as the best-performing NT or TL model and was made available as an opensource MONAI bundle (https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle.git). Conclusion: TL models trained on heterogeneous public datasets and fine-tuned using institutional pediatric data outperformed internal NT models and TotalSegmentator across internal and external pediatric test data. Segmentation performance was better in liver and spleen than in pancreas. Clinical Impact: The selected model may be used for various volumetry applications in pediatric imaging.

Association Between MR Elastography Liver Stiffness and Histologic Liver Fibrosis in Children and Young Adults With Autoimmune Liver Disease.

BACKGROUND. Liver fibrosis is an important clinical endpoint of the progression of autoimmune liver disease (AILD); its monitoring would benefit from noninvasive imaging tools. OBJECTIVE. The purpose of this study was to assess the relationship between MR elastography (MRE) liver stiffness measurements and histologic liver fibrosis, as well as to evaluate the performance of MRE and biochemical-based clinical markers for stratifying histologic liver fibrosis severity, in children and young adults with AILD. METHODS. This retrospective study used an existing institutional registry of children and young adults diagnosed with AILD (primary sclerosing cholangitis [PSC], autoimmune sclerosing cholangitis [ASC], or autoimmune hepatitis [AIH]). The registry was searched to identify patients who underwent both a research abdominal 1.5-T MRI examination that included liver MRE (performed for registry enrollment) and a clinically indicated liver biopsy within 6 months of that examination. MRE used a 2D gradient-recalled echo sequence. One analyst measured mean liver shear stiffness (in kilopascals) for each examination. Laboratory markers of liver fibrosis (aspartate aminotransferase-to-platelet ratio index [APRI] and fibrosis-4 [FIB-4] score) were recorded. For investigational purposes, one pathologist, blinded to clinical and MRI data, determined histologic Metavir liver fibrosis stage. The Spearman rank order correlation coefficient was calculated between MRE liver stiffness and Metavir liver fibrosis stage. ROC analysis was used to evaluate diagnostic performance for identifying advanced fibrosis (i.e., differentiating Metavir F0-F1 from F2-F4 fibrosis), and sensitivity and specificity were calculated using the Youden index. RESULTS. The study included 46 patients (median age, 16.6 years [IQR, 13.7-17.8 years]; 20 female patients, 26 male patients); 12 had PSC, 10 had ASC, and 24 had AIH. Median MRE liver stiffness was 2.9 kPa (IQR, 2.2-4.0 kPa). MRE liver stiffness and Meta-vir fibrosis stage showed strong positive correlation (ρ = 0.68). For identifying advanced liver fibrosis, MRE liver stiffness had an AUC of 0.81, with sensitivity of 65.4% and specificity of 90.0%; APRI had an AUC of 0.72, with sensitivity of 64.0% and specificity of 80.0%; and FIB-4 score had an AUC of 0.71, with sensitivity of 60.0% and specificity of 85.0%. CONCLUSION. MRE liver stiffness measurements were associated with histologic liver fibrosis severity. CLINICAL IMPACT. The findings support a role for MRE in noninvasive monitoring of liver stiffness, a surrogate for fibrosis, in children and young adults with AILD. TRIAL REGISTRATION. ClinicalTrials.gov NCT03175471.

Congestive Hepatopathy: Pathophysiology, Workup, and Imaging Findings with Pathologic Correlation.

Liver congestion is increasingly encountered in clinical practice and presents diagnostic pitfalls of which radiologists must be aware. The complex altered hemodynamics associated with liver congestion leads to diffuse parenchymal changes and the development of benign and malignant nodules. Distinguishing commonly encountered benign hypervascular lesions, such as focal nodular hyperplasia (FNH)-like nodules, from hepatocellular carcinoma (HCC) can be challenging due to overlapping imaging features. FNH-like lesions enhance during the hepatic arterial phase and remain isoenhancing relative to the background liver parenchyma but infrequently appear to wash out at delayed phase imaging, similar to what might be seen with HCC. Heterogeneity, presence of an enhancing capsule, washout during the portal venous phase, intermediate signal intensity at T2-weighted imaging, restricted diffusion, and lack of uptake at hepatobiliary phase imaging point toward the diagnosis of HCC, although these features are not sensitive individually. It is important to emphasize that the Liver Imaging Reporting and Data System (LI-RADS) algorithm cannot be applied in congested livers since major LI-RADS features lack specificity in distinguishing HCC from benign hypervascular lesions in this population. Also, the morphologic changes and increased liver stiffness caused by congestion make the imaging diagnosis of cirrhosis difficult. The authors discuss the complex liver macro- and microhemodynamics underlying liver congestion; propose a more inclusive approach to and conceptualization of liver congestion; describe the pathophysiology of liver congestion, hepatocellular injury, and the development of benign and malignant nodules; review the imaging findings and mimics of liver congestion and hypervascular lesions; and present a diagnostic algorithm for approaching hypervascular liver lesions. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

PURPOSE: To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction. MATERIALS AND METHODS: This prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed. RESULTS: AI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9 s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6 s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01 s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5. CONCLUSIONS: AI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.

Body MRI in pediatrics: where we are and what the future holds.

Body magnetic resonance imaging (MRI) is increasingly used for disease diagnosis, characterization, and monitoring in children of all ages. MRI has numerous advantages when compared to other imaging modalities, including a lack of ionizing radiation, superior soft tissue image contrast, and ability to provide objective, quantitative assessments. As MRI continues to evolve, pediatric body MRI examinations of the future will certainly be different than our current and past protocols. In this review article, we will discuss the present and likely future states of pediatric body MRI, including the increasing application of quantitative MRI methods, faster imaging techniques and implementation of abbreviated targeted protocols, and the growing use of artificial intelligence methods.

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

BACKGROUND: Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice. OBJECTIVE: To assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging. MATERIALS AND METHODS: This prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios. RESULTS: AI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p<0.001) respectively. CONCLUSION: We conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.

A novel collaborative self-supervised learning method for radiomic data.

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.

Pediatric Outpatient Noncontrast Brain MRI: A Time-Driven Activity-Based Costing Analysis at Three U.S. Hospitals.

BACKGROUND. MRI utilization and the use of sedation or anesthesia for MRI have increased in children. Emerging alternative payment models (APMs) require a detailed understanding of the health system costs of performing these examinations. OBJECTIVE. The purpose of this study was to use time-driven activity-based costing (TDABC) to assess health system costs for outpatient noncontrast brain MRI examinations across three children's hospitals. METHODS. Direct costs for outpatient noncontrast brain MRI examinations at three academic free-standing pediatric hospitals were calculated using TDABC. Examinations were categorized as sedated MRI (i.e., sedation or anesthesia), nonsedated MRI, or limited MRI. Process maps were created to describe patient workflows based on input from key personnel and direct observation. Time durations for each process activity were determined; time stamps from retrospective EMR review were used when possible. Capacity cost rates were calculated for resource types within three cost categories (labor, equipment, and space); cost was calculated in a fourth category (supplies). Resources were allocated to each activity, and the cost of each process step was determined by multiplying step-specific capacity costs by the time required for each step. The costs of all steps were summed to yield a base-case total examination cost. Sensitivity analysis for sedated MRI was performed using minimum and maximum time duration inputs for each activity to yield minimum and maximum costs by hospital. RESULTS. The mean base-case cost for a sedated brain MRI examination was $842 (range, $775-924 across hospitals), for a nonsedated brain MRI examination was $262 (range, $240-285), and for a limited brain MRI examination was $135 (range, $127-141). For all examination types, the largest cost category as well as the largest source of difference in cost between hospitals was labor. Sensitivity analysis found that the greatest influence on overall cost at each hospital was the duration of the MRI acquisition. CONCLUSION. The health system cost of performing a sedated MRI examination was substantially greater than that of performing a nonsedated MRI examination. However, the cost of each individual examination type did not vary substantially among hospitals. CLINICAL IMPACT. Health systems operating within APMs can use this comparative cost information for purposes of cost reduction efforts and establishment of bundled prices.

Corrected T1 Mapping in Children and Young Adults With Autoimmune Liver Disease: Correlation With Histology.

The purpose of this study was to assess relationships between liver-corrected T1 (cT1) values (adjusted for T2* effect, MRI system manufacturer, and field strength) and histologic inflammation and fibrosis in 35 participants (15 women and girls, 20 boys and men; median age, 16.0 years) with autoimmune liver disease. At multivariable analysis, inflammation score (ß = 15.5) and sex (ß = 56.0 [female]) were independent predictors of cT1, and fibrosis score (ß = 32.3) and age (ß = 5.5) were independent predictors of cT1 IQR. Liver T1 may have relevance for assessing liver inflammatory activity and fibrosis stage.

The Simplified MR Index of Activity Score in Pediatric Small-Bowel Crohn Disease: An Interreader Agreement and Responsiveness Study.

BACKGROUND. The simplified MR index of activity (MaRIA) score is used to assess the severity of small-bowel inflammation without use of IV contrast material. OBJECTIVE. The purposes of this study were to assess interreader agreement on the use of simplified MaRIA scores for evaluation of the inflammatory activity of terminal ileal Crohn disease in children and young adults and to assess whether simplified MaRIA scores change after biologic medical therapy. METHODS. This analysis was ancillary to a previously reported primary prospective research investigation. The study included 20 children and young adults with newly diagnosed ileal Crohn disease and 15 healthy control participants who underwent research small-bowel MRI examinations between December 2018 and October 2021. The participants with Crohn disease underwent baseline MRI and MRI 6 weeks and 6 months after beginning anti-tumor necrosis factor α-treatment as well as weighted pediatric Crohn disease activity index (wPCDAI) and C-reactive protein (CRP) assessment on the day of each examination. Control participants underwent one MRI examination. Four pediatric radiologists independently assigned simplified MaRIA scores using axial and coronal T2-weighted SSFSE images. Median simplified MaRIA score among readers was computed. Interreader agreement was assessed with Fleiss kappa coefficients and intra-class correlation coefficient (ICC). Analysis included the Mann-Whitney U test, Friedman test, and Spearman rank correlation. RESULTS. Simplified MaRIA scores (across time points and study groups) had substantial interreader agreement (κ = 0.65 [95% CI, 0.56-0.74]; ICC, 0.71 [95% CI, 0.63-0.78]). Median scores were higher in participants with Crohn disease at baseline than in healthy control participants (3.5 [IQR, 2.5-4.9] vs 0.5 [IQR, 0-2.0]; p < .001). Scores decreased after medical treatment in participants with Crohn disease (p = .005). The median score was 3.5 (IQR, 2.5-4.9) at baseline, 2.3 (IQR, 1.6-3.9) at 6 weeks, and 2.0 (IQR, 0.5-2.5) at 6 months. In participants with Crohn disease, median scores had significant correlations with wPCDAI (ρ = 0.46 [95% CI, 0.18-0.64]; p < .001) and CRP level (ρ = 0.48 [95% CI, 0.27-0.65]; p < .001). CONCLUSION. Radiologists had substantial agreement in use of simplified MaRIA scores to assess intestinal inflammation in ileal Crohn disease. Scores changed over time after medical therapy. CLINICAL IMPACT. The results support the simplified MaRIA score as an objective MRI-based clinical measure of intestinal inflammation in children and young adults with Crohn disease.

Respiratory-Triggered Flow-Independent Noncontrast Non-ECG-Gated MRV (REACT) Versus CE-MRV for Central Venous Evaluation in Children and Young Adults: A Six-Reader Study.

BACKGROUND. Contrast-enhanced MRI is commonly used to evaluate thoracic central venous patency in children and young adults. A flow-independent noncontrast non-ECG-gated 3D MRA-MR venography (MRV) technique described in 2019 as "relaxation-enhanced angiography without contrast and triggering (REACT)" may facilitate such evaluation. OBJECTIVE. The purpose of our study was to compare image quality, diagnostic confidence, and interreader agreement between respiratory-triggered REACT and 3D Dixon-based contrast-enhanced MRV (CE-MRV) for evaluating thoracic central venous patency in children and young adults. METHODS. This retrospective study included 42 consecutive children and young adults who underwent MRI of the neck and chest to evaluate central venous patency between August 2019 and January 2021 (median age, 5.2 years; IQR, 1.4-15.1 years; 22 female patients and 20 male patients). Examinations included respiratory-triggered REACT and navigator-gated CE-MRV sequences based on the institution's standard-of-care protocol. Six pediatric radiologists from four different institutions independently reviewed REACT and CE-MRV sequences; they assessed overall image quality (scale, 1-5; 5 = excellent), diagnostic confidence (scale, 1-5; 5 = extremely confident), and presence of clinically relevant artifact(s). Readers classified seven major central vessels as normal or abnormal (e.g., narrowing, thrombosis, or occlusion). Analysis used Wilcoxon signed rank and McNemar tests and Fleiss kappa coefficients. RESULTS. The distribution of overall image quality scores was higher (p = .02) for REACT than for CE-MRV for one reader (both sequences: median score, 5). Image quality scores were not significantly different between the sequences for the remaining five readers (all p > .05). Diagnostic confidence scores and frequency of clinically relevant artifact(s) were not significantly different between sequences for any reader (all p > .05). Interreader agreement for vessel classification as normal or abnormal was similar between sequences for all seven vessels (REACT: κ = 0.37-0.81; CE-MRV: κ = 0.34-0.81). Pooling readers and vessels, 65.4% of vessels were normal by both sequences; 18.7%, abnormal by both sequences; 9.8%, abnormal by REACT only; and 6.1%, abnormal by CE-MRV only. CONCLUSION. Respiratory-triggered REACT, in comparison with CE-MRV, showed no significant difference in image quality (aside from for one of six readers), diagnostic confidence, or frequency of artifact(s), with similar interreader agreement for vessel classification as normal or abnormal. CLINICAL IMPACT. High-resolution 3D MRV performed without IV contrast material can be used to assess central venous patency in children and young adults.

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists.

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Liver T1 Relaxation Quantification Using a 3-Dimensional Interleaved Look-Locker Acquisition With T2 Preparation Pulse Sequence (3D-QALAS): Comparison With Conventional 2-Dimensional MOLLI.

BACKGROUND: Changes in liver magnetic resonance imaging T1 relaxation times are associated with histologic inflammation and fibrosis. OBJECTIVE: To compare liver T1 measurements obtained using a novel single-breath-hold 3-dimensional (3D) whole-liver T1 estimation method (3D-QALAS) to standard-of-care 2-dimensional (2D) modified Look-Locker (2D-MOLLI) measurements. METHODS: With institutional review board approval, research magnetic resonance imaging examinations were performed in 19 participants at 1.5 T. T1 relaxometry of the liver was performed using a novel 3D whole-liver T1 estimation method (3D-QALAS) as well as a 2D modified Look-Locker (2D-MOLLI) method. The 3D method covered the entire liver in a single breath hold, whereas 2D imaging was performed at 4 anatomic levels in 4 consecutive breath holds. T1 measurements from parametric maps were obtained by a single operator, and region-of-interest area-weighted mean T1 values were calculated. Pearson correlation ( r ) was used to assess correlation between T1 estimation methods, and the paired t test and Bland-Altman analysis were used to compare agreement in T1 measurements. RESULTS: In 18 participants (1 participant was excluded from analysis because of respiratory motion artifacts on 3D-QALAS images), 2D-MOLLI and 3D-QALAS mean T1 measurements were strongly correlated ( r = 0.95, [95% CI: 0.87-0.98]; P < 0.0001). 2D-MOLLI T1 values were significantly longer than 3D-QALAS values (647.2 ± 87.3 milliseconds vs. 554.7 ± 75.8 milliseconds; P < 0.0001) with mean bias = 92.5 milliseconds (95% limits of agreement, 36.8, 148.2 milliseconds). CONCLUSION: Whole-liver T1 measurements obtained using a novel single-breath-hold 3D T1 estimation method correlate with a standard-of-care multiple consecutive-breath-hold 2D single-slice method but demonstrate systematic bias that should be considered or corrected when used in a clinical or research setting.