Deep Learning Subtraction Angiography: Improved Generalizability with Transfer Learning.

PURPOSE: To investigate the utility and generalizability of deep learning subtraction angiography (DLSA) for generating synthetic digital subtraction angiography (DSA) images without misalignment artifacts. MATERIALS AND METHODS: DSA images and native digital angiograms of the cerebral, hepatic, and splenic vasculature, both with and without motion artifacts, were retrospectively collected. Images were divided into a motion-free training set (n = 66 patients, 9,161 images) and a motion artifact-containing test set (n = 22 patients, 3,322 images). Using the motion-free set, the deep neural network pix2pix was trained to produce synthetic DSA images without misalignment artifacts directly from native digital angiograms. After training, the algorithm was tested on digital angiograms of hepatic and splenic vasculature with substantial motion. Four board-certified radiologists evaluated performance via visual assessment using a 5-grade Likert scale. Subgroup analyses were performed to analyze the impact of transfer learning and generalizability to novel vasculature. RESULTS: Compared with the traditional DSA method, the proposed approach was found to generate synthetic DSA images with significantly fewer background artifacts (a mean rating of 1.9 [95% CI, 1.1-2.6] vs 3.5 [3.5-4.4]; P = .01) without a significant difference in foreground vascular detail (mean rating of 3.1 [2.6-3.5] vs 3.3 [2.8-3.8], P = .19) in both the hepatic and splenic vasculature. Transfer learning significantly improved the quality of generated images (P < .001). CONCLUSIONS: DLSA successfully generates synthetic angiograms without misalignment artifacts, is improved through transfer learning, and generalizes reliably to novel vasculature that was not included in the training data.

A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.

BACKGROUND: Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. METHODS: Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. RESULTS: The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. CONCLUSIONS: Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update.

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.

BACKGROUND. The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted in- and opposed-phase (IOP) images, although signal FF is prone to biases, leading to inaccurate quantification. OBJECTIVE. The purpose of this study was to compare hepatic fat quantification by use of PDFF inferred from conventional T1-weighted IOP images and deep-learning convolutional neural networks (CNNs) with quantification by use of two-point Dixon signal FF with CSE-MRI PDFF as the reference standard. METHODS. This study entailed retrospective analysis of data from 292 participants (203 women, 89 men; mean age, 53.7 ± 12.0 [SD] years) enrolled at two sites from September 1, 2017, to December 18, 2019, in the Strong Heart Family Study (a prospective population-based study of American Indian communities). Participants underwent liver MRI (site A, 3 T; site B, 1.5 T) including T1-weighted IOP MRI and CSE-MRI (used to reconstruct CSE PDFF and CSE R2* maps). With CSE PDFF as reference, a CNN was trained in a random sample of 218 (75%) participants to infer voxel-by-voxel PDFF maps from T1-weighted IOP images; testing was performed in the other 74 (25%) participants. Parametric values from the entire liver were automatically extracted. Per-participant median CNN-inferred PDFF and median two-point Dixon signal FF were compared with reference median CSE-MRI PDFF by means of linear regression analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. The code is publicly available at github.com/kang927/CNN-inference-of-PDFF-from-T1w-IOP-MR. RESULTS. In the 74 test-set participants, reference CSE PDFF ranged from 1% to 32% (mean, 11.3% ± 8.3% [SD]); reference CSE R2* ranged from 31 to 457 seconds-1 (mean, 62.4 ± 67.3 seconds-1 [SD]). Agreement metrics with reference to CSE PDFF for CNN-inferred PDFF were ICC = 0.99, bias = -0.19%, 95% limits of agreement (LoA) = (-2.80%, 2.71%) and for two-point Dixon signal FF were ICC = 0.93, bias = -1.11%, LoA = (-7.54%, 5.33%). CONCLUSION. Agreement with reference CSE PDFF was better for CNN-inferred PDFF from conventional T1-weighted IOP images than for two-point Dixon signal FF. Further investigation is needed in individuals with moderate-to-severe iron overload. CLINICAL IMPACT. Measurement of CNN-inferred PDFF from widely available T1-weighted IOP images may facilitate adoption of hepatic PDFF as a quantitative bio-marker for liver fat assessment, expanding opportunities to screen for hepatic steatosis and nonalcoholic fatty liver disease.

Multidisciplinary Approach to Chronic Thromboembolic Pulmonary Hypertension: Role of Radiologists.

Management of chronic thromboembolic pulmonary hypertension (CTEPH) should be determined by a multidisciplinary team, ideally at a specialized CTEPH referral center. Radiologists contribute to this multidisciplinary process by helping to confirm the diagnosis of CTEPH and delineating the extent of disease, both of which help determine a treatment decision. Preoperative assessment of CTEPH usually employs multiple imaging modalities, including ventilation-perfusion (V/Q) scanning, echocardiography, CT pulmonary angiography (CTPA), and right heart catheterization with pulmonary angiography. Accurate diagnosis or exclusion of CTEPH at imaging is imperative, as this remains the only form of pulmonary hypertension that is curative with surgery. Unfortunately, CTEPH is often misdiagnosed at CTPA, which can be due to technical factors, patient-related factors, radiologist-related factors, as well as a host of disease mimics including acute pulmonary embolism, in situ thrombus, vasculitis, pulmonary artery sarcoma, and fibrosing mediastinitis. Although V/Q scanning is thought to be substantially more sensitive for CTEPH compared with CTPA, this is likely due to lack of recognition of CTEPH findings rather than a modality limitation. Preoperative evaluation for pulmonary thromboendarterectomy (PTE) includes assessment of technical operability and surgical risk stratification. While the definitive therapy for CTEPH is PTE, other minimally invasive or noninvasive therapies also lead to clinical improvements including greater survival. Complications of PTE that can be identified at postoperative imaging include infection, reperfusion edema or injury, pulmonary hemorrhage, pericardial effusion or hemopericardium, and rethrombosis. ©RSNA, 2022 Online supplemental material is available for this article.

Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI.

Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to correct. Purpose To assess the feasibility of using deep learning to automatically perform image-based background phase error correction in 4D flow MRI and to compare its effectiveness relative to manual image-based correction. Materials and Methods A convenience sample of 139 abdominopelvic 4D flow MRI acquisitions performed between January 2016 and July 2020 was retrospectively collected. Manual phase error correction was performed using dedicated imaging software and served as the reference standard. After reserving 40 examinations for testing, the remaining examinations were randomly divided into training (86% [85 of 99]) and validation (14% [14 of 99]) data sets to train a multichannel three-dimensional U-Net convolutional neural network. Flow measurements were obtained for the infrarenal aorta, common iliac arteries, common iliac veins, and inferior vena cava. Statistical analyses included Pearson correlation, Bland-Altman analysis, and F tests with Bonferroni correction. Results A total of 139 patients (mean age, 47 years ± 14 [standard deviation]; 108 women) were included. Inflow-outflow correlation improved after manual correction (ρ = 0.94, P < .001) compared with that before correction (ρ = 0.50, P < .001). Automated correction showed similar results (ρ = 0.91, P < .001) and demonstrated very strong correlation with manual correction (ρ = 0.98, P < .001). Both correction methods reduced inflow-outflow variance, improving mean difference from -0.14 L/min (95% limits of agreement: -1.61, 1.32) (uncorrected) to 0.05 L/min (95% limits of agreement: -0.32, 0.42) (manually corrected) and 0.05 L/min (95% limits of agreement: -0.38, 0.49) (automatically corrected). There was no significant difference in inflow-outflow variance between manual and automated correction methods (P = .10). Conclusion Deep learning automated phase error correction reduced inflow-outflow bias and variance of volumetric flow measurements in four-dimensional flow MRI, achieving results comparable with manual image-based phase error correction. © RSNA, 2021 See also the editorial by Roldán-Alzate and Grist in this issue.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

Artificial intelligence and machine learning in aortic disease.

PURPOSE OF REVIEW: Discuss foundational concepts for artificial intelligence (AI) and review recent literature on its application to aortic disease. RECENT FINDINGS: Machine learning (ML) techniques are rapidly evolving for the evaluation of aortic disease - broadly categorized as algorithms for aortic segmentation, detection of pathology, and risk stratification. Advances in deep learning, particularly U-Net architectures, have revolutionized segmentation of the aorta and show potential for monitoring the size of aortic aneurysm and characterizing aortic dissection. These algorithms also facilitate application of more complex technologies including analysis of flow dynamics with 4D Flow magnetic resonance imaging (MRI) and computational simulation of fluid dynamics for aortic coarctation. In addition, AI algorithms have been proposed to assist in 'opportunistic' screening from routine imaging exams, including automated aortic calcification score, which has emerged as a strong predictor of cardiovascular risk. Finally, several ML algorithms are being explored for risk stratification of patients with aortic aneurysm and dissection, in addition to prediction of postprocedural complications. SUMMARY: Multiple ML techniques have potential for characterization and risk prediction of aortic aneurysm, dissection, coarctation, and atherosclerotic disease on computed tomography and MRI. This nascent field shows considerable promise with many applications in development and in early preclinical evaluation.

Quantification of hemodynamics of cerebral arteriovenous malformations after stereotactic radiosurgery using 4D flow magnetic resonance imaging.

Stereotactic radiosurgery (SRS) is used to treat cerebral arteriovenous malformations (AVMs). However, early evaluation of efficacy is difficult as structural magnetic resonance imaging (MRI)/magnetic resonance angiography (MRA) often does not demonstrate appreciable changes within the first 6 months. The aim of this study was to evaluate the use of four-dimensional (4D) flow MRI to quantify hemodynamic changes after SRS as early as 2 months. This was a retrospective observational study, which included 14 patients with both pre-SRS and post-SRS imaging obtained at multiple time points from 1 to 27 months after SRS. A 3T MRI Scanner was used to obtain T2 single-shot fast spin echo, time-of-flight MRA, and postcontrast 4D flow with three-dimensional velocity encoding between 150 and 200 cm/s. Post-hoc two-dimensional cross-sectional flow was measured for the dominant feeding artery, the draining vein, and the corresponding contralateral artery as a control. Measurements were performed by two independent observers, and reproducibility was assessed. Wilcoxon signed-rank tests were used to compare differences in flow, circumference, and pulsatility between the feeding artery and the contralateral artery both before and after SRS; and differences in nidus size and flow and circumference of the feeding artery and draining vein before and after SRS. Arterial flow (L/min) decreased in the primary feeding artery (mean: 0.1 ± 0.07 vs. 0.3 ± 0.2; p < 0.05) and normalized in comparison to the contralateral artery (mean: 0.1 ± 0.07 vs. 0.1 ± 0.07; p = 0.068). Flow decreased in the draining vein (mean: 0.1 ± 0.2 vs. 0.2 ± 0.2; p < 0.05), and the circumference of the draining vein also decreased (mean: 16.1 ± 8.3 vs. 15.7 ± 6.7; p < 0.05). AVM volume decreased after SRS (mean: 45.3 ± 84.8 vs. 38.1 ± 78.7; p < 0.05). However, circumference (mm) of the primary feeding artery remained similar after SRS (mean: 15.7 ± 2.7 vs. 16.1 ± 3.1; p = 0.600). 4D flow may be able to demonstrate early hemodynamic changes in AVMs treated with radiosurgery, and these changes appear to be more pronounced and occur earlier than the structural changes on standard MRI/MRA. Level of Evidence: 4 Technical Efficacy Stage: 1.

Quantification of the Hemodynamic Changes of Cirrhosis with Free-Breathing Self-Navigated MRI.

BACKGROUND: Non-invasive assessment of the hemodynamic changes of cirrhosis might help guide management of patients with liver disease but are currently limited. PURPOSE: To determine whether free-breathing 4D flow MRI can be used to quantify the hemodynamic effects of cirrhosis and introduce hydraulic circuit indexes of severity. STUDY TYPE: Retrospective. POPULATION: Forty-seven patients including 26 with cirrhosis. FIELD STRENGTH/SEQUENCE: 3 T/free-breathing 4D flow MRI with soft gating and golden-angle view ordering. ASSESSMENT: Measurements of the supra-celiac abdominal aorta, supra-renal abdominal aorta (SRA), celiac trunk (CeT), superior mesenteric artery (SMA), splenic artery (SpA), common hepatic artery (CHA), portal vein (PV), and supra-renal inferior vena cava (IVC) were made by two radiologists. Measures of hepatic vascular resistance (hepatic arterial relative resistance [HARR]; portal resistive index [PRI]) were proposed and calculated. STATISTICAL ANALYSIS: Bland-Altman, Pearson's correlation, Tukey's multiple comparison, and Cohen's kappa. P < 0.05 was considered significant. RESULTS: Forty-four of 47 studies yielded adequate image quality for flow quantification (94%). Arterial structures showed high inter-reader concordance (range; ρ = 0.948-0.987) and the IVC (ρ = 0.972), with moderate concordance in the PV (ρ = 0.866). Conservation of mass analysis showed concordance between large vessels (SRA vs. IVC; ρ = 0.806), small vessels (celiac vs. CHA + SpA; ρ = 0.939), and across capillary beds (CeT + SMA vs. PV; ρ = 0.862). Splanchnic flow was increased in patients with portosystemic shunting (PSS) relative to control patients and patients with cirrhosis without PSS (P < 0.05, difference range 0.11-0.68 liter/m). HARR was elevated and PRI was decreased in patients with PSS (3.55 and 1.49, respectively) compared to both the control (2.11/3.18) and non-PSS (2.11/2.35) cohorts. DATA CONCLUSION: 4D flow MRI with self-navigation was technically feasible, showing promise in quantifying the hemodynamic effects of cirrhosis. Proposed quantitative metrics of hepatic vascular resistance correlated with PSS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Hemodynamic Assessment of Structural Heart Disease Using 4D Flow MRI: How We Do It.

MRI is an essential diagnostic tool in the anatomic and functional evaluation of cardiovascular disease. In many practices, 2D phase-contrast (2D-PC) MRI has been used for blood flow quantification. Four-dimensional flow MRI is a time-resolved volumetric acquisition that captures the vector field of blood flow along with anatomic images. It also provides a simpler acquisition compared with 2D-PC and facilitates a more accurate and comprehensive hemodynamic assessment. Advancements in accelerated imaging have significantly shortened scanning times for 4D flow MRI while preserving image quality, enabling this technology to transition from the research arena to routine clinical practice. In this article, we review technical optimization based on our more than 10 years of clinical experience with 4D flow MRI. We also present pearls and pitfalls in the practical application of 4D flow MRI, including how to quantify cardiovascular shunts, valvular or vascular stenosis, and valvular regurgitation. As experience increases, and as 4D flow sequences and postprocessing software become more broadly available, 4D flow MRI will likely become an essential component of cardiac imaging in practices involved in the management of congenital and acquired structural heart disease.

Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.

Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To explore the feasibility of using DL to enhance spatial detail from small-matrix MRI acquisitions and evaluate its performance against that of conventional image upscaling methods. Materials and Methods Short-axis cine cardiac MRI examinations performed between January 2012 and December 2018 at one institution were retrospectively collected for algorithm development and testing. Convolutional neural networks (CNNs), a form of DL, were trained to perform super resolution in image space by using synthetically generated low-resolution data. There were 70%, 20%, and 10% of examinations allocated to training, validation, and test sets, respectively. CNNs were compared against bicubic interpolation and Fourier-based zero padding by calculating the structural similarity index (SSIM) between high-resolution ground truth and each upscaling method. Means and standard deviations of the SSIM were reported, and statistical significance was determined by using the Wilcoxon signed-rank test. For evaluation of clinical performance, left ventricular volumes were measured, and statistical significance was determined by using the paired Student t test. Results For CNN training and retrospective analysis, 400 MRI scans from 367 patients (mean age, 48 years ± 18; 214 men) were included. All CNNs outperformed zero padding and bicubic interpolation at upsampling factors from two to 64 (P < .001). CNNs outperformed zero padding on more than 99.2% of slices (9828 of 9907). In addition, 10 patients (mean age, 51 years ± 22; seven men) were prospectively recruited for super-resolution MRI. Super-resolved low-resolution images yielded left ventricular volumes comparable to those from full-resolution images (P > .05), and super-resolved full-resolution images appeared to further enhance anatomic detail. Conclusion Deep learning outperformed conventional upscaling methods and recovered high-frequency spatial information. Although training was performed only on short-axis cardiac MRI examinations, the proposed strategy appeared to improve quality in other imaging planes. © RSNA, 2020 Online supplemental material is available for this article.

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET).

PURPOSE: Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP ) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion-recovery scout to select TINP , without the aid of a human observer. METHODS: We retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion-recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short-term memory to identify the TINP . We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model's transparency. RESULTS: Prediction of TINP from our ensemble VGG19 long short-term memory closely matched with expert annotation (ρ = 0.88). Ninety-four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection. CONCLUSION: In this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion-recovery experiment. Merging the spatial and temporal characteristics of the VGG-19 and long short-term-memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.

Evaluation of atrial septal defects with 4D flow MRI-multilevel and inter-reader reproducibility for quantification of shunt severity.

PURPOSE: With the hypothesis that 4D flow can be used in evaluation of cardiac shunts, we seek to evaluate the multilevel and interreader reproducibility of measurements of the blood flow, shunt fraction and shunt volume in patients with atrial septum defect (ASD) in practice at multiple clinical sites. MATERIALS AND METHODS: Four-dimensional flow MRI examinations were performed at four institutions across Europe and the US. Twenty-nine patients (mean age, 43 years; 11 male) were included in the study. Flow measurements were performed at three levels (valve, main artery and periphery) in both the pulmonary and systemic circulation by two independent readers and compared against stroke volumes from 4D flow anatomic data. Further, the shunt ratio (Qp/Qs) was calculated. Additionally, shunt volume was quantified at the atrial level by tracking the atrial septum. RESULTS: Measurements of the pulmonary blood flow at multiple levels correlate well whether measuring at the valve, main pulmonary artery or branch pulmonary arteries (r = 0.885-0.886). Measurements of the systemic blood flow show excellent correlation, whether measuring at the valve, ascending aorta or sum of flow from the superior vena cava (SVC) and descending aorta (r = 0.974-0.991). Intraclass agreement between the two observers for the flow measurements varies between 0.96 and 0.99. Compared with stroke volume, pulmonic flow is underestimated with 0.26 l/min at the main pulmonary artery level, and systemic flow is overestimated with 0.16 l/min at the ascending aorta level. Direct measurements of ASD flow are feasible in 20 of 29 (69%) patients. CONCLUSION: Blood flow and shunt quantification measured at multiple levels and performed by different readers are reproducible and consistent with 4D flow MRI.

Image Annotation by Eye Tracking: Accuracy and Precision of Centerlines of Obstructed Small-Bowel Segments Placed Using Eye Trackers.

Small-bowel obstruction (SBO) is a common and important disease, for which machine learning tools have yet to be developed. Image annotation is a critical first step for development of such tools. This study assesses whether image annotation by eye tracking is sufficiently accurate and precise to serve as a first step in the development of machine learning tools for detection of SBO on CT. Seven subjects diagnosed with SBO by CT were included in the study. For each subject, an obstructed segment of bowel was chosen. Three observers annotated the centerline of the segment by manual fiducial placement and by visual fiducial placement using a Tobii 4c eye tracker. Each annotation was repeated three times. The distance between centerlines was calculated after alignment using dynamic time warping (DTW) and statistically compared to clinical thresholds for diagnosis of SBO. Intra-observer DTW distance between manual and visual centerlines was calculated as a measure of accuracy. These distances were 1.1 ± 0.2, 1.3 ± 0.4, and 1.8 ± 0.2 cm for the three observers and were less than 1.5 cm for two of three observers (P < 0.01). Intra- and inter-observer DTW distances between centerlines placed with each method were calculated as measures of precision. These distances were 0.6 ± 0.1 and 0.8 ± 0.2 cm for manual centerlines, 1.1 ± 0.4 and 1.9 ± 0.6 cm for visual centerlines, and were less than 3.0 cm in all cases (P < 0.01). Results suggest that eye tracking-based annotation is sufficiently accurate and precise for small-bowel centerline annotation for use in machine learning-based applications.

Volumetric segmentation-free method for rapid visualization of vascular wall shear stress using 4D flow MRI.

PURPOSE: To develop a rapid segmentation-free method to visualize and compute wall shear stress (WSS) throughout the aorta using 4D Flow MRI data. WSS is the drag force-per-area the vessel endothelium exerts on luminal blood; abnormal levels of WSS are associated with cardiovascular pathologies. Previous methods for computing WSS are bottlenecked by labor-intensive manual segmentation of vessel boundaries. A rapid automated segmentation-free method for computing WSS is presented. THEORY AND METHODS: Shear stress is the dot-product of the viscous stress tensor and the inward normal vector. The inward normal vectors are approximated as the gradient of fluid speed at every voxel. Subsequently, a 4D map of shear stress is computed as the partial derivatives of velocity with respect to the inward normal vectors. We highlight the shear stress near the wall by fusing visualization with edge-emphasized anatomical data. RESULTS: As a proof-of-concept, four cases with aortic pathologies are presented. Visualization allows for rapid localization of pathologic WSS. Subsequent analysis of these pathological regions enables quantification of WSS. Average WSS during peak systole measures approximately 50-60 cPa in nonpathological regions of the aorta and is elevated in regions of stenosis, coarctation, and dissection. WSS is reduced in regions of aneurysm. CONCLUSION: A volumetric technique for calculation and visualization of WSS from 4D Flow MRI data is presented. Traditional labor-intensive methods for WSS rely on explicit manual segmentation of vessel boundaries before visualization. This automated volumetric strategy for visualization and quantification of WSS may facilitate its clinical translation.

4D flow MRI quantification of mitral and tricuspid regurgitation: Reproducibility and consistency relative to conventional MRI.

BACKGROUND: In patients with mitral or tricuspid valve regurgitation, evaluation of regurgitant severity is essential for determining the need for surgery. While transthoracic echocardiography is widely accessible, it has limited reproducibility for grading inlet valve regurgitation. Multiplanar cardiac MRI is the quantitative standard but requires specialized local expertise, and is thus not widely available. Volumetric 4D flow MRI has potential for quantitatively grading the severity of inlet valve regurgitation in adult patients. PURPOSE: To evaluate the accuracy and reproducibility of volumetric 4D flow MRI for quantification of inlet valvular regurgitation compared to conventional multiplanar MRI, which may simplify and improve accessibility of cardiac MRI. STUDY TYPE: This retrospective, HIPAA-compliant imaging-based comparison study was conducted at a single institution. SUBJECTS: Twenty-one patients who underwent concurrent multiplanar and 4D flow cardiac MRI between April 2015 and January 2017. FIELD STRENGTH/SEQUENCES: 3T; steady-state free-precession (SSFP), 2D phase contrast (2D-PC), and postcontrast 4D flow. ASSESSMENT: We evaluated the intertechnique (4D flow vs. 2D-PC), intermethod (direct vs. indirect measurement), interobserver and intraobserver reproducibility of measurements of regurgitant flow volume (RFV), fraction (RF), and volume (RVol). STATISTICAL TESTS: Statistical analysis included Pearson correlation, Bland-Altman statistics, and intraclass correlation coefficients. RESULTS: There was high concordance between 4D flow and multiplanar MRI, whether using direct or indirect methods of quantifying regurgitation (r = 0.813-0.985). Direct interrogation of the regurgitant jet with 4D flow showed high intraobserver consistency (r = 0.976-0.999) and interobserver consistency (r = 0.861-0.992), and correlated well with traditional indirect measurements obtained as the difference between stroke volume and forward outlet valve flow. DATA CONCLUSION: 4D flow MRI provides highly reproducible measurements of mitral and tricuspid regurgitant volume, and may be used in place of conventional multiplanar MRI. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1147-1158.

Pelvic Blood Flow Predicts Fibroid Volume and Embolic Required for Uterine Fibroid Embolization: A Pilot Study With 4D Flow MR Angiography.

OBJECTIVE: We report here an initial experience using 4D flow MRI in pelvic imaging-specifically, in imaging uterine fibroids. We hypothesized that blood flow might correlate with fibroid volume and that quantifying blood flow might help to predict the amount of embolic required to achieve stasis at subsequent uterine fibroid embolization (UFE). MATERIALS AND METHODS: Thirty-three patients with uterine fibroids and seven control subjects underwent pelvic MRI with 4D flow imaging. Of the patients with fibroids, 10 underwent 4D flow imaging before UFE and seven after UFE; in the remaining 16 patients with fibroids, UFE had yet to be performed. Four-dimensional flow measurements were performed using Arterys CV Flow. The flow fraction of the internal iliac artery was expressed as the ratio of internal iliac artery flow to external iliac artery flow and was compared between groups. The flow ratios between the internal iliac arteries on each side were calculated. Fibroid volume versus internal iliac flow fraction, embolic volume versus internal iliac flow fraction, and embolic volume ratio between sides versus the ratio of internal iliac artery flows between sides were compared. RESULTS: The mean internal iliac flow fraction was significantly higher in the 26 patients who underwent imaging before UFE (mean ± standard error, 0.78 ± 0.06) than in the seven patients who underwent imaging after UFE (0.48 ± 0.07, p < 0.01) and in the seven control patients without fibroids (0.48 ± 0.08, p < 0.0001). The internal iliac flow fraction correlated well with fibroid volumes before UFE (r = 0.7754, p < 0.0001) and did not correlate with fibroid volumes after UFE (r = -0.3051, p = 0.51). The ratio of embolic required to achieve stasis between sides showed a modest correlation with the ratio of internal iliac flow (r = 0.6776, p = 0.03). CONCLUSION: Internal iliac flow measured by 4D flow MRI correlates with fibroid volume and is predictive of the ratio of embolic required to achieve stasis on each side at subsequent UFE and may be useful for preprocedural evaluation of patients with uterine fibroids.