Deep Learning-based Prescription of Cardiac MRI Planes.

PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article

Deep Learning Image Synthesis for MRI: From Super-Resolution to Cardiovascular Biomechanics

Since its invention in the 1970s, magnetic resonance imaging (MRI) has contributed greatly to our understanding of the human body in health and disease. MRI images anatomy and physiology with high spatiotemporal resolution and without ionizing radiation. Due to these factors, MRI is particularly well suited to studying the heart, and cardiac MRI is considered the clinical gold-standard for assessment of cardiac morphology, flow, and function. However, interpretation of cardiac MRI is highly dependent on image quality and often requires extensive manual annotation and visual analysis. Convolutional neural networks (CNNs), a form of deep learning and artificial intelligence, have potential to revolutionize medical imaging. Broadly, CNNs comprise a series of trainable weights called layers, which iteratively learn the features required to perform a given task. Currently, CNNs are being explored for a variety of computer vision tasks, such as classification, localization, and segmentation, but have untapped potential. Specifically, their ability to perform image synthesis is unknown. Given these challenges in MRI and the untapped potential of CNNs, I asked: can we use deep learning to perform image synthesis for MRI? Using this question as the bedrock for my dissertation, I set out to solve progressively more challenging problems in cardiovascular MRI using CNNs, building towards the ultimate task of automatically quantifying cardiac function and biomechanics. In aim 1, I asked whether existing CNNs can enhance low-resolution cardiac images. That is, can CNNs perform image super-resolution of steady-state free precession (SSFP)? Specifically, I asked which CNN architectures are suitable for this task and how well they perform relative to conventional image upscaling methods. In aim 2, I asked whether I could upgrade the CNN architectures from aim 1 to isolate and remove background signal from 4D Flow MRI. That is, can CNNs perform phase-error correction of 4D Flow MRI acquisitions via synthesis of the background static vector field? To achieve this, I asked what architectural modifications are necessary to infer these multi-component volumetric vector fields. I then compared CNN-based phase error correction with existing manual segmentation-based methods. In aim 3, I asked whether I could further upgrade my phase-error correction CNN from aim 2 to predict intracardiac blood flow from videos of the beating heart. That is, can CNNs infer dynamic blood flow velocity fields from cardiac cine SSFP images? Specifically, I asked how I could incorporate spatiotemporal information and anatomical boundaries into this new architecture I call Triton-Net. I then measured the correlation between the synthesized flow fields and 4D Flow MRI measurements. Lastly, I asked whether I could use these flow values to detect left ventricular outflow obstruction. Finally, in aim 4, I asked whether I could refine Triton-Net to evaluate local myocardial function. That is, could I add explicit physical constraints into Triton-Net to infer dynamic myocardial velocity and strain tensor fields from cardiac cine SSFP images? Realizing that myocardial contraction is periodic, I explored how I may encode net-zero displacement and strain constraints into the Triton-Net architecture, resulting in a heavily modified deep learning synthetic strain (DLSS) CNN. I then characterized DLSS strain in a healthy population and asked whether I could use DLSS strain to identify wall motion abnormalities in an ischemic heart disease population. Lastly, I compared DLSS classification performance against the consensus visual assessment of four cardiothoracic radiologist readers

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

Deep learning phase error correction for cerebrovascular 4D flow MRI

Abstract Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland–Altman, Wilcoxon-signed rank (WSR) and F-tests. Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833–0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945–0.981) and decreased variance (p < 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction

Deep learning phase error correction for cerebrovascular 4D flow MRI.

Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland-Altman, Wilcoxon-signed rank (WSR) and F-tests. Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833-0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945-0.981) and decreased variance (p &lt; 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction

Clinical Performance and Role of Expert Supervision of Deep Learning for Cardiac Ventricular Volumetry: A Validation Study

PurposeTo evaluate the performance of a deep learning (DL) algorithm for clinical measurement of right and left ventricular volume and function across cardiac MR images obtained for a range of clinical indications and pathologies.Materials and methodsA retrospective, Health Insurance Portability and Accountability Act-compliant study was conducted using the first 200 noncongenital clinical cardiac MRI examinations from June 2015 to June 2017 for which volumetry was available. Images were analyzed using commercially available software for automated DL-based and manual contouring of biventricular volumes. Fully automated measurements were compared using Pearson correlations, relative volume errors, and Bland-Altman analyses. Manual, automated, and expert revised contours for 50 MR images were examined by comparing regional Dice coefficients at the base, midventricle, and apex to further analyze the contour quality.ResultsFully automated and manual left ventricular volumes were strongly correlated for end-systolic volume (ESV: Pearson r = 0.99, P &lt; .001), end-diastolic volume (EDV: r = 0.97, P &lt; .001), and ejection fraction (EF: r = 0.94, P &lt; .001). Right ventricular measurements were also correlated for ESV (r = 0.93, P &lt; .001), EDV (r = 0.92, P &lt; .001), and EF (r = 0.73, P &lt; .001). Visual inspection of segmentation quality showed most errors (73%) occurred at the cardiac base. Mean Dice coefficients between manual, automated, and expert revised contours ranged from 0.92 to 0.95, with greatest variance at the base and apex.ConclusionFully automated ventricular segmentation by the tested algorithm provides contours and ventricular volumes that could be used to aid expert segmentation, but can benefit from expert supervision, particularly to resolve errors at the basal and apical slices. Supplemental material is available for this article. © RSNA, 2020

4D Flow Vorticity Visualization Predicts Regions of Quantitative Flow Inconsistency for Optimal Blood Flow Measurement.

PurposeTo evaluate whether automated vorticity mapping four-dimensional (4D) flow MRI can identify regions of quantitative flow inconsistency.Materials and methodsIn this retrospective study, 35 consecutive patients who underwent MR angiography with 4D flow MRI at 3.0 T from December 2017 to October 2018 were analyzed using a λ 2-based technique for vorticity visualization and quantification. The patients were aged 58.6 years ± 14.4 (standard deviation), 12 were women, 18 had ascending aortic aneurysms (maximal diameter &gt; 4.0 cm), and 10 had bicuspid aortic valves. Flow measurements were made in the ascending aorta (aAo), mid-descending aorta, main pulmonary artery, and superior vena cava. Statistical tests included t tests and F tests with a type I error threshold (α) of .05.ResultsThe 35 patients were visually classified as having no (n = 9), mild (n = 8), moderate (n = 11), or severe vorticity (n = 7). Across all patients, standard deviation of cardiac output in the aAo (0.58 L/min ± 0.45) was significantly (P &lt; .001) higher than in the pulmonary arteries (0.15 L/min ± 0.10) and descending aorta and superior vena cava (0.14 L/min ± 0.12). The standard deviation of cardiac output observed in the aAo was significantly greater (P &lt; .01) in patients with moderate or severe vorticity (0.73 L/min ± 0.55) than in those with none or mild vorticity (0.44 L/min ± 0.26).ConclusionCardiac output and blood flow are essential MRI measurements in the evaluation of structural heart disease. Vorticity visualization may be used to help guide optimal location for flow quantification.© RSNA, 2020See also the commentary by Markl in this issue

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

PurposeTo 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 methodsShear 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.ResultsAs 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.ConclusionA 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