Deficiency of the Deubiquitinase UCHL1 Attenuates Pulmonary Arterial Hypertension.

BACKGROUND: The ubiquitin-proteasome system regulates protein degradation and the development of pulmonary arterial hypertension (PAH), but knowledge about the role of deubiquitinating enzymes in this process is limited. UCHL1 (ubiquitin carboxyl-terminal hydrolase 1), a deubiquitinase, has been shown to reduce AKT1 (AKT serine/threonine kinase 1) degradation, resulting in higher levels. Given that AKT1 is pathological in pulmonary hypertension, we hypothesized that UCHL1 deficiency attenuates PAH development by means of reductions in AKT1. METHODS: Tissues from animal pulmonary hypertension models as well as human pulmonary artery endothelial cells from patients with PAH exhibited increased vascular UCHL1 staining and protein expression. Exposure to LDN57444, a UCHL1-specific inhibitor, reduced human pulmonary artery endothelial cell and smooth muscle cell proliferation. Across 3 preclinical PAH models, LDN57444-exposed animals, Uchl1 knockout rats (Uchl1-/-), and conditional Uchl1 knockout mice (Tie2Cre-Uchl1fl/fl) demonstrated reduced right ventricular hypertrophy, right ventricular systolic pressures, and obliterative vascular remodeling. Lungs and pulmonary artery endothelial cells isolated from Uchl1-/- animals exhibited reduced total and activated Akt with increased ubiquitinated Akt levels. UCHL1-silenced human pulmonary artery endothelial cells displayed reduced lysine(K)63-linked and increased K48-linked AKT1 levels. RESULTS: Supporting experimental data, we found that rs9321, a variant in a GC-enriched region of the UCHL1 gene, is associated with reduced methylation (n=5133), increased UCHL1 gene expression in lungs (n=815), and reduced cardiac index in patients (n=796). In addition, Gadd45α (an established demethylating gene) knockout mice (Gadd45α-/-) exhibited reduced lung vascular UCHL1 and AKT1 expression along with attenuated hypoxic pulmonary hypertension. CONCLUSIONS: Our findings suggest that UCHL1 deficiency results in PAH attenuation by means of reduced AKT1, highlighting a novel therapeutic pathway in PAH.

Microvascular Obstruction and Intramyocardial Hemorrhage in Reperfused Myocardial Infarctions: Pathophysiology and Clinical Insights From Imaging.

Microvascular injury immediately following reperfusion therapy in acute myocardial infarction (MI) has emerged as a driving force behind major adverse cardiovascular events in the postinfarction period. Although postmortem investigations and animal models have aided in developing early understanding of microvascular injury following reperfusion, imaging, particularly serial noninvasive imaging, has played a central role in cultivating critical knowledge of progressive damage to the myocardium from the onset of microvascular injury to months and years after in acute MI patients. This review summarizes the pathophysiological features of microvascular injury and downstream consequences, and the contributions noninvasive imaging has imparted in the development of this understanding. It also highlights the interventional trials that aim to mitigate the adverse consequences of microvascular injury based on imaging, identifies potential future directions of investigations to enable improved detection of disease, and demonstrates how imaging stands to play a major role in the development of novel therapies for improved management of acute MI patients.

Non-electrocardiogram-gated, free-breathing, off-resonance reduced, high-resolution, whole-heart myocardial T2 * mapping at 3 T within 5 min.

PURPOSE: Widely used conventional 2D T2 * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T2 * technique that enables rapid whole-heart coverage was developed and validated. METHODS: A continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T2 * imaging. This approach was tested in healthy human volunteers and in swine before and after intravenous administration of ferumoxytol. RESULTS: Spatial-resolution matched T2 * images were acquired with 2-3-fold reduction in scan time using the proposed T2 * mapping approach relative to conventional T2 * mapping. Compared with the conventional approach, T2 * images acquired with the proposed method demonstrated reduced off-resonance and flow artifacts, leading to higher image quality and lower coefficient of variation in T2 *-weighted images of the myocardium of swine and humans. Mean myocardial T2 * values determined using the proposed and conventional approaches were highly correlated and showed minimal bias. CONCLUSION: The proposed non-ECG-gated, free-breathing, 3D T2 * imaging approach can be performed within 5 min or less. It can overcome critical image artifacts from undesirable cardiac and respiratory motion and bulk off-resonance shifts at the heart-lung interface. The proposed approach is expected to facilitate faster and improved cardiac T2 * mapping in those with limited breath-holding capacity or arrhythmias.

The Canadian Cardiovascular Society Classification of Acute Atherothrombotic Myocardial Infarction Based on Stages of Tissue Injury Severity: An Expert Consensus Statement.

Myocardial infarction (MI) remains a leading cause of morbidity and mortality. In atherothrombotic MI (ST-elevation MI and type 1 non-ST-elevation MI), coronary artery occlusion leads to ischemia. Subsequent cardiomyocyte necrosis evolves over time as a wavefront within the territory at risk. The spectrum of ischemia and reperfusion injury is wide: it can be minimal in aborted MI or myocardial necrosis can be large and complicated by microvascular obstruction and reperfusion hemorrhage. Established risk scores and infarct classifications help with patient management but do not consider tissue injury characteristics. This document outlines the Canadian Cardiovascular Society classification of acute MI. It is an expert consensus formed on the basis of decades of data on atherothrombotic MI with reperfusion therapy. Four stages of progressively worsening myocardial tissue injury are identified: (1) aborted MI (no/minimal myocardial necrosis); (2) MI with significant cardiomyocyte necrosis, but without microvascular injury; (3) cardiomyocyte necrosis and microvascular dysfunction leading to microvascular obstruction (ie, "no-reflow"); and (4) cardiomyocyte and microvascular necrosis leading to reperfusion hemorrhage. Each stage reflects progression of tissue pathology of myocardial ischemia and reperfusion injury from the previous stage. Clinical studies have shown worse remodeling and increase in adverse clinical outcomes with progressive injury. Notably, microvascular injury is of particular importance, with the most severe form (hemorrhagic MI) leading to infarct expansion and risk of mechanical complications. This classification has the potential to stratify risk in MI patients and lay the groundwork for development of new, injury stage-specific and tissue pathology-based therapies for MI.

On the loss of image contrast in double-inversion-recovery prepared T2* MRI of Intramyocardial hemorrhage.

PURPOSE: Studies have shown that double-inversion-recovery (DIR) prepared dark-blood T2*-weighted images result in lower SNR, CNR and diagnostic accuracy for intramyocardial hemorrhage (IMH) detection compared to non-DIR-prepared (bright-blood) T2*-weighted images; however, the mechanism contributing to this observation has not been investigated and explained in detail. This work tests the hypothesis that the loss of SNR on dark-blood cardiac T2*-weighted images of IMH stems from spin-relaxation during the long RF pulses in double inversion preparation, as a result, compromising image contrast for intramyocardial hemorrhage detection. METHODS: Phantom and in-vivo animal studies were performed to test the hypothesis of the study. An agar phantom was imaged with multi-gradient-echo T2* imaging protocols with and without double-inversion-recovery (DIR) preparation. Image acquisitions were placed at different delay times (TD) after DIR preparation. SNR, T2* and Coefficient of Variation (COV) were measured and compared between DIR-prepared and non-DIR-prepared images. Canines with hemorrhagic myocardial infarctions were scanned at 3.0 T with DIR-prepared (dark-blood) and non-DIR-prepared (bright-blood) T2* imaging protocols. DIR-prepared T2* images were acquired with short, medium, and long delay times (TD). SNR, CNR, intramyocardial hemorrhage (IMH) extent, T2* and COV were measured and compared between DIR-prepared T2* images with short, medium, and long delay times (TD) to non-DIR-prepared bright-blood T2* images. RESULTS: Phantom studies confirmed the hypothesis that the SNR loss on DIR-prepared T2* images originated from signal loss during DIR preparation. SNR followed T1 recovery curve with increased delay times (TD) indicating that SNR can be recovered with longer time delay between DIR and image acquisition. Myocardial T2* values were not affected by DIR preparation but COV of T2* was elevated. Animal studies supported the hypothesis and showed that DIR-prepared T2* images with insufficient delay time (TD) had impaired sensitivity for IMH detection due to lower SNR and CNR, and higher COV. CONCLUSION: We conclude that lower SNR and CNR on DIR-prepared T2* images originate from signal loss during DIR preparation and insufficient recovery between DIR preparation and image acquisition. Although, the impaired sensitivity can be recovered by extending delay time (TD), it will extend the scan time. Bright-blood T2* imaging protocols should remain the optimal choice for assessment of intramyocardial hemorrhage. DIR-prepared dark-blood T2* imaging protocols should be performed with extra attention on image signal-to-noise ratio when used for intramyocardial hemorrhage detection.

Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets.

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p<0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.

Chronic heart failure following hemorrhagic myocardial infarction: mechanism, treatment and outlook.

Myocardial infarction (MI), the blockage of arterial blood supply of the heart, is among the most common causes of death worldwide. Even when patients receive immediate treatment by re-opening blocked arteries, they often develop chronic heart failure (CHF) in the aftermath of MI events. Yet, the factors that contribute to the development of MI-associated CHF are poorly understood. In our recent study (Nat Commun 13:6394), we link intramyocardial hemorrhage, an injury which can occur during reperfusion of areas affected by MI, to an increased risk of CHF. Mechanistically, our data suggest that an iron-induced adverse cascade of events after hemorrhagic MI drives fatty degeneration of infarcted tissue, which ultimately contributes to negative cardiac remodeling. In this Microreview, we discuss the implications of our findings regarding the molecular mechanism, more targeted treatment options as well as perspectives in the clinical care of CHF after hemorrhagic MI.

Thrombus Signal on T1-Weighted Black-Blood MR Predicts Outcomes of Catheter-Directed Thrombolysis in Acute Deep Vein Thrombosis.

OBJECTIVES: Catheter-directed thrombolysis (CDT) is an effective therapy for acute deep vein thrombosis (DVT). However, predicting the CDT outcomes remains elusive. We hypothesized that the thrombus signal on T1-weighted black-blood magnetic resonance (MR) can provide insight into CDT outcomes in acute DVT patients. METHODS: A total of 117 patients with acute iliofemoral DVT were enrolled for T1-weighted black-blood MR before CDT in this prospective study. Based on the signal contrast between thrombus and adjacent muscle, patients were categorized into the iso-intense thrombus (Iso-IT), hyper-intense thrombus (Hyper-IT), and mixed iso-/hyper-intense thrombi (Mixed-IT) groups. Immediate treatment outcome (i.e., vein patency) and long-term treatment outcome (i.e., the incidence rate of postthrombotic syndrome) were accessed by the same expert. Histological analysis and iron quantification were performed on thrombus samples to characterize the content of fibrin, collagen, and the ratio of Fe3+ to total iron. RESULTS: Compared to Mixed-IT and Hyper-IT groups, the Iso-IT group had the best lytic effect (90.5 ± 1.6% vs. 78.4 ± 2.6% vs. 46.5 ± 3.3%, p < 0.001), lowest bleeding ratio (0.0 vs. 11.8 vs. 13.3, p < 0.001), and the lowest incidence rate of postthrombotic syndrome on 24 months (3.6 vs. 18.4 vs. 63.4%, p < 0.001) following CDT. The Iso-IT group had a significantly lower ratio of Fe3+ to total iron (93.1 ± 3.2% vs. 97.2 ± 2.1%, p = 0.034) and a higher content of fibrin (12.5 ± 5.3% vs. 4.76 ± 3.18%, p = 0.023) than Hyper-IT. CONCLUSION: Thrombus signal characteristics on T1-weighted black-blood MR is associated with CDT outcomes and possesses potential to serve as a noninvasive approach to guide treatment decision making in acute DVT patients. KEY POINTS: · Thrombus signal on T1-weighted black-blood MR is associated with lytic therapeutic outcome in acute DVT patients.. · Presence of iso-intense thrombus revealed by T1-weighted black-blood MRI is associated with successful thrombolysis, low bleeding ratio, and low incidence of the postthrombotic syndrome.. · T1-weighted thrombus signal characteristics may serve as a noninvasive imaging marker to predict CDT treatment outcomes and therefore guide treatment decision making in acute DVT patients..

Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets.

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p < 0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.

MiR-150 blunts cardiac dysfunction in mice with cardiomyocyte loss of ß1-adrenergic receptor/ß-arrestin signaling and controls a unique transcriptome.

The ß1-adrenergic receptor (ß1AR) is found primarily in hearts (mainly in cardiomyocytes [CMs]) and ß-arrestin-mediated ß1AR signaling elicits cardioprotection through CM survival. We showed that microRNA-150 (miR-150) is upregulated by ß-arrestin-mediated ß1AR signaling and that CM miR-150 inhibits maladaptive remodeling post-myocardial infarction. Here, we investigate whether miR-150 rescues cardiac dysfunction in mice bearing CM-specific abrogation of ß-arrestin-mediated ß1AR signaling. Using CM-specific transgenic (TG) mice expressing a mutant ß1AR (G protein-coupled receptor kinase [GRK]-ß1AR that exhibits impairment in ß-arrestin-mediated ß1AR signaling), we first generate a novel double TG mouse line overexpressing miR-150. We demonstrate that miR-150 is sufficient to improve cardiac dysfunction in CM-specific GRK-ß1AR TG mice following chronic catecholamine stimulation. Our genome-wide circular RNA, long noncoding RNA (lncRNA), and mRNA profiling analyses unveil a subset of cardiac ncRNAs and genes as heretofore unrecognized mechanisms for beneficial actions of ß1AR/ß-arrestin signaling or miR-150. We further show that lncRNA Gm41664 and GDAP1L1 are direct novel upstream and downstream regulators of miR-150. Lastly, CM protective actions of miR-150 are attributed to repressing pro-apoptotic GDAP1L1 and are mitigated by pro-apoptotic Gm41664. Our findings support the idea that miR-150 contributes significantly to ß1AR/ß-arrestin-mediated cardioprotection by regulating unique ncRNA and gene signatures in CMs.

Intramyocardial hemorrhage drives fatty degeneration of infarcted myocardium.

Sudden blockage of arteries supplying the heart muscle contributes to millions of heart attacks (myocardial infarction, MI) around the world. Although re-opening these arteries (reperfusion) saves MI patients from immediate death, approximately 50% of these patients go on to develop chronic heart failure (CHF) and die within a 5-year period; however, why some patients accelerate towards CHF while others do not remains unclear. Here we show, using large animal models of reperfused MI, that intramyocardial hemorrhage - the most damaging form of reperfusion injury (evident in nearly 40% of reperfused ST-elevation MI patients) - drives delayed infarct healing and is centrally responsible for continuous fatty degeneration of the infarcted myocardium contributing to adverse remodeling of the heart. Specifically, we show that the fatty degeneration of the hemorrhagic MI zone stems from iron-induced macrophage activation, lipid peroxidation, foam cell formation, ceroid production, foam cell apoptosis and iron recycling. We also demonstrate that timely reduction of iron within the hemorrhagic MI zone reduces fatty infiltration and directs the heart towards favorable remodeling. Collectively, our findings elucidate why some, but not all, MIs are destined to CHF and help define a potential therapeutic strategy to mitigate post-MI CHF independent of MI size.

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

BACKGROUND: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems. METHODS: Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed. RESULTS: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction. CONCLUSIONS: VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.

Intramyocardial Hemorrhage and the "Wave Front" of Reperfusion Injury Compromising Myocardial Salvage.

BACKGROUND: Reperfusion therapy for acute myocardial infarction (MI) is lifesaving. However, the benefit of reperfusion therapy can be paradoxically diminished by reperfusion injury, which can increase MI size. OBJECTIVES: Hemorrhage is known to occur in reperfused MIs, but whether hemorrhage plays a role in reperfusion-mediated MI expansion is not known. METHODS: We studied cardiac troponin kinetics (cTn) of ST-segment elevation MI patients (n = 70) classified by cardiovascular magnetic resonance to be hemorrhagic (70%) or nonhemorrhagic following primary percutaneous coronary intervention. To isolate the effects of hemorrhage from ischemic burden, we performed controlled canine studies (n = 25), and serially followed both cTn and MI size with time-lapse imaging. RESULTS: CTn was not different before reperfusion; however, an increase in cTn following primary percutaneous coronary intervention peaked earlier (12 hours vs 24 hours; P < 0.05) and was significantly higher in patients with hemorrhage (P < 0.01). In hemorrhagic animals, reperfusion led to rapid expansion of myocardial necrosis culminating in epicardial involvement, which was not present in nonhemorrhagic cases (P < 0.001). MI size and salvage were not different at 1 hour postreperfusion in animals with and without hemorrhage (P = 0.65). However, within 72 hours of reperfusion, a 4-fold greater loss in salvageable myocardium was evident in hemorrhagic MIs (P < 0.001). This paralleled observations in patients with larger MIs occurring in hemorrhagic cases (P < 0.01). CONCLUSIONS: Myocardial hemorrhage is a determinant of MI size. It drives MI expansion after reperfusion and compromises myocardial salvage. This introduces a clinical role of hemorrhage in acute care management, risk assessment, and future therapeutics.

A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion.

Quantitative analysis of dynamic contrast-enhanced cardiovascular MRI (cMRI) datasets enables the assessment of myocardial blood flow (MBF) for objective evaluation of ischemic heart disease in patients with suspected coronary artery disease. State-of-the-art MBF quantification techniques use constrained deconvolution and are highly sensitive to noise and motion-induced errors, which can lead to unreliable outcomes in the setting of high-resolution MBF mapping. To overcome these limitations, recent iterative approaches incorporate spatial-smoothness constraints to tackle pixel-wise MBF mapping. However, such iterative methods require a computational time of up to 30 minutes per acquired myocardial slice, which is a major practical limitation. Furthermore, they cannot enforce robustness to residual nonrigid motion which can occur in clinical stress/rest studies of patients with arrhythmia. We present a non-iterative patch-wise deep learning approach for pixel-wise MBF quantification wherein local spatio-temporal features are learned from a large dataset of myocardial patches acquired in clinical stress/rest cMRI studies. Our approach is scanner-independent, computationally efficient, robust to noise, and has the unique feature of robustness to motion-induced errors. Numerical and experimental results obtained using real patient data demonstrate the effectiveness of our approach.Clinical Relevance- The proposed patch-wise deep learning approach significantly improves the reliability of high-resolution myocardial blood flow quantification in cMRI by improving its robustness to noise and nonrigid myocardial motion and is up to 300-fold faster than state-of-the-art iterative approaches.

Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation.

In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.

Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep Learning.

The dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces the accuracy of diagnosis in patients with suspected ischemic heart disease. The main causes for DRA are known to be Gibbs ringing and bulk motion of the heart. The goal of this work is to propose a deep-learning-enabled automatic approach for the detection of motion-induced DRAs in FPP cMRI datasets. To this end, we propose a new algorithm that can detect the DRA in individual time frames by analyzing multiple reconstructions of the same time frame (k-space data) with varying temporal windows. In addition to DRA detection, our approach is also capable of suppressing the extent and severity of DRAs as a byproduct of the same reconstruction-analysis process. In this proof-of-concept study, our proposed method showed a good performance for automatic detection of subendocardial DRAs in stress perfusion cMRI studies of patients with suspected ischemic heart disease. To the best of our knowledge, this is the first approach that performs deep-learning-enabled detection and suppression of DRAs in cMRI.Clinical Relevance- Our approach enables clinicians to provide a more accurate diagnosis of ischemic heart disease by detecting and suppressing subendocardial dark-rim artifacts in first-pass perfusion cMRI datasets.