Design, Synthesis, and Preclinical Evaluation of a High-Affinity 18F-Labeled Radioligand for Myocardial Growth Hormone Secretagogue Receptor Before and After Myocardial Infarction.

The peptide hormone ghrelin is produced in cardiomyocytes and acts through the myocardial growth hormone secretagogue receptor (GHSR) to promote cardiomyocyte survival. Administration of ghrelin may have therapeutic effects on post-myocardial infarction (MI) outcomes. Therefore, there is a need to develop molecular imaging probes that can track the dynamics of GHSR in health and disease to better predict the effectiveness of ghrelin-based therapeutics. We designed a high-affinity GHSR ligand labeled with 18F for imaging by PET and characterized its in vivo properties in a canine model of MI. Methods: We rationally designed and radiolabeled with 18F a quinazolinone derivative ([18F]LCE470) with subnanomolar binding affinity to GHSR. We determined the sensitivity and in vivo and ex vivo specificity of [18F]LCE470 in a canine model of surgically induced MI using PET/MRI, which allowed for anatomic localization of tracer uptake and simultaneous determination of global cardiac function. Uptake of [18F]LCE470 was determined by time-activity curve and SUV analysis in 3 regions of the left ventricle-area of infarct, territory served by the left circumflex coronary artery, and remote myocardium-over a period of 1.5 y. Changes in cardiac perfusion were tracked by [13N]NH3 PET. Results: The receptor binding affinity of LCE470 was measured at 0.33 nM, the highest known receptor binding affinity for a radiolabeled GHSR ligand. In vivo blocking studies in healthy hounds and ex vivo blocking studies in myocardial tissue showed the specificity of [18F]LCE470, and sensitivity was demonstrated by a positive correlation between tracer uptake and GHSR abundance. Post-MI changes in [18F]LCE470 uptake occurred independently of perfusion tracer distributions and changes in global cardiac function. We found that the regional distribution of [18F]LCE470 within the left ventricle diverged significantly within 1 d after MI and remained that way throughout the 1.5-y duration of the study. Conclusion: [18F]LCE470 is a high-affinity PET tracer that can detect changes in the regional distribution of myocardial GHSR after MI. In vivo PET molecular imaging of the global dynamics of GHSR may lead to improved GHSR-based therapeutics in the treatment of post-MI remodeling.

Caveolin-1 knockout mitigates breast cancer metastasis to the lungs via integrin α3 dysregulation in 4T1-induced syngeneic breast cancer model.

Caveolin-1 (Cav-1) is a critical lipid raft protein playing dual roles as both a tumor suppressor and promoter. While its role in tumorigenesis, progression, and metastasis has been recognized, the explicit contribution of Cav-1 to the onset of lung metastasis from primary breast malignancies remains unclear. Here, we present the first evidence that Cav-1 knockout in mammary epithelial cells significantly reduces lung metastasis in syngeneic breast cancer mouse models. In vitro, Cav-1 knockout in 4T1 cells suppressed extracellular vesicle secretion, cellular motility, and MMP secretion compared to controls. Complementing this, in vivo analyses demonstrated a marked reduction in lung metastatic foci in mice injected with Cav-1 knockout 4T1 cells as compared to wild-type cells, which was further corroborated by mRNA profiling of the primary tumor. We identified 21 epithelial cell migration genes exhibiting varied expression in tumors derived from Cav-1 knockout and wild-type 4T1 cells. Correlation analysis and immunoblotting further revealed that Cav-1 might regulate metastasis via integrin α3 (ITGα3). In silico protein docking predicted an interaction between Cav-1 and ITGα3, which was confirmed by co-immunoprecipitation. Furthermore, Cav-1 and ITGα3 knockdown corroborated its role in metastasis in the cell migration assay.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

BACKGROUND: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Enabling Reliable Visual Detection of Chronic Myocardial Infarction with Native T1 Cardiac MRI Using Data-Driven Native Contrast Mapping.

Purpose To investigate whether infarct-to-remote myocardial contrast can be optimized by replacing generic fitting algorithms used to obtain native T1 maps with a data-driven machine learning pixel-wise approach in chronic reperfused infarct in a canine model. Materials and Methods A controlled large animal model (24 canines, equal male and female animals) of chronic myocardial infarction with histologic evidence of heterogeneous infarct tissue composition was studied. Unsupervised clustering techniques using self-organizing maps and t-distributed stochastic neighbor embedding were used to analyze and visualize native T1-weighted pixel-intensity patterns. Deep neural network models were trained to map pixel-intensity patterns from native T1-weighted image series to corresponding pixels on late gadolinium enhancement (LGE) images, yielding visually enhanced noncontrast maps, a process referred to as data-driven native mapping (DNM). Pearson correlation coefficients and Bland-Altman analyses were used to compare findings from the DNM approach against standard T1 maps. Results Native T1-weighted images exhibited distinct pixel-intensity patterns between infarcted and remote territories. Granular pattern visualization revealed higher infarct-to-remote cluster separability with LGE labeling as compared with native T1 maps. Apparent contrast-to-noise ratio from DNM (mean, 15.01 ± 2.88 [SD]) was significantly different from native T1 maps (5.64 ± 1.58; P < .001) but similar to LGE contrast-to-noise ratio (15.51 ± 2.43; P = .40). Infarcted areas based on LGE were more strongly correlated with DNM compared with native T1 maps (R2 = 0.71 for native T1 maps vs LGE; R2 = 0.85 for DNM vs LGE; P < .001). Conclusion Native T1-weighted pixels carry information that can be extracted with the proposed DNM approach to maximize image contrast between infarct and remote territories for enhanced visualization of chronic infarct territories. Keywords: Chronic Myocardial Infarction, Cardiac MRI, Data-Driven Native Contrast Mapping Supplemental material is available for this article. © RSNA, 2024.

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.