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Browsing by Author "Sharif, Behzad"
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Item A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion(IEEE, 2021) Youssef, Khalid; Heydari, Bobby; Rivero, Luis Zamudio; Beaulieu, Taylor; Cheema, Karandeep; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of MedicineQuantitative 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.Item Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation(IEEE, 2021) Yalcinkaya, Dilek Mirgun; Youssef, Khalid; Heydari, Bobby; Zamudio, Luis; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of MedicineIn 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.Item Intramyocardial hemorrhage drives fatty degeneration of infarcted myocardium(Springer Nature, 2022-10-27) Cokic, Ivan; Chan, Shing Fai; Guan, Xingmin; Nair, Anand R.; Yang, Hsin-Jung; Liu, Ting; Chen, Yinyin; Hernando, Diego; Sykes, Jane; Tang, Richard; Butler, John; Dohnalkova, Alice; Kovarik, Libor; Finney, Robert; Kali, Avinash; Sharif, Behzad; Bouchard, Louis S.; Gupta, Rajesh; Krishnam, Mayil Singaram; Vora, Keyur; Tamarappoo, Balaji; Howarth, Andrew G.; Kumar, Andreas; Francis, Joseph; Reeder, Scott B.; Wood, John C.; Prato, Frank S.; Dharmakumar, Rohan; Medicine, School of MedicineSudden 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.Item Reduced myocardial perfusion is common among subjects with ischemia and no obstructive coronary artery disease and heart failure with preserved ejection fraction: a report from the WISE-CVD continuation study(OAE, 2022) Aldiwani, Haider; Nelson, Michael D.; Sharif, Behzad; Wei, Janet; Samuel, T. Jake; Suppogu, Nissi; Quesada, Odayme; Cook-Wiens, Galen; Gill, Edward; Szczepaniak, Lidia S.; Thomson, Louise E. J.; Tamarappoo, Balaji; Asif, Anum; Shufelt, Chrisandra; Berman, Daniel; Merz, C. Noel Bairey; Medicine, School of MedicineAim: Women with evidence of ischemia and no obstructive coronary artery disease (INOCA) have an increased risk of major adverse cardiac events, including heart failure with preserved ejection fraction (HFpEF). To investigate potential links between INOCA and HFpEF, we examined pathophysiological findings present in both INOCA and HFpEF. Methods: We performed adenosine stress cardiac magnetic resonance imaging (CMRI) in 56 participants, including 35 women with suspected INOCA, 13 women with HFpEF, and 8 reference control women. Myocardial perfusion imaging was performed at rest and with vasodilator stress with intravenous adenosine. Myocardial perfusion reserve index was quantified as the ratio of the upslope of increase in myocardial contrast at stress vs. rest. All CMRI measures were quantified using CVI42 software (Circle Cardiovascular Imaging Inc). Statistical analysis was performed using linear regression models, Fisher's exact tests, ANOVA, or Kruskal-Wallis tests. Results: Age (P = 0.007), Body surface area (0.05) were higher in the HFpEF group. Left ventricular ejection fraction (P = 0.02) was lower among the INOCA and HFpEF groups than reference controls after age adjustment. In addition, there was a graded reduction in myocardial perfusion reserve index in HFpEF vs. INOCA vs. reference controls (1.5 ± 0.3, 1.8 ± 0.3, 1.9 ± 0.3, P = 0.02), which was attenuated with age-adjustment. Conclusion: Reduced myocardial perfusion reserve appears to be a common pathophysiologic feature in INOCA and HFpEF patients.Item Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep Learning(IEEE, 2021) Unal, Hazar Benan; Beaulieu, Taylor; Rivero, Luis Zamudio; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of MedicineThe 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.Item Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets(ArXiv, 2023-11-13) Yalcinkaya, Dilek M.; Youssef, Khalid; Heydari, Bobak; Simonetti, Orlando; Dharmakumar, Rohan; Raman, Subha; Sharif, Behzad; Medicine, School of MedicineDynamic 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.