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 Accurate Intramyocardial Hemorrhage Assessment with Fast, Free-running, Cardiac Quantitative Susceptibility Mapping(Radiological Society of North America, 2024) Huang, Yuheng; Guan, Xingmin; Zhang, Xinheng; Yoosefian, Ghazal; Ho, Hao; Huang, Li-Ting; Lin, Hsin-Yao; Anthony, Gregory; Lee, Hsu-Lei; Bi, Xiaoming; Han, Fei; Chan, Shing Fai; Vora, Keyur P.; Sharif, Behzad; Singh, Dhirendra P.; Youssef, Khalid; Li, Debiao; Han, Hui; Christodoulou, Anthony G.; Dharmakumar, Rohan; Yang, Hsin-Jung; Medicine, School of MedicinePurpose: To evaluate the performance of a high-dynamic-range quantitative susceptibility mapping (HDR-QSM) cardiac MRI technique to detect intramyocardial hemorrhage (IMH) and quantify iron content using phantom and canine models. Materials and Methods: A free-running whole-heart HDR-QSM technique for IMH assessment was developed and evaluated in calibrated iron phantoms and 14 IMH female canine models. IMH detection and iron content quantification performance of this technique was compared with the conventional iron imaging approaches, R2*(1/T2*) maps, using measurements from ex vivo imaging as the reference standard. Results: Phantom studies confirmed HDR-QSM’s accurate iron content quantification and artifact mitigation ability by revealing a strong linear relationship between iron concentration and QSM values (R2, 0.98). In in vivo studies, HDR-QSM showed significantly improved image quality and susceptibility homogeneity in nonaffected myocardium by alleviating motion and off-resonance artifacts (HDR-QSM vs R2*: coefficient of variation, 0.31 ± 0.16 [SD] vs 0.73 ± 0.36 [P < .001]; image quality score [five-point Likert scale:], 3.58 ± 0.75 vs 2.87 ± 0.51 [P < .001]). Comparison between in vivo susceptibility maps and ex vivo measurements showed higher performance of HDR-QSM compared with R2* mapping for IMH detection (area under the receiver operating characteristic curve, 0.96 vs 0.75; P < .001) and iron content quantification (R2, 0.71 vs 0.14). Conclusion: In a canine model of IMH, the fast and free-running cardiac QSM technique accurately detected IMH and quantified intramyocardial iron content of the entire heart within 5 minutes without requiring breath holding.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 Diastolic dysfunction in women with ischemia and no obstructive coronary artery disease: Mechanistic insight from magnetic resonance imaging(Elsevier, 2021) Samuel, T. Jake; Wei, Janet; Sharif, Behzad; Tamarappoo, Balaji K.; Pattisapu, Varun; Maughan, Jenna; Cipher, Daisha J.; Suppogu, Nissi; Aldiwani, Haider; Thomson, Louise E. J.; Shufelt, Chrisandra; Berman, Daniel S.; Li, Debiao; Bairey Merz, C. Noel; Nelson, Michael D.; Medicine, School of MedicineBackground: Ischemia with no obstructive coronary artery disease (INOCA) is prevalent in women and is associated with increased risk of developing heart failure with preserved ejection fraction (HFpEF); however, the mechanism(s) contributing to this progression remains unclear. Given that diastolic dysfunction is common in women with INOCA, defining mechanisms related to diastolic dysfunction in INOCA could identify therapeutic targets to prevent HFpEF. Methods: Cardiac MRI was performed in 65 women with INOCA and 12 reference controls. Diastolic function was defined by left ventricular early diastolic circumferential strain rate (eCSRd). Contributors to diastolic dysfunction were chosen a priori as coronary vascular dysfunction (myocardial perfusion reserve index [MPRI]), diffuse myocardial fibrosis (extracellular volume [ECV]), and aortic stiffness (aortic pulse wave velocity [aPWV]). Results: Compared to controls, eCSRd was lower in INOCA (1.61 ± 0.33/s vs. 1.36 ± 0.31/s, P = 0.016); however, this difference was not exaggerated when the INOCA group was sub-divided by low and high MPRI (P > 0.05) nor was ECV elevated in INOCA (29.0 ± 1.9% vs. 28.0 ± 3.2%, control vs. INOCA; P = 0.38). However, aPWV was higher in INOCA vs. controls (8.1 ± 3.2 m/s vs. 6.1 ± 1.5 m/s; P = 0.045), and was associated with eCSRd (r = -0.50, P < 0.001). By multivariable linear regression analysis, aPWV was an independent predictor of decreased eCSRd (standardized β = -0.39, P = 0.003), as was having an elevated left ventricular mass index (standardized β = -0.25, P = 0.024) and lower ECV (standardized β = 0.30, P = 0.003). Conclusions: These data provide mechanistic insight into diastolic dysfunction in women with INOCA, identifying aortic stiffness and ventricular remodeling as putative therapeutic targets.Item Enabling Reliable Visual Detection of Chronic Myocardial Infarction with Native T1 Cardiac MRI Using Data-Driven Native Contrast Mapping(Radiological Society of North America, 2024) Youssef, Khalid; Zhang, Xinheng; Yoosefian, Ghazal; Chen, Yinyin; Chan, Shing Fai; Yang, Hsin-Jung; Vora, Keyur; Howarth, Andrew; Kumar, Andreas; Sharif, Behzad; Dharmakumar, Rohan; Medicine, School of MedicinePurpose: 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.Item Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty–guided space-time analysis(Elsevier, 2024) Yalcinkaya, Dilek M.; Youssef, Khalid; Heydari, Bobak; Wei, Janet; Merz, C. Noel Bairey; Judd, Robert; Dharmakumar, Rohan; Simonetti, Orlando P.; Weinsaft, Jonathan W.; Raman, Subha V.; Sharif, Behzad; Medicine, School of MedicineBackground: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging 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 three 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 inD (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 exDs (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). Conclusion: 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.Item Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis(ArXiv, 2024-08-09) Yalcinkaya, Dilek M.; Youssef, Khalid; Heydari, Bobak; Wei, Janet; Merz, Noel Bairey; Judd, Robert; Dharmakumar, Rohan; Simonetti, Orlando P.; Weinsaft, Jonathan W.; Raman, Subha V.; Sharif, Behzad; Medicine, School of MedicineBackground: 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.Item Intracoronary acetylcholine for vasospasm provocation in women with ischemia and no obstructive coronary artery disease(Elsevier, 2025-03-18) Tjoe, Benita; Pacheco, Christine; Suppogu, Nissi; Samuels, Bruce; Rezaeian, Panteha; Tamarappoo, Balaji; Berman, Daniel S.; Sharif, Behzad; Nelson, Michael; Anderson, R. David; Petersen, John; Pepine, Carl J.; Thomson, Louise E. J.; Merz, C. Noel Bairey; Wei, Janet; Radiology and Imaging Sciences, School of MedicineObjectives: To evaluate the utility of higher dose intracoronary acetylcholine (ACh) during invasive coronary function testing (CFT) in women with suspected ischemia and no obstructive coronary artery disease (INOCA) for detection of epicardial vasospasm, relation to quality of life (QoL) and the presence of scar by late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMRI). Background: CFT is an established method for diagnosis of coronary microvascular dysfunction (CMD). The utility of epicardial vasospasm provocation testing with higher dose ACh infusion is not fully understood. Methods: Women with suspected INOCA undergoing invasive CFT were enrolled in the Women's Ischemia Syndrome Evaluation-Pre-Heart Failure with Preserved Ejection Fraction (WISE Pre-HFpEF) study (NCT03876223). Incremental infusions of 0.364, 36.4 μg and 108 μg ACh were used for vasospasm provocation. Vasospasm was defined as ≥75 % artery diameter reduction compared to post-nitroglycerin diameter and related to QoL and LGE on CMRI. Results: Among 73 women (56 ± 11 years), epicardial vasospasm was detected in 17 (23 %). Among women with vasospasm, the vast majority (94 %) had coronary endothelial dysfunction and few (12 %) had other abnormal CFT measures. Those with vasospasm had more nocturnal angina symptoms, calcium channel blocker use, poorer QoL (all p = 0.001) and disease perception (p = 0.02) than those without. LGE scar by CMRI was not associated with vasospasm (p = 0.22). Conclusions: Among women with suspected INOCA, intracoronary Ach spasm testing provoked epicardial vasospasm in one fourth. Women with epicardial vasospasm overwhelmingly had concomitant endothelial dysfunction, worse QoL but not more frequent myocardial scar on CMRI.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 Left ventricular circumferential strain and coronary microvascular dysfunction: A report from the Women’s Ischemia Syndrome Evaluation Coronary Vascular Dysfunction (WISE-CVD) Project(Elsevier, 2021) Tamarappoo, Balaji; Samuel, T. Jake; Elboudwarej, Omeed; Thomson, Louise E. J.; Aldiwani, Haider; Wei, Janet; Mehta, Puja; Cheng, Susan; Sharif, Behzad; AlBadri, Ahmed; Handberg, Eileen M.; Petersen, John; Pepine, Carl J.; Nelson, Michael D.; Bairey Merz, C. Noel; Graduate Medical Education, School of MedicineAims: Women with ischemia but no obstructive coronary artery disease (INOCA) often have coronary microvascular dysfunction (CMD). Left ventricular (LV) circumferential strain (CS) is often lower in INOCA compared to healthy controls; however, it remains unclear whether CS differs between INOCA women with and without CMD. We hypothesized that CS would be lower in women with CMD, consistent with CMD-induced LV mechanical dysfunction. Methods and results: Cardiac magnetic resonance (cMR) images were examined from women enrolled in the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction Project. CS by feature tracking in INOCA women with CMD, defined as myocardial perfusion reserve index (MPRI) <1.84 during adenosine-stress perfusion cMR, was compared with CS in women without CMD. In a subset who had invasive coronary function testing (CFT), the relationship between CS and CFT metrics, LV ejection fraction (LVEF) and cardiovascular risk factors was investigated. Among 317 women with INOCA, 174 (55%) had CMD measured by MPRI. CS was greater in women with CMD compared to those without CMD (23.2 ± 2.5% vs. 22.1 ± 3.0%, respectively, P = 0.001). In the subset with CFT (n = 153), greater CS was associated with increased likelihood of reduced vasodilator capacity (OR = 1.33, 95%CI = 1.02-1.72, p = 0.03) and discriminated abnormal vs. normal coronary vascular function compared to CAD risk factors, LVEF and LV concentricity (AUC: 0.82 [0.73-0.96 95%CI] vs. 0.65 [0.60-0.71 95%CI], respectively, P = 0.007). Conclusion: The data indicate that LV circumferential strain is related to and predicts CMD, although in a direction contrary with our hypothesis, which may represent an early sign of LV mechanical dysfunction in CMD.