Browsing by Author "Yang, Hsin-Jung"

Now showing 1 - 3 of 3
  • Thumbnail Image
    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 Medicine
    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.
  • Thumbnail Image
    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 Medicine
    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.
  • Thumbnail Image
    Item
    Reliable Off-Resonance Correction in High-Field Cardiac MRI Using Autonomous Cardiac B0 Segmentation with Dual-Modality Deep Neural Networks
    (MDPI, 2024-02-23) Li, Xinqi; Huang, Yuheng; Malagi, Archana; Yang, Chia-Chi; Yoosefian, Ghazal; Huang, Li-Ting; Tang, Eric; Gao, Chang; Han, Fei; Bi, Xiaoming; Ku, Min-Chi; Yang, Hsin-Jung; Han, Hui; Medicine, School of Medicine
    B0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B0 shimming is the most straightforward way to improve the B0 homogeneity. However, today’s standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B0 shim compared to the standard method (SD(B0Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines.