A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion
dc.contributor.author | Youssef, Khalid | |
dc.contributor.author | Heydari, Bobby | |
dc.contributor.author | Rivero, Luis Zamudio | |
dc.contributor.author | Beaulieu, Taylor | |
dc.contributor.author | Cheema, Karandeep | |
dc.contributor.author | Dharmakumar, Rohan | |
dc.contributor.author | Sharif, Behzad | |
dc.contributor.department | Medicine, School of Medicine | |
dc.date.accessioned | 2023-11-20T14:01:04Z | |
dc.date.available | 2023-11-20T14:01:04Z | |
dc.date.issued | 2021 | |
dc.description.abstract | 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. | |
dc.eprint.version | Author's manuscript | |
dc.identifier.citation | Youssef K, Heydari B, Rivero LZ, et al. A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:4045-4051. doi:10.1109/EMBC46164.2021.9629630 | |
dc.identifier.uri | https://hdl.handle.net/1805/37163 | |
dc.language.iso | en_US | |
dc.publisher | IEEE | |
dc.relation.isversionof | 10.1109/EMBC46164.2021.9629630 | |
dc.relation.journal | Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) | |
dc.rights | Publisher Policy | |
dc.source | PMC | |
dc.subject | Coronary artery disease | |
dc.subject | Coronary circulation | |
dc.subject | Deep learning | |
dc.subject | Magnetic resonance imaging | |
dc.title | A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion | |
dc.type | Article |