2021/03/31 by Hossam El‐Rewaidy, Hossam El-Rewaidy, Rui Guo +13
Engineering · Medicine · Physics and Astronomy · #Advanced MRI Techniques and Applications #Cardiac Imaging and Diagnostics #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #eess.IV #electronic engineering #information engineering #physics.med-ph
paper · pdf · doi:10.48550/arxiv.2104.00143
34 pages, 10 figrues, 3 tables
arxiv created 2021/03/31 · openalex publication_date 2021/03/31 · arxiv updated 2021/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Purpose: To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses neural networks (NN) to estimate voxel-wise myocardial T1 and extracellular (ECV) from T1-weighted images collected after a single inversion pulse over 4-5 heartbeats. Method: MyoMapNet utilizes a simple fully-connected NN to estimate T1 values from 5 (native) or 4 (post-contrast) T1-weighted images. Native MOLLI-5(3)3 T1 was collected in 717 subjects (386 males, 55±16.5 years) and post-contrast MOLLI-4(1)3(1)2 in 535 subjects (232 male, 56.5±15 years). The dataset was divided into training (80%) and testing (20%), where 20% of the training set was used to optimize MyoMapNet architecture (size and loss functions). We used MyoMapNet to estimate T1 and ECV maps with the first 5 (native) or 4 (post-contrast) T1-weighted images from the corresponding MOLLI sequence compared to the conventional and an abbreviated MOLLI using similar number of T1-weighted images with 3-parameter curve-fitting. Results: In our preliminary optimizaiton step, we determined that a 5-layers NN trained using mean-absolute-error loss yields lower estimation errors and was used subsequently in independent testing study. The myocardial T1 by MyoMapNet was similar to MOLLI (1200±45ms vs. 1199±46ms; P=0.3 for native T1, and 27.3±3.5% vs. 27.1±4%; P=0.4 for ECV). MyoMapNet had significantly smaller errors in T1 estimations compared to abbreviated-MOLLI (1±17ms vs. 31±34ms, P<0.01 for in native T1, and 0.1±1.3% vs. 1.9±2.5%, P<0.01 for ECV). The duration of T1 estimation was approximately 2 ms per slice using MyoMapNet. Conclusion: MyoMapNet T1 mapping enables myocardial T1 quantification in 4-5 heartbeats with near-instantaneous map estimation time with similar accuracy and precision as MOLLI. Keywords: Myocardial T1 mapping, MOLLI, T1 reconstruction, Neural network, Deep Learning.