End-to-End Deep Learning-Based Motion Correction and Reconstruction for Accelerated Whole-Heart Joint T/T Mapping.

Purpose: To accelerate 3D whole-heart joint T/T mapping for myocardial tissue characterization using an end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data.

Methods: A free-breathing high-resolution motion-compensated 3D joint T/T water/fat sequence is employed. The sequence consists of the acquisition of four interleaved volumes with 2-echo encoding, resulting in eight volumes with different contrasts. An end-to-end non-rigid motion-corrected reconstruction network is used to estimate high quality motion-corrected reconstructions from the eight multi-contrast undersampled data for subsequent joint T/T mapping. Reconstruction with the proposed approach was compared against state-of-the-art motion-corrected HD-PROST reconstruction.

Results: The proposed approach yields images with good visual agreement compared to the reference reconstructions. The comparison of the quantitative values in the T and T maps showed the absence of systematic errors, and a small bias of -6.35 ms and -1.8 ms, respectively. The proposed reconstruction time was 24 seconds in comparison to 2.5 hours with motion-corrected HD-PROST, resulting in a reconstruction speed-up of over 370 times.

Conclusion: In conclusion, this study presents a promising method for efficient whole-heart myocardial tissue characterization. Specifically, the research highlights the potential of the multi-contrast end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data. The findings underscore its ability to compute T and T values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.