End-to-End Deep Learning-Based Motion Correction and Reconstruction for Accelerated Whole-Heart Joint T1/T2 Mapping

Purpose

To accelerate 3D whole-heart joint T₁/T₂ 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 T₁/T₂ 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 T₁/T₂ 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 T₁ and T₂ 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 T₁ and T₂ values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.