Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data

Abstract

Purpose

To accelerate respiratory triggered free-breathing T2 mapping of the abdomen while maintaining high-quality anatomical images, accurate T2 maps, and fast reconstruction times.

Methods

We developed a flexible deep learning framework that can be trained in a fully supervised manner to improve T2-weighted images or in a self-supervised manner to reconstruct T2 maps.

Results

For retrospectively undersampled data, anatomical images and T2 maps reconstructed by the proposed deep learning method demonstrated reduced voxel-wise error compared to existing traditional and compressed sensing techniques. Reconstruction times were approximately 1 s per slice, significantly faster than existing compressed sensing techniques. Prospectively undersampled data were also acquired to assess the model.

Conclusion

The proposed deep-learning framework reconstructed high-quality anatomical images and accurate T2 maps from datasets undersampled to only 160 total radial views (5 views per echo time), enabling full liver coverage in under three minutes on average with per-slice reconstruction times of approximately one second.