Accelerated calibrationless parallel transmit mapping using joint transmit and receive low-rank tensor completion

TL;DR

This paper evaluates a novel calibrationless parallel imaging algorithm for reconstructing undersampled transmit maps in MRI, demonstrating up to eight-fold acceleration with low RMS error, improving imaging efficiency.

Contribution

The study introduces a low-rank tensor completion method for calibrationless parallel transmit mapping, enabling higher acceleration factors than existing approaches.

Findings

TxLR method achieved up to 8-fold acceleration with RMS error below 0.1.

Virtual coils method failed at high noise or acceleration levels.

Joint receiver coils method worked well up to 4-fold acceleration.

Abstract

Purpose: To evaluate an algorithm for calibrationless parallel imaging to reconstruct undersampled parallel transmit field maps for the body and brain. Methods: Using synthetic data, body, and brain measurements of relative transmit maps, three different approaches to a joint transmit-receive low-rank tensor completion algorithm are evaluated. These methods included: (i) virtual coils using the product of receive and transmit sensitivities, (ii) joint-receiver coils that enforces a low rank structure across receive coils of all transmit modes, and (iii) transmit low rank (TxLR) that uses a low rank structure for both receive and transmit modes simultaneously. The performance of each are investigated for different noise levels and different acceleration rates on an 8-channel parallel transmit 7T system. Results: The virtual coils method broke down with increasing noise levels or acceleration rates greater than two producing RMS error greater than 0.1. The joint receiver coils method worked well up to acceleration factors of four, beyond which the RMS error exceeded 0.1. While TxLR enabled an eight-fold acceleration with most RMS errors remaining below 0.1. Conclusion: This work demonstrates that under-sampling factors of up to eight-fold are feasible for transmit array mapping and can be reconstructed using calibrationless parallel imaging methods.