Compressed sensing reconstruction of 7 Tesla ²³Na multi-channel breast data using ¹H MRI constraint

To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging (²³Na MRI) using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS) with anatomical ¹H prior knowledge.

An iterative reconstruction for ²³Na MRI with multi-channel receiver coils is presented. Based on CS it utilizes a second order total variation (TV⁽²⁾), adopted by anatomical weighting factors (AnaWeTV⁽²⁾) obtained from a high-resolution ¹H image. A support region is included as additional regularization. Simulated and measured ²³Na multi-channel data sets (n = 3) of the female breast acquired at 7 T with different undersampling factors (USF = 1.8/3.6/7.2/14.4) were reconstructed and compared to a conventional gridding reconstruction. The structural similarity was used to assess image quality of the reconstructed simulated data sets and to optimize the weighting factors for the CS reconstruction.

Compared with a conventional TV⁽²⁾, the AnaWeTV⁽²⁾ reconstruction leads to an improved image quality due to preserving of known structure and reduced partial volume effects. An additional incorporated support region shows further improvements for high USFs. Since the decrease in image quality with higher USFs is less pronounced compared to a conventional gridding reconstruction, proposed algorithm is beneficial especially for higher USFs. Acquisition time can be reduced by a factor of 4 (USF = 7.2), while image quality is still similar to a nearly fully sampled (USF = 1.8) gridding reconstructed data set.

Especially for high USFs, the proposed algorithm allows improved image quality for multi-channel ²³Na MRI data sets.