Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data

TL;DR

This paper introduces SIDER, a novel MRI reconstruction method that incorporates prior knowledge of signal decay to significantly accelerate diffusion imaging, enabling higher resolution and coverage during breath-hold scans.

Contribution

The study presents SIDER, a new compressed sensing approach that outperforms traditional methods like TV, allowing for faster lung MRI scans with preserved image quality at high acceleration factors.

Findings

SIDER achieves acceleration factors up to x10 with minimal loss of image quality.

Compared to TV, SIDER produces lower errors and more accurate alveolar dimension estimates.

SIDER enables more data to be acquired during breath-hold, improving lung microstructure analysis.

Abstract

Diffusion MRI measurements using hyperpolarized gases are generally acquired during patient breath hold, which yields a compromise between achievable image resolution, lung coverage and number of b-values. In this work, we propose a novel method that accelerates the acquisition of MR diffusion data by undersampling in both spatial and b-value dimensions, thanks to incorporating knowledge about the signal decay into the reconstruction (SIDER). SIDER is compared to total variation (TV) reconstruction by assessing their effect on both the recovery of ventilation images and estimated mean alveolar dimensions (MAD). Both methods are assessed by retrospectively undersampling diffusion datasets of normal volunteers and COPD patients (n=8) for acceleration factors between x2 and x10. TV led to large errors and artefacts for acceleration factors equal or larger than x5. SIDER improved TV, presenting lower errors and histograms of MAD closer to those obtained from fully sampled data for accelerations factors up to x10. SIDER preserved image quality at all acceleration factors but images were slightly smoothed and some details were lost at x10. In conclusion, we have developed and validated a novel compressed sensing method for lung MRI imaging and achieved high acceleration factors, which can be used to increase the amount of data acquired during a breath-hold. This methodology is expected to improve the accuracy of estimated lung microstructure dimensions and widen the possibilities of studying lung diseases with MRI.