SPIRiT Regularization: Parallel MRI with a Combination of Sensitivity Encoding and Linear Predictability

TL;DR

This paper introduces SPIRiT regularization, a novel method combining sensitivity encoding and linear predictability in parallel MRI to improve image reconstruction quality from fewer samples.

Contribution

The paper proposes a new regularization technique that integrates compressed sensing with two parallel imaging methods, enhancing MRI reconstruction.

Findings

Improved image quality in brain, knee, and ankle MRI data.

Effective combination of sensitivity encoding and linear predictability.

Enhanced reconstruction from undersampled data.

Abstract

Accelerated Magnetic Resonance Imaging (MRI) permits high quality images from fewer samples that can be collected with a faster scan. Two established methods for accelerating MRI include parallel imaging and compressed sensing. Two types of parallel imaging include linear predictability, which assumes that the Fourier samples are linearly related, and sensitivity encoding, which incorporates a priori knowledge of the sensitivity maps. In this work, we combine compressed sensing with both types of parallel imaging using a novel regularization term: SPIRiT regularization. When combined, the reconstructed images are improved. We demonstrate results on data of a brain, a knee, and an ankle.