Calibrationless MRI Reconstruction with a Plug-in Denoiser

TL;DR

This paper introduces a calibrationless MRI reconstruction method that integrates a plug-in denoiser within a high-dimensional convolutional framework, enabling faster imaging from under-sampled data without requiring calibration data.

Contribution

It presents a novel combination of a plug-in denoiser with the HICU framework for calibrationless MRI reconstruction, enhancing flexibility and acceleration in MRI imaging.

Findings

Feasibility demonstrated on 2D brain data.

Improved reconstruction quality from highly under-sampled data.

Enhanced flexibility in sampling pattern design.

Abstract

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI's clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (HICU) with a plug-in denoiser and demonstrate its feasibility using 2D brain data.