Reducing the Sampling Burden of Fourier Sensing with a Non-rectangular Field-of-View

TL;DR

This paper introduces a method to efficiently sample and reconstruct images within arbitrary non-rectangular fields-of-view in Fourier sensing, reducing sampling burden and computational costs for MRI imaging.

Contribution

It proposes a novel sampling pattern and direct reconstruction algorithm for arbitrary FOVs, along with improvements to existing model-based reconstruction methods.

Findings

Reduced number of samples needed for high-quality images

Decreased computational cost of reconstruction algorithms

Validated with MRI data of ankle, pineapple, and brain

Abstract

With Fourier sensing, it is commonly the case that the field-of-view (FOV), the area of space to be imaged, is known prior to reconstruction. To date, reconstruction algorithms have focused on FOVs with simple geometries: a rectangle or a hexagon. This yields sampling patterns that are more burdensome than necessary. Due to the reduced area of imaging possible with an arbitrary (e.g., non-rectangular) FOV, the number of samples required for a high-quality images is reduced. However, when an arbitrary FOV has been considered, the reconstruction algorithm is computationally expensive. In this manuscript, we present a method to reduce the sampling pattern for an arbitrary FOV with an accompanying direct (non-iterative) reconstruction algorithm. We also present a method to decrease the computational cost of the (iterative) model-based reconstruction (MBR) algorithm. We present results using MRI data of an ankle, a pineapple, and a brain.