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arXiv · 2406.16214

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

Nicholas Dwork·Erin K. Englund·Alex J. Barker
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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.

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Nicholas Dwork, Erin K. Englund, Alex J. Barker. 2024-06-23. Reducing the Sampling Burden of Fourier Sensing with a Non-rectangular Field-of-View. https://arxiv.org/abs/2406.16214

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