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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Correlation between k-space sampling pattern and MTF in compressed sensing MRSI.

A A Heikal¹, K Wachowicz², B G Fallone¹

¹Department of Medical Physics, Cross Cancer Institute, 11560 University Avenue, Edmonton, Alberta T6G 1Z2, Canada and Departments of Oncology and Physics, University of Alberta, 11560 University Avenue, Edmonton, Alberta T6G 1Z2, Canada.

Medical Physics

|October 27, 2016

Summary

This summary is machine-generated.

Researchers explored how k-space sampling affects the modulation transfer function (MTF) in compressed sensing MR spectroscopic imaging (CS-MRSI). Findings show a predictable relationship, allowing customizable metabolite map frequency content for better undersampling patterns.

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Area of Science:

Magnetic Resonance Imaging
Spectroscopy
Image Reconstruction

Background:

Compressed Sensing MR Spectroscopic Imaging (CS-MRSI) enables faster data acquisition by undersampling k-space.
The Modulation Transfer Function (MTF) characterizes the spatial frequency response of imaging systems, crucial for metabolite map quality.

Purpose of the Study:

To investigate the relationship between k-space sampling patterns in CS-MRSI and the resulting MTF of metabolite maps.
To explore how this relationship can be leveraged to quantitatively tailor the frequency content of metabolite maps.

Main Methods:

Simulations of a phantom were used to compute MTFs for Nyquist-sampled (NS) and undersampled CS-MRSI.
The impact of different k-space sampling patterns, generated using various probability distribution functions (PDFs), on the CS-MTF was evaluated.
A modified algorithm constrained sampling ratios to adhere to PDFs to assess its effect on CS-MTF reproducibility.

Main Results:

A strong correlation was observed between the CS-MRSI MTF and the product of the frequency-dependent sampling ratio and the NS MTF.
Probability distribution function (PDF)-constrained sampling patterns demonstrated higher reproducibility of the CS-MTF.
Enhanced correlations were found between PDF-constrained patterns and the predicted MTF.

Conclusions:

A predictable and customizable theoretical solution for the MTF of CS-MRSI has been established.
This relationship empowers users to tailor metabolite map frequency content by designing specific undersampling patterns.
The findings facilitate the optimization of CS-MRSI acquisition strategies for desired image quality.