Improving self super resolution in magnetic resonance images

Sachin Goyal; Can Zhao; Amod Jog; Jerry L. Prince; Aaron Carass

doi:10.1117/12.2295366

12 March 2018 Improving self super resolution in magnetic resonance images

Sachin Goyal, Can Zhao, Amod Jog, Jerry L. Prince, Aaron Carass

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1057814 (2018) https://doi.org/10.1117/12.2295366
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Magnetic resonance (MR) images (MRI) are routinely acquired with high in-plane resolution and lower through-plane resolution. Improving the resolution of such data can be achieved through post-processing techniques knows as super-resolution (SR), with various frameworks in existence. Many of these approaches rely on external databases from which SR methods infer relationships between low and high resolution data. The concept of self super-resolution (SSR) has been previously reported, wherein there is no external training data with the method only relying on the acquired image. The approach involves extracting image patches from the acquired image constructing new images based on regression and combining the new images by Fourier Burst Accumulation. In this work, we present four improvements to our previously reported SSR approach. We demonstrate these improvements have a significant effect on improving image quality and the measured resolution.

Conference Presentation

Citation Download Citation

Sachin Goyal, Can Zhao, Amod Jog, Jerry L. Prince, and Aaron Carass "Improving self super resolution in magnetic resonance images", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1057814 (12 March 2018); https://doi.org/10.1117/12.2295366

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