@inproceedings{44f33540e45744668d06c3cb0a3d6303,
title = "Improving self super resolution in magnetic resonance images",
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.",
keywords = "MRI, super-resolution, unsupervised methods",
author = "Sachin Goyal and Can Zhao and Amod Jog and Prince, {Jerry L.} and Aaron Carass",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 11-02-2018 Through 13-02-2018",
year = "2018",
doi = "10.1117/12.2295366",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2018",
}