no code implementations • 1 Feb 2023 • Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, Aaron Carass
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.
no code implementations • 12 Dec 2022 • Lianrui Zuo, Yihao Liu, Yuan Xue, Blake E. Dewey, Murat Bilgel, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Susan M. Resnick, Jerry L. Prince, Aaron Carass
HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
no code implementations • 24 Mar 2021 • Lianrui Zuo, Blake E. Dewey, Aaron Carass, Yihao Liu, Yufan He, Peter A. Calabresi, Jerry L. Prince
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
no code implementations • 4 Mar 2021 • Dzung L. Pham, Yi-Yu Chou, Blake E. Dewey, Daniel S. Reich, John A. Butman, Snehashis Roy
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.
1 code implementation • 7 Jul 2020 • Yufan He, Aaron Carass, Lianrui Zuo, Blake E. Dewey, Jerry L. Prince
However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.
1 code implementation • 11 Dec 2018 • Jacob C. Reinhold, Blake E. Dewey, Aaron Carass, Jerry L. Prince
Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.
no code implementations • 26 Feb 2018 • Can Zhao, Aaron Carass, Blake E. Dewey, Jerry L. Prince
This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.