no code implementations • 4 Jun 2025 • Savannah P. Hays, Lianrui Zuo, Anqi Feng, Yihao Liu, Blake E. Dewey, Jiachen Zhuo, Ellen M. Mowry, Scott D. Newsome Jerry L. Prince, Aaron Carass
It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data. The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei.
1 code implementation • 14 Mar 2025 • Samuel W. Remedios, Shuwen Wei, Shuo Han, Jinwei Zhang, Aaron Carass, Kurt G. Schilling, Dzung L. Pham, Jerry L. Prince, Blake E. Dewey
Super-resolution (SR) methods aim to address this problem, but previous methods do not address all of the following: slice profile shape estimation, slice gap, domain shift, and non-integer or arbitrary upsampling factors.
1 code implementation • 10 Sep 2024 • Anqi Feng, Zhangxing Bian, Blake E. Dewey, Alexa Gail Colinco, Jiachen Zhuo, Jerry L. Prince
In this work, we introduce RATNUS, which uses synthetic T1-weighted images with many inversion times along with diffusion-derived features to enhance the visibility of nuclei within the thalamus.
no code implementations • 29 Aug 2024 • Savannah P. Hays, Samuel W. Remedios, Lianrui Zuo, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Blake E. Dewey, Jerry L. Prince
In this paper, we evaluate the impact of image resolution on harmonization using a pretrained harmonization algorithm.
no code implementations • 5 Jan 2024 • Ho Hin Lee, Adam M. Saunders, Michael E. Kim, Samuel W. Remedios, Lucas W. Remedios, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Chloe Cho, Louise A. Mawn, Tonia S. Rex, Kevin L. Schey, Blake E. Dewey, Jeffrey M. Spraggins, Jerry L. Prince, Yuankai Huo, Bennett A. Landman
These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
no code implementations • 7 Dec 2023 • Zejun Wu, Samuel W. Remedios, Blake E. Dewey, Aaron Carass, Jerry L. Prince
Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.
no code implementations • 3 Dec 2023 • Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Dzung L. Pham, Aaron Carass, Jerry L. Prince
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.
no code implementations • 31 Oct 2023 • Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass
Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.
1 code implementation • 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, Samuel W. Remedios, Savannah P. Hays, Murat Bilgel, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Susan M. Resnick, Jerry L. Prince, Aaron Carass
Furthermore, 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.