Computer Number: 1

H. Neeli, I. Seimenis, D. Martin, N. Tsekos, P. Martin
University of Houston, Houston, United States

Impact: This work serves as a proof of concept, in which our optical flow-based deep learning technique has the potential to reduce MRI acquisition times, overcome issues of image distortions, and enhance overall image quality.

Computer Number: 2

M. Mostapha, R. Miron, N. Janardhanan, M. Nadar, O. Darwish, T. Huelnhagen, T. Würfl, D. Grodzki, R. Schneider
Siemens Healthineers, Princeton, United States

Impact: We present a denoising method that accelerates DWI scans through a PnP diffusion model that utilizes noise maps for guidance. This approach improves scanning efficiency while preserving image quality, showcasing promise for future DWI clinical applications.

Computer Number: 3

H. Jing Han, W. Yu, S. Huang
Singapore University of Technology and Design, Singapore, Singapore

Impact: PeNN effectively and efficiently corrects the spatial encoding magnetic field in a portable MRI system using model-based imaging, saving regular labor-intensive mapping of magnetic fields. The proposed PeNN does not necessarily require the physics concept to be differentiable for backpropagation.

Computer Number: 4

A. Shazly, D. Kim, A. Al-Fakih, A. Rezk, K. Ryu, M. Al-masni
Sejong University, Seoul, Korea, Republic of

Impact: This methodology eliminates the need for perfectly aligned inputs, which are often unavailable in practice. By using the misaligned CT image as a proxy label, the proposed self-supervised approach leverages CBCT information to enhance the high-quality aligned CT (aCT) format.

Computer Number: 5

W. Jin, Z. Xu, Y. Li, Y. Zhao, R. Guo, Y. Li, Z-P Liang
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States

Impact: This proposed method provides an effective way to enhance the sensitivity of MRSI using physics-based machine learning incorporating side information. The method may further enhance the practical utility of MRSI for research and clinical applications. 

Computer Number: 6

S. Vasylechko, O. Afacan, S. Kurugol
Boston Children's Hospital, Harvard Medical School, Boston, United States

Impact: The demonstrated combination of diffusion posterior sampling with self-supervised learning establishes a framework for artifact-robust medical image restoration. This advances both computational efficiency in MRI post-processing and enables new research into automated quality assessment of cardiac functional metrics. 

Computer Number: 7

G. Lao, X. Zong, Y. Zhang, H. Wei
Shanghai Jiao Tong University, Shanghai, China

Impact: The proposed method can simultaneously generate motion-robust multiparametric quantitative maps of the whole brain without the need for k-space correction, increasing the clinical usability of multiparametric quantitative MRI.

Computer Number: 8

S. Chatterjee, R. Sundaresan, S. Rajamani, D. Shanbhag
GE HealthCare, Bengaluru, India

Impact: Proposed method enables training MRI DL denoising models without requirement to accurately model MRI noise. This approach becomes helpful while training denoising methods for low SNR MRI data.

Computer Number: 9

Z. Zhou, L. Xiang, H. Gandhi, A. Shankaranarayanan
Subtle Medical Inc, Menlo Park, United States

Impact: Synthetic phase allows convenient clinical deployment of complex DL denoising (CpxDN) models that shows advantage in high level and structured noise suppression. More clinical evaluation and optimization on CpxDN performance worth further investigation.

Computer Number: 10

W. Chen, Y. Fan, Z. Li, C. Liu, Y. Wang, Q. Tian, D. Shen, X. Song
Tsinghua University, Beijing, China

Impact: Our model facilitates multi-contrast MRI super-resolution in the absence of ground-truth HR images, which not only substantially reduces MRI acquisition time, but enables the obtaining of certain HR sequences that are difficult to acquire in clinical settings.

Computer Number: 11

S. Chatterjee, M. Lebel
GE HealthCare, Bengaluru, India

Impact: A model-based approach for MRI denoising provides guarantees against contrast and fine structure alterations during denoising. By virtue of the proposed method, we shall have robust and generalizable MRI denoisers.

Computer Number: 12

S. Yun, S-H You, B. Kim, D-H Kim, H. Seo
Bionics Research Center, Korea Institute of Science and Technology, Seoul, Korea, Republic of

Impact: This study advances 7T MRA imaging by providing a ground truth-free denoising method, potentially improving the diagnosis accuracy of cerebrovascular diseases and enhancing the clinical utility of 7T MRI in vascular imaging applications.

Computer Number: 13

R. Wu, J. Cheng, C. Li, J. Zou, W. Fan, Y. Liang, S. Wang
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Impact: Simultaneously enhancing the spatial and angular resolution of dMRI data can effectively reduce acquisition time, improve the accuracy of quantitative parameter estimation, and enhance clinical diagnostic efficiency.

Computer Number: 14

M. Safari, Z. Eidex, S. Wang, C-W Chang, R. L. Liu, H. Mao, E. H. Middlebrooks, X. Yang
Emory University, Atlanta, United States

Impact: The proposed model can reduce the scan time required for generating high-resolution T1 maps within a clinically acceptable time. Its capacity to produce high-quality brain images with reduced artifacts may improve diagnosis and accelerate advancements in neuroimaging research.

Computer Number: 15

P. Cheng, C. Yang, C. Wu, K. Wang, L. Yang, L. Hu, J. Yuan, X. Sun
NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Harbin, China

Impact: Our deep learning approach markedly improves non-proton nuclei image quality in simulcast multi-nuclei MRI, advancing rigorous scientific research in simulcast multi-nuclei MRI, especially in settings with limited access to advanced multi-nuclei simultaneous imaging technologies.

Computer Number: 16

C. Zaki, C. Millard, M. Chiew
University of Toronto, Toronto, Canada

Impact: This research demonstrates improved denoising performance on low-noise OOD MRI data, addressing a key challenge in generalizing to diverse imaging conditions with limited training data.