Q. Liu, A. Zhu, X. Wang, D. Erdogmus, L. O’Donnell, L. Ning, Y. Rathi
Brigham and Women's Hospital, Harvard Medical School, Boston, United States

Impact: This study shows that inter-scanner variability in diffusion MRI cannot be mitigated using a unified reconstruction algorithm if the acquisition sequences themselves are different. Prospective harmonization of dMRI may benefit more from harmonizing the sequence itself.

P. Fuchs, K. Shmueli
University of Antwerp, Antwerp, Belgium

Impact: The tensor structure of multi-echo fMRI data can be exploited to perform tensor-based MP-PCA denoising, which greatly outperforms conventional matrix-based MP-PCA denoising, decreasing standard deviation and increasing temporal SNR. Tensor-based MP-PCA denoising should improve multi-echo fMRI and fQSM studies.

A. Brenes, Z. Tan, J. Bae, E. Solomon, E. Goesche, F. Knoll, S. G. Kim
Weill Cornell Medical College, New York City, United States

Impact: This proposed direct reconstruction method aims to streamline the complex image reconstruction and data analysis processes of DCE-MRI and has the potential to make DCE MRI a more efficient, faster, and accessible tool for breast cancer exams. 

M. McIntosh, S. Gerard, J. Altman, E. Ramirez, R. Meyer, S. Khodaei, C. Wharff, R. Bello, A. Kizhakke Puliyakote, A. Hahn, S. Fain
University of Iowa, Iowa City, United States

Impact: This method may be used to develop quantitative parenchymal density measures on UTE MRI as a biomarker to assess cross-sectional differences in disease populations, disease progression and treatment response without the ionizing radiation inherent to gold-standard computed tomography.

R. Adams, E. Alzaga Goñi, P-T Yap, D. Ma
Case Western Reserve University, Cleveland, United States

Impact: UTN is the first thalamus segmentation tool that works across different contrast types, matching the performance of top tools, which are limited to white matter-nulled images. This allows for volumetric thalamic nuclei studies with any type of MR data.

E-G Ha, S-S Han, D-H Kim
Yonsei university, Seoul, Korea, Republic of

Impact: The proposed model has the potential to assist dental clinicians by improving the accuracy and consistency of TMJ MRI interpretation while reducing manual effort. This approach could facilitate further exploration into automated diagnostics across various imaging modalities, enhancing clinical workflows.

S. Li, S-F Shih, J. Finn, D. Ruan, K-L Nguyen, X. Zhong
Department of Radiological Sciences, University of California Los Angeles, Los Angeles, United States

Impact: This research advances automated LV segmentation in DENSE MRI by integrating UDA and foundation models, leveraging cine SSFP images and established annotations to improve deep learning model performance despite limited availability of specialized MR images like DENSE.

R. Rodriguez Cruces, A. Ngo, D. Gift Cabalo, J. Royer, Y. Hwang, N. Eichert, P. Herholz, J. DeKraker, I. Leppert, C. Tardif, B. Bernhardt
McGill, Montreal, Canada

Impact: This standardized workflow ensures consistent, high-resolution analysis and generates sharable outputs for large-scale data processing. It promotes reproducibility and collaboration, advancing our understanding of brain structure-function relationships and enhancing scientific impact across the research community.

J. A. Chong Chie, S. Persohn, P. Salama, P. Territo
Stark Neurosciences Research Institute, Indianapolis, United States

Impact: This study presents a 3D multimodal registration framework invariant to image contrast, spatial resolutions, and anatomical and functional variations; thus, enabling direct comparisons of brain anatomy and function(s) across subjects, while minimizing inter-subject variability, permitting quantitative multimodal analysis.

Z. Huang, N. Jiang, Y. Sui
National Institute of Health Data Science, Peking University, Beijing, China

Impact: We presents an unsupervised 3D image registration method based on deep learning principles. By incorporating local-global self-attention training, this method is robust across different registration tasks. It ensures consistently accurate registration results and is applicable to multi clinical registration scenarios.