The paper introduces MR‑DiffuSR, a 3‑D latent diffusion framework that uses high‑resolution T1w structural priors to guide super‑resolution of thick‑slice FLAIR MRI scans. By applying cross‑modality structural swin attention and a mixed‑scale degradation strategy, the method avoids hallucinations and remains robust across varying slice thicknesses. On ADNI datasets, MR‑DiffuSR outperforms CNN and 2‑D diffusion baselines, achieving high PSNR, SSIM, and low LPIPS, and maintains strong white‑matter hyperintensity segmentation performance even at 7 mm equivalent slice thickness.
By Haoyu Lan, Jiazhen Zhang, John Onofrey, Bino Varghese, Nasim Sheikh-Bahaei, Arthur W. Toga, Jeiran Choupan
arXiv:2601. 20503v2 Announce Type: replace-cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) detectable on magnetic resonance imaging (MRI).
By Jesse Phitidis, Alison Q. Smithard, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Vald\'es Hern\'andez
arXiv:2606. 03566v1 Announce Type: cross Abstract: Background: The lateral ventricle choroid plexus (LVCP) is gaining recognition as a key imaging biomarker for multiple sclerosis (MS) related to physical disability and neuroinflammation.
By Po-Jui Lu, Alessandro Cagol, Mario Ocampo-Pineda, Federico Spagnolo, Marina Mastantuono, Andreea-Alexandra Aldea, Jannis M\"uller, \"Ozg\"ur Yaldizli, Matthias Weigel, Lester Melie-Garcia, Roberta Magliozzi, Maria Pia Sormani, Ludwig Kappos, Jens Kuhle, Cristina Granziera
arXiv:2606. 07381v1 Announce Type: cross Abstract: Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which are difficult to acquire.
By Prabhjot Kaur, Hakim Ouaalam, Sedat Kandemirli, Sanjay P. Prabhu, Simon K. Warfield
The study investigates how image‑domain Poisson perturbations affect the classification of ischemic core/penumbra in non‑contrast CT slices. Using a CPAISD cohort, the authors compared direct ResNet‑18 classification with a denoising‑then‑classifying pipeline and found that denoising generally reduced performance. A prospective experiment with joint denoising‑classification models showed no statistically significant advantage over direct noisy classification, indicating limited robustness to Poisson noise.
By Rhea Ghosal, Ronok Ghosal, Eileen Lou
MIRTO is an evaluation protocol for unsupervised anomaly segmentation in brain MRI that explicitly documents key methodological choices—such as registration alignment, threshold setting, and false‑positive budgeting—and measures their impact. It applies a registration check, uses validation data for thresholding, reports realized false‑positive volumes, and repeats each comparison across 15,552 evaluation pipelines with bootstrap intervals. In a study on four UAD methods and 312 BraTS 2020 subjects, MIRTO revealed that an axis‑order mismatch dramatically lowered a diffusion model’s voxel AUROC, and that many performance differences were driven by lesion definition and threshold transfer rather than model quality.
By Negin Kafee Hernashki, Soumick Chatterjee
The study introduces ART‑Net, an anatomy‑aware residual attention network designed to refine four‑fold accelerated SENSE brain MRI. In a prospective paired study of 80 participants, ART‑Net achieved the highest peak signal‑to‑noise ratio and structural similarity index among evaluated methods, and it preserved anatomical fidelity with superior Dice coefficients for medial temporal and whole‑brain structures. Radiologist assessments also indicated improved gradient fidelity, regional contrast, and overall structural quality.
By Changjing Chai, Bin Huang, Libo Xu, Jian Zhou, Boyang Pan, Kristen W Yeom, Qiyong Gong, Nan-Jie Gong
The study introduces the RSM framework, which includes the ARNAI artifact removal network, to enhance automated measurement of spinopelvic parameters on postoperative lumbar spine radiographs containing implants. Adding ARNAI to the Transformer-based FCBFormer model raised the mean Dice similarity coefficient from 0.814 to 0.870 and significantly reduced the mean L4–L5 segmental Cobb angle error from about 15.8° to 4.7°, a 70% improvement. The framework also improved intraclass correlation coefficients for key parameters, surpassing 0.70 for pelvic tilt, lumbar lordosis, and sacral slope.
By Sang-Jin Park, Jinyoung Choi, Seokwon Kim, Seungeon Song, Insu Park, Dougho Park, Taeyeon Kim, Youjin Lee, Donghoon Yang, Jaeman Cho, Joongwon Yang, Mansu Kim, Heumdai Kwon, Hong Gyu Baek, Dae Chul Cho, Injung Kim
arXiv:2608. 06429v1 Announce Type: cross Abstract: Interpretability methods for large language models (LLMs) describe internal state but do not directly test whether that state is causally sufficient to produce the observed behavior.
By Yong Yang, Roger Newman-Norlund, Xiang Guan, Saeed Ahmadi, Regan Willis, Nadra Salman, Kalil Warren, Sophie Arheix-Parras, Srihari Nelakuditi, Leonardo Bonilha, Christopher Rorden, Rutvik H. Desai, Julius Fridriksson
CATCH is a conditional 3D diffusion model operating in an invertible Haar-wavelet domain designed to inpaint masked regions in T1‑weighted brain MRI with plausible, tumor‑free tissue while preserving observed anatomy. The model’s denoiser uses noisy target coefficients, voided‑image coefficients, and a signed mask, guided by tumor‑excluded wavelet reconstruction and a hole‑focused loss, and hard compositing ensures observed voxels remain unchanged. Experiments on BraTS data show that a weighted mixture of tumor‑derived, irregular‑blob, and ellipsoidal masks yields the best performance, achieving higher SSIM, PSNR, and lower MSE compared to fixed or random augmentation baselines.
By Simon Winther Albertsen, Hjalte Bjoernstrup, Said Djafar Said, Mostafa Mehdipour Ghazi
arXiv:2608. 19769v1 Announce Type: cross Abstract: Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning.
By Maunil Shah, Vaanathi Sundaresan
The paper introduces AGW-PBR, a brain MRI super‑resolution method that emphasizes tissue‑transition regions affected by the partial‑volume effect. It uses a low‑resolution backbone guided by Sobel edges, soft latent‑basis assignment, and grid‑anchored warping, while training‑time weights are derived from tissue‑mixture entropy computed from registered T1/T2/PD images. Experiments on IXI T2‑weighted scans at 2×, 4×, and 6× show improved full‑image reconstruction and regional fidelity, and the backbone also performs well on fastMRI data without PVE supervision.
By Xiao Tong, Wenyun Yang, Ziheng Zhang, Jingzhi Han, Zhaochu Luo, Jinbo Yang