VoxelSynth3D is a training‑free 3D image‑domain framework that reduces metal artifacts in postoperative musculoskeletal CT by combining support masking, normalized tissue synthesis, deviation gating, and restricted edge refinement. The method preserves implant voxels while correcting surrounding tissue artifacts and was evaluated on a newly constructed Synthetic CLINIC‑Metal benchmark, showing a reduction in RMSE from 801.48 to 786.18 HU on 40 held‑out cases. Compared to a 3D Gaussian smoother, VoxelSynth3D achieved a 13.58 HU improvement and maintained clean-edge agreement beyond 5 mm from metal.
By Amritesh Banerjee, Abdul Basit, Renil Renji Joseph, Nouhaila Innan, Muhammad Shafique
The paper introduces FLEX‑MRI, a method that accelerates MRI reconstruction by partitioning the recovery process into a few finite intervals defined by progressive k‑space support changes. Instead of learning fine‑grained local dynamics, FLEX‑MRI directly learns the reconstruction change across each interval and employs stochastic exploration during inference to sample alternative transition paths. Experiments on multi‑coil MRI datasets demonstrate that FLEX‑MRI outperforms competing methods while keeping computational efficiency comparable to few‑step baselines.
By Muhammad Usama Mirza, Esin \"Ozt\"urk I\c{s}{\i}k, Tolga \c{C}ukur
arXiv:2607. 28759v1 Announce Type: cross Abstract: In X-ray CT, metallic objects cause beam hardening, photon starvation, and scattering, leading to projection inconsistency, streaks, dark bands, and structural distortions that compromise clinical diagnosis and quantitative analysis.
By Heran Wang, Jianing Sun, Xu Jiang, Genwei Ma, Xing Zhao, Jigang Duan
Zero-Shot Self-Supervised Learning (ZS-SSL) has emerged as a promising paradigm for accelerated Magnetic Resonance Imaging (MRI) reconstruction, eliminating the reliance on fully-sampled external datasets. However, learning solely from a single under-sampled scan suffers from supervision scarcity and optimization instability, often leading to overfitting or artifacts.
arXiv:2603. 04438v3 Announce Type: replace-cross Abstract: Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction.
By Qingyong Zhu, Yumin Tan, Xiang Gu, Dong Liang
The paper introduces CoSMo-RecNet, a modular framework for multi-contrast MRI reconstruction that operates effectively in low-data regimes. It leverages a reusable content/style prior learned from large, unpaired multi-contrast image datasets, allowing a lightweight unrolled network to refine reconstructions using only a few task‑specific training samples. Experiments on low‑field 0.3 T and ultra‑low‑field 47 mT datasets demonstrate that CoSMo-RecNet outperforms parameter‑matched MoDL, classical reconstruction, transfer learning, and zero‑shot methods, achieving higher quality with as few as five training subjects.
By Chinmay Rao, Efe Il{\i}cak, Matthias J. P. van Osch, Mariya Doneva, Laurens Beljaards, Navid Jabarimani, Nicola Pezzotti, Marius Staring
arXiv:2608.28787v1 Announce Type: new
Abstract: Joint-embedding predictive architectures (JEPAs) have primarily been developed for self-supervised representation learning. Denoising JEPA (D-JEPA) rec...
By Meng Zhou, Wenhao You, Yuxing Chen, Yueying Tian
arXiv:2606. 00146v1 Announce Type: cross Abstract: Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability.
By Honglin Xiong, Yuxian Tang, Feng Li, Yulin Wang, Lei Xiang, Dinggang Shen, Qian Wang
arXiv:2609.39083v1 Announce Type: new
Abstract: Super-resolution and quality enhancement of 1.5\,T brain MRI are normally validated with image-fidelity metrics, although their purpose is to improve d...
By Kavitha Viswanathan, Harsh Choudhary, Amit Sethi
AGGR-Mamba is a lightweight output‑stage refinement network for undersampled brain MRI reconstruction that builds on a Mamba backbone. The method first generates an initial reconstruction and then applies an Attention Refine Head to recalibrate local anatomical structures, edge textures, and residual‑error regions, followed by a gated residual refinement branch that selectively preserves informative residual features while suppressing redundant responses. Experiments on the IXI brain MRI dataset with T1‑ and T2‑weighted images at acceleration factors R=4 and R=8 show that AGGR‑Mamba achieves competitive PSNR and SSIM performance across different reconstruction settings.
By Yue Zhuo, Wenbin Deng, Lingjun Liu, Xuhang Chen, Zhonghua Xie
arXiv:2606. 00100v1 Announce Type: cross Abstract: Self-supervised deep learning-based methods have shown great promise for accelerated magnetic resonance imaging (MRI) reconstruction, achieving high image quality without requiring fully sampled data for training.
By Tongxi Song, Ziyu Li, Zihan Li, Wen Zhong, Congyu Liao, Yang Yang, Hua Guo, Wenchuan Wu, Qiyuan Tian
Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts--especially beam hardening--when imaging materials with high attenuation such as metals. These artifacts arise from the polychromatic nature of X-rays and are not properly addressed by conventional monochromatic reconstruction algorithms.