# Magnetic Resonance Imaging

On structural MRI from three independent cohorts, Alzheimer's disease classifiers use age as a shortcut: older patients receive higher predicted risk. Residualizing age linearly in feature space (PMDN) brings the age-prediction correlation near zero in cognitively normal and mild cognitive impairment groups, with balanced accuracy 0.71 and Brier score about 0.22, and far less fold-to-fold spread than the adversarial CF-Net. Post hoc calibration further lowers probability error, with nonparametric isotonic regression and Bayesian binning performing best.

On structural MRI from three independent cohorts, Alzheimer's disease classifiers use age as a shortcut: older patients receive higher predicted risk. Residualizing age linearly in feature space (PMDN) brings the age-prediction correlation near zero in cognitively normal and mild cognitive impairment groups, with balanced accuracy 0.71 and Brier score about 0.22, and far less fold-to-fold spread than the adversarial CF-Net. Post hoc calibration further lowers probability error, with nonparametric isotonic regression and Bayesian binning performing best.

MRI foundation models have largely learned image-level representations that struggle to separate normal from abnormal findings within one organ. A4-MR is pretrained in two stages on roughly 50k MRI studies and 491k series, pushing image-text alignment down to the anatomy level and learning separate embeddings for healthy and unhealthy anatomies. With a frozen encoder it gains 1.7 AUROC on abnormality detection over MedSigLIP, 1.4 Dice on knee segmentation over MedNeXt, stays within 0.4 Dice of the best model on abdominal organ segmentation, and localizes 3D anatomies without any prompt on par with MedSAM2 given a ground-truth box.

MRI foundation models have largely learned image-level representations that struggle to separate normal from abnormal findings within one organ. A4-MR is pretrained in two stages on roughly 50k MRI studies and 491k series, pushing image-text alignment down to the anatomy level and learning separate embeddings for healthy and unhealthy anatomies. With a frozen encoder it gains 1.7 AUROC on abnormality detection over MedSigLIP, 1.4 Dice on knee segmentation over MedNeXt, stays within 0.4 Dice of the best model on abdominal organ segmentation, and localizes 3D anatomies without any prompt on par with MedSAM2 given a ground-truth box.

SimCortex v2 reconstructs the left and right white-matter and pial cortical surfaces jointly in one shared deformation space, matching the strongest baseline's surface-distance accuracy on 560 T1-weighted MRI cases from 14 cohorts while showing no detected inter-surface collision in 92.14% of cases, whereas every baseline produced at least one collision in every case. It initializes topologically correct meshes from a volumetric segmentation, then predicts multi-scale stationary velocity fields with a ribbon-conditioned U-Net and regularizes surface separation during training.

SimCortex v2 reconstructs the left and right white-matter and pial cortical surfaces jointly in one shared deformation space, matching the strongest baseline's surface-distance accuracy on 560 T1-weighted MRI cases from 14 cohorts while showing no detected inter-surface collision in 92.14% of cases, whereas every baseline produced at least one collision in every case. It initializes topologically correct meshes from a volumetric segmentation, then predicts multi-scale stationary velocity fields with a ribbon-conditioned U-Net and regularizes surface separation during training.

The work proposes UniField, a unified multi-modality, multi-task MRI field-strength enhancement framework that consolidates T1, T2, and FLAIR modalities and cross-field tasks such as 64mT-to-3T and 3T-to-7T into a single model, embeds 3D structural priors by operating in the latent space of the pretrained video super-resolution model FlashVSR with LoRA fine-tuning, and introduces a Field-Aware Spectral Rectification Mechanism (FASRM) that adjusts low-, mid-, and high-frequency loss weights according to the physical properties of the source and target fields; it also organizes and publicly releases a paired multi-field MRI dataset from five institutions that is an order of magnitude larger than existing benchmarks, reporting average improvements of about 1.81 dB in PSNR and 9.

The work proposes UniField, a unified multi-modality, multi-task MRI field-strength enhancement framework that consolidates T1, T2, and FLAIR modalities and cross-field tasks such as 64mT-to-3T and 3T-to-7T into a single model, embeds 3D structural priors by operating in the latent space of the pretrained video super-resolution model FlashVSR with LoRA fine-tuning, and introduces a Field-Aware Spectral Rectification Mechanism (FASRM) that adjusts low-, mid-, and high-frequency loss weights according to the physical properties of the source and target fields; it also organizes and publicly releases a paired multi-field MRI dataset from five institutions that is an order of magnitude larger than existing benchmarks, reporting average improvements of about 1.81 dB in PSNR and 9.

The study introduces NeuralShift, a U-Net-based model that takes only preoperative MRI plus a hemisphere indicator encoding resection laterality and predicts a dense displacement field mapping preoperative to intraoperative MRI, the intraoperative brain mask, and its signed distance function; evaluated on 98 paired preoperative and intraoperative T1-weighted MRI scans from epilepsy patients undergoing temporal lobe resection with 9-fold cross-validation, it achieved a Dice of 0.97±0.01 between predicted and intraoperative masks (versus 0.92±0.01 for the preoperative mask) and reduced landmark TRE on the resection side and midline from about 4.58 mm to about 2.96 mm (left) and from about 4.41 mm to about 2.89 mm (right), with a minimum of 1.12 mm.

The study introduces NeuralShift, a U-Net-based model that takes only preoperative MRI plus a hemisphere indicator encoding resection laterality and predicts a dense displacement field mapping preoperative to intraoperative MRI, the intraoperative brain mask, and its signed distance function; evaluated on 98 paired preoperative and intraoperative T1-weighted MRI scans from epilepsy patients undergoing temporal lobe resection with 9-fold cross-validation, it achieved a Dice of 0.97±0.01 between predicted and intraoperative masks (versus 0.92±0.01 for the preoperative mask) and reduced landmark TRE on the resection side and midline from about 4.58 mm to about 2.96 mm (left) and from about 4.41 mm to about 2.89 mm (right), with a minimum of 1.12 mm.

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.