📄 Category

radiology and imaging

97 papers

Radiology and imaging serve as the eyes of modern medicine, allowing doctors to peer inside the human body without making a single incision. This rapidly evolving field uses technologies like X-rays, MRI scans, and ultrasound to detect diseases, guide treatments, and monitor patient recovery. As new research emerges, these visual tools become increasingly sophisticated, offering deeper insights into conditions ranging from broken bones to complex neurological disorders.

At Gist.Science, we bridge the gap between raw scientific data and public understanding by processing every new preprint in this category from medRxiv. Our team translates these complex studies into both plain-language overviews and detailed technical summaries, ensuring that breakthroughs in medical imaging are accessible to everyone, from students to specialists. Below are the latest papers in radiology and imaging, ready for you to explore.

📄 radiology and imaging

Adaptive Post-Processing Recovers Most of the Gap to nnU-Net v2 in Head and Neck GTV Segmentation: A Paired Three-Arm HECKTOR 2025 Benchmark

This HECKTOR 2025 benchmark demonstrates that while adaptive post-processing significantly narrows the performance gap between a lightweight MiniUNet3D and the larger nnU-Net v2 for head and neck tumor segmentation, it fails to fully eliminate differences in nodal disease accuracy or prevent a higher rate of catastrophic failures on small primary tumors.

Oyarzun Silva, R., Hernandez Hernandez, P.2026-08-31
📄 radiology and imaging

Image transmission through a multimode fibre in reflection mode with physics-guided deep learning towards ultrathin endoscopy

This paper presents a physics-guided deep learning framework that combines a reflected real-valued intensity transmission matrix with image restoration networks to enable single-shot, phase-retrieval-free image recovery through multimode fibres in reflection mode, significantly improving image quality and generalizability for ultrathin endoscopy.

Ye, Z., He, F., Zhao, T., Xia, W.2026-08-31
📄 radiology and imaging

Label-Free Threshold Selection for Out-of-Distribution Detection in Liver CT Segmentation

This paper proposes a label-free framework for calibrating out-of-distribution detection thresholds in liver CT segmentation by fitting a log-t distribution to pairwise surface DSC scores, enabling statistically principled failure risk categorization without requiring expert-labeled failure data.

Nielsen, M., Castelo, A., Altaie, M., Bennett, J., Anthony, A., Siddiqi, N. S., Gupta, A. C., Brock, K. K., Woodland, M.2026-08-24
📄 radiology and imaging

Quantifying Human-AI Workflow in Abdominal Ultrasound: A Prospective Randomised Crossover Study

This prospective randomised crossover study demonstrates that vendor-integrated AI software significantly improves operational efficiency and reduces mental demand and physical effort for sonographers performing abdominal ultrasound, although the magnitude of time savings varies between operators and the technology reshapes rather than eliminates the workflow.

Hsiao, N., Clifford, M., Lin, S.-Z., Premasiri, S., Roots, J., Allen, H., Robertson, A. P., Moafa, K., Wardle, J., Edwar (…)2026-08-19
📄 radiology and imaging

Small but systematic bias introduced by EEG electrodes in PET imaging

This study demonstrates that using CT images with EEG electrodes and an extended Hounsfield unit range provides the most accurate attenuation correction for simultaneous PET/EEG imaging, introducing only a small, systematic bias compared to scans without electrodes, while metal artifact reduction techniques offer minimal additional benefit.

Stöhrmann, P., Ponce de Leon, M., Dörl, G., Milz, C., Graf, S., Eggerstorfer, B., Murgas, M., Reed, M. B., Falb, P. C. (…)2026-08-13
📄 radiology and imaging

Clinical selectivity and failure modes of automated chest radiograph report evaluation metrics: a cross-dataset analysis of ReXErr-v1 and RadEvalX

This cross-dataset analysis reveals that while standard automated metrics like BLEU and ROUGE are highly sensitive to textual changes, they fail to distinguish clinically meaningful errors, with CheXbert emerging as the most aligned metric for assessing radiology report quality despite only moderate overall performance.

Naidu, J., Muralidharan, S., Prashani, A., Baskaradoss, V.2026-08-12
📄 radiology and imaging

Pushing a Frozen CXR Foundation Model: A LoRA Partial-Fine-Tuning Study on NIH ChestX-ray14 with a Model-Conditional Label-Flip Sensitivity Analysis

This retrospective study demonstrates that applying Low-Rank Adaptation (LoRA) to a frozen Rad-DINO ViT-B/14 foundation model improves multi-label classification performance on the NIH ChestX-ray14 dataset, while explicitly clarifying that the reported results are descriptive rather than confirmatory due to prior exposure to test labels and highlighting the sensitivity of metrics to counterfactual label-flip analyses.

BAI, T.-C., YEH, S.-C.2026-08-11
📄 radiology and imaging

CT ECV Mapper: an interactive 3D Slicer application with a batch-capable pipeline for voxelwise CT-derived extracellular volume mapping of the liver and hepatic tumors

The author presents CT ECV Mapper, an open-source 3D Slicer application that enables both interactive and unattended batch processing of voxelwise extracellular volume mapping for the liver and hepatic tumors using conventional single-energy multiphase CT, demonstrating high feasibility and robust quality control on a large public dataset.

Suzuki, M.2026-08-11