📄 Category

radiology and imaging

69 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

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
📄 radiology and imaging

Report-Guided Semi-Supervised Learning for Scalable Prostate Cancer Detection on Biparametric MRI: Multicenter Prospective Validation and Multimodal Integration

This multicenter prospective study validates a report-guided semi-supervised learning framework with a lesion-only teacher model (RG-SSL-LOC) for biparametric MRI, demonstrating superior lesion segmentation and robust case-level detection of clinically significant prostate cancer that significantly enhances multimodal diagnosis and reduces unnecessary biopsies compared to existing methods.

Calado, A., de Almeida, J. G., Verde, A. S. C., Tsiknakis, M., Marias, K., Regge, D., Papanikolaou, N., ProCAncer-I Cons (…)2026-08-07
📄 radiology and imaging

BioDeformUNet: A Deep Learning Model for Biomechanically Informed Liver Image Registration

BioDeformUNet is a deep learning model that achieves near real-time, biomechanically informed liver image registration with accuracy comparable to traditional biomechanical algorithms while offering a 34-fold speedup in inference time to facilitate efficient intra-procedural evaluation of minimal ablative margins.

Zhang, X., OConnor, C., Castelo, A., Woodland, M., Daoud, B., Paolucci, I., Albuquerque, J., Altaie, M. A., Siddiqi, N. (…)2026-08-05
📄 radiology and imaging

The Mammillary Body-Fornix Gate in Long COVID An exploratory structural and diffusion MRI study of tremor-like symptoms, internal vibrations, and neuromuscular fatigue

This exploratory neuroimaging study of 122 participants suggests that severe Long COVID symptoms, including tremor-like sensations and neuromuscular fatigue, may be associated with structural and microstructural abnormalities at the mammillary body-fornix-hypothalamic interface and connected brainstem-cerebellar pathways, though the findings require prospective replication to establish causal mechanisms.

Ziaja, C. P., Young, S. Y., Stark, M. S.-C., Zurek, G., Sedlacik, J., Wright, F. M.2026-08-03
📄 radiology and imaging

Robustness of Long Axial Field-of-View PET to Defective Detector Blocks: Impact on Quantitative Accuracy

This study demonstrates that the quantitative accuracy of long axial field-of-view PET systems is primarily determined by the spatial distribution of defective detector blocks rather than just their number, revealing that sparse defects are tolerable in higher numbers while clustered defects cause significant localized biases, thereby supporting a re-evaluation of quality control thresholds to balance system uptime with diagnostic reliability.

Lan, W., Vrakidis, K. D., Bharkhada, D., Linder, P. M., Yaqub, M. M., la Fougere, C., Boellaard, R., Schmidt, F.2026-07-31
📄 radiology and imaging

Dual-Filament 3D Printing of Patient-Specific CT Phantoms with Embedded Implants and Tunable Metal-Artifact Intensity

This paper presents a dual-filament, voxel-level 3D printing method that fabricates patient-specific CT phantoms with embedded metallic implants and tunable artifact intensity, enabling objective benchmarking of metal artifact reduction algorithms and spectral CT performance.

Pasyar, P., Mei, K., Im, J. Y., Roshkovan, L., Geagan, M., Noël, P. B.2026-07-20
📄 radiology and imaging

FreqFuseNet: Resolving Feature-Scale Mismatch in Dual-Frequency Fusion for Thin-Wall Head-and-Neck OAR Segmentation

FreqFuseNet addresses a critical 863-fold activation-scale mismatch in dual-frequency feature fusion by normalizing the FcaNet branch to the FFT scale, thereby enabling a stable 5% residual injection that significantly improves the segmentation accuracy of thin-wall head-and-neck organs-at-risk while maintaining a lightweight architecture.

Chen, W.-Y., Wan, S.-Y., Lin, G.-Y.2026-07-13
📄 radiology and imaging

Automated Design of Patient-Specific 4D-Printed Phantoms for Quality Assurance of Adaptive Radiotherapy on a 1.5T MR-Linac

This paper presents an automated end-to-end workflow for generating patient-specific, 4D-printed, multi-material phantoms that replicate realistic anatomy, physiological motion, and multimodal imaging properties to enable independent quality assurance of adaptive radiotherapy on 1.5T MR-Linac systems.

Hamkins, H. M., Tam, K. H., Sobremonte, A., Jogi, S., Koay, E., Hassanzadeh, C., Segars, P., Tyagi, N., Subashi, E.2026-07-13