82 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.
In cognitively unimpaired individuals from the Baltimore Longitudinal Study of Aging, plasma p-tau217 demonstrated the strongest association with both subsequent Alzheimer's disease-related dementia and longitudinal changes in AD-specific brain atrophy patterns compared to other plasma biomarkers.
This study demonstrates that visible/near-infrared hyperspectral imaging, particularly when analyzed using full-spectrum machine learning algorithms, can accurately and rapidly classify cellulitis, non-cellulitis edema, and healthy lower-leg tissue in emergency department patients across diverse skin pigmentation types.
This proof-of-concept study identified a set of reproducible and repeatable radiomics features from gadoxetic acid-enhanced MRI that significantly correlate with quantitative liver function indices, establishing a foundation for future liver function-related radiomics research.
This phantom study evaluates the accuracy of CTA, 3DRA, and 2D DSA in measuring intracranial vessel diameters against physical ground truth, revealing that while all modalities generally measure within 0.5 mm of true dimensions, they exhibit consistent biases that vary by vessel size, contrast concentration, and detection algorithm, with maximum-gradient edge detection proving superior to FWHM and fixed-HU methods.
This paper introduces Residual Endpoint Flow Matching (REFM), a single-step deep learning method that leverages the inherent anatomical similarity between low-dose and standard-dose CT images to achieve real-time, high-quality denoising comparable to iterative diffusion models while drastically reducing computational costs.
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.
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.
This paper introduces MARLA, an agentic framework that enables clinicians to autonomously conduct complex multimodal medical AI research by automatically translating high-level study objectives into executable, self-correcting research loops, thereby eliminating the need for specialized AI expertise.
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.
This paper introduces an open-source 3D Slicer extension that automates the precise localisation of intracranial electrode contacts for SEEG analysis, achieving high accuracy with minimal manual correction and offering a user-friendly, integrated solution to streamline clinical workflows.