62 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.
This clinical pilot study demonstrates that incorporating polygenic risk scores into breast cancer screening for women aged 40–49 can effectively stratify risk and personalize screening recommendations without inducing significant anxiety among participants.
This paper outlines the protocol for the AIMS Norway randomized controlled trial, which aims to determine if an AI-stratified mammography reading strategy—where low-risk cases are read by a single radiologist and high-risk cases by two—is non-inferior to standard double reading in terms of screen-detected breast cancer rates.
This exploratory study demonstrates that photoacoustic imaging can non-invasively detect significant differences in muscle water, lipid, and hemoglobin ratios between patients with m.3243A>G mitochondrial myopathy and healthy controls, highlighting its potential as a novel biomarker for monitoring disease progression.
This study reveals that increased functional connectivity within white matter networks, but not between white and gray matter, is significantly associated with social impairment severity in individuals with Autism Spectrum Disorder, offering new insights into the neural mechanisms underlying the disorder.
This study demonstrates that a transfer learning-based nnU-Net model, pretrained on TOF-MRA and fine-tuned on 7T 4D Flow MRI data, outperforms existing deep learning architectures in intracranial artery segmentation and provides the most accurate, automated hemodynamic quantification, thereby confirming that segmentation precision directly impacts the reliability of derived flow metrics.
This paper proposes a resource-efficient, two-stage conditional diffusion framework that generates synthetic PET scans from CT data to enable equitable oncology screening in rural settings by preserving critical metabolic biomarkers like SUV under simulated low-resource imaging conditions.
This prospective R-IDEAL Stage 0-2a study demonstrates that a 6-point quantitative Dixon sequence offers superior geometric accuracy, quantitative concordance, and reproducibility for PDFF and R2* estimation across 1.5T and 3T MR-Simulation and MR-Linac devices compared to 2- and 3-point methods, thereby validating its use for adaptive radiation therapy and bone marrow characterization in the pelvis and head and neck.
This study demonstrates the technical feasibility of implementing 3D-QALAS on a 1.5T MR-Linac to achieve whole-brain, 1 mm isotropic quantitative T1, T2, and PD mapping with high accuracy and reproducibility within a 7-minute acquisition time, paving the way for integrating these biomarkers into adaptive radiation therapy workflows.
This multi-institutional study demonstrates that converting chest radiographs from 16-bit to 8-bit depth does not significantly affect the performance of deep learning models in classifying sex, age, and obesity, suggesting that 8-bit images can be used for efficient data storage and processing without compromising diagnostic accuracy.
This study demonstrates that mask-aware embeddings derived from a medical foundational segmentation model (MedSAM2), when fused with clinical data, significantly improve the prediction of progression-free survival in multiple myeloma patients compared to clinical-only or radiomics baselines.