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.
This study demonstrates that ultrasound time-harmonic elastography can simultaneously quantify and distinguish the independent and superimposed effects of voluntary muscle contraction and blood flow restriction on the elastic and viscous properties of skeletal muscle in vivo.
This paper introduces an open, reproducible gamma-variate pipeline for CT-perfusion analysis that ensures cross-site comparability through standardized ASIST-Japan visualization and validates its accuracy and bit-stable reproducibility using synthetic benchmarks with known ground truth.
This study utilized the UK7T travelling head dataset to demonstrate that 7T MRI measurements of hippocampal subfield susceptibility and volume exhibit high reproducibility across repeated acquisitions, with intra-subject variability significantly lower than inter-subject differences and intraclass correlation coefficients exceeding 0.82 for all subfields.
This paper presents a fine-tuned SAM2 model for coronary artery segmentation in X-ray fluoroscopy that significantly outperforms zero-shot baselines (achieving a Dice score of 0.767 on the ARCADE dataset) and demonstrates robust vessel tracking in video studies by effectively distinguishing arteries from confounding structures like ribs and stents.
This study demonstrates that a deep learning model can accurately quantify breast arterial calcification from screening mammograms to identify women at elevated cardiovascular risk, although adding this AI-derived biomarker to existing risk scores like PREVENT offers minimal improvement in overall predictive discrimination.
This study introduces and validates a novel functionally focused comparative protocol for evaluating wearable EEG headsets, which successfully assesses both cognitive resolution and user usability while offering a more comprehensive alternative to traditional, function-agnostic analysis methods.
This study presents a secondary analysis of the LOSEIT trial data designed to evaluate the agreement and equivalence between a commercially available AI tool and expert radiologist measurements of joint space width in weight-bearing, fixed-flexion knee radiographs of patients with osteoarthritis.
This study demonstrates that while B1+ inhomogeneity correction at 7 T MRI consistently improves image quality metrics, its impact on morphometric statistical inferences is method-dependent, underscoring the necessity of explicit correction and customized preprocessing to establish reliable biomarkers.
This study demonstrates that a multimodal approach combining [18F]florbetapir and [18F]flortaucipir PET imaging with MRI-derived brain atrophy data significantly improves the prediction of cognitive impairment in Alzheimer's disease compared to using PET biomarkers alone.
This paper demonstrates that a Singular Value Decomposition-based coil combination algorithm (SVD-B1) significantly enhances the accuracy and noise robustness of Quantitative Susceptibility Maps in high-field MRI by eliminating artifacts and outperforming conventional methods in both in-vivo and postmortem human brain studies.