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 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.
This retrospective study of 66 adult patients in Gujranwala, Pakistan, demonstrates that age-related cerebral atrophy observed on non-contrast CT scans is significantly correlated with specific neurological symptoms such as slurred speech, ataxia, and numbness, independent of age, supporting the integration of standardized atrophy reporting into routine radiology practice in resource-limited settings.
This study introduces a novel framework that utilizes geometric brain signatures derived from multiple neuroimaging modalities to accurately identify distinct Alzheimer's disease subtypes and progression trajectories, outperforming conventional localized features in stability and biological relevance.
This paper proposes a modularized Bayesian framework combining Bayesian Additive Regression Trees (BART) for flexible, uncertainty-aware normative modeling of Multiple Sclerosis deviations and a SoftBART survival model to propagate this uncertainty, demonstrating superior calibration and prediction accuracy over traditional two-step approaches in large clinical datasets.
This paper presents expert consensus-based technical recommendations for the clinical translation of renal Dynamic Contrast-Enhanced (DCE) MRI, aiming to standardize protocols and improve cross-site comparability through a modified Delphi process involving an international panel of experts.
Next-generation large language models, specifically Claude Opus 4.7 and GPT-5.5, equipped with retrieval-augmented generation using domain-specific nuclear cardiology resources, achieved mean accuracy rates of approximately 86% on the ASNC Board Preparation Exam, surpassing both the estimated passing threshold and the average performance of human fellows-in-training.