42 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 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.
This study demonstrates that while activating external laser positioning systems (ELPS) during MRI simulation generally preserves quantitative values, it significantly degrades image quality—specifically causing a four-fold signal-to-noise ratio drop and geometric distortion errors when using the integrated body coil—necessitating clear clinical guidelines to avoid ELPS interference during imaging.
The Holder-Optimized Elastography (HOE) method enhances the non-invasive detection of breast cancer-related lymphedema by stabilizing ultrasound probes to visualize fluid-filled lymphatic obstructions as High-Velocity Areas, offering a promising adjunct for monitoring treatment response despite current limitations in sensitivity and specificity.
This paper identifies and resolves the "NLP-to-Expert Gap" in chest X-ray AI by demonstrating that models optimized on automated report labels overfit to labeling errors, whereas superior diagnostic performance is achieved by using expert labels as a validation compass, employing early stopping to prevent memorization, and relying on frozen ImageNet features with regularization rather than direct metric optimization.
This paper benchmarks transfer learning strategies for dense breast tissue segmentation on small datasets, demonstrating that CNNs with full fine-tuning, multi-view self-supervised pre-training, and hybrid loss functions outperform transformer-based models and parameter-efficient updates to achieve optimal accuracy and efficiency for annotation-limited mammography workflows.
This study introduces a robust, data-driven framework that identifies five distinct white matter hyperintensity spatial subtypes across large cohorts, revealing their unique associations with vascular risk factors and demonstrating that regional lesion patterns offer superior predictive value for future disease progression compared to total lesion burden alone.