📄 Category

radiology and imaging

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.

📄 radiology and imaging

The Effects of External Laser Positioning Systems for MRI Simulation on Image Quality and Quantitative MRI Values

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.

McCullum, L., Ding, Y., Fuller, C. D., Taylor, B. A.2026-03-07
📄 radiology and imaging

Real-Time Detection of Breast Cancer-Related Lymphedema with Shear-Wave Elastography: The Holder-Optimized Elastography Method

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.

Hoe, Z. Y., Ding, R.-S., Chou, C.-P., Hu, C., Lee, C.-H., Tzeng, Y.-D., Pan, C.-T., Lee, M.-C., Lee, E. K.-L.2026-03-02
📄 radiology and imaging

The NLP-to-Expert Gap in Chest X-ray AI

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.

Fisher, G. R.2026-03-02
📄 radiology and imaging

Benchmarking Transfer Learning for Dense Breast Tissue Segmentation on Small Mammogram Datasets

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.

Qu, B., Liu, W., Zhou, L., Guo, X., Malin, B., Yin, Z.2026-02-24
📄 radiology and imaging

Location patterns and longitudinal progression of white matter hyperintensities

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.

Zhao, X., Malone, I. B., Brown, T. M., Wong, A., Cash, D. M., Chaturvedi, N., Hughes, A. D., Schott, J., Barkhof, F., Ba (…)2026-02-23
📄 radiology and imaging

Quality versus quantity of training datasets for artificial intelligence-based whole liver segmentation

This study demonstrates that while highly curated, smaller datasets can achieve equivalent 3D segmentation performance to much larger mixed-curation datasets, the latter offers superior generalizability and local improvements, indicating that the optimal balance between data quality and quantity depends on specific training goals.

Castelo, A., O'Connor, C., Gupta, A. C., Anderson, B. M., Woodland, M., Altaie, M., Koay, E. J., Odisio, B. C., Tang, T. (…)2026-02-18
📄 radiology and imaging

Intraoperative Metabolomic-Guided Precision Surgery for Pediatric Brain Tumors: A Systematic Review of Multi-Modal Molecular Imaging Platforms and Artificial Intelligence Integration

This systematic review evaluates the current landscape of intraoperative molecular imaging and AI integration in pediatric brain tumor surgery, highlighting the proven efficacy of intraoperative MRI and selective fluorescence guidance while identifying critical gaps in pediatric-specific metabolomic platforms and standardized protocols that must be addressed to optimize oncological and neurodevelopmental outcomes.

Sirkin, N. J., Harper, T., Lamey, E., Wilhelm, J. N., Rought, G., Yerrapragada, A.2026-02-12
📄 radiology and imaging

Calibrated simulations for dynamic focusing of ultrasound through the temporal window

This paper presents a calibration framework using axisymmetric projections and sparse sampling to optimize skull attenuation coefficients, thereby improving the accuracy of acoustic simulations and safety assessments for dynamic focused ultrasound neuromodulation through the temporal window across 157 subjects.

Dadgar-Kiani, E., Hebbale, V., Attalla, G., Alvarez, J. L., Dunsford, S., Caulfield, K. A., Good, C. H., Krystal, A. D. (…)2026-01-30
📄 radiology and imaging

A Hybrid CNN-Transformer Deep Learning Model for Differentiating Benign and Malignant Breast Tumors Using Multi-View Ultrasound Images

This study presents a robust hybrid CNN-Transformer deep learning model that effectively integrates multi-view ultrasound images to achieve superior accuracy in differentiating benign and malignant breast tumors compared to conventional single-image analysis methods.

qi, z., Li, R., Pan, T., Jianxing, Z., Yang, G., Lin, Z., Zhang, M., Miao, C.2026-01-29