📄 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

Reproducibility of Apparent Diffusion Coefficient and Restriction Spectrum Imaging Restriction Score in the Prostate Across MRI Sessions, Vendors, and Acquisition Settings: a Prospective Study

This prospective study demonstrates that while Apparent Diffusion Coefficient (ADC) shows limited reproducibility across MRI sessions and vendors, the Restriction Spectrum Imaging restriction score maximum value (RSIrs-max) exhibits significantly stronger cross-session reproducibility in prostate cancer detection, even under varying acquisition settings and vendor conditions.

song, y., Conlin, C. C., Lee, K.-L., Dornisch, A., Barrett, T., Do, S., Do, D. D., Margolis, D. J., Rakow-Penner, R., Da (…)2026-05-13
📄 radiology and imaging

Reconsidering Brain Age: Why Age-Prediction Models Fail as Measures of Brain Aging

This paper argues that current brain age models are fundamentally flawed as biomarkers for accelerated aging because they are trained to prioritize shared chronological patterns while ignoring individual trajectories, leading to misleading conclusions that stable anatomical differences are signs of neurodegeneration.

Grodem, E. O. S., Smith, S. M., Vidal-Pineiro, D., Elliott, M. L., for the Alzheimer's Disease Neuroimaging Initiative, (…)2026-05-08
📄 radiology and imaging

A Low-Cost, Microcontroller-Based Gas Delivery System for Respiratory Stimuli in MRI Studies

This paper presents the design, validation, and successful application of a low-cost, microcontroller-based gas delivery system that automates and synchronizes fixed respiratory stimuli with MRI acquisition, demonstrating reliable physiological and BOLD signal responses in cerebrovascular reactivity studies.

Blockley, N. P., Alzaidi, A. A., Milbourn, C. C., Bulte, D. P., Rudgewick-Brown, A., Rieger, S. W.2026-05-07
📄 radiology and imaging

Retrospective safety testing of the CT Clock method for identifying treatment-eligible patients with ischaemic stroke of unknown onset time: Study Protocol

This study protocol outlines a retrospective safety analysis of the "CT Clock" method, a simple technique using standard CT scans to identify ischaemic stroke patients with unknown onset times who may safely receive thrombolysis beyond the standard four-and-a-half-hour window, aiming to expand treatment access to hospitals lacking advanced imaging capabilities.

Mair, G., Chappell, F. M.2026-05-03
📄 radiology and imaging

Longitudinal MAP-MRI-based Assessment of Tissue Microstructural Alterations in Acute mTBI

This longitudinal study utilizing advanced MAP-MRI techniques found no significant microstructural alterations in acute mild traumatic brain injury (mTBI) patients compared to controls, suggesting that such injuries may not be detectable with current diffusion MRI methods despite the presence of clinical symptoms.

Gangolli, M., Perkins, N. J., Marinelli, L., Basser, P. J., Avram, A. V.2026-04-13
📄 radiology and imaging

Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma

This study presents MoEMIL, a multi-task deep learning model that integrates pretreatment MRI and whole slide images to outperform traditional staging and single-modality approaches in predicting induction chemotherapy response and overall survival for patients with locally advanced nasopharyngeal carcinoma, thereby offering a promising tool for personalized treatment decision-making.

Hou, J., Yi, X., Li, C., Li, J., Cao, H., Lu, Q., Yu, X.2026-04-11
📄 radiology and imaging

Data-efficient Self-Supervised Diffusion Learning for Detecting Myofascial Pain in Upper Trapezius Muscle with B-mode Ultrasound Videos

This paper demonstrates that a self-supervised Video Diffusion Encoder can effectively detect Myofascial Pain Syndrome in the upper trapezius muscle using B-mode ultrasound videos from a small prospective cohort, offering a data-efficient alternative to conventional deep learning methods that require large annotated datasets.

Lu, H.-E., Koivisto, D., Lou, Y., Zeng, Z., Yu, T., Wang, J., Meng, X., Nowikow, C., Wilson, R., Kumbhare, D., Pu, J.2026-04-08
📄 radiology and imaging

Spectral normative modeling of brain structure

This paper introduces Spectral Normative Modeling (SNM), a computationally efficient framework that leverages brain eigenmodes to generate high-resolution, adaptable normative growth charts for brain structure, enabling precise characterization of individual neurodevelopmental trajectories and neurodegenerative patterns like Alzheimer's disease.

Mansour L, S., Di Biase, M. A., Zhang, C., Tian, F., Zhang, S., Yan, H., Xue, A., Chong, J. S. X., Dehestani, N., Ng, E. (…)2026-04-05