258 papers
This category explores the fascinating intersection where the fundamental laws of physics meet the complexities of human health and medicine. Here, researchers apply principles of mechanics, thermodynamics, and quantum theory to solve critical medical challenges, from understanding how blood flows through vessels to designing advanced imaging technologies that reveal disease at a cellular level. These studies bridge the gap between abstract scientific theory and tangible improvements in patient care, offering new ways to diagnose and treat illnesses.
Every paper featured in this section is a fresh preprint sourced directly from arXiv, the leading archive for rapid scientific sharing. At Gist.Science, we process each new submission to ensure accessibility for everyone, providing both a detailed technical breakdown for experts and a clear, plain-language summary for curious readers. This dual approach ensures that groundbreaking insights in medical physics reach a wider audience without losing their scientific rigor.
Below are the latest preprints in this field, offering a glimpse into the cutting-edge research shaping the future of healthcare.
This paper introduces a continuous 3-D latent diffusion framework that utilizes a coordinate-conditioned local implicit image function decoder to enable efficient, high-resolution medical image generation and measurement-guided reconstruction on a single GPU while avoiding patch seams and minimizing memory usage.
This review synthesizes current evidence on in vivo oxygen measurement and radiation chemistry to propose that the FLASH radiotherapy sparing effect arises from a complex interplay between dose rate, local tissue oxygen availability, and altered radical yields, occurring specifically at intermediate baseline oxygen levels.
This paper presents a fast, ungated 5D-MRI reconstruction method based on the CMR-MOTUS framework that enables rapid, high-quality, and personalized cardiorespiratory motion characterization in just seven minutes, demonstrating strong potential for integration into MRI-guided stereotactic arrhythmia radio-ablation treatments.
This paper proposes a Green AI-guided adaptive encoder freezing strategy for federated learning in MRI-to-CT conversion that significantly reduces energy consumption and CO2 emissions by up to 23% while maintaining or improving model performance, thereby promoting equitable and sustainable healthcare AI.
This paper introduces the "One-for-All Adaptive Radiotherapy Planning Agent," a unified foundation-model framework that autonomously generates clinically acceptable, treatment-specific online adaptive radiotherapy plans from daily CBCT scans in under two minutes across diverse cancer types while maintaining a human-in-the-loop oversight mechanism.
The paper introduces MORSE, a computationally efficient, open-source method for parallel MRI reconstruction that robustly estimates multiple coil sensitivities per voxel to produce high-quality, real-time feasible images while effectively addressing artifacts and noise.
This roadmap outlines the current research status, underlying physics, and detector technologies for Quantum Entanglement PET (QE-PET), a novel imaging modality that leverages the polarization entanglement of annihilation photons to potentially enhance image reconstruction and introduce new diagnostic biomarkers for tissue pathology, oxygenation, and pH mapping.
This paper proposes a joint reconstruction framework for PET activity and attenuation that utilizes wavelet diffusion models and diffusion posterior sampling to eliminate the need for auxiliary anatomical scans, demonstrating superior performance over existing methods in simulated 3D TOF data while showing promising potential for clinical non-TOF applications.
This paper demonstrates that surface relaxivity, arising from wall collisions in myelinated axons, systematically biases microstructure imaging estimates by over-weighting intra-axonal signals in diffusion MRI and slightly inflating myelin water fraction measurements in relaxometry, with the magnitude of these errors depending on fiber packing density and axon caliber.
This paper experimentally investigates and compares the sensitivity of classical Linear Programming (LP) and Linear Superiorization (LinSup) algorithms to increasing condition numbers in linear constraint systems, specifically evaluating their respective abilities to handle ill-posed problems and error propagation.