Resolution dependence of vessel size index across various brain regions.

Magnetic resonance imaging (MRI) excels at detecting quantitative changes in microvascular parameters such as cerebral blood volume, cerebral blood flow, and vessel size index (VSI), which are essential for diagnosing and monitoring cerebrovascular diseases. Absolute VSI estimation, often utilizing superparamagnetic iron oxide nanoparticles as contrast agents, relies on measuring transverse relaxation rates (∆R and ∆R). This study systematically investigates the spatial resolution dependence of VSI using Monte Carlo simulations and in vivo rat brain MRI experiments.

Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning.

This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent magnetic resonance imaging (MRI)-to-CT image conversion.

Automated volumetric determination of high R regions in substantia nigra: A feasibility study of quantifying substantia nigra atrophy in progressive supranuclear palsy.

The establishment of an unbiased protocol for the automated volumetric measurement of iron-rich regions in the substantia nigra (SN) is clinically important for diagnosing neurodegenerative diseases exhibiting midbrain atrophy, such as progressive supranuclear palsy (PSP). This study aimed to automatically quantify the volume and surface properties of the iron-rich 3D regions in the SN using the quantitative MRI-R map. Three hundred and sixty-seven slices of R map and susceptibility-weighted imaging (SWI) at 3-T MRI from healthy control (HC) individuals and Parkinson's disease (PD) patients were used to train customized U-net++ convolutional neural network based on expert-segmented masks.

Quantitative analysis of blood cells from microscopic images using convolutional neural network.

Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the presence of infection, inflammation, edema, bleeding, and other blood-related issues. Current microscopic methods used for blood cell counting are very tedious and are highly prone to different sources of errors.