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Related Concept Videos

Diffusion01:12

Diffusion

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion01:21

Diffusion

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Facilitated Diffusion01:16

Facilitated Diffusion

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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
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Updated: Jan 30, 2026

In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
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In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice

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An efficient 3D latent diffusion model for T1-contrast enhanced MRI generation.

Zach Eidex1, Mojtaba Safari1, Jie Ding1,2

  • 1Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.

Biomedical Physics & Engineering Express
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces T1C-RFlow, a 3D deep learning model that generates contrast-enhanced MRI images (T1C) from pre-contrast scans, offering a faster, contrast-agent-free alternative for brain tumor imaging.

Keywords:
MRIT1-contrastdeep learninggliomaintramodal synthesisrectified flow

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Gadolinium-based contrast agents (GBCAs) enhance MRI lesion visualization but pose risks like nephrogenic systemic fibrosis.
  • GBCA administration variations can lead to inconsistent imaging results.
  • There is a need for advanced methods to generate contrast-enhanced MRI without GBCAs.

Purpose of the Study:

  • To develop an efficient 3D deep-learning framework, T1C-RFlow, for generating T1-contrast enhanced (T1C) MRI images from pre-contrast multiparametric MRI.
  • To overcome limitations associated with GBCA use in MRI.
  • To enable contrast-agent-free brain tumor imaging.

Main Methods:

  • Proposed the 3D latent rectified flow (T1C-RFlow) model utilizing a pretrained autoencoder for latent space representation of T1w and T2-FLAIR images.
  • Trained a rectified flow diffusion model in the latent space.
  • Evaluated performance on BraTS 2024 datasets (glioma, meningioma, metastases) using NMSE and SSIM metrics.

Main Results:

  • T1C-RFlow demonstrated superior performance compared to benchmark 3D models (pix2pix, DDPM, DiT-3D).
  • Achieved high SSIM scores (e.g., 0.935 for glioma) and low NMSE (e.g., 0.044 for glioma).
  • Showcased significantly faster denoising times (6.9 s/volume) than conventional DDPM models.

Conclusions:

  • The T1C-RFlow method efficiently generates synthetic T1C images closely resembling ground truth.
  • The model offers a substantial time reduction compared to previous diffusion models.
  • This approach holds promise for developing practical contrast-agent-free MRI for brain tumors.