Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
69
Biological Effects of Radiation02:59

Biological Effects of Radiation

15.4K
All radioactive nuclides emit high-energy particles or electromagnetic waves. When this radiation encounters living cells, it can cause heating, break chemical bonds, or ionize molecules. The most serious biological damage results when these radioactive emissions fragment or ionize molecules. For example, α and β particles emitted from nuclear decay reactions possess much higher energies than ordinary chemical bond energies. When these particles strike and penetrate matter, they...
15.4K
Drug Distribution: Overview01:11

Drug Distribution: Overview

180
Drug distribution within the body is a dynamic process involving the movement of a drug in two directions across various compartments: from the bloodstream into tissues (tissue uptake) and from tissues back into the bloodstream (tissue release or redistribution). This process is passive and primarily driven by two variables: the concentration gradient between the bloodstream and the extravascular tissues and the drug's ability to cross the cell membrane.
Initially, the free drug in the...
180
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

78
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...
78
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

70
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
70
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

182
The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug...
182

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

IL-6/STAT3 signaling pathway-mediated apoptosis induced by medical ozone water in liver cancer: A mechanistic study.

Biomedical reports·2026
Same author

SAM-guided structural consistency constraints for unsupervised MR-to-CT synthesis.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same author

A multimodal interpretable deep learning-radiomics framework for predicting lymph node metastasis following neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter validation study.

NPJ precision oncology·2026
Same author

Unleashing Diffusion and State Space Models for Medical Image Segmentation.

Journal of imaging informatics in medicine·2026
Same author

Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.

Nature communications·2026
Same author

Automated CT-derived body composition predicts pathologic response to neoadjuvant immunotherapy in non-small cell lung cancer.

Cancer letters·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 2025

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.8K

DoseDiff: Distance-Aware Diffusion Model for Dose Prediction in Radiotherapy.

Yiwen Zhang, Chuanpu Li, Liming Zhong

    IEEE Transactions on Medical Imaging
    |April 2, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces DoseDiff, a novel deep learning model for radiotherapy treatment planning. DoseDiff accurately predicts radiation dose distributions by incorporating distance information, improving efficiency and precision for medical physicists.

    More Related Videos

    Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
    08:34

    Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

    Published on: February 6, 2019

    20.3K
    Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
    06:20

    Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

    Published on: March 11, 2021

    7.2K

    Related Experiment Videos

    Last Updated: Jun 29, 2025

    Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
    07:57

    Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

    Published on: March 24, 2022

    2.8K
    Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
    08:34

    Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

    Published on: February 6, 2019

    20.3K
    Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
    06:20

    Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

    Published on: March 11, 2021

    7.2K

    Area of Science:

    • Medical Physics
    • Radiotherapy
    • Artificial Intelligence

    Background:

    • Radiotherapy treatment planning is a complex, time-consuming process.
    • Current deep learning models for dose prediction lack effective utilization of spatial and distance information.
    • This leads to suboptimal accuracy and information loss in predicted dose distribution maps.

    Purpose of the Study:

    • To develop a novel deep learning model for precise dose distribution prediction in radiotherapy.
    • To enhance treatment planning efficiency and accuracy by leveraging distance information.
    • To address limitations of existing methods in utilizing spatial context and ray path characteristics.

    Main Methods:

    • Proposed a distance-aware diffusion model (DoseDiff) for dose prediction.
    • Utilized computed tomography (CT) images and signed distance maps (SDMs) as input conditions.
    • Introduced a multi-encoder and multi-scale fusion network (MMFNet) for enhanced feature fusion.

    Main Results:

    • DoseDiff demonstrated superior performance compared to state-of-the-art methods.
    • The model achieved higher quantitative accuracy in dose prediction.
    • Visual quality of the predicted dose distribution maps was significantly improved.

    Conclusions:

    • DoseDiff offers a precise and efficient approach to radiotherapy dose prediction.
    • The integration of SDMs and advanced fusion networks enhances prediction accuracy.
    • This method has the potential to significantly improve radiotherapy treatment planning workflows.