Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Assessment of Diffusion and Perfusion

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...

You might also read

Related Articles

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

Sort by

Same author

Spleen-targeted neoantigen mRNA vaccine induces ISG15<sup>+</sup> CD8<sup>+</sup> T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma.

Cell reports. Medicine·2026

Same author

Hybrid experimental-machine learning framework for media optimization enhances antioxidant production in Micrococcus endophyticus SS-1.

Journal of microbiological methods·2026

Same author

Neoantigens and shared MICB α3 antigen dual-targeted vaccine generates potent antitumor immunity.

EMBO molecular medicine·2026

Same author

Corrigendum to "Enhanced underwater adhesion of chitooligosaccharide-based pressure-sensitive adhesives and their potential in wound healing applications" [Carbohydrate Polymers 368 (2025) 124227].

Carbohydrate polymers·2026

Same author

Residential greenness and atopic dermatitis in Japanese children: Findings from the TMM BirThree cohort study.

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology·2026

Same author

Meta-path and context-aware learning for attribute completion in heterogeneous graphs.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026

Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 13, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.

Geng Chen¹, Yoonmi Hong¹, Khoi Minh Huynh¹

¹Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.

Medical Image Analysis

|January 22, 2023

Summary

This summary is machine-generated.

New deep learning loss functions improve diffusion MRI data prediction by focusing on microstructural details. These novel methods enhance the quality of derived diffusion scalars for better tissue microstructure quantification.

Keywords:

Data prediction Deep learning Diffusion MRI Loss functions

More Related Videos

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: Aug 13, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Area of Science:

Medical Imaging
Neuroscience
Machine Learning

Background:

Deep learning models for diffusion MRI (DMRI) prediction require effective loss functions.
Current loss functions primarily assess signal-wise differences, neglecting the quality of derived diffusion scalars essential for microstructure quantification.

Purpose of the Study:

To introduce novel loss functions, microstructural loss and spherical variance loss, for DMRI prediction.
To explicitly incorporate the quality of both predicted DMRI data and derived diffusion scalars into the loss calculation.

Main Methods:

Development and application of two new loss functions: microstructural loss and spherical variance loss.
Utilizing these loss functions for multi-shell DMRI data prediction and angular resolution enhancement.
Evaluation on both infant and adult DMRI datasets.

Main Results:

The proposed microstructural loss and spherical variance loss functions enhance the quality of derived diffusion scalars.
These novel losses improve the prediction of multi-shell DMRI data and angular resolution.
Demonstrated effectiveness in both infant and adult DMRI data.

Conclusions:

Microstructural loss and spherical variance loss offer a significant advancement in deep learning for DMRI analysis.
These methods provide a more comprehensive approach to DMRI prediction by considering microstructural integrity.
Improved diffusion scalar quality facilitates more accurate quantification of brain tissue microstructure.