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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by
Same authorSame journal

CODE: A SELF-SUPERVISED CONSISTENCY MODEL FRAMEWORK FOR MRI DENOISING.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

Digitised histopathology slides now ready for artificial intelligence: predicting the molecular signatures of gliomas.

The Lancet. Digital health·2026
Same author

VisionOnc: a dynamic data visualiser for oncology.

The Lancet. Digital health·2026
Same author

PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI.

Medical image analysis·2026
Same author

A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.

The journal of machine learning for biomedical imaging·2025
Same author

Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2024

Related Experiment Video

Updated: Jul 16, 2026

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

HIMAC: HISTOGRAM-DRIVEN AND MASK-AWARE CONSISTENCY MODELS FOR MRI RECONSTRUCTION.

Qiudi He1, Kai Zhao1,2, Kaifeng Pang1

  • 1Department of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.

Proceedings. IEEE International Symposium on Biomedical Imaging
|July 15, 2026
PubMed
Summary

HiMaC accelerates Magnetic Resonance Imaging (MRI) reconstruction using Consistency Models (CMs) for faster, high-fidelity results. This novel framework improves generalization across undersampling patterns by incorporating histogram consistency and mask awareness.

Keywords:
Accelerated MRI reconstructionconsistency modelsdeep learningdiffusion models

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Related Experiment Videos

Last Updated: Jul 16, 2026

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Science

Background:

  • Accelerated Magnetic Resonance Imaging (MRI) reconstruction methods, particularly diffusion-based ones, face challenges with slow multi-step sampling and limited generalization.
  • Existing techniques often struggle with poor generalization due to reliance on a fixed set of undersampling patterns.

Purpose of the Study:

  • To introduce HiMaC (Histogram-driven and Mask-aware Consistency Model), a unified framework for fast, high-fidelity MRI reconstruction.
  • To address limitations of current diffusion-based methods by enabling one-step or few-step reconstruction with reduced inference cost.

Main Methods:

  • Leveraged Consistency Models (CMs) for efficient MRI reconstruction.
  • Introduced a distribution alignment regularization to minimize histogram divergence between reconstructed and input images.
  • Incorporated the downsampling mask as an auxiliary input to embed sampling information and guide reconstruction.

Main Results:

  • Achieved one-step or few-step MRI reconstruction, significantly reducing inference time.
  • Demonstrated enhanced reconstruction fidelity through histogram consistency.
  • Improved generalization capability across diverse undersampling patterns by utilizing mask-aware consistency.

Conclusions:

  • HiMaC offers a unified framework for fast, high-fidelity, and robust MRI reconstruction.
  • The proposed methods effectively address brightness shifts and improve generalization in accelerated MRI.
  • HiMaC represents a significant advancement in efficient and reliable MRI reconstruction techniques.