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 author

SWI combined with cMRI and CT in the differentiating of intracranial Rosai-Dorfman disease from fibrous meningioma.

BMC medical imaging·2026
Same author

Evaluation of fat fraction and morphologic characteristics of mandibular condyles in patients with temporomandibular disorders using Q-Dixon.

European journal of radiology·2026
Same author

Convergent imaging and genetic signatures of gray matter atrophy in Parkinson's disease.

NeuroImage·2026
Same author

Multimodal MRI-based nomogram integrating clinical-radiological, radiomic, and habitat features to discriminate solitary fibrous tumors from atypical meningiomas.

Scientific reports·2026
Same author

Non-invasive prediction of B-cell lymphoma-2 and Ki-67 expression in primary central nervous system lymphoma using whole-tumor histogram analysis of diffusion weighted imaging, diffusional kurtosis imaging and intravoxel incoherent motion.

BMC medical imaging·2026
Same author

Shared genetic architecture of gray matter deficits in schizophrenia and bipolar disorder: evidence from structural neuroimaging-genetic analyses.

Psychological medicine·2026
Same journal

Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography.

Physics in medicine and biology·2026
Same journal

Improving Plan Quality in Adaptive Proton Therapy Using an Interactive Dose Modification Tool.

Physics in medicine and biology·2026
Same journal

Technical Note: Real-Time MLC Control and Latency Measurement Optimization with External Verification.

Physics in medicine and biology·2026
Same journal

Fetus-Specific Hematopoietic Stem Cell Dosimetry Framework for Leukemia-Relevant Target Cells During Prenatal Development.

Physics in medicine and biology·2026
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

Improved pharmacokinetic parameter estimation from DCE-MRI via spatial-temporal information-driven unsupervised

Xinyi He1, Lu Wang2, Qizhi Yang2

  • 1Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian 361102, People's Republic of China.

Physics in Medicine and Biology
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning method, STUDE, effectively estimates pharmacokinetic (PK) parameters from dynamic contrast-enhanced MRI (DCE-MRI) by integrating spatial and temporal data. This approach improves accuracy and aids in identifying glioma mutation status.

Keywords:
attention mechanismdynamic contrast-enhanced MRIpharmacokinetic parameter estimationspatial-temporal informationunsupervised learningvision transformer

More Related Videos

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.6K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.2K

Related Experiment Videos

Last Updated: Jun 20, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.6K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.2K

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) yields pharmacokinetic (PK) parameters crucial for tissue characterization.
  • Current deep learning models often analyze spatial or temporal features independently, neglecting their combined impact on DCE-MRI data.
  • This limitation hinders precise PK parameter estimation and its clinical utility.

Purpose of the Study:

  • To develop a novel deep learning framework, STUDE, that fully integrates spatial and temporal information from DCE-MRI for enhanced PK parameter estimation.
  • To validate the accuracy and diagnostic performance of STUDE against established methods using both phantom and clinical glioma datasets.

Main Methods:

  • Proposed STUDE, a spatial-temporal information-driven unsupervised deep learning method utilizing CNNs and a Vision Transformer for feature extraction.
  • Implemented a spatial-temporal attention fusion module for adaptive feature integration.
  • Incorporated the extended Tofts model to impose physical constraints for unsupervised training.
  • Compared STUDE with Non-Linear Least Squares (NLLS) and other deep learning models (GRU, CNN, U-Net, VTDCE-Net) on a numerical phantom and 87 glioma patients.

Main Results:

  • STUDE achieved the lowest systematic and random errors in PK parameter mapping on a numerical phantom, even at low signal-to-noise ratios (SNR=10 dB).
  • On glioma data, STUDE produced less noisy parameter maps with superior structural clarity compared to NLLS and other deep learning methods.
  • STUDE demonstrated high accuracy in predicting glioma isocitrate dehydrogenase (IDH) mutation status, with AUCs of 0.840 for Ktrans and 0.908 for Ve, further improving to 0.926 when all PK parameters were combined.

Conclusions:

  • STUDE represents a significant advancement in leveraging integrated spatial-temporal data for PK parameter estimation using DCE-MRI.
  • The physics-informed, unsupervised learning approach of STUDE enhances precision and demonstrates considerable potential for clinical applications in neuro-oncology.