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

You might also read

Related Articles

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

Sort by
Same author

Early Knee Osteoarthritis Detection by Multi-Component T<sub>2</sub> Mapping.

Bioengineering (Basel, Switzerland)·2026
Same author

Adiabatic Pulse Shape Influence on the Orientation Dependence of T<sub>1ρ</sub> Relaxation.

Magnetic resonance in medicine·2026
Same author

Detection of Early Knee Osteoarthritis Using Multi-Component T<sub>1ρ</sub> Mapping.

Journal of magnetic resonance imaging : JMRI·2025
Same author

Feasibility of a UTE Stack-of-Spirals Sequence for T<sub>1ρ</sub> Mapping of Achilles Tendinopathy.

NMR in biomedicine·2025
Same author

Fine-Tuning Deep Learning Model for Quantitative Knee Joint Mapping With MR Fingerprinting and Its Comparison to Dictionary Matching Method: Fine-Tuning Deep Learning Model for Quantitative MRF.

NMR in biomedicine·2025
Same author

HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.

Bioengineering (Basel, Switzerland)·2025
Same journal

Reproducibility of Splanchnic Blood Flow Measured Using Phase-Contrast MRI.

NMR in biomedicine·2026
Same journal

Restriction-Weighted Q-Space Trajectory Imaging (ResQ): Toward Mapping Diffusion-Time Effects With Tensor-Valued Diffusion Encoding in Human Prostate Cancer Xenografts.

NMR in biomedicine·2026
Same journal

In Vivo Quantitative Detection of PEGylated Macromolecules by Magnetic Resonance Spectroscopy.

NMR in biomedicine·2026
Same journal

Metabolic Assessment in Human Pluripotent Stem Cell-Derived Cerebral Organoids Using HR-MAS NMR Spectroscopy.

NMR in biomedicine·2026
Same journal

Characterizing Metabolic and Compositional Heterogeneity of Calf Muscle Using CEST MRI at 3 T.

NMR in biomedicine·2026
Same journal

Estimating the Sodium Content: A Case Series of Benign and Malignant Renal Tumours Using <sup>23</sup>Na-MRI at 3 T.

NMR in biomedicine·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

740

HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.

Dilbag Singh1, Ravinder R Regatte1, Marcelo V W Zibetti1

  • 1Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.

NMR in Biomedicine
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

Synthetic data-guided deep learning (SGDNet) enables fast and accurate knee joint T1ρ mapping. This method, combined with nonlinear least squares (HSGDNet), significantly reduces errors and computation time for various relaxation models.

Keywords:
T1ρ mappingattention moduledeep learningknee cartilagemagnetic resonance imagingnonlinear least squaresosteoarthritis

More Related Videos

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

3.2K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

681

Related Experiment Videos

Last Updated: Sep 13, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

740
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

3.2K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

681

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Image Analysis
  • Computational Biology

Background:

  • Multi-component T1ρ mapping of the knee joint is crucial for diagnosing conditions like osteoarthritis.
  • Traditional nonlinear least squares (NLS) methods are computationally intensive, limiting their clinical applicability.
  • Deep learning (DL) offers speed but typically requires extensive training datasets.

Purpose of the Study:

  • To develop an efficient and accurate method for multi-component T1ρ mapping of the knee joint.
  • To overcome the limitations of computational intensity in NLS methods and data requirements in DL.
  • To introduce a hybrid deep learning approach for accelerated and precise T1ρ quantification.

Main Methods:

  • Proposed Synthetic data-Guided supervised Deep Learning Network (SGDNet) using synthetically generated data for training.
  • Integrated residual connections and a self-attention module into SGDNet for improved gradient flow and accuracy.
  • Developed a hybrid approach (HSGDNet) combining SGDNet outputs with NLS for enhanced precision and speed.
  • Employed a customized loss function to ensure parameter fidelity and data consistency.

Main Results:

  • HSGDNet achieved significant average error reductions: 91.4% (ME), 31.5% (SE), and 36.0% (BE).
  • HSGDNet accelerated T1ρ fitting by approximately 67.4× (ME), 53.9× (SE), and 42.3× (BE) compared to NLS.
  • Validated HSGDNet on an early osteoarthritis (EOA) dataset, demonstrating robustness under pathological and protocol variations.

Conclusions:

  • HSGDNet provides a rapid, precise, and robust solution for multi-component T1ρ mapping in the knee joint.
  • The synthetic data-driven approach eliminates the need for large experimental datasets, facilitating DL model training.
  • HSGDNet shows potential for improved clinical diagnosis and monitoring of knee joint pathologies.