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

You might also read

Related Articles

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

Sort by

Same author

Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.

Tomography (Ann Arbor, Mich.)·2026

Same author

Objective quality assessment for precision functional MRI data.

Neuron·2026

Same author

Integrating neuroscience across species and scales.

Nature neuroscience·2026

Same author

A Sub-Microsecond Switch Enabling SWIFT <sup>23</sup>Na Imaging at 10.5 T.

Magnetic resonance in medicine·2026

Same author

A Systematic Characterization of Causal Interactions Between Human Visual Areas.

bioRxiv : the preprint server for biology·2026

Same author

Dissociating stimulus encoding and task demands in ECoG responses from human visual cortex.

bioRxiv : the preprint server for biology·2026

Same journal

Influence of gadolinium-based contrast agent (GBCA) on the diffusion weightings of breast lesions: an intra-patient analysis.

Magma (New York, N.Y.)·2026

Same journal

Evaluation of the diffusion time dependence of the IVIM effect based on realistic capillary flow simulations in mouse brain.

Magma (New York, N.Y.)·2026

Same journal

An evaluation of brain volume and cortical thickness measurement at 0.55 T.

Magma (New York, N.Y.)·2026

Same journal

Net zero emission MR imaging using a permanent 0.4 T magnet.

Magma (New York, N.Y.)·2026

Same journal

Special issue on "deuterium metabolic imaging".

Magma (New York, N.Y.)·2026

Same journal

Black-blood dynamic contrast-enhanced MRI of abdominal aortic aneurysms.

Magma (New York, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 19, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Improved quantitative parameter estimation for prostate T₂ relaxometry using convolutional neural networks.

Patrick J Bolan^1,2, Sara L Saunders³, Kendrick Kay^4,5

¹Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA. bolan@umn.edu.

Magma (New York, N.Y.)

|July 23, 2024

Summary

This summary is machine-generated.

Neural networks (NN) show superior T2 mapping in the prostate compared to traditional curve fitting. A convolutional neural network (CNN) trained on synthetic data achieved higher accuracy and robustness, especially in noisy conditions.

Keywords:

T 2 mapping Magnetic resonance imaging Neural networks Prostate Relaxometry

More Related Videos

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Published on: May 19, 2023

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Jun 19, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Published on: May 19, 2023

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Imaging
Computational Biology

Background:

Quantitative parameter mapping in MRI typically uses curve fitting.
Estimating T2 parameters is crucial for prostate imaging.
Current methods face challenges, particularly in low signal-to-noise regions.

Purpose of the Study:

To compare conventional curve fitting techniques with neural network (NN) methods for T2 measurement in the prostate.
To evaluate the accuracy, precision, and noise robustness of different NN architectures and training strategies against established curve fitting methods.

Main Methods:

Generation of large, physics-based synthetic datasets simulating T2 mapping acquisitions for NN training and performance comparison.
Implementation and comparison of four NN combinations (architectures and training corpora) against four curve fitting strategies.
Quantitative evaluation using synthetic data with known ground truth and in vivo data with noise augmentation.

Main Results:

A convolutional neural network (CNN) trained on naturalistic synthetic data demonstrated the highest accuracy and precision on synthetic datasets.
The best-performing CNN produced low-noise T2 maps on in vivo data.
This CNN method exhibited the least performance degradation with increasing input noise levels.

Conclusions:

Supervised training of a CNN using synthetic data can yield superior T2 estimation performance compared to conventional curve fitting.
NN-based T2 mapping shows particular promise for improving accuracy in low signal-to-noise ratio regions of the prostate.
This approach offers a potential advancement for quantitative MRI in prostate imaging.