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Magnetic Resonance Imaging

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent

Misha P T Kaandorp¹, Frank Zijlstra², Davood Karimi³

¹Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Center for MR Research, University Children's Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland.

Medical Image Analysis

|December 31, 2024

Summary

This study introduces a novel deep learning approach for diffusion-weighted imaging (DWI) parameter estimation, leveraging spatial correlations in neighboring voxels. This method significantly improves accuracy over traditional techniques for medical image analysis.

Keywords:

Deep learning parameter estimation Quantitative magnetic resonance imaging Supervised attention models Synthetic data generation

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Last Updated: Jun 4, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

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

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Published on: August 14, 2019

Area of Science:

Medical image analysis
Biophysical modeling
Diffusion-weighted magnetic resonance imaging (DWI)

Background:

Accurate parameter estimation in DWI is challenging due to low signal-to-noise ratio (SNR) and ill-posed inverse problems.
Conventional methods analyze voxels independently, ignoring spatial correlations in tissue microenvironments.
Exploiting spatial information can enhance the accuracy of biophysical model parameter estimation.

Purpose of the Study:

To develop and evaluate a novel deep learning approach for DWI parameter estimation that incorporates spatial information from neighboring voxels.
To assess the performance of different deep learning architectures using self-supervised and supervised learning.
To validate the approach using synthetic data with spatial correlations and in vivo DWI data.

Main Methods:

Training neural networks on synthetic data patches that include correlations between neighboring voxels.
Evaluating deep learning architectures for incorporating spatial information via self-supervised and supervised learning.
Quantitative assessment using fractal-noise-based synthetic data and application to in vivo DWI data.

Main Results:

Supervised training on larger patch sizes with attention models demonstrated substantial performance improvements.
The novel deep learning approach outperformed conventional voxelwise model fitting.
Convolution-based approaches were also surpassed by the proposed method incorporating spatial information.

Conclusions:

Deep learning approaches that incorporate spatial information from neighboring voxels offer significant advantages for DWI parameter estimation.
The proposed method effectively utilizes spatial correlations, leading to improved accuracy in medical image analysis.
Attention models and larger patch sizes in supervised training are key to achieving enhanced performance in DWI parameter estimation.