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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Junjie He1, Yunsong Peng2, Bangkang Fu2

  • 1Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, No. 2870, Huaxi Avenue South, Guiyang 550025, Guizhou, China; Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China.

Neuroimage
|May 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel self-supervised deep learning method for quantitative susceptibility mapping (QSM) that overcomes resolution limitations and improves accuracy. The method effectively measures iron deposition in neurodegenerative diseases like Alzheimer's and Parkinson's.

Keywords:
Alzheimer’s diseaseArbitrary resolutionParkinson’s diseaseSelf-supervised learningSusceptibility quantitative mapping

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Artificial Intelligence

Background:

  • Quantitative susceptibility mapping (QSM) is crucial for measuring iron deposition and aiding neurodegenerative disease diagnosis.
  • A key challenge in QSM is the dipole inversion problem, and current deep learning solutions often require ground-truth data.
  • Training QSM models across different resolutions without ground-truth data remains difficult.

Purpose of the Study:

  • To develop a self-supervised deep learning method for QSM that is independent of acquisition resolution and does not require ground-truth data.
  • To improve the accuracy and efficiency of QSM reconstruction.
  • To validate the method's utility in diagnosing neurodegenerative diseases.

Main Methods:

  • Proposed a self-supervised deep learning framework for QSM utilizing a morphological QSM builder.
  • Introduced a morphological loss function to reduce artifacts and training time.
  • Tested the method on human and animal data across various resolutions.

Main Results:

  • The method successfully reconstructs QSM at arbitrary resolutions, outperforming previous unsupervised methods.
  • Achieved a 3.6% higher PSNR, 16.2% lower NRMSE, and 22.1% lower HFEN compared to the best unsupervised method.
  • Reduced training time by 22.1% compared to cycle gradient loss.
  • Demonstrated robust measurement of increased striatal magnetic susceptibility in Alzheimer's and substantia nigra susceptibility in Parkinson's disease patients.

Conclusions:

  • The proposed self-supervised morphological deep learning method for QSM is effective for arbitrary resolutions.
  • It achieves state-of-the-art performance among unsupervised methods and offers significant improvements in accuracy and efficiency.
  • The method shows promise as an auxiliary diagnostic tool for Alzheimer's and Parkinson's disease by accurately quantifying iron deposition changes.