The Utility of a Convolutional Neural Network for Generating a Myelin Volume Index Map from Rapid Simultaneous Relaxometry Imaging

Autor: Akifumi Hagiwara, Tatsuya Higashi, Misaki Nakazawa, Jeff Kershaw, Nobutaka Hattori, Riwa Kishimoto, Kazumasa Yokoyama, Yasuhiko Tachibana, Takayuki Obata, Shigeki Aoki, Tokuhiko Omatsu, Masaaki Hori
Rok vydání: 2020
Předmět:
Adult
Male
Relaxometry
Wilcoxon signed-rank test
brain
convolutional neural network
Convolutional neural network
030218 nuclear medicine & medical imaging
rapid simultaneous relaxometry imaging
White matter
03 medical and health sciences
Deep Learning
Imaging
Three-Dimensional
0302 clinical medicine
Image Processing
Computer-Assisted
medicine
Humans
Radiology
Nuclear Medicine and imaging
Prospective Studies
Magnetization transfer
Gray Matter
Myelin Sheath
Aged
Brain Mapping
Pixel
business.industry
Atlas (topology)
Pattern recognition
Middle Aged
Magnetic Resonance Imaging
White Matter
Healthy Volunteers
medicine.anatomical_structure
myelin volume index
Metric map
Female
Neural Networks
Computer
Artificial intelligence
Tomography
X-Ray Computed
business
Algorithms
Major Paper
030217 neurology & neurosurgery
Zdroj: Magnetic Resonance in Medical Sciences
ISSN: 1880-2206
1347-3182
DOI: 10.2463/mrms.mp.2019-0075
Popis: Purpose: A current algorithm to obtain a synthetic myelin volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI) has a potential problem, that it does not incorporate information from surrounding pixels. The purpose of this study was to develop a method that utilizes a convolutional neural network (CNN) to overcome this problem. Methods: RSRI and magnetization transfer images from 20 healthy volunteers were included. A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the MVI map calculated from magnetization transfer images (MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared by testing how well they correlated with the MTMVI map. The correlations were evaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and (ii) pixel-based comparison for ROIs defined in four different tissue types (cortical and subcortical gray matter, white matter, and whole brain). Results: For atlas-based ROIs, the overall correlation with the MTMVI map was higher for the GenMVI map than for the SyMVF map. In the pixel-based comparison, correlation with the MTMVI map was stronger for the GenMVI map than for the SyMVF map, and the difference in the distribution for the volunteers was significant (Wilcoxon sign-rank test, P < 0.001) in all tissue types. Conclusion: The proposed method is useful, as it can incorporate more specific information about local tissue properties than the existing method. However, clinical validation is necessary.
Databáze: OpenAIRE
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