@article{RN268,
   author = {Drenthen, G. S. and Backes, W. H. and Jansen, J. F. A.},
   title = {Estimating myelin-water content from anatomical and diffusion images using spatially undersampled myelin-water imaging through machine learning},
   journal = {Neuroimage},
   volume = {226},
   pages = {117626},
   note = {Drenthen, Gerhard S
Backes, Walter H
Jansen, Jacobus F A
eng
Neuroimage. 2021 Feb 1;226:117626. doi: 10.1016/j.neuroimage.2020.117626. Epub 2020 Dec 8.},
   abstract = {Myelin is vital for healthy neuronal development, and can therefore provide valuable information regarding neuronal maturation. Anatomical and diffusion weighted images (DWI) possess information related to the myelin content and the current study investigates whether quantitative myelin markers can be extracted from anatomical and DWI using neural networks. Thirteen volunteers (mean age 29y) are included, and for each subject, a residual neural network was trained using spatially undersampled reference myelin-water markers. The network is trained on a voxel-by-voxel basis, resulting in a large amount of training data for each volunteer. The inputs used are the anatomical contrasts (cT1w, cT2w), the standardized T1w/T2w ratio, estimates of the relaxation times (T1, T2) and their ratio (T1/T2), and common DWI metrics (FA, RD, MD, lambda1, lambda2, lambda3). Furthermore, to estimate the added value of the DWI metrics, neural networks were trained using either the combined set (DWI, T1w and T2w) or only the anatomical (T1w and T2w) images. The reconstructed myelin-water maps are in good agreement with the reference myelin-water content in terms of the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC). A 6-fold undersampling using both anatomical and DWI metrics resulted in ICC = 0.68 and CoV = 5.9%. Moreover, using twice the training data (3-fold undersampling) resulted in an ICC that is comparable to the reproducibility of the myelin-water imaging itself (CoV = 5.5% vs. CoV = 6.7% and ICC = 0.74 vs ICC = 0.80). To achieve this, beside the T1w, T2w images, DWI is required. This preliminary study shows the potential of machine learning approaches to extract specific myelin-content from anatomical and diffusion-weighted scans.},
   keywords = {Adult
Body Water/*diagnostic imaging
Brain/*diagnostic imaging
Diffusion Magnetic Resonance Imaging/methods
Humans
Image Interpretation, Computer-Assisted/*methods
*Machine Learning
Magnetic Resonance Imaging/*methods
*Myelin Sheath
*Neural Networks, Computer
Neuroimaging/*methods
*Artificial intelligence
*Magnetic resonance imaging
*Myelin-water fraction
*Neural networks},
   ISSN = {1095-9572 (Electronic)
1053-8119 (Linking)},
   DOI = {10.1016/j.neuroimage.2020.117626},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/33301943},
   year = {2021},
   type = {Journal Article}
}

{"_id":"jpvww36SQqv73cvhz","bibbaseid":"drenthen-backes-jansen-estimatingmyelinwatercontentfromanatomicalanddiffusionimagesusingspatiallyundersampledmyelinwaterimagingthroughmachinelearning-2021","author_short":["Drenthen, G. S.","Backes, W. H.","Jansen, J. F. A."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Drenthen"],"firstnames":["G.","S."],"suffixes":[]},{"propositions":[],"lastnames":["Backes"],"firstnames":["W.","H."],"suffixes":[]},{"propositions":[],"lastnames":["Jansen"],"firstnames":["J.","F.","A."],"suffixes":[]}],"title":"Estimating myelin-water content from anatomical and diffusion images using spatially undersampled myelin-water imaging through machine learning","journal":"Neuroimage","volume":"226","pages":"117626","note":"Drenthen, Gerhard S Backes, Walter H Jansen, Jacobus F A eng Neuroimage. 2021 Feb 1;226:117626. doi: 10.1016/j.neuroimage.2020.117626. Epub 2020 Dec 8.","abstract":"Myelin is vital for healthy neuronal development, and can therefore provide valuable information regarding neuronal maturation. Anatomical and diffusion weighted images (DWI) possess information related to the myelin content and the current study investigates whether quantitative myelin markers can be extracted from anatomical and DWI using neural networks. Thirteen volunteers (mean age 29y) are included, and for each subject, a residual neural network was trained using spatially undersampled reference myelin-water markers. The network is trained on a voxel-by-voxel basis, resulting in a large amount of training data for each volunteer. The inputs used are the anatomical contrasts (cT1w, cT2w), the standardized T1w/T2w ratio, estimates of the relaxation times (T1, T2) and their ratio (T1/T2), and common DWI metrics (FA, RD, MD, lambda1, lambda2, lambda3). Furthermore, to estimate the added value of the DWI metrics, neural networks were trained using either the combined set (DWI, T1w and T2w) or only the anatomical (T1w and T2w) images. The reconstructed myelin-water maps are in good agreement with the reference myelin-water content in terms of the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC). A 6-fold undersampling using both anatomical and DWI metrics resulted in ICC = 0.68 and CoV = 5.9%. Moreover, using twice the training data (3-fold undersampling) resulted in an ICC that is comparable to the reproducibility of the myelin-water imaging itself (CoV = 5.5% vs. CoV = 6.7% and ICC = 0.74 vs ICC = 0.80). To achieve this, beside the T1w, T2w images, DWI is required. This preliminary study shows the potential of machine learning approaches to extract specific myelin-content from anatomical and diffusion-weighted scans.","keywords":"Adult Body Water/*diagnostic imaging Brain/*diagnostic imaging Diffusion Magnetic Resonance Imaging/methods Humans Image Interpretation, Computer-Assisted/*methods *Machine Learning Magnetic Resonance Imaging/*methods *Myelin Sheath *Neural Networks, Computer Neuroimaging/*methods *Artificial intelligence *Magnetic resonance imaging *Myelin-water fraction *Neural networks","issn":"1095-9572 (Electronic) 1053-8119 (Linking)","doi":"10.1016/j.neuroimage.2020.117626","url":"https://www.ncbi.nlm.nih.gov/pubmed/33301943","year":"2021","bibtex":"@article{RN268,\n author = {Drenthen, G. S. and Backes, W. H. and Jansen, J. F. A.},\n title = {Estimating myelin-water content from anatomical and diffusion images using spatially undersampled myelin-water imaging through machine learning},\n journal = {Neuroimage},\n volume = {226},\n pages = {117626},\n note = {Drenthen, Gerhard S\nBackes, Walter H\nJansen, Jacobus F A\neng\nNeuroimage. 2021 Feb 1;226:117626. doi: 10.1016/j.neuroimage.2020.117626. Epub 2020 Dec 8.},\n abstract = {Myelin is vital for healthy neuronal development, and can therefore provide valuable information regarding neuronal maturation. Anatomical and diffusion weighted images (DWI) possess information related to the myelin content and the current study investigates whether quantitative myelin markers can be extracted from anatomical and DWI using neural networks. Thirteen volunteers (mean age 29y) are included, and for each subject, a residual neural network was trained using spatially undersampled reference myelin-water markers. The network is trained on a voxel-by-voxel basis, resulting in a large amount of training data for each volunteer. The inputs used are the anatomical contrasts (cT1w, cT2w), the standardized T1w/T2w ratio, estimates of the relaxation times (T1, T2) and their ratio (T1/T2), and common DWI metrics (FA, RD, MD, lambda1, lambda2, lambda3). Furthermore, to estimate the added value of the DWI metrics, neural networks were trained using either the combined set (DWI, T1w and T2w) or only the anatomical (T1w and T2w) images. The reconstructed myelin-water maps are in good agreement with the reference myelin-water content in terms of the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC). A 6-fold undersampling using both anatomical and DWI metrics resulted in ICC = 0.68 and CoV = 5.9%. Moreover, using twice the training data (3-fold undersampling) resulted in an ICC that is comparable to the reproducibility of the myelin-water imaging itself (CoV = 5.5% vs. CoV = 6.7% and ICC = 0.74 vs ICC = 0.80). To achieve this, beside the T1w, T2w images, DWI is required. 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