TY - JOUR
T1 - Statistical estimation of white matter microstructure from conventional MRI
AU - Suttner, Leah H.
AU - Mejia, Amanda
AU - Dewey, Blake
AU - Sati, Pascal
AU - Reich, Daniel S.
AU - Shinohara, Russell T.
N1 - Funding Information:
We thank Dr. Govind Nair for his insightful comments and discussions about this work. We acknowledge the contribution of the NINDS Neuroimmunology Clinic, which recruited the patients and performed clinical evaluations, and the NIH Functional MRI Facility, where scanning took place. Suttner, Mejia, and Shinohara are partially funded by the NIH grant RO1 NS085211 from the National Institute of Neurological Disorders and Stroke (NINDS) ( Z01-NS003119 ). Shinohara was also partially funded by R21 NS093349 . The study was supported in part by the Intramural Research Program of NINDS . This work represents the opinions of the researchers and not necessarily that of the granting organizations.
Publisher Copyright:
© 2016
PY - 2016
Y1 - 2016
N2 - Diffusion tensor imaging (DTI) has become the predominant modality for studying white matter integrity in multiple sclerosis (MS) and other neurological disorders. Unfortunately, the use of DTI-based biomarkers in large multi-center studies is hindered by systematic biases that confound the study of disease-related changes. Furthermore, the site-to-site variability in multi-center studies is significantly higher for DTI than that for conventional MRI-based markers. In our study, we apply the Quantitative MR Estimation Employing Normalization (QuEEN) model to estimate the four DTI measures: MD, FA, RD, and AD. QuEEN uses a voxel-wise generalized additive regression model to relate the normalized intensities of one or more conventional MRI modalities to a quantitative modality, such as DTI. We assess the accuracy of the models by comparing the prediction error of estimated DTI images to the scan-rescan error in subjects with two sets of scans. Across the four DTI measures, the performance of the models is not consistent: Both MD and RD estimations appear to be quite accurate, while AD estimation is less accurate than MD and RD; the accuracy of FA estimation is poor. Thus, in some cases when assessing white matter integrity, it may be sufficient to acquire conventional MRI sequences alone.
AB - Diffusion tensor imaging (DTI) has become the predominant modality for studying white matter integrity in multiple sclerosis (MS) and other neurological disorders. Unfortunately, the use of DTI-based biomarkers in large multi-center studies is hindered by systematic biases that confound the study of disease-related changes. Furthermore, the site-to-site variability in multi-center studies is significantly higher for DTI than that for conventional MRI-based markers. In our study, we apply the Quantitative MR Estimation Employing Normalization (QuEEN) model to estimate the four DTI measures: MD, FA, RD, and AD. QuEEN uses a voxel-wise generalized additive regression model to relate the normalized intensities of one or more conventional MRI modalities to a quantitative modality, such as DTI. We assess the accuracy of the models by comparing the prediction error of estimated DTI images to the scan-rescan error in subjects with two sets of scans. Across the four DTI measures, the performance of the models is not consistent: Both MD and RD estimations appear to be quite accurate, while AD estimation is less accurate than MD and RD; the accuracy of FA estimation is poor. Thus, in some cases when assessing white matter integrity, it may be sufficient to acquire conventional MRI sequences alone.
KW - Image synthesis
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U2 - 10.1016/j.nicl.2016.09.010
DO - 10.1016/j.nicl.2016.09.010
M3 - Article
C2 - 27722085
AN - SCOPUS:84989169830
SN - 2213-1582
VL - 12
SP - 615
EP - 623
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
ER -