| Autor: |
Zhu H; Hongtu Zhu is Associate Professor, Department of Biostatistics and Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( hzhu@bios.unc.edu ). Yimei Li is a Ph.D. student, Department of Biostatistics and Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( liyimei@email.uce.edu ). Joshep G. Ibrahim is Alumni Distinguished Professor, Department of Biostatistics and Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( ibrahim@bios.unc.edu ). Xiaoyan Shi is a Ph.D. student, Department of Biostatistics and Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( xyshi@email.unc.edu . Hongyu An is Research Assistant Professor, Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( HongyuAn@med.unc.edu ). Yashen Chen is Research Fellow, Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( yasheng.chen@med.unc.edu ). Wei Gao is a Ph.D. student, Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( wgao@med.unc.edu ). Weili Lin is Professor, Department of Radiology, University of North Carolina at Chapel Hill, NC 27599 ( weili.lin@med.unc.edu ). Daniel B. Rowe is Associate Professor, Department of Biophysics, Medical College of Wisconsin, Milwaudee, WI 53226 ( dbrowe@mcw.edu ). Bradley S. Peterson is Professor, Department of Psychiatry, Columbia Medical Center and the New York State Psychiatric Institiute, New York, NY 10032 ( petersob@childpsych.columbia.edu )., Li Y, Ibrahim JG, Shi X, An H, Chen Y, Gao W, Lin W, Rowe DB, Peterson BS |
| Abstrakt: |
Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models. |
