Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines


Autoři: Seyyed M. H. Haddad aff001;  Christopher J. M. Scott aff002;  Miracle Ozzoude aff002;  Melissa F. Holmes aff002;  Stephen R. Arnott aff003;  Nuwan D. Nanayakkara aff001;  Joel Ramirez aff002;  Sandra E. Black aff002;  Dar Dowlatshahi aff005;  Stephen C. Strother aff003;  Richard H. Swartz aff004;  Sean Symons aff008;  Manuel Montero-Odasso aff009;  ;  Robert Bartha aff001
Působiště autorů: Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada aff001;  L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada aff002;  Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada aff003;  Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada aff004;  Ottawa Hospital Research Institute, Ottawa, Ontario, Canada aff005;  Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada aff006;  Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Ontario, Canada aff007;  Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada aff008;  Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, University of Western Ontario, London, Ontario, Canada aff009;  Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada aff010
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226715

Souhrn

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Klíčová slova:

Computational pipelines – Data acquisition – Data processing – Diffusion tensor imaging – Diffusion weighted imaging – Image processing – Magnetic resonance imaging – Graphics pipelines


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2019 Číslo 12