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

Abstract

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.

Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions

“…MRI protocol were harmonised with the Canadian Dementia Imaging Protocol (CDIP) (37), and were in compliance with the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards (38). Detailed MRI protocols are reported elsewhere (39,40). In brief, the structural MRI used in the current study include: a high-resolution 3D T1-weighted (T1), an interleaved proton density (PD) and T2-weighted (T2), and a T2 fluid-attenuated inversion recovery (FLAIR) images.…”

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI methods & outcome measures

“…Neuroimaging was acquired at the following sites using each respective 3T (Nestor et al, 2013) which was fully integrated into the pipeline and are included in the final volumetric spreadsheet. Microbleed Rating, Resting State fMRI Analysis, and Diffusion Tensor Imaging (DTI) analyses are processed separately, however, the DTI pipeline (Haddad et al, 2020) and Cortical Thickness pipelines are dependent on some components of the primary pipeline, thus, results from these processes are also provided in separate spreadsheets. Table 1.…”

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures

“…In brief, the following structural MRI sequences were obtained for each study participant: 3D T1-weighted (T1), T2-weighted fluid attenuated inversion recovery (FLAIR), interleaved T2-weighted and proton density (T2/PD), and T2 * gradient recalled echo (GRE). It should be noted that additional imaging protocol included a 30/32 direction diffusion tensor imaging (DTI), resting state functional MRI, and arterial spin labeling (acquired only at one site), but are beyond the scope of this paper and will be presented elsewhere (1). Prior to image processing for volumetric quantification, MRI were fully evaluated by a neuroradiologist (SS) for incidental findings and for imaging quality by a medical biophysics scientist (RB).…”