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Comparative Study
. 2019 Dec 20;14(12):e0226715.
doi: 10.1371/journal.pone.0226715. eCollection 2019.

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

Seyyed M H Haddad  1 , Christopher J M Scott  2 , Miracle Ozzoude  2 , Melissa F Holmes  2 , Stephen R Arnott  3 , Nuwan D Nanayakkara  1 , Joel Ramirez  2 , Sandra E Black  2   4 , Dar Dowlatshahi  5 , Stephen C Strother  3   6 , Richard H Swartz  4   7 , Sean Symons  8 , Manuel Montero-Odasso  9 ; ONDRI Investigators; Robert Bartha  1   10
Affiliations
Comparative Study

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

Seyyed M H Haddad et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schematic representation of the three pipelines utilized for brain DTI data processing.
The T1- and T2-weighted imaging processing subpipelines are common to all DTI processing pipelines. The blue, red, and purple arrows depict the 1st, 2nd, and 3rd DTI processing pipelines, respectively. T1 = T1-weighted image, T2 = T2-weighted image, b0 = b0 image.
Fig 2
Fig 2
(A) FA, (B) MD, (C) AD, and (D) RD maps produced from the raw ground truth DWI data by the three DTI processing pipelines and directly from the ground truth data. First, second, and third columns correspond to the 1st, 2nd, and 3rd DTI processing pipeline, respectively. The fourth column corresponds to the maps obtained directly from the ground truth data. (E) The corresponding T1-weighted structural image and (F) full-brain segmentation of cerebral tissues (WM, GM, and CSF) are also provided. The images were not interpolated.
Fig 3
Fig 3. FA maps produced by the three proposed DTI processing pipelines from the DWI datasets containing artefacts.
First, second, and third columns correspond to the 1st, 2nd, and 3rd DTI processing pipeline, respectively. First, second, third, and forth rows correspond to datasets LM-20, LM-40, SM-20, and SM-40, respectively. The corresponding T1-weighted structural image and full-brain segmentation of cerebral tissues are provided in panels e and f of Fig 2, respectively. The images were not interpolated.
Fig 4
Fig 4. MD maps produced by the three proposed DTI processing pipelines from the DWI datasets containing artefacts.
First, second, and third columns correspond to the 1st, 2nd, and 3rd DTI processing pipeline, respectively. First, second, third, and forth rows correspond to datasets LM-20, LM-40, SM-20, and SM-40, respectively. The corresponding T1-weighted structural image and full-brain segmentation of cerebral tissues are provided in panels e and f of Fig 2, respectively. The images were not interpolated.
Fig 5
Fig 5. Processing error associated with the pipelines for FA calculation in (A) WM and in (B) GM, and MD calculation in (C) WM and in (D) GM.
Fig 6
Fig 6. Distribution of FA values in the WM region generated by the three pipelines after processing the ground-truth data.
Fig 7
Fig 7. Distribution of the MD values in the WM region generated by the three pipelines after processing of the ground-truth data.
Fig 8
Fig 8. Percentage error associated with each pipeline when processing data containing artefacts.
(A) shows FA in WM, (B) shows MD in WM region.
Fig 9
Fig 9. FA measurements in NAWM from 20 ONDRI VCI subjects.
Fig 10
Fig 10. MD measurements in NAWM from 20 ONDRI VCI subjects.
Fig 11
Fig 11. Percentage error in calculation of FA in WM for the dataset LM-20 when using the gradient anisotropic diffusion (GAD) filter and Rician linear minimum mean square error (LMMSE) filter.
All pipelines produced lower percentage error with the GAD filter.
Fig 12
Fig 12. FA maps for the dataset LM-20 obtained by different pipelines when using the gradient anisotropic diffusion (GAD) filter (first row) and Rician linear minimum mean square error (LMMSE) filter (second row).
First, second, and third columns correspond to the 1st, 2nd, and 3rd DTI processing pipeline, respectively. The images were not interpolated.
See this image and copyright information in PMC

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Publication types

Grants and funding

This study was funded by Ontario Brain Institute through the Ontario Neurodegenerative Disease Research Initiative (ONDRI). ONDRI is a provincial collaboration between more than 80 of Ontario’s world-class neurodegenerative disease researchers and clinicians, four patient advocacy groups, the industrial sector, and more than 15 clinical, academic and research centers carried out in partnership with the Ontario Brain Institute. The five-year funding formula for the Ontario Neurodegenerative Disease Research Initiative (ONDRI) includes $19 million from the Ontario Brain Institute (OBI) and $9.5 million in partner (healthcare and academic and research institutions) contributions. Of the $9.5 million required in partner contributions, over $5 million has currently been raised through philanthropic donations. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The ONDRI project is funded by the Ontario Brain Institute through the Government of Ontario with matching funds provided by participating hospital and research institute foundations, including the Baycrest Foundation, Bruyère Research Institute, Centre for Addiction and Mental Health Foundation, London Health Sciences Foundation, McMaster University Faculty of Health Sciences, Ottawa Brain and Mind Research Institute, Queen’s University Faculty of Health Sciences, Providence Care (Kingston), Sunnybrook Health Sciences Foundation, the Thunder Bay Regional Health Sciences Centre, the University of Ottawa Faculty of Medicine, and the Windsor/Essex County ALS Association. The Temerty Family Foundation provided the major infrastructure matching funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Dr. Haddad was supported by an ONDRI postdoctoral scholar fellowship. The Centre for Functional and Metabolic Mapping is supported by Brain Canada and the Canada First Research Excellence Fund (CFREF).