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Subject-Motion Correction in HARDI Acquisitions: Choices and Consequences.

Elhabian S, Gur Y, Vachet C, Piven J, Styner M, Leppert IR, Pike GB, Gerig G - Front Neurol (2014)

Bottom Line: The scientific community offers strong theoretical and experimental work on noise reduction and orientation distribution function (ODF) reconstruction techniques for HARDI data, where post-acquisition motion correction is widely performed, e.g., using the open-source DTIprep software (1), FSL (the FMRIB Software Library) (2), or TORTOISE (3).Nonetheless, effects and consequences of the selection of motion correction schemes on the final analysis, and the eventual risk of introducing confounding factors when comparing populations, are much less known and far beyond simple intuitive guessing.Effects of various motion correction choices are systematically explored and illustrated, leading to a general conclusion of discouraging users from setting ad hoc thresholds on the estimated motion parameters beyond which volumes are claimed to be corrupted.

View Article: PubMed Central - PubMed

Affiliation: Scientific Computing and Imaging Institute , Salt Lake City, UT , USA ; Faculty of Computers and Information, Cairo University , Cairo , Egypt.

ABSTRACT
Diffusion-weighted imaging (DWI) is known to be prone to artifacts related to motion originating from subject movement, cardiac pulsation, and breathing, but also to mechanical issues such as table vibrations. Given the necessity for rigorous quality control and motion correction, users are often left to use simple heuristics to select correction schemes, which involves simple qualitative viewing of the set of DWI data, or the selection of transformation parameter thresholds for detection of motion outliers. The scientific community offers strong theoretical and experimental work on noise reduction and orientation distribution function (ODF) reconstruction techniques for HARDI data, where post-acquisition motion correction is widely performed, e.g., using the open-source DTIprep software (1), FSL (the FMRIB Software Library) (2), or TORTOISE (3). Nonetheless, effects and consequences of the selection of motion correction schemes on the final analysis, and the eventual risk of introducing confounding factors when comparing populations, are much less known and far beyond simple intuitive guessing. Hence, standard users lack clear guidelines and recommendations in practical settings. This paper reports a comprehensive evaluation framework to systematically assess the outcome of different motion correction choices commonly used by the scientific community on different DWI-derived measures. We make use of human brain HARDI data from a well-controlled motion experiment to simulate various degrees of motion corruption and noise contamination. Choices for correction include exclusion/scrubbing or registration of motion corrupted directions with different choices of interpolation, as well as the option of interpolation of all directions. The comparative evaluation is based on a study of the impact of motion correction using four metrics that quantify (1) similarity of fiber orientation distribution functions (fODFs), (2) deviation of local fiber orientations, (3) global brain connectivity via graph diffusion distance (GDD), and (4) the reproducibility of prominent and anatomically defined fiber tracts. Effects of various motion correction choices are systematically explored and illustrated, leading to a general conclusion of discouraging users from setting ad hoc thresholds on the estimated motion parameters beyond which volumes are claimed to be corrupted.

No MeSH data available.


Related in: MedlinePlus

Sample tractography selection for the uncinate fasciculus (UNC) from the untilted motion-free dataset as well as selections from motion-corrected datasets with 10, 30, and 70% corrupted gradient directions. Correction choices shown include outlier-based (i.e., motion scrubbing) and registration-based (using baseline and model-based reference volumes). Notice the inaccurate UNC tract being detected from the motion scrubbing choice at high percentages of motion corruption.
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Figure 12: Sample tractography selection for the uncinate fasciculus (UNC) from the untilted motion-free dataset as well as selections from motion-corrected datasets with 10, 30, and 70% corrupted gradient directions. Correction choices shown include outlier-based (i.e., motion scrubbing) and registration-based (using baseline and model-based reference volumes). Notice the inaccurate UNC tract being detected from the motion scrubbing choice at high percentages of motion corruption.

Mentions: Table 2 shows the average Cohen’s Kappa statistic computed for corpus callosum (CC), corticospinal tract (CST), and inferior fronto-occipital tract (IFO) (where other pathways showed similar trend) based on automatic tractography selection using whole brain tractography of raw datasets (denoised datasets showed similar trends due to the robust fODF estimation, yet their graphs were omitted due to space limitation). Figures 8–12 show sample tractography selections for the aforementioned tracts from the untilted motion-free dataset as well as selections from motion-corrected datasets with different corrupted gradient directions. Correction choices shown include outlier-based (i.e., motion scrubbing) and registration-based (using baseline and model-based reference volumes). Also pass-through (in green) and not-pass-through (in red) volumes (i.e., isosurfaces) are shown. Their definitions along with the geometric constraints employed to remove fibers, which do not belong to the pathway of interest, can be found in Ref. (64).


Subject-Motion Correction in HARDI Acquisitions: Choices and Consequences.

Elhabian S, Gur Y, Vachet C, Piven J, Styner M, Leppert IR, Pike GB, Gerig G - Front Neurol (2014)

Sample tractography selection for the uncinate fasciculus (UNC) from the untilted motion-free dataset as well as selections from motion-corrected datasets with 10, 30, and 70% corrupted gradient directions. Correction choices shown include outlier-based (i.e., motion scrubbing) and registration-based (using baseline and model-based reference volumes). Notice the inaccurate UNC tract being detected from the motion scrubbing choice at high percentages of motion corruption.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4260507&req=5

Figure 12: Sample tractography selection for the uncinate fasciculus (UNC) from the untilted motion-free dataset as well as selections from motion-corrected datasets with 10, 30, and 70% corrupted gradient directions. Correction choices shown include outlier-based (i.e., motion scrubbing) and registration-based (using baseline and model-based reference volumes). Notice the inaccurate UNC tract being detected from the motion scrubbing choice at high percentages of motion corruption.
Mentions: Table 2 shows the average Cohen’s Kappa statistic computed for corpus callosum (CC), corticospinal tract (CST), and inferior fronto-occipital tract (IFO) (where other pathways showed similar trend) based on automatic tractography selection using whole brain tractography of raw datasets (denoised datasets showed similar trends due to the robust fODF estimation, yet their graphs were omitted due to space limitation). Figures 8–12 show sample tractography selections for the aforementioned tracts from the untilted motion-free dataset as well as selections from motion-corrected datasets with different corrupted gradient directions. Correction choices shown include outlier-based (i.e., motion scrubbing) and registration-based (using baseline and model-based reference volumes). Also pass-through (in green) and not-pass-through (in red) volumes (i.e., isosurfaces) are shown. Their definitions along with the geometric constraints employed to remove fibers, which do not belong to the pathway of interest, can be found in Ref. (64).

Bottom Line: The scientific community offers strong theoretical and experimental work on noise reduction and orientation distribution function (ODF) reconstruction techniques for HARDI data, where post-acquisition motion correction is widely performed, e.g., using the open-source DTIprep software (1), FSL (the FMRIB Software Library) (2), or TORTOISE (3).Nonetheless, effects and consequences of the selection of motion correction schemes on the final analysis, and the eventual risk of introducing confounding factors when comparing populations, are much less known and far beyond simple intuitive guessing.Effects of various motion correction choices are systematically explored and illustrated, leading to a general conclusion of discouraging users from setting ad hoc thresholds on the estimated motion parameters beyond which volumes are claimed to be corrupted.

View Article: PubMed Central - PubMed

Affiliation: Scientific Computing and Imaging Institute , Salt Lake City, UT , USA ; Faculty of Computers and Information, Cairo University , Cairo , Egypt.

ABSTRACT
Diffusion-weighted imaging (DWI) is known to be prone to artifacts related to motion originating from subject movement, cardiac pulsation, and breathing, but also to mechanical issues such as table vibrations. Given the necessity for rigorous quality control and motion correction, users are often left to use simple heuristics to select correction schemes, which involves simple qualitative viewing of the set of DWI data, or the selection of transformation parameter thresholds for detection of motion outliers. The scientific community offers strong theoretical and experimental work on noise reduction and orientation distribution function (ODF) reconstruction techniques for HARDI data, where post-acquisition motion correction is widely performed, e.g., using the open-source DTIprep software (1), FSL (the FMRIB Software Library) (2), or TORTOISE (3). Nonetheless, effects and consequences of the selection of motion correction schemes on the final analysis, and the eventual risk of introducing confounding factors when comparing populations, are much less known and far beyond simple intuitive guessing. Hence, standard users lack clear guidelines and recommendations in practical settings. This paper reports a comprehensive evaluation framework to systematically assess the outcome of different motion correction choices commonly used by the scientific community on different DWI-derived measures. We make use of human brain HARDI data from a well-controlled motion experiment to simulate various degrees of motion corruption and noise contamination. Choices for correction include exclusion/scrubbing or registration of motion corrupted directions with different choices of interpolation, as well as the option of interpolation of all directions. The comparative evaluation is based on a study of the impact of motion correction using four metrics that quantify (1) similarity of fiber orientation distribution functions (fODFs), (2) deviation of local fiber orientations, (3) global brain connectivity via graph diffusion distance (GDD), and (4) the reproducibility of prominent and anatomically defined fiber tracts. Effects of various motion correction choices are systematically explored and illustrated, leading to a general conclusion of discouraging users from setting ad hoc thresholds on the estimated motion parameters beyond which volumes are claimed to be corrupted.

No MeSH data available.


Related in: MedlinePlus