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Proceedings Paper

SHARD: spherical harmonic-based robust outlier detection for HARDI methods
Author(s): Vishwesh Nath; Kurt G. Schilling; Allison E. Hainline; Prasanna Parvathaneni; Justin A. Blaber; Ilwoo Lyu; Adam W. Anderson; Hakmook Kang; Allen T. Newton; Baxter P. Rogers; Bennett A. Landman
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Paper Abstract

High Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion, physiologic, and imaging artifacts. In diffusion tensor imaging, robust statistical approaches have been shown to greatly reduce these adverse factors without human intervention. Similar approaches would be possible with HARDI methods, but robust versions of each distinct HARDI approach would be necessary. To avoid the computational and pragmatic burdens of creating individual robust HARDI analysis variants, we propose a robust outlier imputation model to mitigate outliers prior to traditional HARDI analysis. This model uses a weighted spherical harmonic fit of diffusion weighted magnetic resonance imaging scans to estimate the values which had been corrupted during acquisition to restore them. Briefly, spherical harmonics of 6th order were used to generate basis function which were weighted by diffusion signal for detection of outliers. For validation, a single healthy volunteer was scanned for a single session comprising of two scans one without head movement and the other with deliberate head movement at a b-value of 3000 s/mm2 with 64 diffusion weighted directions with a single b0 (5 averages) per scan. The deliberate motion from the volunteer created natural artifacts in the acquisition of one of the scans. The imputation model shows reduction in root mean squared error of the raw signal intensities and improvement for the HARDI method Q-ball in terms of the Angular Correlation Coefficient. The results reveal that there is quantitative and qualitative improvement. The proposed model can be used as general pre-processing model before implementing any HARDI model in general to restore the artifacts which are created because of the outlier diffusion signal in certain gradient volumes.

Paper Details

Date Published: 2 March 2018
PDF: 11 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740X (2 March 2018); doi: 10.1117/12.2293727
Show Author Affiliations
Vishwesh Nath, Vanderbilt Univ. (United States)
Kurt G. Schilling, Vanderbilt Univ. Institute of Imaging Science (United States)
Allison E. Hainline, Vanderbilt Univ. (United States)
Prasanna Parvathaneni, Vanderbilt Univ. (United States)
Justin A. Blaber, Vanderbilt Univ. (United States)
Ilwoo Lyu, Vanderbilt Univ. (United States)
Adam W. Anderson, Vanderbilt Univ. Institute of Imaging Science (United States)
Hakmook Kang, Vanderbilt Univ. (United States)
Allen T. Newton, Vanderbilt Univ. Institute of Imaging Science (United States)
Baxter P. Rogers, Vanderbilt Univ. Institute of Imaging Science (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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