Entropy based DTI quality control via regional orientation distribution

Abstract

Diffusion Tensor Imaging (DTI) has received increasing attention in the neuroimaging community. However, the complex Diffusion Weighted Images (DWI) acquisition protocol are prone to artifacts induced by motion and low signal-to-noise rations(SNRs). A rigorous quality control (QC) and error correction procedure is absolutely necessary for DTI data analysis. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts. We propose a new regional, alignment-independent DTI-QC measure that is based in the DTI domain employing the entropy of the regional distribution of the principal directions. This new QC measurement is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Experiments show that our automatic method can reliably detect and potentially correct such residual artifacts. The results indicate its usefulness for general quality assessment in DTI studies.

DTIPrep: quality control of diffusion-weighted images

“…Recent studies [Hiltunen et al, 2006, Gallichan et al, 2010, Farzinfar et al, 2012, 2013b] have demonstrated a new kind of artifact that was not previously detected by DTIPrep. These artifacts manifest themselves as a strong bias in the measured principal direction of diffusion (PD).…”

“…However, the vibration artifact presents itself either as a global disruption in the intensities or as local, gradual intensity change in neighboring slices over a subset of DWI's. To detect these artifacts, we have recently proposed a novel approach using an entropy-based measurement of the PD distribution (Farzinfar et al, 2012, 2013b). The orientation distribution is computed via a spherical histogram, using an icosahedron subdivision scheme to create the histogram bins.…”

“…The proposed method (Farzinfar et al, 2012, 2013b) evaluates the entropy of the orientation distribution within WM, gray matter and whole brain regions. In order to detect scans with suspicious values of entropy (lower values of entropy for artifacts of dominating directions), we compare a given DT scan's entropy to values learned from a training set of artifact-free samples.…”

“…We have proposed (Farzinfar et al, 2012, 2013b) an iterative leave-one-out-strategy over all individual DWI images by re-computing the DTI and the corresponding entropies. The DWI whose omission yields the most improvement in entropy is excluded from the scan.…”

DTI quality control assessment via error estimation from Monte Carlo simulations

“…The DWI-QC techniques aim to detect and correct these artifacts including inter/intra-slice intensity change [5], venetian blind [5], dropout signal intensities and vibration artifacts [6, 7] and eddy-current and motion artifacts [8, 5]; prior to tensor estimation. It is important to note that there are some pitfalls associated with these QC approaches [9] in the result of QC after correcting these artifacts.…”