Projects:DTI DWI QualityControl

Back to UNC Algorithms

Diffusion Tensor and Diffusion Weighted Imaging Quality Control

DWIs data suffer from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework, called DTIPrep, for assessing and correcting DWIs and DTI.

Description

DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipeline steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baseline images, 6) Eddy current and motion correction, 7) gradient-wise checking of residual motion/deformations, 8) Computing DTI measurements and saving and 9) Optional visual QC.

Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep

3D view of gradients before and after Quality Control procedures

As further extended-DTIprep, our experiments show that the residual artifacts presence after DTIPrep DWIs QC can be detected and corrected using knowledge of DTI. We introduce new approach in DTI QC including the detection and the corrections steps. Our approach use new entropy-based benchmark comes up with Principal Directions (PDs) histogram implemented by multi-level subdivision icosahedron within different regions of brain. Given training of the measurement, the quality of DTI is categorized into acceptable, suspicious and unacceptable groups using calculated the standard scores.

Comparison between control subject and green and red artifacts in terms of their corresponding PDs histogram on sphere.

The z-score of orientational entropies for autism sub- jects. The detected images are indexed from 13 and all suffer from color artifac.

We employ the correction step by excluding gradients which have the most contribution in the artifacts. We continue ex- cluding gradients till the z-score of whole brain of updated image will be close enough to the trained.

The correction step result (the corrected image in right side) shows much more improvement in observing cc and fx tracts. The FA profiles of the genu and splenium are shown in bottom ( blue color: corrected image)

Publications

• NA-MIC Publication Database on DTI/DWI QC

Key Investigators

• UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
• Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard

Links

• DTIPrep on NITRC