Quality Control of Diffusion Weighted Images

Abstract

Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. DWI data suffers 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. Currently, routine DTI QC procedures are conducted manually by visually checking the DWI data set in a gradient by gradient and slice by slice way. The results often suffer from low consistence across different data sets, lack of agreement of different experts, and difficulty to judge motion artifacts by qualitative inspection. Additionally considerable manpower is needed for this step due to the large number of images to QC, which is common for group comparison and longitudinal studies, especially with increasing number of diffusion gradient directions. We present a framework for automatic DWI QC. We developed a tool called DTIPrep which pipelines the QC steps with a detailed protocoling and reporting facility. And it is fully open source. This framework/tool has been successfully applied to several DTI studies with several hundred DWIs in our lab as well as collaborating labs in Utah and Iowa. In our studies, the tool provides a crucial piece for robust DTI analysis in brain white matter study.

Keywords: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Eddy Current Artifact; Intensity Artifact; Motion Artifact; Quality Control.

Figure 1

DTIPrep is running in GUI mode. In this picture, the #4 gradient shows intensity artifacts in several slices and it is marked as EXCLUDE in the QC result table, indicating that it will be excluded when the Save DWI button is clicked.

Figure 2

Correlation (in vertical) vs. slice number (in horizon) plot for slice-wise checking shows that gradient-18 is abnormal at several slice locations indicating intensity artifacts in these slices.

Figure 3

Motion parameters (in vertical) vs. gradient number (in horizon) plot for interlace-wise checking shows a large translation in Z in gradient-18. Correlation (in vertical) vs. gradient number (in horizon) plot also shows a drop for gradient-18. This implies that those slice intensity artifacts are very likely originating from motions between the interleaved acquisitions.

Figure 4

Motion parameters (in vertical) vs. gradient number (in horizon) plot for gradient-wise checking shows large motion occurred after gradient-17. This kind of artifacts can be corrected by registering those gradient volumes to the baseline (usually the first volume if no baseline acquired in a multiple b-valued acquisition).

Figure 5

Examples of intensity artifacts detected with DTIPrep. a shows a electromagnetic interference-like artifact; b shows severe signal loss in the middle and anterior part; c shows checker board-like artifact in a small area in the occipital part; d shows Venetian blind artifact due to the motion between the acquisition of the interleaved parts.

Figure 6

Orthogonal views of the color coded FA map calculated from a test DWI data set before (a: axial, b: sagittal, c: coronal) and after (d: axial, e: sagittal, f: coronal) QC using DTIPrep.