DTIPrep: quality control of diffusion-weighted images

Abstract

In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.

Keywords: diffusion MRI; diffusion tensor imaging; open-source; preprocessing; quality control; software.

Figure 1

Examples of intensity artifacts detected with DTIPrep. (A) An electromagnetic interference-like artifact, (B) severe signal loss in the anterior and middle regions, (C) venetian blind artifact, (D) inter-slice and intra-slice intensity artifact, and (E) checkerboard artifact.

Figure 2

Head-motion artifacts. Rigid registration parameters between each gradient and baseline image of the original DWIs (A) and corrected DWIs (B). (C) Overlay comparison between original DWI #2 before (red) and after (green) correction.

Figure 3

QC reduces measurement noise in DTI. Standard deviation of FA in five healthy subjects in six regions are shown. QC reduces FA standard deviation considerably even for a single scan; the improvement is dramatic when the number of included repeat scans (horizontal axis) is increased. Percentage figures indicate number of gradient directions excluded by DTIPrep. Courtesy of V. Magnotta (University of Iowa).

Figure 4

DWI-based QC results using DTIPrep through three steps. (1) converting the DWI image from DICOM to NRRD format, (2) loading the protocol and running the software, and (3) if necessary, visual checking and saving the final DWI dataset. In this example, gradient #11 suffers from intensity artifact and is excluded. The sphere shows a 3D view of the gradient distribution before (blue dots) and after running DTIPrep (green dots, visualized on top of the blue dots), respectively. In this particular example, a large number of DWI's were excluded (missing green dots on the 3D sphere). The 3D sphere also reveals a highly non-uniform distribution of the input diffusion gradient, indicating a non-optimal acquisition protocol.

Figure 5

DTIPrep workflow.

Figure 6

Vibration artifacts. (A) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (B) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (C) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.

Figure 7

Bias analysis of different DWI schemes via MC simulation. (A–C) Gradient direction distribution for three common DWI acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (D–F) Estimated error distribution in PD computation. (G–I) Estimated error distribution in FA computation as a percentage of true FA. 200,000 MC simulation were performed for this experiment, with a true FA value of 0.4 and SNR = 10.