Abstract
Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called “corrected Inter-Slice Intensity Discontinuity” (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.
Highlights
Diffusion tensor imaging (DTI) is an MRI technique that measures the anisotropy of water incoherent motion, which can generate unique image contrasts to display the brain white matter [1,2]
Least squares-based tensor fitting According to the DTI theory, the relation between the b0 image (S0) and diffusion-weighted signals (Sb) is as follows: Sb = S0exp(2bTDb), where the vector, b, is decided by the diffusion gradients, and the diffusion tensor, D, is a 363 positive symmetric matrix to be determined [13], and the superscript, T, represents vector or matrix transposition
Motion-induced artifacts are often observed in diffusion-weighted images (DWIs), which could lead to significant inaccuracy in tensor calculation
Summary
Diffusion tensor imaging (DTI) is an MRI technique that measures the anisotropy of water incoherent motion, which can generate unique image contrasts to display the brain white matter [1,2]. In DTI, a diffusion tensor is typically estimated by a least squares error fit of the intensity at each pixel of diffusion-weighted images (DWIs) and non-diffusion-weighted (b0) images. Sensitive to molecular motion on the order of 10 mm, DWIs often suffer from a large amount of signal loss (corruption). An image slice that is acquired during bulk motion could lead to complete signal loss of the entire slice. Sub-pixel elastic brain motion caused by cardiac pulsation is known to cause regional signal loss [3,4,5,6,7,8]. The image corruption leads to errors in the subsequent tensor estimation. The mis-registration can be lessened by post-processing image alignment, for corrupted images, the only available solution is to discard the affected image slice (slice rejection) or pixels (pixel rejection)
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