Quality control of diffusion weighted images

Liu, Zhexing; Wang, Yi; Gerig, Guido; Gouttard, Sylvain; Tao, Ran; Fletcher, Thomas; Styner, Martin

doi:10.1117/12.844748

Cited by 132 publications

(143 citation statements)

References 6 publications

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“…[38][39][40] For example, if the OOA likelihoods are aggregated over subjects/diffusion weighted volumes, one could quantify the OOA likelihood for a given subject/image for fully automated quality control. Additionally, due to the huge amount of data within a single DTI dataset, the proposed framework could provide a mechanism for localizing the regions/slices of high artifact likelihood.…”

Section: Iiie Dti Resultsmentioning

confidence: 99%

Out‐of‐atlas likelihood estimation using multi‐atlas segmentation

et al. 2013

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Purpose: Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures that are anatomically consistent across a large population of potential target subjects (i.e., multi-atlas segmentation is limited to "in-atlas" applications). Herein, the authors propose a technique to determine the likelihood that a multi-atlas segmentation estimate is representative of the problem at hand, and, therefore, identify anomalous regions that are not well represented within the atlases. Methods: The authors derive a technique to estimate the out-of-atlas (OOA) likelihood for every voxel in the target image. These estimated likelihoods can be used to determine and localize the probability of an abnormality being present on the target image. Results: Using a collection of manually labeled whole-brain datasets, the authors demonstrate the efficacy of the proposed framework on two distinct applications. First, the authors demonstrate the ability to accurately and robustly detect malignant gliomas in the human brain-an aggressive class of central nervous system neoplasms. Second, the authors demonstrate how this OOA likelihood estimation process can be used within a quality control context for diffusion tensor imaging datasets to detect large-scale imaging artifacts (e.g., aliasing and image shading). Conclusions:The proposed OOA likelihood estimation framework shows great promise for robust and rapid identification of brain abnormalities and imaging artifacts using only weak dependencies on anomaly morphometry and appearance. The authors envision that this approach would allow for application-specific algorithms to focus directly on regions of high OOA likelihood, which would (1) reduce the need for human intervention, and (2) reduce the propensity for false positives. Using the dual perspective, this technique would allow for algorithms to focus on regions of normal anatomy to ascertain image quality and adapt to image appearance characteristics.

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Section: Iiie Dti Resultsmentioning

confidence: 99%

Out‐of‐atlas likelihood estimation using multi‐atlas segmentation

et al. 2013

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“…Further there is no guarantee that the head will ever move back to the original position. Alternatively, the exclusion of one or more gradients bearing strong motion artifacts can be exercised [5], a.k.a motion scrubbing in functional MRI, however, this limits the ability to reconstruct crossing fibers especially at small separation angles. As such, post-acquisition motion correction is imperative to guarantee voxel-wise correspondence between different DWIs referring to the same anatomical structure.…”

Section: Introductionmentioning

confidence: 99%

Motion Is Inevitable: The Impact of Motion Correction Schemes on HARDI Reconstructions

Elhabian¹,

Gur²,

Vachet³

et al. 2014

Computational Diffusion MRI

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Diffusion weighted imaging (DWI) is known to be prone to artifacts related to motion originating from subject movement, cardiac pulsation and breathing, but also to mechanical issues such as table vibrations. Given the necessity for rigorous quality control and motion correction, users are often left to use simple heuristics to select correction schemes, but do not fully understand the consequences of such choices on the final analysis, moreover being at risk to introduce confounding factors in population studies. This paper reports work in progress towards a com- prehensive evaluation framework of HARDI motion correction to support selection of optimal methods to correct for even subtle motion. We make use of human brain HARDI data from a well controlled motion experiment to simulate various degrees of motion corruption. Choices for correction include exclusion or registration of motion corrupted directions, with different choices of interpolation. The comparative evaluation is based on studying effects of motion correction on three different metrics commonly used when using DWI data, including similarity of fiber orientation distribution functions (fODFs), global brain connectivity via Graph Diffusion Distance (GDD), and reproducibility of prominent and anatomically defined fiber tracts. Effects of various settings are systematically explored and illustrated, leading to the somewhat surprising conclusion that a best choice is the alignment and interpolation of all DWI directions, not only directions considered as corrupted.

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“…Also, during DWI acquisition, fast gradient switching existing in echo-planar imaging (EPI) will yield an amplification effect to various image distortions. Therefore, DWI screening is an essential step in any DTI-based study to eliminate any bias that may exist with data collection, which potentially could affect the final outcome of the study [152]. Some examples of DWI intensity artifacts are shown in Figure 22.…”

Section: Dwi Image Preprocessingmentioning

confidence: 99%

“…Some examples of DWI intensity artifacts are shown in Figure 22. In order to perform the required DWI quality control (QC), DTIprep software [152] was used, which automatically detects and removes various scan artifacts, and correct motion and eddy current distortions. Using the DTIprep generated comprehensive report and the obtained results, scans with strong remaining artifacts were eliminated.…”

Section: Dwi Image Preprocessingmentioning

confidence: 99%

“…In addition, resultant data sets with low number of gradient directions, which will produce DTI estimates with low SNR, were excluded from any further processing. The main steps involved in the QC procedure per-formed by DTIprep software are summarized in Algorithm 1 [152], and the complete DTIprep software details could be found in [152].…”

Section: Dwi Image Preprocessingmentioning

confidence: 99%

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A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Mostapha¹

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Quality control of diffusion weighted images

Cited by 132 publications

References 6 publications

Out‐of‐atlas likelihood estimation using multi‐atlas segmentation

Out‐of‐atlas likelihood estimation using multi‐atlas segmentation

Motion Is Inevitable: The Impact of Motion Correction Schemes on HARDI Reconstructions

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

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