Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

Zhou, Zhenyu; Liu, Wei; Cui, Jiali; Wang, Xun‐Heng; Arias, Diana; Wen, Ying; Bansal, Ravi; Hao, Xuejun; Wang, Zhishun; Peterson, Bradley S.; Xu, Dongrong

doi:10.1016/j.mri.2010.06.022

Cited by 31 publications

(31 citation statements)

References 20 publications

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“…Several automated techniques for the detection of artefacts in diffusion-weighted images have been developed, which operate on a slice-wise (Zhou et al, 2011) or on a voxel-wise basis (Chang et al, 2005(Chang et al, , 2012. Compared to voxel-wise approaches, the slice-wise detection technique (Zhou et al, 2011) does not rely on a specific model of diffusion, is computationally less expensive, and potentially provides improved detection of scanner-induced pattern artefacts.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to voxel-wise approaches, the slice-wise detection technique (Zhou et al, 2011) does not rely on a specific model of diffusion, is computationally less expensive, and potentially provides improved detection of scanner-induced pattern artefacts. However, it cannot identify artefacts caused by cardiac pulsation, which only affect a small portion of the image slice.…”

Section: Introductionmentioning

confidence: 99%

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HOMOR: Higher Order Model Outlier Rejection for high b-value MR diffusion data

et al. 2012

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HOMOR: Higher Order Model Outlier Rejection for high b-value MR diffusion data

et al. 2012

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“…Large signal drop-outs are problematic with through-plane motion correction because thereafter, the incorrect signal intensities affect 2 sections, resulting in possible incorrect tensor estimations. Zhou et al 47 investigated the use of local texture features to identify and reject outlier images automatically before estimating the diffusion tensor. Their method is fast and removes sections that are corrupted and cannot be used for tensor estimation, resulting in more accurate data.…”

Section: Motion/distortion Correctionmentioning

confidence: 99%

Acquisition Guidelines and Quality Assessment Tools for Analyzing Neonatal Diffusion Tensor MRI Data

Heemskerk

Leemans

Plaisier

et al. 2013

AJNR Am J Neuroradiol

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SUMMARY:Diffusion tensor imaging is a valuable measure in clinical settings to assess diagnosis and prognosis of neonatal brain development. However, obtaining reliable images is not straightforward because of the tissue characteristics of the neonatal brain and the high likelihood of motion artifacts. In this review, we present guidelines on how to acquire DTI data of the neonatal brain and recommend high-quality data acquisition and processing as an essential means to obtain accurate and robust parametric maps. Sudden head movements are problematic for DTI in neonates, and these may lead to incorrect values. We describe strategies to minimize the corrupting effects both in terms of acquisition (eg, more gradient directions) and postprocessing (eg, tensor estimation methods). In addition, tools are described that can help assess whether a dataset is of sufficient quality for further assessment. ABBREVIATIONS:MD ϭ mean diffusivity; FA ϭ fractional anisotropy; RESTORE ϭ robust estimation of tensors by outlier rejection; TBSS ϭ tract-based spatial

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“…Therefore, accurate prediction models need to be built. So far the most common modeling methods are partial least squares (PLS) [4], support vector machine (SVM) [5] and back propagation neural network (BPNN) [6]. These methods have been used widely in electrophysiological studies [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment

Feng

Cao

Wang

et al. 2015

BME

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Abstract. The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.

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Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

Cited by 31 publications

References 20 publications

HOMOR: Higher Order Model Outlier Rejection for high b-value MR diffusion data

HOMOR: Higher Order Model Outlier Rejection for high b-value MR diffusion data

Acquisition Guidelines and Quality Assessment Tools for Analyzing Neonatal Diffusion Tensor MRI Data

Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment

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