A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging

Koay, Cheng Guan; Chang, Lin‐Ching; Carew, John D.; Pierpaoli, Carlo; Basser, Peter J.

doi:10.1016/j.jmr.2006.06.020

Cited by 221 publications

(218 citation statements)

References 20 publications

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“…5 Because diffusion tensor estimation techniques differ considerably in principle, speed, and accuracy, 29 awareness of the benefits and pitfalls is essential: the LLS method is fast and mostly used but assumes that errors are identically distributed, which can result in inaccurate estimation of the tensor. 30 The WLLS method is slightly slower but provides more accurate results because it considers errors to be heterogeneously distributed. 31 NLLS iteratively minimizes errors and results in more reliable estimation but needs considerably longer processing time and may get stuck in local optima during optimization.…”

Section: Discussionmentioning

confidence: 99%

Choice of Diffusion Tensor Estimation Approach Affects Fiber Tractography of the Fornix in Preterm Brain

Plaisier¹,

Pieterman

Lequin³

et al. 2014

American Journal of Neuroradiology

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BACKGROUND AND PURPOSE:Neonatal DTI enables quantitative assessment of microstructural brain properties. Although its use is increasing, it is not widely known that vast differences in tractography results can occur, depending on the diffusion tensor estimation methodology used. Current clinical work appears to be insufficiently focused on data quality and processing of neonatal DTI. To raise awareness about this important processing step, we investigated tractography reconstructions of the fornix with the use of several estimation techniques. We hypothesized that the method of tensor estimation significantly affects DTI tractography results.

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Section: Discussionmentioning

confidence: 99%

Choice of Diffusion Tensor Estimation Approach Affects Fiber Tractography of the Fornix in Preterm Brain

Plaisier¹,

Pieterman

Lequin³

et al. 2014

American Journal of Neuroradiology

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“…Lab-developed software written in Matlab (MathWorks, Natick, MA) was used to derive the diffusion tensors independently for each voxel from the diffusion-weighted images using a weighted linear least-squares method (Koay et al, 2006). The eigenvalue decomposition was then applied to each tensor, yielding a set of eigenvalues (l 1 !…”

Section: In-vivo Diffusion Tensor Imaging Data Analysesmentioning

confidence: 99%

Diffusion Tensor Imaging at 3 Hours after Traumatic Spinal Cord Injury Predicts Long-Term Locomotor Recovery

Kim

Loy

Wang

et al. 2010

Journal of Neurotrauma

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Accurate diagnosis of spinal cord injury (SCI) severity must be achieved before highly aggressive experimental therapies can be tested responsibly in the early phases after trauma. These studies demonstrate for the first time that axial diffusivity (ljj), derived from diffusion tensor imaging (DTI) within 3 h after SCI, accurately predicts long-term locomotor behavioral recovery in mice. Female C57BL=6 mice underwent sham laminectomy or graded contusive spinal cord injuries at the T9 vertebral level (5 groups, n ¼ 8 for each group). In-vivo DTI examinations were performed immediately after SCI. Longitudinal measurements of hindlimb locomotor recovery were obtained using the Basso mouse scale (BMS). Injured and spared regions of ventrolateral white matter (VLWM) were reliably separated in the hyperacute phase by threshold segmentation. Measurements of ljj were compared with histology in the hyperacute phase and 14 days after injury. The spared normal VLWM determined by hyperacute ljj and 14-day histology correlated well (r ¼ 0.95). A strong correlation between hindlimb locomotor function recovery and ljj-determined spared normal VLWM was also observed. The odds of significant locomotor recovery increased by 18% with each 1% increase in normal VLWM measured in the hyperacute phase (odds ratio ¼ 1.18, p ¼ 0.037). The capability of measuring subclinical changes in spinal cord physiology and murine genetic advantages offer an early window into the basic mechanisms of SCI that was not previously possible. Although significant obstacles must still be overcome to derive similar data in human patients, the path to clinical translation is foreseeable and achievable.

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“…The linear leastsquares method is commonly used by vendors and is the default setting for commonly used DTI postprocessing software such as FSL (http://www.fmrib.ox.ac.uk/fsl) 55 ; however, this method proved to be the least appropriate to estimate the tensor because the method assumptions are restrictive and physically implausible. 56 Fig 3 shows the effect of the tensor estimation method for a dataset with few outliers and a dataset corrupted as the result of motion. The difference between the methods is clearly visible, with the ordinary least-squares method providing obviously erroneous results, whereas the weighted least-squares method, which only takes a fraction more computational time, provides more accurate results.…”

Section: Estimation Of the Tensormentioning

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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A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging

Cited by 221 publications

References 20 publications

Choice of Diffusion Tensor Estimation Approach Affects Fiber Tractography of the Fornix in Preterm Brain

Choice of Diffusion Tensor Estimation Approach Affects Fiber Tractography of the Fornix in Preterm Brain

Diffusion Tensor Imaging at 3 Hours after Traumatic Spinal Cord Injury Predicts Long-Term Locomotor Recovery

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

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