Simulation and experimental verification of the diffusion in an anisotropic fiber phantom

Fieremans, Els; Deene, Yves De; Delputte, Steven; Ozdemir, Mahir; D’Asseler, Yves; Vlassenbroeck, Jelle; Deblaere, Karel; Achten, Eric; Lemahieu, Ignace

doi:10.1016/j.jmr.2007.10.014

Cited by 112 publications

(125 citation statements)

References 41 publications

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“…However, the measured FA value will depend on a variety of factors, including the voxel dimension [55] (larger voxels can average out some of the anisotropy if fibres are not exactly parallel), the amount of image noise [56] and the precise size and location of the ROI (since partial volume effects are quite strong); large within-centre variations have been reported [57]. Some anisotropic phantoms are under development [58,59], although these are currently quite complex to manufacture and are quite small (up to about 2 cm in diameter).…”

Section: Diffusionmentioning

confidence: 99%

Multicentre imaging measurements for oncology and in the brain

Tofts

Collins²

2011

BJR

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ABSTRACT. Multicentre imaging studies of brain tumours (and other tumour and brain studies) can enable a large group of patients to be studied, yet they present challenging technical problems. Differences between centres can be characterised, understood and minimised by use of phantoms (test objects) and normal control subjects. Normal white matter forms an excellent standard for some MRI parameters (e.g. diffusion or magnetisation transfer) because the normal biological range is low (,2-3%) and the measurements will reflect this, provided the acquisition sequence is controlled. MR phantoms have benefits and they are necessary for some parameters (e.g. tumour volume). Techniques for temperature monitoring and control are given. In a multicentre study or treatment trial, between-centre variation should be minimised. In a cross-sectional study, all groups should be represented at each centre and the effect of centre added as a covariate in the statistical analysis. In a serial study of disease progression or treatment effect, individual patients should receive all of their scans at the same centre; the power is then limited by the within-subject reproducibility. Sources of variation that are generic to any imaging method and analysis parameters include MR sequence mismatch, B 1 errors, CT effective tube potential, region of interest generation and segmentation procedure. Specific tissue parameters are analysed in detail to identify the major sources of variation and the most appropriate phantoms or normal studies. These include dynamic contrast-enhanced and dynamic susceptibility contrast gadolinium imaging, T 1 , diffusion, magnetisation transfer, spectroscopy, tumour volume, arterial spin labelling and CT perfusion. There have been many approaches to carrying out multicentre imaging studies. A common difficulty is a measurement made at one centre is often not reproducible at another centre and to pool measurements from several centres into a large trial reduces its statistical power. Yet there is a strong imperative to be able to carry out meaningful multicentre studies so clinical drug trials with a large number of subjects can take place in a reasonable time. Analysing the sources of intercentre variation, with insight into the MR physics aspects of the imaging process, and then reducing them where possible has allowed progress to be made. Measuring actual quantities, such as volume, relaxation time or transfer constant, has meant in principle we are able to obtain values independent of the scanner used and the particular way the measurement was carried out.Multicentre imaging studies are desirable for several reasons. 1. In clinical treatment trials, they are usually the only way of achieving the large number of patients needed to statistically power the study. 2. Measurement of good intercentre agreement demonstrates the imaging technique is good and the results of clinical or scientific research can be applied to other centres. 3. Good intercentre agreement also demonstrates that the physical factors involv...

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Section: Diffusionmentioning

confidence: 99%

Multicentre imaging measurements for oncology and in the brain

Tofts

Collins²

2011

BJR

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“…Software models have the advantage that they can be easily modified to account for different scanner parameters, image noise or artifacts. While much of the previous work has focused on simple phantoms in which fiber bundles were represented as cylindrical tubes or helices (see for example (Fieremans, De Deene, Delputte, Ozdemir, D'Asseler, Vlassenbroeck, Deblaere, Achten & Lemahieu, 2008;Lori et al, 2002)), in this section we suggest a framework in which it is possible to realistically model specific neural fiber bundles, simulating both the smooth transition between the actual white matter pathway and the surrounding tissue and the partial volume effects caused by the possible contemporary presence of white matter, grey matter and cerebrospinal fluid in one voxel. We focus on generating a phantom of the corticospinal tract.…”

Section: Assessing Fiber Tracking Accuracy Via Diffusion Tensor Softwmentioning

confidence: 99%

On the Reliability of Diffusion Neuroimaging

Klein¹,

Barbieri²,

Stuke³

et al. 2010

Neuroimaging

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“…Large efforts have been devoted to the development of physical phantoms (Lin et al, 2001;Campbell et al, 2005;Perrin et al, 2005;Fieremans et al, 2008;Tournier et al, 2008). Côté et al (2013) conducted a thorough review of tractography methodologies using the so-called FiberCup phantom (Poupon et al, 2008;Fillard et al, 2011).…”

Section: Introductionmentioning

confidence: 99%