Multicenter stability of diffusion tensor imaging measures: A European clinical and physical phantom study

Teipel, Stefan J.; Reuter, Sigrid; Stieltjes, Bram; Acosta‐Cabronero, Julio; Ernemann, Ulrike; Fellgiebel, Andreas; Filippi, Massimo; Frisoni, Giovanni B.; Hentschel, Frank; Jessen, Frank; Klöppel, Stefan; Meindl, Thomas; Pouwels, Petra J. W.; Kh, Hauenstein; Hampel, Harald

doi:10.1016/j.pscychresns.2011.05.012

Cited by 100 publications

(116 citation statements)

References 32 publications

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“…However, these first monocentric studies are generally limited by relatively small sample sizes, and diagnostic and prognostic findings within the highly controlled experimental conditions of these 152 S. Teipel et al studies, such as uniform DTI acquisition protocols and selected patient populations, will probably not translate seamlessly into the broader clinical context. Only recently, multicenter acquisition and analysis of DTI data have begun to be explored in the framework of the European DTI Study on Dementia (EDSD) (Teipel, Reuter, et al, 2011). Using a physical and clinical phantom study, the EDSD showed an at least 50% increase of between scanner variability compared to anatomical MRI acquisitions (Teipel, Reuter, et al, 2011).…”

Section: Contribution Of Multimodal Imaging Of Connectivity To Diagnomentioning

confidence: 99%

Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications

Teipel

Grothe

Zhou

et al. 2016

J Int Neuropsychol Soc

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Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138-163)

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Section: Contribution Of Multimodal Imaging Of Connectivity To Diagnomentioning

confidence: 99%

Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications

Teipel

Grothe

Zhou

et al. 2016

J Int Neuropsychol Soc

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“…With many centers contributing relatively few patients, variability between sites must be minimized. There have been several reports comparing diffusion measurements in multicenter studies 5 , 6 , 7 , 8 documenting significant variability for scanners of the same model using essentially identical technique and parameters. Many aspects of scanner performance have been reported to affect the measurement results and introduce discrepancies in the measurement (14) .…”

Section: Discussionmentioning

confidence: 99%

“…Repeatability and reproducibility data were not collected from sites participating in this study. Therefore, it is difficult to assess whether the variability of all the metrics is due to the systematic difference of machines from different vendors, as mentioned in the studies by Volimar et al, Zhu et al, Fox et al, and Teipel et al, 5 , 6 , 7 , 8 or due to random errors in the MRI measurements. Since the goal of this study was to evaluate a QC methodology for multicenter trial, knowing under what circumstance the site needs to undergo hardware/software adjustment is important.…”

Section: Discussionmentioning

confidence: 99%

“…These technique‐related effects may both mask and mimic biological changes in white matter. There have been several reports comparing diffusion measurements in multicenter studies 5 , 6 , 7 , 8 documenting significant variability for scanners of the same model using essentially identical technique and parameters. Conducting a multicenter study using quantitative analysis of diffusion tensor data requires a uniform quality control procedure to ensure that: (i) data from different sites are not biased and can be combined for statistical analysis; and (ii) data of substandard quality are excluded.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of diffusion tensor image quality across sites and vendors using the American College of Radiology head phantom

Wang

Seo

Huang

et al. 2016

J Applied Clin Med Phys

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The purpose of this study was to explore the feasibility of assessing quality of diffusion tensor imaging (DTI) from multiple sites and vendors using American College of Radiology (ACR) phantom. Participating sites (Siemens false(n=2false), GE false(n=2false), and Philips false(n=4false)) reached consensus on parameters for DTI and used the widely available ACR phantom. Tensor data were processed at one site. B0 and eddy current distortions were assessed using grid line displacement on phantom Slice 5; signal‐to‐noise ratio (SNR) was measured at the center and periphery of the b=0 image; fractional anisotropy (FA) and mean diffusivity (MD) were assessed using phantom Slice 7. Variations of acquisition parameters and deviations from specified sequence parameters were recorded. Nonlinear grid line distortion was higher with linear shimming and could be corrected using the 2nd order shimming. Following image registration, eddy current distortion was consistently smaller than acquisition voxel size. SNR was consistently higher in the image periphery than center by a factor of 1.3–2.0. ROI‐based FA ranged from 0.007 to 0.024. ROI‐based MD ranged from 1.90×10−3 to 2.33×10−3mm2/sfalse(median=2.04×10−3mm2/sfalse). Two sites had image void artifacts. The ACR phantom can be used to compare key quality measures of diffusion images acquired from multiple vendors at multiple sites.PACS number(s): 87.57.‐s, 87.19.lf

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“…Many researchers have tried to investigate the reproducibility and reliability of the acquired networks through different imaging protocols [139,149,150] , different imaging sites [151,152] , and different parameters for constructing networks [19,153] . However, this is still an open question for the Brainnetome era [12] .…”

Section: Future Directions Of Dmri For Brainnetomementioning

confidence: 99%

Diffusion magnetic resonance imaging for Brainnetome: A critical review

Zuo¹,

Cheng²,

Jiang³

2012

Neurosci. Bull.

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Abstract:Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of smallworldness, hierarchy and modularity. The "connectome" was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the "Brainnetome" (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.

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Multicenter stability of diffusion tensor imaging measures: A European clinical and physical phantom study

Cited by 100 publications

References 32 publications

Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications

Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications

Assessment of diffusion tensor image quality across sites and vendors using the American College of Radiology head phantom

Diffusion magnetic resonance imaging for Brainnetome: A critical review

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