Basic MR sequence parameters systematically bias automated brain volume estimation

Haller, Sven; Falkovskiy, Pavel; Meuli, Reto; Thiran, Jean‐Philippe; Krueger, Gunnar; Lövblad, Karl‐Olof; Kober, Tobias; Roche, Alexis; Maréchal, Bénédicte

doi:10.1007/s00234-016-1737-3

Cited by 23 publications

(20 citation statements)

References 23 publications

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“…However, the agreement in terms of absolute volumes varies with acquisition protocols and field strength. For example, change in voxel size can lead to systematic errors in the range of 5% for hippocampal volume [ 181 ]. Methods to correct for these variabilities are being investigated [ 86 ].…”

Section: Imaging As An Outcome Measure In Trialsmentioning

confidence: 99%

Secondary prevention of Alzheimer’s dementia: neuroimaging contributions

et al. 2018

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BackgroundIn Alzheimer’s disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations.Main bodyAmyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation.ConclusionWe propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.Electronic supplementary materialThe online version of this article (10.1186/s13195-018-0438-z) contains supplementary material, which is available to authorized users.

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Section: Imaging As An Outcome Measure In Trialsmentioning

confidence: 99%

Secondary prevention of Alzheimer’s dementia: neuroimaging contributions

et al. 2018

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“…Finally, recent work ( Haller et al, 2016 ) has shown that protocol specific MR parameters can systematically bias the results of automated volume estimation of a number of brain structures by 4–5%. We must therefore consider the possibility of a similar effect could being present when estimating lesion volume.…”

Section: Resultsmentioning

confidence: 99%

Brain lesion segmentation through image synthesis and outlier detection

Bowles

Chen

Guerrero

et al. 2017

NeuroImage: Clinical

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Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.

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“…10,11 Voxel-based morphometry has been used successfully to investigate a wide number of disorders such as Alzheimer's disease (AD), 12 Parkinson's disease, 13 depression, 14 and multiple sclerosis. 15 Clinical application of these methods would appear promising in the assessment of neurodegenerative conditions; automated brain morphometry is increasingly recognized as a biomarker for AD 16 and atrophy measurement from serial scans is an attractive way to characterize diseases such as multiple sclerosis. 17,18 However, while a number of these sophisticated methods of analysis are available to quantify local and global atrophy from MRI, relatively little progress has been made to integrate these into clinical workflows due to special hardware requirements, prohibitively long processing times and dependency on specific acquisition techniques.…”

Section: Introductionmentioning

confidence: 99%

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Holmes¹,

Negus²,

Wiltshire³

et al. 2020

Medical Physics

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Purpose: Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast. Methods: To investigate these issues we have created a three-dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom. Results: Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9-35.8 phantom, 29.9-34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other. Conclusion: The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

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Basic MR sequence parameters systematically bias automated brain volume estimation

Cited by 23 publications

References 23 publications

Secondary prevention of Alzheimer’s dementia: neuroimaging contributions

Secondary prevention of Alzheimer’s dementia: neuroimaging contributions

Brain lesion segmentation through image synthesis and outlier detection

Creation of an anthropomorphic CT head phantom for verification of image segmentation

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