Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity

Khanal, Bishesh; Ayache, Nicholas; Pennec, Xavier

doi:10.3389/fnins.2017.00132

Cited by 13 publications

(16 citation statements)

References 50 publications

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“…To tackle this problem, a few simulators have been proposed in the literature [3,4,8,6]. The recent SimulAtrophy [5] uses a computational model based on fluid mechanics. This approach combines two deformation fields: one from a biophysical model and the other obtained by non-rigid registration of two real images.…”

Section: Introductionmentioning

confidence: 99%

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

Ravì

Alexander

Oxtoby

et al. 2019

Lecture Notes in Computer Science

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Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited by imposing a parametric model for atrophy and are extremely resource-demanding. Recent advances in deep learning have yielded datadriven models for longitudinal studies (e.g., face ageing) that are capable of generating synthetic images in real-time. Similar solutions can be used to model trajectories of atrophy in the brain, although new challenges need to be addressed to ensure accurate disease progression modelling. Here we propose Degenerative Adversarial NeuroImage Net (DaniNet) -a new deep learning approach that learns to emulate the effect of neurodegeneration on MRI by simulating atrophy as a function of ages, and disease progression. DaniNet uses an underlying set of Support Vector Regressors (SVRs) trained to capture the patterns of regional intensity changes that accompany disease progression. DaniNet produces whole output images, consisting of 2D-MRI slices that are constrained to match regional predictions from the SVRs. DaniNet is also able to maintain the unique brain morphology of individuals. Adversarial training ensures realistic brain images and smooth temporal progression. We train our model using 9652 T1-weighted (longitudinal) MRI extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We perform quantitative and qualitative evaluations on a separate test set of 1283 images (also from ADNI) demonstrating the ability of Da-niNet to produce accurate and convincing synthetic images that emulate disease progression.*Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at

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Section: Introductionmentioning

confidence: 99%

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

Ravì

Alexander

Oxtoby

et al. 2019

Lecture Notes in Computer Science

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“…Whether one or the other is more accurate or reflective of the actual pathology is unclear and out of the scope of this work. In this regard, we suggest using atrophy generator pipelines in the future to have a sense of ground truth [30][31][32]. Second, we only used one method for assessing brain volume change, and thus it is difficult to generalise our findings to other medical image analysis tools.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging

Carvajal-Camelo¹,

Bernal²,

Oliver³

et al. 2021

Applied Sciences

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Atrophy quantification is fundamental for understanding brain development and diagnosing and monitoring brain diseases. FSL-SIENA is a well-known fully automated method that has been widely used in brain magnetic resonance imaging studies. However, intensity variations arising during image acquisition may compromise evaluation, analysis and even diagnosis. In this work, we studied whether intensity standardisation could improve longitudinal atrophy quantification using FSL-SIENA. We evaluated the effect of six intensity standardisation methods—z-score, fuzzy c-means, Gaussian mixture model, kernel density estimation, histogram matching and WhiteStripe—on atrophy detected by FSL-SIENA. First, we evaluated scan–rescan repeatability using scans taken during the same session from OASIS (n=122). Except for WhiteStripe, intensity standardisation did not compromise the scan–rescan repeatability of FSL-SIENA. Second, we compared the mean annual atrophy for Alzheimer’s and control subjects from OASIS (n=122) and ADNI (n=147) yielded by FSL-SIENA with and without intensity standardisation, after adjusting for covariates. Our findings were threefold: First, the use of histogram matching was counterproductive, primarily as its assumption of equal tissue proportions does not necessarily hold in longitudinal studies. Second, standardising with z-score and WhiteStripe before registration affected the registration performance, thus leading to erroneous estimates. Third, z-score was the only method that consistently led to increased effect sizes compared to when omitted (no standardisation: 0.39 and 0.43 for OASIS and ADNI; z-score: 0.45 for both datasets). Overall, we found that incorporating z-score right after registration led to reduced inter-subject inter-scan intensity variability and benefited FSL-SIENA. Our work evinces the relevance of appropriate intensity standardisation in longitudinal cerebral atrophy assessments using FSL-SIENA.

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“…Current MRI simulators can be divided into two categories: i) biomechanical/physics-based models which describe the brain deformations in mechanical terms such as strain, displacement and stress. These models consider geometry, boundary conditions, loading, and material properties in their definition ( Miller, Joldes, Bourantas, Warfield, Hyde, Kikinis, Wittek, 2019 , Khanal, Ayache, Pennec, 2017 ); ii) data-driven/learning-based models capable of understanding and predicting disease progression. These approaches often use machine learning, including deep learning techniques to distil information from big data ( Ravìet al., 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

Ravì

Blumberg

Ingala

et al. 2022

Medical Image Analysis

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Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity

Cited by 13 publications

References 50 publications

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging

Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

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