On the estimation and correction of bias in local atrophy estimations using example atrophy simulations

Sharma, Swati; Rousseau, François; Heitz, Fabrice; Rumbach, L.; Armspach, Jean‐Paul

doi:10.1016/j.compmedimag.2013.07.002

Cited by 8 publications

(11 citation statements)

References 32 publications

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“…Since the cortical surface lying near the skull is an important area for AD, it is desirable not to have error in the obtained Jacobian in these areas. Finally, when only volume loss is prescribed, as seems to be the case in the evaluation experiments of [8,10], it is not clear which regions of the brain expand to compensate for the volume loss since the volume within the skull must be constant when skull invariance is imposed. The spatial distribution of the resulting non-zero error in the desired vs. obtained Jacobian map is not easy to control in this case.…”

Section: Introductionmentioning

confidence: 99%

“…Concerning the realistic MR image generation block, previous works in [14,8,10] provide an interesting framework for adding different kinds of intensity noise on the simulated images for the benchmarking of atrophy measurements tools. Even though this is a desirable component of a generic atrophy simulation tool, intensity noise accounts only for a small part of the variability of atrophy measurement tools.…”

Section: Introductionmentioning

confidence: 99%

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A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease

et al. 2016

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We propose a framework for developing a comprehensive biophysical model that could predict and simulate realistic longitudinal MRIs of patients with Alzheimer's disease (AD). The framework includes three major building blocks: i) atrophy generation, ii) brain deformation, and iii) realistic MRI generation. Within this framework, this paper focuses on a detailed implementation of the brain deformation block with a carefully designed biomechanics-based tissue loss model. For a given baseline brain MRI, the model yields a deformation field imposing the desired atrophy at each voxel of the brain parenchyma while allowing the CSF to expand as required to globally compensate for the locally prescribed volume loss. Our approach is inspired by biomechanical principles and involves a system of equations similar to Stokes equations in fluid mechanics but with the presence of a non-zero mass source term. We use this model to simulate longitudinal MRIs by prescribing complex patterns of atrophy. We present experiments that provide an insight into the role of different biomechanical parameters in the model. The model allows simulating images with exactly the same tissue atrophy but with different underlying deformation fields in the image. We explore the influence of different spatial distributions of atrophy on the image appearance and on the measurements of atrophy reported by various global and local atrophy estimation algorithms. We also present a pipeline that allows evaluating atrophy estimation algorithms by simulating longitudinal MRIs from large number of real subject MRIs with complex subject-specific atrophy patterns. The proposed framework could help understand the implications of different model assumptions, regularization choices, and spatial priors for the detection and measurement of brain atrophy from longitudinal brain MRIs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease

et al. 2016

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“…Such simulators have been used for the validation of registration or segmentation based atrophy estimation algorithms (Camara et al, 2008; Pieperhoff et al, 2008; Sharma et al, 2010), to estimate the bias in such algorithms, and also to estimate uncertainty in the measured atrophy (Sharma et al, 2013). These studies have estimated the bias by simulating simple atrophy patterns in a small number of brain regions or uniform diffused global atrophies.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation.

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“…Atrophy simulators [11] [5] have been proposed in the literature and used mostly for the validation of registration or segmentation methods [4] [16], or to estimate uncertainty in the measured atrophy [17]. The simulators in [11][14] [16] use a Jacobian based methods where the desired level of atrophy is set at each voxel, and the deformation that best approximates the desired level of atrophy is found.…”

Section: Introductionmentioning

confidence: 99%

Simulating Patient Specific Multiple Time-Point MRIs from a Biophysical Model of Brain Deformation in Alzheimer’s Disease

Khanal

Lorenzi

Ayache

et al. 2016

Computational Biomechanics for Medicine

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This paper proposes a framework to simulate patient specific structural Magnetic Resonance Images (MRIs) from the available MRI scans of Alzheimer's Disease(AD) subjects. We use a biophysical model of brain deformation due to atrophy that can generate biologically plausible deformation for any given desired volume changes at the voxel level of the brain MRI. Large number of brain regions are segmented in 45 AD patients and the atrophy rates per year are estimated in these regions from the available two extremal time-point scans. Assuming linear progression of atrophy, the volume changes in scans closest to the half way time period is computed. These atrophy maps are prescribed to the baseline images to simulate the middle time-point images by using the biophysical model of brain deformation. From the baseline scans, the volume changes in real middle time-point scans are compared to the ones in simulated middle time-point images. This present framework also allows to introduce desired atrophy patterns at different time-points to simulate non-linear progression of atrophy. This opens a way to use a biophysical model of brain deformation to evaluate methods that study the temporal progression and spatial relationships of atrophy of different regions in the brain with AD.

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On the estimation and correction of bias in local atrophy estimations using example atrophy simulations

Cited by 8 publications

References 32 publications

A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease

A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease

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

Simulating Patient Specific Multiple Time-Point MRIs from a Biophysical Model of Brain Deformation in Alzheimer’s Disease

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