525 research outputs found
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
The generation of virtual populations (VPs) of anatomy is essential for
conducting in silico trials of medical devices. Typically, the generated VP
should capture sufficient variability while remaining plausible and should
reflect the specific characteristics and demographics of the patients observed
in real populations. In several applications, it is desirable to synthesise
virtual populations in a \textit{controlled} manner, where relevant covariates
are used to conditionally synthesise virtual populations that fit a specific
target population/characteristics. We propose to equip a conditional
variational autoencoder (cVAE) with normalising flows to boost the flexibility
and complexity of the approximate posterior learnt, leading to enhanced
flexibility for controllable synthesis of VPs of anatomical structures. We
demonstrate the performance of our conditional flow VAE using a data set of
cardiac left ventricles acquired from 2360 patients, with associated
demographic information and clinical measurements (used as
covariates/conditional information). The results obtained indicate the
superiority of the proposed method for conditional synthesis of virtual
populations of cardiac left ventricles relative to a cVAE. Conditional
synthesis performance was evaluated in terms of generalisation and specificity
errors and in terms of the ability to preserve clinically relevant biomarkers
in synthesised VPs, that is, the left ventricular blood pool and myocardial
volume, relative to the real observed population.Comment: Accepted at MICCAI 202
Double Diffusion Encoding Prevents Degeneracy in Parameter Estimation of Biophysical Models in Diffusion MRI
Purpose: Biophysical tissue models are increasingly used in the
interpretation of diffusion MRI (dMRI) data, with the potential to provide
specific biomarkers of brain microstructural changes. However, the general
Standard Model has recently shown that model parameter estimation from dMRI
data is ill-posed unless very strong magnetic gradients are used. We analyse
this issue for the Neurite Orientation Dispersion and Density Imaging with
Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from
Single Diffusion Encoding (SDE) to Double Diffusion Encoding (DDE) solves the
ill-posedness and increases the accuracy of the parameter estimation. Methods:
We analyse theoretically the cumulant expansion up to fourth order in b of SDE
and DDE signals. Additionally, we perform in silico experiments to compare SDE
and DDE capabilities under similar noise conditions. Results: We prove
analytically that DDE provides invariant information non-accessible from SDE,
which makes the NODDIDA parameter estimation injective. The in silico
experiments show that DDE reduces the bias and mean square error of the
estimation along the whole feasible region of 5D model parameter space.
Conclusions: DDE adds additional information for estimating the model
parameters, unexplored by SDE, which is enough to solve the degeneracy in the
NODDIDA model parameter estimation.Comment: 22 pages, 7 figure
Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease
The estimation of disease progression in Alzheimer’s disease
(AD) based on a vector of quantitative biomarkers is of high interest
to clinicians, patients, and biomedical researchers alike. In this work,
quantile regression is employed to learn statistical models describing the
evolution of such biomarkers. Two separate models are constructed using
(1) subjects that progress from a cognitively normal (CN) stage to mild
cognitive impairment (MCI) and (2) subjects that progress from MCI
to AD during the observation window of a longitudinal study. These
models are then automatically combined to develop a multi-stage disease
progression model for the whole disease course. A probabilistic approach
is derived to estimate the current disease progress (DP) and the disease
progression rate (DPR) of a given individual by fitting any acquired
biomarkers to these models. A particular strength of this method is that
it is applicable even if individual biomarker measurements are missing
for the subject. Employing cognitive scores and image-based biomarkers,
the presented method is used to estimate DP and DPR for subjects from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the
potential use of these values as features for different classification tasks
is demonstrated. For example, accuracy of 64% is reached for CN vs.
MCI vs. AD classification
Construction of a Statistical Atlas of the Whole Heart from a Large 4D CT Database
International audienceWe present in this work an efficient and robust framework for the construction of a high-resolution and spatio-temporal atlas of the whole heart from a database of 138 CT 4D images, the largest sample to be used for cardiac statistical modeling to date. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. In the proposed technique, spatial and temporal normalization based on non-rigid image registration are used to synthesize a population mean image from all CT image. With the resulting transformation, a detailed 3D mesh representation of the atlas is warped to fit all images in each subject and phase. The obtained level of anatomical detail (a total of 13 cardiac structures) and the extendability of the atlas present an advantage over most existing cardiac models published previously
Constrained manifold learning for the characterization of pathological deviations from normality
International audienceThis paper describes a technique to (1) learn the representation of a pathological motion pattern from a given population, and (2) compare individuals to this population. Our hypothesis is that this pattern can be modeled as a deviation from normal motion by means of non-linear embedding techniques. Each subject is represented by a 2D map of local motion abnormalities, obtained from a statistical atlas of myocardial motion built from a healthy population. The algorithm estimates a manifold from a set of patients with varying degrees of the same disease, and compares individuals to the training population using a mapping to the manifold and a distance to normality along the manifold. The approach extends recent manifold learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Interpolation techniques using locally adjustable kernel improve the accuracy of the method. The technique is applied in the context of cardiac resynchronization therapy (CRT), focusing on a specific motion pattern of intra-ventricular dyssynchrony called septal flash (SF). We estimate the manifold from 50 CRT candidates with SF and test it on 37 CRT candidates and 21 healthy volunteers. Experiments highlight the relevance of nonlinear techniques to model a pathological pattern from the training set and compare new individuals to this pattern
A Generative Shape Compositional Framework: Towards Representative Populations of Virtual Heart Chimaeras
Generating virtual populations of anatomy that capture sufficient variability
while remaining plausible is essential for conducting in-silico trials of
medical devices. However, not all anatomical shapes of interest are always
available for each individual in a population. Hence,
missing/partially-overlapping anatomical information is often available across
individuals in a population. We introduce a generative shape model for complex
anatomical structures, learnable from datasets of unpaired datasets. The
proposed generative model can synthesise complete whole complex shape
assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We
applied this framework to build virtual chimaeras from databases of whole-heart
shape assemblies that each contribute samples for heart substructures.
Specifically, we propose a generative shape compositional framework which
comprises two components - a part-aware generative shape model which captures
the variability in shape observed for each structure of interest in the
training population; and a spatial composition network which assembles/composes
the structures synthesised by the former into multi-part shape assemblies (viz.
virtual chimaeras). We also propose a novel self supervised learning scheme
that enables the spatial composition network to be trained with partially
overlapping data and weak labels. We trained and validated our approach using
shapes of cardiac structures derived from cardiac magnetic resonance images
available in the UK Biobank. Our approach significantly outperforms a PCA-based
shape model (trained with complete data) in terms of generalisability and
specificity. This demonstrates the superiority of the proposed approach as the
synthesised cardiac virtual populations are more plausible and capture a
greater degree of variability in shape than those generated by the PCA-based
shape model.Comment: 15 pages, 4 figure
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