A framework for scalable biophysics-based image analysis

Gholami, Amir; Mang, Andreas; Scheufele, Klaudius; Davatzikos, Christos; Mehl, Miriam; Biros, George

doi:10.1145/3126908.3126930

Cited by 18 publications

(97 citation statements)

References 49 publications

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“…In what follows, we describe the main building blocks of our formulation, our solver, and its implementation, and introduce new features that distinguish this work from our former work on constrained diffeomorphic image registration [47,[82][83][84][85]87]. We use a globalized preconditioned, inexact, reduced space Gauss-Newton-Krylov method to solve (1).…”

Section: Methodsmentioning

confidence: 99%

“…The hyperbolic transport equations that appear in our formulation are integrated using a semi-Lagrangian method [41,117]. Our solver uses distributed-memory parallelism and can be scaled up to thousands of cores [47,83]. The linear solvers and the Gauss-Newton optimizer are built on top of PETSc [14] and TAO [98].…”

mentioning

confidence: 99%

“…17. high-order numerical methods (second order time integration, cubic interpolation, and spectral differentiation). Our solver uses MPI for parallelism and has been deployed to high performance platforms [47,83]. This allows us to target applications of unprecedented scale such as CLARITY imaging [120], a novel optical imaging technique that delivers sub-micron resolution.…”

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confidence: 99%

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CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

Mang

Gholami

Davatzikos

et al. 2019

SIAM J. Sci. Comput.

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112

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We introduce CLAIRE, a distributed-memory algorithm and software for solving constrained large deformation diffeomorphic image registration problems in three dimensions. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss-Newton-Krylov scheme.We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousand of distributed memory nodes on high-end clusters. Our improved, parallel implementation features parameter-, scale-, and grid-continuation schemes to speedup the computations and reduce the likelihood to get trapped in local minima. We also implement an improved preconditioner for the reduced space Hessian to speedup the convergence.We test registration performance on synthetic and real data. We demonstrate registration accuracy on 16 neuroimaging datasets. We compare the performance of our scheme against different flavors of the DEMONS algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5× with a peak performance of up to 17× compared to our former work. Introduction.Deformable registration is a key technology in the medical imaging. It is about computing a map y that establishes a meaningful spatial correspondence between two (or more) images m R (the reference (fixed) image) and m T (the template (deformable or moving) image; image to be registered) of the same scene [43,96]. Numerous approaches for formulating and solving image registration problems have appeared in the past; we refer to [43,60,96,97,116] for lucid overviews. Image registration is typically formulated as a variational optimization problem that consists of a data fidelity term and a Tikhonov regularization functional to over-come ill-posedness [40,43]. A key concern is that y is a diffeomorphism, i.e., the map y is differentiable, a bijection, and has a differentiable inverse. We require that the determinant of the deformation gradient det ∇y does not vanish or change sign. An intuitive approach to safeguard against nondiffeomorphic maps y is to add hard and/or soft constraints on det ∇y to the variational optimization problem [29,58,103,108]. An alternative strategy to ensure regularity is to introduce a pseudo-time variable t and invert for a smooth velocity field v that encodes the map y [16,37,94,126]; existence of a diffeomorphism y can be guaranteed if v is adequately smooth [16,30,37,121]. This model is, as opposed to many traditional formulations that directly invert for y [58,88,95,108], adequate for recovering large, highly nonlinear deformations. Our approach falls into this category. T...

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

Mang

Gholami

Davatzikos

et al. 2019

SIAM J. Sci. Comput.

Self Cite

112

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“…This is primarily due to the rich initial conditions produced by the L 2 solver which impedes its ability to predict the correct reaction scaling using our method. We note that if the reaction scaling is known beforehand, the L 2 solver can potentially have better performance (see [16,56] for similar synthetic experiments). This problem is magnified for larger tumors (AT-C2 and AT-C3) where the predicted reaction coefficient shows about 66% and 54% relative error in the L 2 solver for the two test-cases respectively (as compared to around 15% and 1% error with sparsity constraints).…”

Section: Test Casementioning

confidence: 99%

“…Setup: Since we do not have access to the healthy patient brain, we cannot resort to direct inversion of model parameters. There have been many approaches in literature (see [16,56] for a comprehensive review) to approximate the healthy brain through methods such as diffeomorphic image registration and registration-tumor coupled optimization formulations. While such techniques are necessary to capture the healthy patient brain, we do not pursue them since this test-case is intended to primarily serve as an illustration.…”

Section: Real Tumor (Rt) Test-casementioning

confidence: 99%

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

et al. 2020

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We present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction-diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of Magnetic Resonance Imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver. Related work.Although there has been a lot of work on forward problems for tumor growth, there has been less work on inverse problems. The latter has different aspects. The first is the underlying biophysical model. The second is the inverse problem setup, observation operators and the existence of scans at multiple points, the noise models, inversion parameters and constraints. And the third one is the solution algorithm.Regarding the underlying model, like us, most researchers focus on parameter calibration of a handful of model parameters using single-species reaction-diffusion equations [7,22,24,28,33,39,51,58,59]. While more complex models describing processes like mass effect, angiogenesis and chemotaxis [23,26,48,54,60] exist, they have not been considered for calibration due to theoretical and computational challenges. However, several groups, including ours, are working to address these challenges.Regarding the inverse problem setup, in most studies t...

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Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect

Subramanian

Scheufele

Himthani

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models-including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.Index Terms-tumor growth model personalization, inverse problem, mass effect, glioblastoma (Preprint submitted to IEEE Transactions on Medical Imaging) I. INTRODUCTIONComputational oncology is an emerging field that attempts to integrate biophysical models with imaging with the goal of assisting image analysis in a clinical setting. Typically, the integration is accomplished by calibrating PDE model parameters from images. A significant challenge in brain tumors, and in particular, GBMs, is the single-scan calibration, that becomes even harder in the presence of mass effect. However, modeling provides the capability for automatic quantification of mass effect along with additional biomarkers related to

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A framework for scalable biophysics-based image analysis

Cited by 18 publications

References 49 publications

CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect

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