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Le M, Delingette H, Kalpathy-Cramer J, Gerstner ER, Batchelor T, Unkelbach J, Ayache N. MRI Based Bayesian Personalization of a Tumor Growth Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2329-2339. [PMID: 27164582 DOI: 10.1109/tmi.2016.2561098] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution. Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images of a given patient. The estimation of the posterior probability is based on: 1) a highly parallelized implementation of the reaction-diffusion equation using the Lattice Boltzmann Method (LBM), and 2) a high acceptance rate Monte Carlo technique called Gaussian Process Hamiltonian Monte Carlo (GPHMC). We compare this personalization approach with two commonly used methods based on the spherical asymptotic analysis of the reaction-diffusion model, and on a derivative-free optimization algorithm. We demonstrate the performance of the method on synthetic data, and on seven patients with a glioblastoma, the most aggressive primary brain tumor. This Bayesian personalization produces more informative results. In particular, it provides samples from the regions of interest and highlights the presence of several modes for some patients. In contrast, previous approaches based on optimization strategies fail to reveal the presence of different modes, and correlation between parameters.
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Kayvanpour E, Mansi T, Sedaghat-Hamedani F, Amr A, Neumann D, Georgescu B, Seegerer P, Kamen A, Haas J, Frese KS, Irawati M, Wirsz E, King V, Buss S, Mereles D, Zitron E, Keller A, Katus HA, Comaniciu D, Meder B. Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart. PLoS One 2015; 10:e0134869. [PMID: 26230546 PMCID: PMC4521877 DOI: 10.1371/journal.pone.0134869] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 07/14/2015] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. METHODS AND RESULTS State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. CONCLUSION This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.
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Affiliation(s)
- Elham Kayvanpour
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Tommaso Mansi
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Farbod Sedaghat-Hamedani
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Ali Amr
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Dominik Neumann
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Bogdan Georgescu
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Philipp Seegerer
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Ali Kamen
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Jan Haas
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Karen S. Frese
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Maria Irawati
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Emil Wirsz
- Siemens AG, Corporate Technology, Erlangen, Germany
| | - Vanessa King
- Siemens Corporation, Corporate Technology, Sensor Technologies, Princeton, New Jersey, United States of America
| | - Sebastian Buss
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Derliz Mereles
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Edgar Zitron
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Andreas Keller
- Biomarker Discovery Center Heidelberg, Heidelberg, Germany
- Department of Human Genetics, Saarland University, Homburg, Germany
| | - Hugo A. Katus
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
- Klaus Tschira Institute for Computational Cardiology, Heidelberg, Germany
| | - Dorin Comaniciu
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Benjamin Meder
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
- Klaus Tschira Institute for Computational Cardiology, Heidelberg, Germany
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Audigier C, Mansi T, Delingette H, Rapaka S, Mihalef V, Carnegie D, Boctor E, Choti M, Kamen A, Ayache N, Comaniciu D. Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1576-1589. [PMID: 30132760 DOI: 10.1109/tmi.2015.2406575] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Radiofrequency ablation (RFA) is an established treatment for liver cancer when resection is not possible. Yet, its optimal delivery is challenged by the presence of large blood vessels and the time-varying thermal conductivity of biological tissue. Incomplete treatment and an increased risk of recurrence are therefore common. A tool that would enable the accurate planning of RFA is hence necessary. This manuscript describes a new method to compute the extent of ablation required based on the Lattice Boltzmann Method (LBM) and patient-specific, pre-operative images. A detailed anatomical model of the liver is obtained from volumetric images. Then a computational model of heat diffusion, cellular necrosis, and blood flow through the vessels and liver is employed to compute the extent of ablated tissue given the probe location, ablation duration and biological parameters. The model was verified against an analytical solution, showing good fidelity. We also evaluated the predictive power of the proposed framework on ten patients who underwent RFA, for whom pre- and post-operative images were available. Comparisons between the computed ablation extent and ground truth, as observed in postoperative images, were promising (DICE index: 42%, sensitivity: 67%, positive predictive value: 38%). The importance of considering liver perfusion while simulating electrical-heating ablation was also highlighted. Implemented on graphics processing units (GPU), our method simulates 1 minute of ablation in 1.14 minutes, allowing near real-time computation.
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Talbot H, Marchesseau S, Duriez C, Sermesant M, Cotin S, Delingette H. Towards an interactive electromechanical model of the heart. Interface Focus 2014; 3:20120091. [PMID: 24427533 DOI: 10.1098/rsfs.2012.0091] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Accepted: 01/08/2013] [Indexed: 02/06/2023] Open
Abstract
In this work, we develop an interactive framework for rehearsal of and training in cardiac catheter ablation, and for planning cardiac resynchronization therapy. To this end, an interactive and real-time electrophysiology model of the heart is developed to fit patient-specific data. The proposed interactive framework relies on two main contributions. First, an efficient implementation of cardiac electrophysiology is proposed, using the latest graphics processing unit computing techniques. Second, a mechanical simulation is then coupled to the electrophysiological signals to produce realistic motion of the heart. We demonstrate that pathological mechanical and electrophysiological behaviour can be simulated.
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Affiliation(s)
- Hugo Talbot
- Shacra Team, Inria Lille - North Europe, Lille, France ; Asclepios Team, Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France
| | | | | | - Maxime Sermesant
- Asclepios Team, Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France
| | | | - Hervé Delingette
- Asclepios Team, Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France
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Zettinig O, Mansi T, Georgescu B, Kayvanpour E, Sedaghat-Hamedani F, Amr A, Haas J, Steen H, Meder B, Katus H, Navab N, Kamen A, Comaniciul D. Fast data-driven calibration of a cardiac electrophysiology model from images and ECG. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2013; 16:1-8. [PMID: 24505642 DOI: 10.1007/978-3-642-40811-3_1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in aproximately 3 s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5 ms for QRS duration and 2 degree for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization.
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Affiliation(s)
- Oliver Zettinig
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Tommaso Mansi
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Bogdan Georgescu
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Elham Kayvanpour
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Farbod Sedaghat-Hamedani
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Ali Amr
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Jan Haas
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Henning Steen
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Benjamin Meder
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Hugo Katus
- University Hospital Heidelberg, Department of Internal Medicine III-Cardiology, Angiology and Pneumology, Heidelberg, Germany
| | - Nassir Navab
- Computer Aided Medical Procedures, Technische Universitkit Miinchen, Germany
| | - Ali Kamen
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Dorin Comaniciul
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
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