In Silico Tumor Growth: Application to Glioblastomas

Clatz, Olivier; Bondiau, P.-Y.; Delingette, Hervé; Malandain, Grégoire; Sermesant, Maxime; Warfield, Simon K.; Ayache, Nicholas

doi:10.1007/978-3-540-30136-3_42

Cited by 25 publications

(22 citation statements)

References 7 publications

(12 reference statements)

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“…Choosing the Young modulus for the brain tissue and assuming slow and small deformations ( 10%), we have shown that the maximum error measured on the Young modulus with respect to the state of the art brain constitutive equation [11] is less than 7% [40]. We chose a Poisson's ratio , modeling an almost incompressible brain tissue.…”

Section: ) Segmentationmentioning

confidence: 99%

Robust nonrigid registration to capture brain shift from intraoperative MRI

Clatz

Delingette

Talos

et al. 2005

IEEE Trans. Med. Imaging

218

214

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Abstract-We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue.To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.Index Terms-Brain shift, finite element model, intraoperative magnetic resonance imaging, nonrigid registration.

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Section: ) Segmentationmentioning

confidence: 99%

Robust nonrigid registration to capture brain shift from intraoperative MRI

Clatz

Delingette

Talos

et al. 2005

IEEE Trans. Med. Imaging

218

214

View full text Add to dashboard Cite

show abstract

“…These models utilize mathematical tools such as reaction-diffusion equations [13], mechanical models [13], or cellular automation [14], and provide a remarkable insight into the growth process. The paper by Swanson et al [15] and the book by Chaplain [16] describe several of the existing models used in studying tumor growth.…”

Section: A Comparison With Past Workmentioning

confidence: 99%

“…We seek a growth model capable of modeling growth in a variety of anatomical parts, including tumors. Our approach can lead to a stochastic differential equation modeling local deformations, similar to the diffusion model of [13], but our framework allows for a more efficient representation of a wider class of deformation patterns. Ling and He [17] have studied certain biological growth models using entropy considerations.…”

Section: A Comparison With Past Workmentioning

confidence: 99%

A Pattern-Theoretic Characterization of Biological Growth

Grenander

Srivastava

Saini

2007

IEEE Trans. Med. Imaging

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Abstract-Mathematical and statistical modeling of biological growth is an important problem in medical diagnostics. Here, we seek tools to analyze changes in anatomical parts using images collected over time. We introduce a structured model, called Growth by Random Iterated Diffeomorphisms (GRID), that treats a cumulative growth deformation as a composition of several elementary deformations. Each elementary deformation applies to a small region by capturing deformation local to that region and is characterized by a seed and a radial deformation pattern around that seed. These GRID variables-seed locations and radial deformation patterns-are estimated from observed images in two steps: 1) estimate a cumulative deformation over an observation interval; 2) estimate GRID variables using maximum-likelihood criterion from this estimated cumulative deformation. We demonstrate this framework using an MRI image data of a rat's brain growth. For future statistical analysis, we propose a time-varying Poisson process for the seed placements and a random drawing from a predetermined catalog of deformations for the radial deformation patterns.

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“…We use the registered and reoriented DT-MRI to simulate the tissue infiltration process, similar to the approach done by Clatz et al [7,8]. However, registration and reorientation are generally insufficient to account for the mass effect.…”

Section: Tumor and Edema Infiltrationmentioning

confidence: 99%

Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation

Prastawa¹,

Bullitt²,

Gerig

2005

Lecture Notes in Computer Science

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Abstract. Validation and method of comparison for segmentation of magnetic resonance images (MRI) presenting pathology is a challenging task due to the lack of reliable ground truth. We propose a new method for generating synthetic multi-modal 3D brain MRI with tumor and edema, along with the ground truth. Tumor mass effect is modeled using a biomechanical model, while tumor and edema infiltration is modeled as a reaction-diffusion process that is guided by a modified diffusion tensor MRI. We propose the use of warping and geodesic interpolation on the diffusion tensors to simulate the displacement and the destruction of the white matter fibers. We also model the process where the contrast agent tends to accumulate in cortical csf regions and active tumor regions to obtain contrast enhanced T1w MR image that appear realistic. The result is simulated multi-modal MRI with ground truth available as sets of probability maps. The system will be able to generate large sets of simulation images with tumors of varying size, shape and location, and will additionally generate infiltrated and deformed healthy tissue probabilities.

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In Silico Tumor Growth: Application to Glioblastomas

Cited by 25 publications

References 7 publications

Robust nonrigid registration to capture brain shift from intraoperative MRI

Robust nonrigid registration to capture brain shift from intraoperative MRI

A Pattern-Theoretic Characterization of Biological Growth

Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation

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