Ashraf Mohamed

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A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on(More)
An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the(More)
Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To(More)
We present an approach for the automatic generation of patient-specific tetrahedral finite-element (FE) meshes from multiple-label segmented medical images. The approach uses a mesh refinement method with guaranteed tetrahedral element quality and includes a post-processing step with operations to change the mesh topol-ogy. Results indicate good(More)
An approach for estimating the deformation of the prostate caused by transrectal ultrasound (TRUS) probe insertion is presented. This work is particularly useful during brachytherapy procedures, in which planning for radioactive seed insertion is performed on preopera-tive scans, and significant deformation of the prostate can occur during the procedure.(More)
A deformable registration method is proposed to register a brain atlas with tumor-bearing brain scans. The tumor mass effect is first simulated in the (normal) atlas, using a biomechanical model of mass effect. The tumor-bearing atlas is subsequently warped to the patient's scan by a deformable registration method, built upon the idea of HAMMER registration(More)
BACKGROUND AND PURPOSE We evaluated several hemodynamic parameters for the prediction of rupture in a data set of initially unruptured aneurysms, including aneurysms that ruptured during follow-up observation. METHODS Aneurysm geometry was extracted from CT angiographic images and analyzed using a mathematical formula for fluid flow under pulsatile blood(More)
A general statistical approach for predicting anatomical deformations is presented. Emphasis in this paper is on estimating deformations induced in the brain anatomy due to tumor growth. The presented approach utilizes the principal modes of co-variation between deformed (after tumor growth) and undeformed (before tumor growth) anatomy to estimate one given(More)
We consider the problem of clustering neural fiber pathways, produced from diffusion MRI data via tractography, into different bundles. Existing clustering methods often suffer from the burden of computing pairwise fiber (dis)similarities, which escalates quadratically as the number of fiber pathways increases. To address this challenge, we adopt the(More)