Hashim Uddin Ahmed

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A deformable registration method is described that enables automatic alignment of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland. The method employs a novel "model-to-image" registration approach in which a deformable model of the gland surface, derived from an MR image, is registered automatically to a TRUS volume(More)
There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability(More)
A method is described for registering preoperative magnetic resonance (MR) to intraoperative transrectal ultrasound (TRUS) images of the prostate gland. A statistical motion model (SMM) of the prostate is first built using training data provided by biomechanical simulations of the motion of a patient-specific finite element model, derived from a(More)
A method is described for generating a patient-specific, statistical motion model (SMM) of the prostate gland. Finite element analysis (FEA) is used to simulate the motion of the gland using an ultrasound-based 3D FE model over a range of plausible boundary conditions and soft-tissue properties. By applying principal component analysis to the displacements(More)
Statistical shape models of soft-tissue organ motion provide a useful means of imposing physical constraints on the displacements allowed during non-rigid image registration, and can be especially useful when registering sparse and/or noisy image data. In this paper, we describe a method for generating a subject-specific statistical shape model that(More)
A direct approach to using finite element analysis (FEA) to predict organ motion typically requires accurate estimates of soft-tissue properties, which are very difficult to measure and are known to vary significantly between patients. In this paper, we describe a method that combines FEA with a statistical approach to overcome these problems. We show how a(More)
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