Optimization Transfer Approach to Joint Registration / Reconstruction for Motion-Compensated Image Reconstruction

Motion artifacts in image reconstruction problems can be reduced by performing image motion estimation and image reconstruction jointly using a penalized-likelihood cost function. However, updating the motion parameters by conventional gradient-based iterations can be computationally demanding due to the system model required in inverse problems. This paper describes an optimization transfer approach that leads to minimization steps for the motion parameters that have comparable complexity to those needed in image registration problems. This approach can simplify the implementation of motion-compensated image reconstruction (MCIR) methods when the motion parameters are estimated jointly with the reconstructed image.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85845/1/Fessler247.pd

A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.85-95Pubblicat

GPU Accelerated Viscous-fluid Deformable Registration for Radiotherapy

In cancer treatment organ and tissue deformation betweenradiotherapy sessions represent a significant challenge to op-timal planning and delivery of radiation doses. Recent de-velopments in image guided radiotherapy has caused a soundrequest for more advanced approaches for image registrationto handle these deformations. Viscous-fluid registration isone such deformable registration method. A drawback withthis method has been that it has required computation timesthat were too long to make the approach clinically appli-cable. With recent advances in programmability of graph-ics hardware, complex user defined calculations can now beperformed on consumer graphics cards (GPUs). This pa-per demonstrates that the GPU can be used to drasticallyreduce the time needed to register two medical 3D imagesusing the viscous-fluid registration method. This facilitatesan increased incorporation of image registration in radio-therapy treatment of cancer patients, potentially leading tomore efficient treatment with less severe side effects

Accelerating 3D Non-Rigid Registration using Graphics Hardware

International audienceThere is an increasing need for real-time implementation of 3D image analysis processes, especially in the context of image-guided surgery. Among the various image analysis tasks, non-rigid image registration is particularly needed and is also computationally prohibitive. This paper presents a GPU (Graphical Processing Unit) implementation of the popular Demons algorithm using a Gaussian recursive filtering. Acceleration of the classical method is mainly achieved by a new filtering scheme on GPU which could be reused in or extended to other applications and denotes a significant contribution to the GPU-based image processing domain. This implementation was able to perform a non-rigid registration of 3D MR volumes in less than one minute, which corresponds to an acceleration factor of 10 compared to the corresponding CPU implementation. This demonstrated the usefulness of such method in an intra-operative context

A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the syste

Fast registration of contrast-enhanced magnetic resonance images of the breast

Master'sMASTER OF ENGINEERIN