18,007 research outputs found
GPU acceleration of brain image proccessing
Durante los últimos años se ha venido demostrando el alto poder computacional
que ofrecen las GPUs a la hora de resolver determinados problemas.
Al mismo tiempo, existen campos en los que no es posible beneficiarse completamente
de las mejoras conseguidas por los investigadores, debido principalmente
a que los tiempos de ejecución de las aplicaciones llegan a ser extremadamente
largos. Este es por ejemplo el caso del registro de imágenes en medicina.
A pesar de que se han conseguido aceleraciones sobre el registro de imágenes,
su uso en la práctica clínica es aún limitado. Entre otras cosas, esto se debe
al rendimiento conseguido.
Por lo tanto se plantea como objetivo de este proyecto, conseguir mejorar los
tiempos de ejecución de una aplicación dedicada al resgitro de imágenes en medicina,
con el fin de ayudar a aliviar este problema
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
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
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
Using high resolution displays for high resolution cardiac data
The ability to perform fast, accurate, high resolution visualization is fundamental
to improving our understanding of anatomical data. As the volumes of data
increase from improvements in scanning technology, the methods applied to rendering
and visualization must evolve. In this paper we address the interactive display of
data from high resolution MRI scanning of a rabbit heart and subsequent histological
imaging. We describe a visualization environment involving a tiled LCD panel
display wall and associated software which provide an interactive and intuitive user
interface.
The oView software is an OpenGL application which is written for the VRJuggler
environment. This environment abstracts displays and devices away from the
application itself, aiding portability between different systems, from desktop PCs to
multi-tiled display walls. Portability between display walls has been demonstrated
through its use on walls at both Leeds and Oxford Universities. We discuss important
factors to be considered for interactive 2D display of large 3D datasets,
including the use of intuitive input devices and level of detail aspects
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