An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm

This paper proposes a research of An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm and combines 3D graphic application interfaces, such as DirectX3D and OpenCV to reconstruct the 3D imaging system for Magnetic Resonance Imaging (MRI), and adds Level of Detail (LOD) algorithm to the system. The system uses the volume rendering method to perform 3D reconstruction for brain imaging. The process, which is based on the level of detail algorithm that converts and formulates functions from differing levels of detail and scope, significantly reduces the complexity of required processing and computation, under the premises of maintaining drawing quality. To validate the system's efficiency enhancement on brain imaging reconstruction, this study operates the system on various computer platforms, and uses multiple sets of data to perform rendering and 3D object imaging reconstruction, the results of which are then verified and compared

Cubing Units Using Carry-Save Array Implementations

This study is aimed at designing a specialized functional unit to perform the operation of Cubing. The design has been implemented based on the concept of carry-save array multipliers. The carry save concept aims at accelerating the process of addition by delaying the carry propagation operation till the last step. The motivation behind using array structures is simple. Array structures are regular and easy to design. This paper first looks at a simple cubing unit that can accept only unsigned inputs. This design is then modified based on mathematical derivations and architecture for signed cubing unit or two's complement cubing unit is derived. The designs have been tested, synthesized and compared with the traditional multiplication techniques for area and delay. When the different designs for various bit sizes of operands were tested and synthesized, some really interesting results were arrived at. The algorithmic analysis showed that the cubing unit implementation would require more delay and area as compared to two passes for the same operation through corresponding traditional CSAMs. After synthesis, while the results agreed with the area requirement, for the 6-bit version, it was seen that the cubing design is actually faster as compared to the traditional implementation. The interesting thing about the cubing unit design is that its area requirement and delay increases almost exponentially with the length of the input operands. Careful floorplanning and layout techniques must be employed in order to arrive at a good design.School of Electrical & Computer Engineerin

True 4D Image Denoising on the GPU

The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512  × 512  × 445  × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly

Desarrollo de un entorno virtual para la simulación de intervenciones quirúrgicas en neurocirugía

RESUMEN: Los sistemas de neurocirugía guiada por imágenes han surgido como una herramienta útil en la formación médica para enfrentar las deficiencias de los métodos tradicionales de enseñanza. El hecho de que cada año muchas personas mueran en los hospitales por errores en los procedimientos quirúrgicos ayuda a entender el posible impacto del entrenamiento médico y revela que la insuficiencia de éste podría costar vidas humanas. Aunque en el medio existen diferentes tipos de entornos desarrollados para la aplicación de sistemas de neurocirugía guiada por imágenes, con una alta calidad en la visualización y la interacción con los modelos, en general estos sistemas presentan un costo muy elevado, por lo que su aplicación con fines educativos se ve disminuida, además, tampoco ofrecen todas las opciones de visualización existentes ya que las opciones más avanzadas requieren conocimientos mayores de procesamiento de imágenes. Por esta razón se desarrolló un entorno para el acceso a operaciones avanzadas de procesamiento de imágenes y la manipulación intuitiva de imágenes volumétricas, el cual puede manipularse por medio de un periférico de entrada diferente al mouse, mejorando y facilitando la forma de interacción usuario-sistema. El entorno fue desarrollado en 3D Slicer, que es un software de uso libre, y facilitará el acceso de los neurocirujanos en formación de la ciudad, a las técnicas de craneotomía virtual y manipulación de representaciones tridimensionales