GPGPU Acceleration Algorithm for Medical Image Reconstruction

Virasin Archirapatkave, Hendra Sumilo, Simon Chong Wee See, Tiranee Achalakul
IEEE 9th International Symposium on Parallel and Distributed Processing with Applications (ISPA), 2011


   title={GPGPU Acceleration Algorithm for Medical Image Reconstruction},

   author={Archirapatkave, V. and Sumilo, H. and See, S.C.W. and Achalakul, T.},

   booktitle={Parallel and Distributed Processing with Applications (ISPA), 2011 IEEE 9th International Symposium on},





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Medical imaging techniques such as X-ray, Ultrasound, CT and MRI scan are widely used for diagnosis. The 2D medical images from these scans are difficult to interpret because they can only show cross section views of a human body. Interpreting these images requires experts or trained professionals. Reconstructing 2D images into 3D models can help with the interpretation process. However, such model reconstruction is normally time-consuming and costly. It requires high performance computation, such as grid or parallel computing. This research, thus, proposes a high performance 3D reconstruction method using the General-Purpose computation on Graphics Processing Units (GPGPU). The GPGPU has a high computational performance. Parallel computing method on GPU can thus regenerate a model for real time 3D visualization. In other words, the GPU computational speed sufficiently improves the visualization effectiveness of both images and models to the point where a real-time navigation of the data set is possible. In our work, the 3D reconstruction process reconstructs a set of 2D cross-section images and stacks them to generate a volume data, and then transform them into a 3D model. The generated models are then displayed on the user interface developed with OpenGL. Finally, the performance of the GPU acceleration is presented in this paper.
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