ArticleProceedings of SPIE--the International Society for Optical Engineering2012
Graphics Processing Unit (GPU) implementation of image processing algorithms to improve system performance of the Control, Acquisition, Processing, and Image Display System (CAPIDS) of the Micro-Angiographic Fluoroscope (MAF).
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Combination of Spatial and Recursive Temporal Filtering for Noise Reduction when Using Region of Interest (ROI) Fluoroscopy for Patient Dose Reduction in Image Guided Vascular Interventions with Significant Anatomical Motion.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Detector system comparison using relative CNR for specific imaging tasks related to neuro-endovascular image-guided interventions (neuro-EIGIs).Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Workflow for the use of a high-resolution image detector in endovascular interventional procedures.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
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Authors and funding
6 authors.
Funding
Abstract
We present the image processing upgrades implemented on a Graphics Processing Unit (GPU) in the Control, Acquisition, Processing, and Image Display System (CAPIDS) for the custom Micro-Angiographic Fluoroscope (MAF) detector. Most of the image processing currently implemented in the CAPIDS system is pixel independent; that is, the operation on each pixel is the same and the operation on one does not depend upon the result from the operation on the other, allowing the entire image to be processed in parallel. GPU hardware was developed for this kind of massive parallel processing implementation. Thus for an algorithm which has a high amount of parallelism, a GPU implementation is much faster than a CPU implementation. The image processing algorithm upgrades implemented on the CAPIDS system include flat field correction, temporal filtering, image subtraction, roadmap mask generation and display window and leveling. A comparison between the previous and the upgraded version of CAPIDS has been presented, to demonstrate how the improvement is achieved. By performing the image processing on a GPU, significant improvements (with respect to timing or frame rate) have been achieved, including stable operation of the system at 30 fps during a fluoroscopy run, a DSA run, a roadmap procedure and automatic image windowing and leveling during each frame.
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Registered trials
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