Over the last decade, many methods for adaptively filtering a data stream have been proposed. Those methods have applications in two dimensional imaging as well as in three dimensional image reconstruction. Although the primary objective of this filtering technique is to reduce the noise while avoiding to blur the edges, diagnostic, automated segmentation and surgery show a growing interest in enhancing the features contained in the image flow. Most of the methods proposed so far emerged from thorough studies of the physics of the considered modality and therefore show only a marginal capability to be extended across modalities. Moreover, adaptive filtering belongs to the family of processing intensive algorithms. Existing technology has often driven to simplifications and modality specific optimization to sustain the expected performances. In the specific case of real time digital X-ray as used surgery, the system has to sustain a throughput of 30 frames per second. In this study, we take a generalized approach for adaptive filtering based on multiple oriented filters. Mapping the filtering part to the embedded real time image processing while a user/application defined adaptive recombination of the filter outputs allow to change the smoothing and edge enhancement properties of the filter without changing the oriented filter parameters. We have implemented the filtering on a Cell Broadband Engine processor and the adaptive recombination on an off-the-shelf PC, connected via Gigabit Ethernet. This implementation is capable of filtering images of 5122 pixels at a throughput in excess of 40 frames per second while allowing to change the parameters in real time.
Over the last few decades, the medical imaging community has passionately debated over different approaches to implement reconstruction algorithms for Spiral CT. Numerous alternatives have been proposed. Whether they are approximate, exact or, iterative, those implementations generally include a backprojection step. Specialized compute platforms have been designed to perform this compute-intensive algorithm within a timeframe compatible with hospitalworkflow requirements. Solving the performance problem in a cost-effective way had driven designers to use a combination of digital signal processor (DSP) chips, general-purpose processors, application-specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs). The Cell processor by IBM offers an interesting alternative for implementing the backprojection, especially since it offers a good level of parallelism and vast I/O capabilities. In this paper, we consider the implementation of a straight backprojection algorithm on the Cell processor to design a costeffective system that matches the performance requirements of clinically deployed systems. The effects on performance of system parameters such as pitch and detector size are also analyzed to determine the ideal system size for modern CT scanners.
Adaptive filtering is a compute-intensive algorithm aimed at effectively reducing noise without blurring the structures contained in a set of digital images. In this study, we take a generalized approach for adaptive filtering based on seven oriented filters, each individual filter implemented by a two-dimensional (2D) convolution with a mask size of 11 by 11 pixels. Digital radiology workflow imposes severe real-time constraints that require the use of hardware acceleration such as provided by multicore processors. Implementing complex algorithms on heterogeneous multicore architectures is a complex task especially for taking advantage of the DMA engines. We have implemented the algorithm on a Cell Broadband Engine (CBE) processor clocked at 3.2 GHz using a generic framework for multicore processors. This implementation is capable of filtering images of 512 2 pixels at a throughput of 40 frames per second while allowing changing the parameters in real time. The resulting images are directed to the DR monitor or to the real-time computed tomography (CT) reconstruction engine.
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