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A scanning system for intelligent imaging: I-ImaS

Longo, R; Rokvic, T; Venanzi, C; Asimidis, A; Manthos, N; Triantis, F; ... Van Der Stelt, PF; + view all (2007) A scanning system for intelligent imaging: I-ImaS. Progress in Biomedical Optics and Imaging - Proceedings of SPIE , 6510 (PART 1) 10.1117/12.708457.

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Abstract

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce adaptive x-ray imaging systems using Monolithic Active Pixel Sensors (MAPS) to create optimal diagnostic images. Initial systems concentrate on mammography and cephalography. The on-chip intelligence available to MAPS technology will allow real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. In our system, the exposure in each image region is optimized and the beam intensity is a function not only of tissue thickness and attenuation, but also of local physical and statistical parameters found in the image itself. Using a linear array of detectors with on-chip intelligence, the system will perform an on-line analysis of the image during the scan and then will optimize the X-ray intensity in order to obtain the maximum diagnostic information from the region of interest while minimizing exposure of less important, or simply less dense, regions. This paper summarizes the testing of the sensors and their electronics carried out using synchrotron radiation, x-ray sources and optical measurements. The sensors are tiled to form a 1.5D linear array. These have been characterised and appropriate correction techniques formulated to take into account misalignments between individual sensors. Full testing of the mammography and cephalography I-ImaS prototype is now underway and the system intelligence is constantly being upgraded through iterative testing in order to obtain the optimal algorithms and settings. In preliminary simulations the dose savings between the regulated images and the reference images were estimated to between 30 to 70%.

Type:Article
Title:A scanning system for intelligent imaging: I-ImaS
DOI:10.1117/12.708457
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Medical Sciences > Surgery and Interventional Science (Division of)
UCL > School of BEAMS > Faculty of Engineering Science > Institute of Biomedical Engineering
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering

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