18 March 2016 Partition-based acquisition model for speed up navigated beta-probe surface imaging
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Abstract
Although gross total resection in low-grade glioma surgery leads to a better patient outcome, the in-vivo control of resection borders remains challenging. For this purpose, navigated beta-probe systems combined with 18F-based radiotracer, relying on activity distribution surface estimation, have been proposed to generate reconstructed images. The clinical relevancy has been outlined by early studies where intraoperative functional information is leveraged although inducing low spatial resolution in reconstruction. To improve reconstruction quality, multiple acquisition models have been proposed. They involve the definition of attenuation matrix for designing radiation detection physics. Yet, they require high computational power for efficient intraoperative use. To address the problem, we propose a new acquisition model called Partition Model (PM) considering an existing model where coefficients of the matrix are taken from a look-up table (LUT). Our model is based upon the division of the LUT into averaged homogeneous values for assigning attenuation coefficients. We validated our model using in vitro datasets, where tumors and peri-tumoral tissues have been simulated. We compared our acquisition model with the o_-the-shelf LUT and the raw method. Acquisition models outperformed the raw method in term of tumor contrast (7.97:1 mean T:B) but with a difficulty of real-time use. Both acquisition models reached the same detection performance with references (0.8 mean AUC and 0.77 mean NCC), where PM slightly improves the mean tumor contrast up to 10.1:1 vs 9.9:1 with the LUT model and more importantly, it reduces the mean computation time by 7.5%. Our model gives a faster solution for an intraoperative use of navigated beta-probe surface imaging system, with improved image quality.
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Frédéric Monge, Dzhoshkun I. Shakir, Nassir Navab, Pierre Jannin, "Partition-based acquisition model for speed up navigated beta-probe surface imaging", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97862P (18 March 2016); doi: 10.1117/12.2216231; https://doi.org/10.1117/12.2216231
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