18 March 2015 Data fusion for planning target volume and isodose prediction in prostate brachytherapy
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Abstract
In low-dose prostate brachytherapy treatment, a large number of radioactive seeds is implanted in and adjacent to the prostate gland. Planning of this treatment involves the determination of a Planning Target Volume (PTV), followed by defining the optimal number of seeds, needles and their coordinates for implantation. The two major planning tasks, i.e. PTV determination and seed definition, are associated with inter- and intra-expert variability. Moreover, since these two steps are performed in sequence, the variability is accumulated in the overall treatment plan. In this paper, we introduce a model based on a data fusion technique that enables joint determination of PTV and the minimum Prescribed Isodose (mPD) map. The model captures the correlation between different information modalities consisting of transrectal ultrasound (TRUS) volumes, PTV and isodose contours. We take advantage of joint Independent Component Analysis (jICA) as a linear decomposition technique to obtain a set of joint components that optimally describe such correlation. We perform a component stability analysis to generate a model with stable parameters that predicts the PTV and isodose contours solely based on a new patient TRUS volume. We propose a framework for both modeling and prediction processes and evaluate it on a dataset of 60 brachytherapy treatment records. We show PTV prediction error of 10:02±4:5% and the V100 isodose overlap of 97±3:55% with respect to the clinical gold standard.
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Saman Nouranian, Saman Nouranian, Mahdi Ramezani, Mahdi Ramezani, S. Sara Mahdavi, S. Sara Mahdavi, Ingrid Spadinger, Ingrid Spadinger, William J. Morris, William J. Morris, Septimiu E. Salcudean, Septimiu E. Salcudean, Purang Abolmaesumi, Purang Abolmaesumi, } "Data fusion for planning target volume and isodose prediction in prostate brachytherapy", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 94151I (18 March 2015); doi: 10.1117/12.2081055; https://doi.org/10.1117/12.2081055
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