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17 March 2020 Intraoperative thermographic perfusion mapping in neurosurgery using regularized semiparametric regression (Conference Presentation)
Juliane Müller, Nico Hoffmann, Martin Oelschlägel, Christian Schnabel, Gerald Steiner, Edmund Koch, Stephan B. Sobottka, Gabriele Schackert, Matthias Kirsch
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Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113150N (2020) https://doi.org/10.1117/12.2549641
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States
Abstract
The success of brain surgery depends on the exact and effective treatment of pathological alterations while preserving functional tissue and essential vessels. Varying intraoperative imaging methods have been developed to achieve this goal. For cortical perfusion imaging, application of time-resolved thermography in combination with an intravenously applied cold bolus became a promising approach. This work provides a regularized semiparametric regression framework that detects the cold signal response function while compensating arbitrary background signals using penalized B-Splines. This enables weak thermal signal detection to give information about blood flow even in small vessels, applicable for intraoperative cortical blood flow mapping.
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Juliane Müller, Nico Hoffmann, Martin Oelschlägel, Christian Schnabel, Gerald Steiner, Edmund Koch, Stephan B. Sobottka, Gabriele Schackert, and Matthias Kirsch "Intraoperative thermographic perfusion mapping in neurosurgery using regularized semiparametric regression (Conference Presentation)", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150N (17 March 2020); https://doi.org/10.1117/12.2549641
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KEYWORDS
Signal detection
Brain mapping
Blood circulation
Interference (communication)
Signal processing
Thermal modeling
Cerebral blood flow
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