Kenneth Poels1, Jennifer Dhont2, Dirk Verellen3, Oliver Blanck4, Floris Ernst5, Jef Vandemeulebroucke6, Tom Depuydt7, Guy Storme3, Mark De Ridder3. 1. Department of Radiotherapy, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium. Electronic address: poels.kenneth@gmail.com. 2. Department of Radiotherapy, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium; Vrije Universiteit Brussel, Medical Imaging and Physical Sciences Group, Faculty of Medicine and Pharmacy, Belgium. 3. Department of Radiotherapy, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium. 4. Department of Radiation Oncology, University Clinic Schleswig-Holstein, Kiel, Germany; Saphir Radiosurgery Center Northern Germany, Güstrow, Germany. 5. Institute for Robotics and Cognitive Systems, University of Lübeck, Germany. 6. Department of Medical Information Technology, iMinds, Department of Electronics and Informatics (ETRO), VUB, Belgium. 7. Department of Radiation Oncology, University Hospitals Leuven, Belgium.
Abstract
PURPOSE: A head-to-head comparison of two clinical correlation models with a focus on geometrical accuracy for internal tumor motion estimation during real-time tumor tracking (RTTT). METHODS AND MATERIALS: Both the CyberKnife (CK) and the Vero systems perform RTTT with a correlation model that is able to describe hysteresis in the breathing motion. The CK dual-quadratic (DQ) model consists of two polynomial functions describing the trajectory of the tumor for inhale and exhale breathing motion, respectively. The Vero model is based on a two-dimensional (2D) function depending on position and speed of the external breathing signal to describe a closed-loop tumor trajectory. In this study, 20 s of internal motion data, using an 11 Hz (on average) full fluoroscopy (FF) sequence, was used for training of the CK and Vero models. Further, a subsampled set of 15 internal tumor positions (15p) equally spread over the different phases of the breathing motion was used for separate training of the CK DQ model. Also a linear model was trained using 15p and FF tumor motion data. Fifteen liver and lung cancer patients, treated on the Vero system with RTTT, were retrospectively evaluated comparing the CK FF, CK 15p and Vero FF models using an in-house developed simulator. The distance between estimated target position and the tumor position localized by X-ray imaging was measured in the beams-eye view (BEV) to calculate the 95th percentile BEV modeling errors (ME(95,BEV)). Additionally, the percentage of ME(95,BEV) smaller than 5 mm (P(5mm)) was determined for all correlation models. RESULTS: In general, no significant difference (p>0.05, paired t-test) was found between the CK FF and Vero models. Based on patient-specific evaluation of the geometrical accuracy of the linear, CK DQ and Vero correlation models, no statistical necessity (p>0.05, two-way ANOVA) of including hysteresis in correlation models was proven, although during inhale breathing motion, the linear model resulted in a decreased P(5mm) with 5-6% compared to both the DQ CK and Vero models. CONCLUSION: Dual-quadratic CyberKnife and 2D Vero correlation models were interchangeable in terms of geometrical accuracy with the CK linear ME(95,BEV)=4.1 mm, CK dual-quadratic ME(95,BEV)=3.9 mm and Vero ME(95,BEV)=3.7 mm, when modeled with FF sequence. CK DQ modeling based on 15p acquired in 20 s may lead to problems for internal motion estimation.
PURPOSE: A head-to-head comparison of two clinical correlation models with a focus on geometrical accuracy for internal tumor motion estimation during real-time tumor tracking (RTTT). METHODS AND MATERIALS: Both the CyberKnife (CK) and the Vero systems perform RTTT with a correlation model that is able to describe hysteresis in the breathing motion. The CK dual-quadratic (DQ) model consists of two polynomial functions describing the trajectory of the tumor for inhale and exhale breathing motion, respectively. The Vero model is based on a two-dimensional (2D) function depending on position and speed of the external breathing signal to describe a closed-loop tumor trajectory. In this study, 20 s of internal motion data, using an 11 Hz (on average) full fluoroscopy (FF) sequence, was used for training of the CK and Vero models. Further, a subsampled set of 15 internal tumor positions (15p) equally spread over the different phases of the breathing motion was used for separate training of the CK DQ model. Also a linear model was trained using 15p and FF tumor motion data. Fifteen liver and lung cancerpatients, treated on the Vero system with RTTT, were retrospectively evaluated comparing the CK FF, CK 15p and Vero FF models using an in-house developed simulator. The distance between estimated target position and the tumor position localized by X-ray imaging was measured in the beams-eye view (BEV) to calculate the 95th percentile BEV modeling errors (ME(95,BEV)). Additionally, the percentage of ME(95,BEV) smaller than 5 mm (P(5mm)) was determined for all correlation models. RESULTS: In general, no significant difference (p>0.05, paired t-test) was found between the CK FF and Vero models. Based on patient-specific evaluation of the geometrical accuracy of the linear, CK DQ and Vero correlation models, no statistical necessity (p>0.05, two-way ANOVA) of including hysteresis in correlation models was proven, although during inhale breathing motion, the linear model resulted in a decreased P(5mm) with 5-6% compared to both the DQ CK and Vero models. CONCLUSION: Dual-quadratic CyberKnife and 2D Vero correlation models were interchangeable in terms of geometrical accuracy with the CK linear ME(95,BEV)=4.1 mm, CK dual-quadratic ME(95,BEV)=3.9 mm and Vero ME(95,BEV)=3.7 mm, when modeled with FF sequence. CK DQ modeling based on 15p acquired in 20 s may lead to problems for internal motion estimation.
Authors: Thomas Woolcot; Evanthia Kousi; Emma Wells; Katharine Aitken; Helen Taylor; Maria A Schmidt Journal: Phys Imaging Radiat Oncol Date: 2018-09-06