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At least 109 records · Page 6

SU-E-J-57: First Development of Adapting to Intrafraction Relative Motion Between Prostate and Pelvic Lymph Nodes Targets

Purpose Large intrafraction relative motion of multiple targets is common in advanced head and neck, lung, abdominal, gynaecological and urological cancer, jeopardizing the treatment outcomes. The objective of this study is to develop a real-time adaptation strategy, for the first time, to accurately correct for the relative motion of multiple targets by reshaping the treatment field using the multi-leaf collimator (MLC). Methods The principle of tracking the simultaneously treated but differentially moving tumor targets is to determine the new aperture shape that conforms to the shifted targets. Three dimensional volumes representing the individual targets are projected to the beam’s eye view. The leaf openings falling inside each 2D projection will be shifted according to the measured motion of each target to form the new aperture shape. Based on the updated beam shape, new leaf positions will be determined with optimized trade-off between the target underdose and healthy tissue overdose, and considerations of the physical constraints of the MLC. Taking a prostate cancer patient with pelvic lymph node involvement as an example, a preliminary dosimetric study was conducted to demonstrate the potential treatment improvement compared to the state-of- art adaptation technique which shifts the whole beam to track only one target. Results The world-first intrafraction adaptation system capable of reshaping the beam to correct for the relative motion of multiple targets has been developed. The dose in the static nodes and small bowel are closer to the planned distribution and the V45 of small bowel is decreased from 110cc to 75cc, corresponding to a 30% reduction by this technique compared to the state-of-art adaptation technique. Conclusion The developed adaptation system to correct for intrafraction relative motion of multiple targets will guarantee the tumour coverage and thus enable PTV margin reduction to minimize the high target dose to the adjacent organs-at-risk. The authors acknowledge funding support from the Australian NHMRC Australia Fellowship and NHMRC Project Grant No. APP1042375.

60 APPLIED LIFE SCIENCES↗

SU-E-T-57: A Novel Method to Improve Dose Heterogeneity of Target and Organs at Risk Sparing in the Intensity-Modulated Radiotherapy for Stage III Lung Cancer

Purpose: Intensity-modulated radiotherapy (IMRT) treatment for lung cancer is difficult due to the heterogeneous dose distribution and excessive dose to the organs at risk (OARs). We introduce a simple method based on the base dose function (BDF) in Eclipse treatment planning system to overcome the difficulties. Methods: Thirteen patients suffered from stage III non-small cell lung cancer (NSCLC) were enrolled in the study. Three kinds of approaches were applied to obtain clinically acceptable treatment plans: 1) conventionally optimizing method with hot and cold spots re-optimization (CO); 2) target-divided optimizing method (TDO) in which the optimization objective in the lung density of planning target volume (PTV) was set to 2 to 4 Gy higher than in the soft tissue density; 3) base dose function (BDF) in which the treatment plan was produced based on the original plan for re-optimization. CO, TDO and BDF methods were then compared in terms of conformity index (CI), homogeneity index (HI), OARs sparing and monitor units (MUs). Additionally, delta4, portal dosimetry and IMSure were used to measure the dose delivering accuracy. Results: The BDF technique provided more superior CI and HI than the other two methods. Moreover, the new method also reduced the lung, esophagus, heart and spinal cord dose. However, the BDF plans needed extra 15% and 10% MUs than the CO and TDO methods. Dose verification results demonstrated good and comparable γ pass rates among the three methods. Conclusion: The proposed BDF method greatly improves the dose homogeneity and OARs sparing in the IMRT treatment for lung cancer.

60 APPLIED LIFE SCIENCES↗

SU-F-I-57: Evaluate and Optimize PET Acquisition Overlap in 18F-FDG Oncology Wholebody PET/CT: Can We Scan PET Faster?

Purpose: The longer patient has to remain on the table during PET imaging, the higher the likelihood of motion artifacts due to patient discomfort. This study was to investigate and optimize PET acquisition overlap in 18F-FDG oncology wholebody PET/CT to speed up PET acquisition and improve patient comfort. Methods: Wholebody 18F-FDG PET/CT of phantoms, 8 pre-clinical patients (beagles) and 5 clinical oncology patients were performed in 90s/bed on a time-of-flight Gemini TF 64 system. Imaging of phantoms and beagles was acquired with reduced PET overlaps (40%, 33%, 27%, 20%, 13% and no overlap) in addition to the system default (53%). In human studies, 1 or 2 reduced overlaps from the listed options were used to acquire PET/CT sweeps right after the default standard of care imaging. Image quality was blindly reviewed using visual scoring criteria and quantitative SUV assessment. NEMA PET sensitivity was performed under different overlaps. Results: All PET exams demonstrated no significant impact on the visual grades for overlaps >20%. Blinded reviews assigned the best visual scores to PET using overlaps 53%–27%. Reducing overlap to 27% for oncology patients (12-bed) saved an average of ∼40% acquisition time (11min) compared to using the default overlap (18min). No significant SUV variances were found when reducing overlap to half of default for cerebellum, lung, heart, aorta, liver, fat, muscle, bone marrow, thighs and target lesions (p>0.05), except expected variability in urinary system. Conclusion: This study demonstrated by combined phantom, pre-clinical and clinical PET/CT scans that PET acquisition overlap in axial of today’s systems can be reduced and optimized. It showed that a reduction of PET acquisition overlap to 27% (half of system default) can be implemented to reduce table time by ∼40% to improve patient comfort and minimize potential motion artifacts, without prominently degrading image quality or compromising PET quantification.

60 APPLIED LIFE SCIENCES↗

SU-F-J-57: Effectiveness of Daily CT-Based Three-Dimensional Image Guided and Adaptive Proton Therapy

Purpose: Daily CT-based three-dimensional image-guided and adaptive (CTIGRT-ART) proton therapy system was designed and developed. We also evaluated the effectiveness of the CTIGRT-ART. Methods: Retrospective analysis was performed in three lung cancer patients: Proton treatment planning was performed using CT image datasets acquired by Toshiba Aquilion ONE. Planning target volume and surrounding organs were contoured by a well-trained radiation oncologist. Dose distribution was optimized using 180-deg. and 270-deg. two fields in passive scattering proton therapy. Well commissioned Simplified Monte Carlo algorithm was used as dose calculation engine. Daily consecutive CT image datasets was acquired by an in-room CT (Toshiba Aquilion LB). In our in-house program, two image registrations for bone and tumor were performed to shift the isocenter using treatment CT image dataset. Subsequently, dose recalculation was performed after the shift of the isocenter. When the dose distribution after the tumor registration exhibits change of dosimetric parameter of CTV D90% compared to the initial plan, an additional process of was performed that the range shifter thickness was optimized. Dose distribution with CTV D90% for the bone registration, the tumor registration only and adaptive plan with the tumor registration was compared to the initial plan. Results: In the bone registration, tumor dose coverage was decreased by 16% on average (Maximum: 56%). The tumor registration shows better coverage than the bone registration, however the coverage was also decreased by 9% (Maximum: 22%) The adaptive plan shows similar dose coverage of the tumor (Average: 2%, Maximum: 7%). Conclusion: There is a high possibility that only image registration for bone and tumor may reduce tumor coverage. Thus, our proposed methodology of image guidance and adaptive planning using the range adaptation after tumor registration would be effective for proton therapy. This research is partially supported by Japan Agency for Medical Research and Development (AMED).

60 APPLIED LIFE SCIENCES↗

SU-F-T-57: Delivered Activity Accuracy of Radium 223 Dichloride Injections, When Being Administrated for Castration Resistant Prostate Cancer, Symptomatic Bone Metastases. The Impact of Residual Activity in the Spent Syringe and Dispensing Accuracy of Ra 223

Purpose: To quantify the delivered activity accuracy of Radium 223 dichloride injections, when being administrated for castration – resistant prostate cancer, symptomatic bone metastases. The impact of residual activity in the spent syringe and dispensing accuracy of Ra 223. Methods: The administration is by slow intravenous injection over 1 minute followed by double flushing of the 10 mL syringe and IV with saline. Eighty (80) procedures was used to investigate variations in the activity from the amount prescribed (µCi) = 1.35 × Patient weight Kg. The Activity dispensed into a 10mL syringe using a NIST traceable Capintec CRC-25R Chamber and a cross calibrated capintec CRC-15R to measure activity in the syringe immediately before and after administration Results: The patients weight range from 121Ib to 235lb and doses ranging 74.25 µCi to 144.2 µCi. The deviation of dispensed dose vs Prescribed dose average +2.1% with a range of −1.1% to +5.7%. The Dose measured before administration ranges 79.3 µCi to 154.9 µCi. Deviation from the dispensed dose was show to average +2.9% with a range of −0.8% to +7.3%. The average residual dose post injection was 2.5 µCi or 2.2% of the pre injection activity. Ranging from 0.9 µCi to 6.2 µCi, 0.7% to 5.4% respectively. Subtracting the residual activity from that measured activity before injection and comparing it to prescription dose was shown to have an average variation of +2.7% with a range of −0.8% to 7.4%. Conclusion: The case resulted in the 6.2 µCi maximum residual dose had two syringes. A small, 82.8 µCi activity, case resulted in the 7.4% maximum variation in measures less residual verses prescription dose. The average +2.1 % dispenses activity of Ra 223 over the prescription dosage was seen to counteract the average 2.2% residual dosage found to remain in the syringe.

60 APPLIED LIFE SCIENCES↗

Materials Data on RbMo2(PO4)3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on TlMo2(PO4)3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on TlVO3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on Na5VO5 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on Mn3O4 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on UVO5 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on LiMo2(PO4)3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on LiNi(PO3)3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on Mo2(PO4)3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on Ni(PO3)3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Materials Data on CsVO3 (SG:57) by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗