Hsiang-Chi Kuo

TU‐F‐CAMPUS‐J‐04: Evaluation of Metal Artifact Reduction Technique for the Radiation Therapy Planning

Purpose: To evaluate the feasibility of using a metal artifact reduction technique in depleting metal artifact and its application in improving dose calculation in External Radiation Therapy Planning. Methods: CIRS electron density phantom was scanned with and without steel drill bits placed in some plug holes. Meta artifact reduction software with Metal Deletion Technique (MDT) was used to remove metal artifacts for scanned image with metal. Hounsfield units of electron density plugs from artifact free reference image and MDT processed images were compared. To test the dose calculation improvement after the MDT processed images, clinically approved head and neck plan with manual dental artifact correction was tested. Patient images were exported and processed with MDT and plan was recalculated with new MDT image without manual correction. Dose profiles near the metal artifacts were compared. Results: The MDT used in this study effectively reduced the metal artifact caused by beam hardening and scatter. The windmill around the metal drill was greatly improved with smooth rounded view. Difference of the mean HU in each density plug between reference and MDT images were less than 10 HU in most of the plugs. Dose difference between original plan and MDT images were minimal. Conclusion: Most metal artifact reduction methods were developed for diagnostic improvement purpose. Hence Hounsfield unit accuracy was not rigorously tested before. In our test, MDT effectively eliminated metal artifacts with good HU reproduciblity. However, it can introduce new mild artifacts so the MDT images should be checked with original images.

SU‐E‐T‐122: Anisotropic Analytical Algorithm (AAA) Vs. Acuros XB (AXB) in Stereotactic Treatment Planning

Purpose: To evaluate dosimetric differences between superposition beam model (AAA) and determinant photon transport solver (AXB) in lung SBRT and Cranial SRS dose computations. Methods: Ten Cranial SRS and ten Lung SBRT plans using Varian, AAA _11.0 were re-planned using Acuros _XB_11.0 with fixed MU. 6MV photon Beam model with HD120_MLC used for dose calculations. Four non-coplanar conformal arcs used to deliver 21Gy or 18Gy to SRS targets (0.4 to 6.2cc). 54Gy (3Fractions) or 50Gy (5Fractions) was planned for SBRT targets (7.3 to 13.9cc) using two VAMT non-coplanar arcs. Plan comparison parameters were dose to 1% PTV volume (D1), dose to 99% PTV volume( D99), Target mean (Dmean), Conformity index (ratio of prescription isodose volume to PTV), Homogeneity Index [ (D2%-D98%)/Dmean] and R50 (ratio of 50% of prescription isodose volume to PTV). OAR parameters were Brain volume receiving 12Gy dose (V12Gy) and maximum dose (D0.03) to Brainstem for SRS. For lung SBRT, maximum dose to Heart and Cord, Mean lung dose (MLD) and volume of lung receiving 20Gy (V20Gy) were computed. PTV parameters compared by percentage difference between AXB and AAA parameters. OAR parameters and HI compared by absolute difference between two calculations. For analysis, paired t-test performed over the parameters. Results: Compared to AAA, AXB SRS plans have on average 3.2% lower D99, 6.5% lower CI and 3cc less Brain_V12. However, AXB SBRT plans have higher D1, R50 and Dmean by 3.15%, 1.63% and 2.5%. For SRS and SBRT, AXB plans have average HI 2 % and 4.4% higher than AAA plans. In both techniques, all other parameters vary within 1% or 1Gy. In both sets only two parameters have P>0.05. Conclusion: Even though t-test results signify difference between AXB and AAA plans, dose differences in dose estimations by both algorithms are clinically insignificant.

TU‐F‐18C‐09: Mammogram Surveillance Using Texture Analysis for Breast Cancer Patients After Radiation Therapy

PURPOSE To study the feasibility of applying cancer risk model established from treated patients to predict the risk of recurrence on follow-up mammography after radiation therapy for both ipsilateral and contralateral breast. METHODS An extensive set of textural feature functions was applied to a set of 196 Mammograms from 50 patients. 56 Mammograms from 28 patients were used as training set, 44 mammograms from 22 patients were used as test set and the rest were used for prediction. Feature functions include Histogram, Gradient, Co-Occurrence Matrix, Run-Length Matrix and Wavelet Energy. An optimum subset of the feature functions was selected by Fisher Coefficient (FO) or Mutual Information (MI) (up to top 10 features) or a method combined FO, MI and Principal Component (FMP) (up to top 30 features). One-Nearest Neighbor (1-NN), Linear Discriminant Analysis (LDA) and Nonlinear Discriminant Analysis (NDA) were utilized to build a risk model of breast cancer from the training set of mammograms at the time of diagnosis. The risk model was then used to predict the risk of recurrence from mammogram taken one year and three years after RT. RESULTS FPM with NDA has the best classification power in classifying the training set of the mammogram with lesions versus those without lesions. The model of FPM with NDA achieved a true positive (TP) rate of 82% compared to 45.5% of using FO with 1-NN. The best false positive (FP) rates were 0% and 3.6% in contra-lateral breast of 1-year and 3-years after RT, and 10.9% in ipsi-lateral breast of 3-years after RT. CONCLUSION Texture analysis offers high dimension to differentiate breast tissue in mammogram. Using NDA to classify mammogram with lesion from mammogram without lesion, it can achieve rather high TP and low FP in the surveillance of mammogram for patient with conservative surgery combined RT.