Investigating beam range uncertainty in proton prostate treatment using pelvic-like biological phantoms.
Wencheng ShaoYunhe XieJianan WuLiyan ZhangSchuemann JanHsiao-Ming LuPublished in: Physics in medicine and biology (2021)
This study aims to develop a method for verifying site-specific and/or beam path specific proton beam range, which could reduce range uncertainty margins and the associated treatment complications. It investigates the range uncertainties from both CT HU to relative stopping power conversion and patient positioning errors for prostate treatment using pelvic-like biological phantoms. Three 25 × 14 × 12 cm3phantoms, made of fresh animal tissues mimicking the pelvic anatomies of prostate patients, were scanned with a general electric CT simulator. A 22 cm circular passive scattering beam with 29 cm range and 8 cm modulation width was used to measure the water equivalent path lengths (WEPL) through the phantoms at multiple points using the dose extinction method with a MatriXXPT detector. The measured WEPLs were compared to those predicted by TOPAS simulations and ray-tracing WEPL calculations. For the three phantoms, the WEPL differences between measured and theoretical prediction (WDMT) are below 1.8% for TOPAS, and 2.5% for ray-tracing. WDMT varies with phantom anatomies by about 0.5% for both TOPAS and ray-tracing. WDMT also correlates with the tissue types of a specific treated region. For the regions where the proton beam path is parallel to sharp bone edges, the WDMTs of TOPAS and ray-tracing respectively reach up to 1.8% and 2.5%. For the region where proton beams pass through just soft tissues, the WDMT is mostly less than 1% for both TOPAS and ray-tracing. For prostate treatments, range uncertainty depends on the tissue types within a specific treated region, patient anatomies and the range calculation methods in the planning algorithms. Our study indicates range uncertainty is less than 2.5% for the whole treated region with both ray-tracing and TOPAS, which suggests the potential to reduce the current 3.5% range uncertainty margin used in the clinics by at least 1% even for single-energy CT data.
Keyphrases
- monte carlo
- prostate cancer
- image quality
- computed tomography
- gene expression
- newly diagnosed
- primary care
- case report
- dual energy
- end stage renal disease
- benign prostatic hyperplasia
- molecular dynamics
- peritoneal dialysis
- machine learning
- magnetic resonance imaging
- emergency department
- prognostic factors
- postmenopausal women
- bone mineral density
- big data
- risk assessment
- deep learning
- molecular dynamics simulations
- combination therapy
- density functional theory
- artificial intelligence
- pet ct