Monte Carlo calculation of dose distributions in oligometastatic patients planned for spine stereotactic ablative radiotherapy

Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

PURPOSE

The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and "4π" delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of "concurrent" Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner.

METHODS

The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively.

RESULTS

The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7-8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform.

CONCLUSIONS

This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.

Effective dose reduction in spine radiographic imaging by choosing the less radiation-sensitive side of the body

BACKGROUND CONTEXT

X-ray absorption is highest in the organs and tissues located closest to the radiation source. The photon flux that crosses the body decreases from the entry surface toward the image receptor. The internal organs absorb x-rays and shield each other during irradiation. Therefore, changing the x-ray projection angle relative to the patient for specific spine procedures changes the radiation dose that each organ receives. Every organ has different radiation sensitivity, so irradiation from different sides of the body changes the biological influence and radiation risk potential on the total body, that is the effective dose (ED).

PURPOSE

The study aimed to determine the less radiation-sensitive sides of the body during lateral and anterior-posterior (AP) or posterior anterior (PA) directions.

STUDY DESIGN

The study used exposure of patient phantoms and Monte Carlo simulation of the effective doses.

PATIENT SAMPLE

Calculations for adults and 10-year-old children were included because the pediatric population has a greater lifetime radiation risk than adults.

OUTCOME MEASURES

Pediatric and adult tissue and organ doses and ED from cervical, thoracic, and lumbar x-ray spine examinations were performed from different projections.

METHODS

Standard mathematical phantoms for adults and 10-year-old children, using PCXMC 2.0 software based on Monte Carlo simulations, were used to calculate pediatric and adult tissue and organ doses and ED. The study was not funded. The authors have no conflicts of interest to declare.

RESULTS

Spine x-ray exposure from various right (RT) LAT projection angles was associated with lower ED compared with the same left (LT) LAT projections (up to 28% and 27% less for children aged 10 and adults, respectively). The PA spine projections showed up to 64% lower ED for children aged 10 and 65% for adults than AP projections. The AP projection at the thoracic spine causes an excess breast dose of 543.3% and 597.0% for children aged 10 and adults, respectively.

CONCLUSIONS

Radiation ED in spine procedures can be significantly reduced by performing x-ray exposures through the less radiation-sensitive sides of the body, which are PA in the frontal position and right lateral in the lateral position.