Anterior Interosseous Nerve to Ulnar Nerve Transfer: A systematic review

Background

Objectives

Methods

Results

Conclusion

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.

Initial characterization of a gel patch dosimeter for in vivo dosimetry

In vivo dosimetry is a greatly underutilized tool for patient safety in clinical external beam radiotherapy treatments, despite being recommended by several national and international organizations (AAPM, ICRU, IAEA, NACP). The reasons for this underutilization mostly relate to the feasibility and cost of in vivo dosimetry methods. Due to the increase in the number of beam angles and dose per fraction in modern treatments, there is a compelling need for a novel dosimeter that is robust and affordable while able to operate properly in these complex conditions. This work presents a gel patch dosimeter as a novel method of in vivo dosimetry. DEFGEL, a 6% T normoxic polyacrylamide gel, was injected into 1 cm thick acrylic molds to create 1 cm thick small cylindrical patch dosimeters. To evaluate the change in optical density due to radiation induced polymerization, dosimeters were scanned before and after irradiation using an in-house developed laser densitometer. The dose-responses of three separate batches of gel were evaluated and compared to check for linearity and repeatability. The response development time was evaluated to ensure that the patch dosimeter could be high throughput. Additionally, the potential of this system to be used as an in vivo dosimeter was tested with a clinically relevant end-to-end in vivo phantom test. All irradiations were performed with a Varian Clinac 21EX at the University of Wisconsin Medical Radiation Research Center (UWMRRC). The dose-response of all three batches of gel was found to be linear within the range of 2-20 Gy. At doses below 0.5 Gy the statistical uncertainties were prohibitively large to make quantitative assessments of the results. The three batches demonstrated good repeatability in the range of 2 Gy to up to 10 Gy, with only slight variations in response at higher doses. For low doses the dosimeter fully developed within an hour while at higher doses they fully developed within four hours. During the in vivo phantom test the predicted patch absorbed dose was 4.23 Gy while the readout dose was evaluated to be 4.37 Gy, which corresponds to a 3.2% discrepancy. The dosimeter and densitometer pairing shows promise as an in vivo dosimetry system, especially for hypofractionated or MRI-guided radiotherapy treatments where higher doses are prescribed.

Consistency evaluation between EGSnrc and Geant4 charged particle transport in an equilibrium magnetic field

Following the proposal by several groups to integrate magnetic resonance imaging (MRI) with radiation therapy, much attention has been afforded to examining the impact of strong (on the order of a Tesla) transverse magnetic fields on photon dose distributions. The effect of the magnetic field on dose distributions must be considered in order to take full advantage of the benefits of real-time intra-fraction imaging. In this investigation, we compared the handling of particle transport in magnetic fields between two Monte Carlo codes, EGSnrc and Geant4, to analyze various aspects of their electromagnetic transport algorithms; both codes are well-benchmarked for medical physics applications in the absence of magnetic fields. A water-air-water slab phantom and a water-lung-water slab phantom were used to highlight dose perturbations near high- and low-density interfaces. We have implemented a method of calculating the Lorentz force in EGSnrc based on theoretical models in literature, and show very good consistency between the two Monte Carlo codes. This investigation further demonstrates the importance of accurate dosimetry for MRI-guided radiation therapy (MRIgRT), and facilitates the integration of a ViewRay MRIgRT system in the University of Wisconsin-Madison's Radiation Oncology Department.

Comparison of MCNPX and Geant4 proton energy deposition predictions for clinical use

Several different Monte Carlo codes are currently being used at proton therapy centers to improve upon dose predictions over standard methods using analytical or semi-empirical dose algorithms. There is a need to better ascertain the differences between proton dose predictions from different available Monte Carlo codes. In this investigation Geant4 and MCNPX, the two most-utilized Monte Carlo codes for proton therapy applications, were used to predict energy deposition distributions in a variety of geometries, comprising simple water phantoms, water phantoms with complex inserts and in a voxelized geometry based on clinical CT data. The Gamma analysis was used to evaluate the differences of the predictions between the codes. The results show that in all the cases the agreement was better than clinical acceptance criteria.