Application of an inverse kernel concept to Monte Carlo based IMRT

Abstract

Inverse treatment planning by means of pencil beam algorithms can lead to errors in the calculation of dose in areas without secondary electron equilibrium. Monte Carlo (MC) simulations give accurate results in such areas but result in increased computation times. We present a new, so-called inverse kernel concept that offers MC precision in inverse treatment planning with acceptable computation times and memory consumption. Inverse kernels are matrices that describe the dose contribution from all bixels of a beam to a distinct voxel of the patient phantom. The concept is similar to other generalized pencil-beam concepts, except that inverse kernel elements are precalculated using a single MC simulation and stored as binary trees. In this procedure a modified MC code (XVMC) is applied to trace the photon history for each dose deposition. Iterative optimization is then applied in a second step. The inverse process is separated into (i) a slower MC simulation and (ii) a faster iterative optimization, followed by (iii) the segmentation procedure, and (iv) a final MC dose calculation step including a segment weight reoptimization. Inverse kernel optimization, or IKO, with segmentation and reoptimization steps is demonstrated by means of a lung cancer case. To demonstrate the superiority of an inverse MC system over pencil-beam or collapsed-cone based systems, the final result of the IKO is compared to plans where all segments have been calculated by pencil beam or collapsed cone, respectively. Dose-volume histograms and dose-difference histograms show remarkable differences, which can be attributed to systematic errors in both algorithms. IKO is a precise, nonhybrid, inverse MC treatment planning system which suits current clinical needs, as several optimization steps can follow one single MC-simulation step for a distinct beam setup.

A Biologically Adapted Dose-Escalation Approach, Demonstrated for 18F-FET-PET in Brain Tumors

“…The dose-painting model in this work is implemented into the inverse Monte Carlo treatment-planning system IKO [4] developed at our department. To achieve the dose distribution, we interpret an 18 F-FET-PET as a map of radioresistant areas in GBM.…”

“…After the delineation of target volume and OARs by a physician, an initial treatment setup was generated using five to seven coplanar beams adapted to the target volume. PET, CT, volume of interest, and the initial treatment setup were then transferred into the inverse Monte Carlo treatment-planning system IKO [4,16].…”

“…The core of the IKO treatment-planning system is the separation of the time-consuming Monte Carlo dose algorithms from the optimization procedure [4]. For this study, the Monte Carlo algorithms XVMC [9] including the head model VEFM [10] for photons was used.…”

Entropy as a quality descriptor for the dose distribution — theory and practice for the patient target volume

“…We also add a margin of unspecified tissue (UT) around the target as another organ at risk (OAR) to avoid the hot spots close to the target. We compare three plans obtained by different methods for the same patient and constraint setup: -Monte Carlo calculation of a set of segments obtained from the commercial system (denoted as TMS); -Monte Carlo calculation of the same set of segments with weights optimised to obtain required dose distribution, segment kernel optimisation -denoted as SKO, [18]; -Monte Carlo calculation of fluence maps defined by inverse kernel optimisation, denoted as IKO, [19].…”

18F-FET-PET-Based Dose Painting by Numbers with Protons

“…In three brain tumor planning studies, we demonstrate the implementation of a voxel-by-voxel dose painting with protons based on 18 F-FET-PET imaging. The optimization of the proton plans was done using the inverse Monte Carlo treatment-planning system (iMCTPS) IKO (inverse kernel optimization) developed at our department [3,21]. Finally, the results are compared to previously published photon plans [27].…”