Clinical implications of the anisotropic analytical algorithm for IMRT treatment planning and verification

PURPOSE: To determine the implications of the use of the Anisotropic Analytical Algorithm(AAA) for the production and dosimetric verification of IMRT plans for treatments of the prostate, parotid, nasopharynx and lung. METHODS: 72 IMRT treatment plans produced using the Pencil Beam Convolution (PBC)algorithm were recalculated using the AAA and the dose distributions compared. 24 of the plans were delivered to inhomogeneous phantoms and verification measurements made using a pinpoint ionisation chamber. The agreement between the AAA and measurement was determined. RESULTS: Small differences were seen in the prostate plans, with the AAA predicting slightly lower minimum PTV doses. In the parotid plans, there were small increases in the lens and contralateral parotid doses while the nasopharyngeal plans revealed a reduction in the volume of the PTV covered by the 95% isodose (the V95%) when the AAA was used. Large changes were seen in the lung plans, the AAA predicting reductions in the minimum PTV dose and large reductions in the V95%. The AAA also predicted small increases in the mean dose to the normal lung and the V20. In the verification measurements, all AAA calculations were within 3% or 3.5mm distance to agreement of the measured doses. Conclusions: The AAA should be used in preference to the PBC algorithm for treatments involving low density tissue but this may necessitate re-evaluation of plan acceptability criteria. Improvements to the Multi-Resolution Dose Calculation algorithm used in the inverse planning are required to reduce the convergence error in the presence of lung tissue. There was excellent agreement between the AAA and verification measurements for all sites

Clinical implications of the anisotropic analytical algorithm for IMRT treatment planning and verification

PURPOSE: To determine the implications of the use of the Anisotropic Analytical Algorithm(AAA) for the production and dosimetric verification of IMRT plans for treatments of the prostate, parotid, nasopharynx and lung. METHODS: 72 IMRT treatment plans produced using the Pencil Beam Convolution (PBC)algorithm were recalculated using the AAA and the dose distributions compared. 24 of the plans were delivered to inhomogeneous phantoms and verification measurements made using a pinpoint ionisation chamber. The agreement between the AAA and measurement was determined. RESULTS: Small differences were seen in the prostate plans, with the AAA predicting slightly lower minimum PTV doses. In the parotid plans, there were small increases in the lens and contralateral parotid doses while the nasopharyngeal plans revealed a reduction in the volume of the PTV covered by the 95% isodose (the V95%) when the AAA was used. Large changes were seen in the lung plans, the AAA predicting reductions in the minimum PTV dose and large reductions in the V95%. The AAA also predicted small increases in the mean dose to the normal lung and the V20. In the verification measurements, all AAA calculations were within 3% or 3.5mm distance to agreement of the measured doses. Conclusions: The AAA should be used in preference to the PBC algorithm for treatments involving low density tissue but this may necessitate re-evaluation of plan acceptability criteria. Improvements to the Multi-Resolution Dose Calculation algorithm used in the inverse planning are required to reduce the convergence error in the presence of lung tissue. There was excellent agreement between the AAA and verification measurements for all sites

A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite size pencil beam (FSPB) algorithm with a 3D-density correction method on GPU. This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework [Gu et al. Phys. Med. Biol. 54 6287-97, 2009]. Dosimetric evaluations against Monte Carlo dose calculations are conducted on 10 IMRT treatment plans (5 head-and-neck cases and 5 lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 second (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (~5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning

Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime

Utilizing Log Files For Treatment Planning And Delivery Qa In Radiotherapy

Purpose: Monte Carlo-based log file quality assurance (LF-MC QA) is investigated as an alternative method to phantom-based patient-specific quality assurance in radiotherapy (e.g. ArcCHECK QA (AC QA)). Methods: First, the shortcomings of AC QA were investigated. The sensitivity dependence of ArcCHECK diodes on dose rate (in-field) and energy (primarily out-of-field) was quantified. LF-MC QA was then analyzed on the phantom geometry. Planned (‘Plan’) and LF-reconstructed CS and MC doses were compared with each other and AC measurement via statistical (mean ± StdDev(σ)) and gamma analyses to isolate dosimetric uncertainties and quantify the relative accuracies of AC QA and LF-MC QA. LF-MC QA was then analyzed on the patient geometry. Calculation algorithm dependent (Plan-MC vs Plan-CS) and delivery error (LF-MC vs Plan-MC) dependent dosimetric discrepancies were isolated. Dose discrepancies were evaluated using PTV Dmean, D99, and D1 as well as tumor control probability (TCP). Dose discrepancy due to calculation algorithm was further assessed as a function of heterogeneity and beam modulation complexity (MU/Rx). LF QA results were compared to clinical AC QA results. Various LF-MC QA pass/fail protocols were assessed. Results: Calculation and ArcCHECK measurement differed up to 1.5% in-field due to variations in dose rate and up to 5% out-of-field due to energy effects. On the ArcCHECK geometry, phantom-dependent, calculation algorithm-dependent (MC vs. CS), and delivery error-dependent dose uncertainties were 0.8±1.2%, 0.2±1.1%, and 0.1±0.9% respectively. On the patient anatomy, percent differences in [PTV Dmean, D99, D1] were [-0.1±0.1%, 0.0±0.2%, -0.2±0.2%] for machine delivery error, [-3.4±1.9%, -4.6±2.8%, -1.2±2.8%] for dose calculation difference, and [0.5±2.0%, 0.2±1.2%, 2.6±4.1%] due to limited VMAT beam sampling. Drop in TCP due to calculation difference (MC-CS) was -3.1±1.8% [min -5.7%]. 41% of PTV D99 dose calculation difference was due to beam complexity. Heterogeneity effects were negligible for H&N. For lung, 18% of dose calculation difference on PTV Dmean was due to heterogeneity Conclusions: ArcCHECK QA was consistently incapable of catching clinically relevant dose discrepancies as calculated on the patient anatomy using LF-MC QA

Dosimetric accuracy of tomotherapy dose calculation in thorax lesions

<p>Abstract</p> <p>Background</p> <p>To analyse limits and capabilities in dose calculation of collapsed-cone-convolution (CCC) algorithm implemented in helical tomotherapy (HT) treatment planning system for thorax lesions.</p> <p>Methods</p> <p>The agreement between measured and calculated dose was verified both in homogeneous (Cheese Phantom) and in a custom-made inhomogeneous phantom. The inhomogeneous phantom was employed to mimic a patient's thorax region with lung density encountered in extreme cases and acrylic inserts of various dimensions and positions inside the lung cavity. For both phantoms, different lung treatment plans (single or multiple metastases and targets in the mediastinum) using HT technique were simulated and verified. Point and planar dose measurements, both with radiographic extended-dose-range (EDR2) and radiochromic external-beam-therapy (EBT2) films, were performed. Absolute point dose measurements, dose profile comparisons and quantitative analysis of gamma function distributions were analyzed.</p> <p>Results</p> <p>An excellent agreement between measured and calculated dose distributions was found in homogeneous media, both for point and planar dose measurements. Absolute dose deviations <3% were found for all considered measurement points, both inside the PTV and in critical structures. Very good results were also found for planar dose distribution comparisons, where at least 96% of all points satisfied the gamma acceptance criteria (3%-3 mm), both for EDR2 and for EBT2 films. Acceptable results were also reported for the inhomogeneous phantom. Similar point dose deviations were found with slightly worse agreement for the planar dose distribution comparison: 96% of all points passed the gamma analysis test with acceptable levels of 4%-4 mm and 5%-4 mm, for EDR2 and EBT2 films respectively. Lower accuracy was observed in high dose/low density regions, where CCC seems to overestimate the measured dose around 4-5%.</p> <p>Conclusions</p> <p>Very acceptable accuracy was found for complex lung treatment plans calculated with CCC algorithm implemented in the tomotherapy TPS even in the heterogeneous phantom with very low lung-density.</p

Monte Carlo vs. Pencil Beam based optimization of stereotactic lung IMRT

<p>Abstract</p> <p>Background</p> <p>The purpose of the present study is to compare finite size pencil beam (fsPB) and Monte Carlo (MC) based optimization of lung intensity-modulated stereotactic radiotherapy (lung IMSRT).</p> <p>Materials and methods</p> <p>A fsPB and a MC algorithm as implemented in a biological IMRT planning system were validated by film measurements in a static lung phantom. Then, they were applied for static lung IMSRT planning based on three different geometrical patient models (one phase static CT, density overwrite one phase static CT, average CT) of the same patient. Both 6 and 15 MV beam energies were used. The resulting treatment plans were compared by how well they fulfilled the prescribed optimization constraints both for the dose distributions calculated on the static patient models and for the accumulated dose, recalculated with MC on each of 8 CTs of a 4DCT set.</p> <p>Results</p> <p>In the phantom measurements, the MC dose engine showed discrepancies < 2%, while the fsPB dose engine showed discrepancies of up to 8% in the presence of lateral electron disequilibrium in the target. In the patient plan optimization, this translates into violations of organ at risk constraints and unpredictable target doses for the fsPB optimized plans. For the 4D MC recalculated dose distribution, MC optimized plans always underestimate the target doses, but the organ at risk doses were comparable. The results depend on the static patient model, and the smallest discrepancy was found for the MC optimized plan on the density overwrite one phase static CT model.</p> <p>Conclusions</p> <p>It is feasible to employ the MC dose engine for optimization of lung IMSRT and the plans are superior to fsPB. Use of static patient models introduces a bias in the MC dose distribution compared to the 4D MC recalculated dose, but this bias is predictable and therefore MC based optimization on static patient models is considered safe.</p

Validation of an in vivo transit dosimetry algorithm using Monte Carlo simulations and ionization chamber measurements

Purpose Transit dosimetry is a safety tool based on the transit images acquired during treatment. Forward-projection transit dosimetry software, as PerFRACTION, compares the transit images acquired with an expected image calculated from the DICOM plan, the CT, and the structure set. This work aims to validate PerFRACTION expected transit dose using PRIMO Monte Carlo simulations and ionization chamber measurements, and propose a methodology based on MPPG5a report. Methods The validation process was divided into three groups of tests according to MPPG5a: basic dose validation, IMRT dose validation, and heterogeneity correction validation. For the basic dose validation, the fields used were the nine fields needed to calibrate PerFRACTION and three jaws-defined. For the IMRT dose validation, seven sweeping gaps fields, the MLC transmission and 29 IMRT fields from 10 breast treatment plans were measured. For the heterogeneity validation, the transit dose of these fields was studied using three phantoms: 10 , 30 , and a 3 cm cork slab placed between 10 cm of solid water. The PerFRACTION expected doses were compared with PRIMO Monte Carlo simulation results and ionization chamber measurements. Results Using the 10 cm solid water phantom, for the basic validation fields, the root mean square (RMS) of the difference between PerFRACTION and PRIMO simulations was 0.6%. In the IMRT fields, the RMS of the difference was 1.2%. When comparing respect ionization chamber measurements, the RMS of the difference was 1.0% both for the basic and the IMRT validation. The average passing rate with a ¿(2%/2 mm, TH = 20%) criterion between PRIMO dose distribution and PerFRACTION expected dose was 96.0% ± 5.8%. Conclusion We validated PerFRACTION calculated transit dose with PRIMO Monte Carlo and ionization chamber measurements adapting the methodology of the MMPG5a report. The methodology presented can be applied to validate other forward-projection transit dosimetry software.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Postprint (published version