| 1. |
- Aarup, Lasse Rye, et al.
(författare)
-
The effect of different lung densities on the accuracy of various radiotherapy dose calculation methods: Implications for tumour coverage
- 2009
-
Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 1879-0887 .- 0167-8140. ; 91:3, s. 405-414
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: To evaluate against Monte-Carlo the performance of various dose calculations algorithms regarding lung turnout coverage in stereotactic body radiotherapy (SBRT) conditions. Materials and methods: Dose distributions in virtual lung phantoms have been calculated using four commercial Treatment Planning System (TPS) algorithms and one Monte Carlo (MC) system (EGSnrc). We compared the performance of the algorithms in calculating the target dose for different degrees of lung inflation. The phantoms had a cubic 'body' and 'lung' and a central 2-cm diameter spherical 'tumour' (the body and turnout have unit density). The lung tissue was assigned five densities (rho(lung)): 0.01, 0.1, 0.2, 0.4 and 1 g/cm(3). Four-field treatment plans were calculated with 6- and 18 MV narrow beams for each value of rho(lung). We considered the Pencil Beam Convolution (PBCEl) and the Analytical Anisotropic Algorithm (AAA(ECl)) from Varian Eclipse and the Pencil Beam Convolution (PBCOMP) and the Collapsed Cone Convolution (CCCOMP) algorithms from Oncentra MasterPlan. Results: When changing rho(lung) from 0.4 to 0.1 g/cm(3), the MC median target dose decreased from 89.2% to 74.9% for 6 MV and from 83.3% to 61.6% for 18 MV (of dose maximum in the homogenous case at both energies), while for both PB algorithms the median target dose was virtually independent of lung density. Conclusions: Both PB algorithms overestimated the target dose, the overestimation increasing as rho(lung) decreased. Concerning target dose, the AAA(ECl) and CCCOMP algorithms appear to be adequate alternatives to MC. (C) 2009 Elsevier Ireland Ltd. All rights reserved. Radiotherapy and oncology 91 (2009) 405-414
|
|
| 2. |
- Ceberg, Sofie, et al.
(författare)
-
RapidArc treatment verification in 3D using polymer gel dosimetry and Monte Carlo simulation.
- 2010
-
Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 1361-6560 .- 0031-9155. ; 55:17, s. 4885-4898
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to verify the advanced inhomogeneous dose distribution produced by a volumetric arc therapy technique (RapidArc) using 3D gel measurements and Monte Carlo (MC) simulations. The TPS (treatment planning system)-calculated dose distribution was compared with gel measurements and MC simulations, thus investigating any discrepancy between the planned dose delivery and the actual delivery. Additionally, the reproducibility of the delivery was investigated using repeated gel measurements. A prostate treatment plan was delivered to a 1.3 liter nPAG gel phantom using one single arc rotation and a target dose of 3.3 Gy. Magnetic resonance imaging of the gel was carried out using a 1.5 T scanner. The MC dose distributions were calculated using the VIMC-Arc code. The relative absorbed dose differences were calculated voxel-by-voxel, within the volume enclosed by the 90% isodose surface (VOI(90)), for the TPS versus gel and TPS versus MC. The differences between the verification methods, MC versus gel, and between two repeated gel measurements were investigated in the same way. For all volume comparisons, the mean value was within 1% and the standard deviation of the differences was within 2.5% (1SD). A 3D gamma analysis between the dose matrices were carried out using gamma criteria 3%/3 mm and 5%/5 mm (% dose difference and mm distance to agreement) within the volume enclosed by the 50% isodose surface (VOI(50)) and the 90% isodose surface (VOI(90)), respectively. All comparisons resulted in very high pass rates. More than 95% of the TPS points were within 3%/3 mm of both the gel measurement and MC simulation, both inside VOI(50) and VOI(90). Additionally, the repeated gel measurements showed excellent consistency, indicating reproducible delivery. Using MC simulations and gel measurements, this verification study successfully demonstrated that the RapidArc plan was both accurately calculated and delivered as planned.
|
|
| 3. |
- Ceberg, Sofie, et al.
(författare)
-
Tumor-tracking radiotherapy of moving targets; verification using 3D polymer gel, 2D ion-chamber array and biplanar diode array
- 2010
-
Ingår i: Journal of Physics: Conference Series. - : IOP Publishing. - 1742-6588 .- 1742-6596. ; 250:1, s. 235-239
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to carry out a dosimetric verification of a dynamic multileaf collimator (DMLC)-based tumor-tracking delivery during respiratory-like motion. The advantage of tumor-tracking radiation delivery is the ability to allow a tighter margin around the target by continuously following and adapting the dose delivery to its motion. However, there are geometric and dosimetric uncertainties associated with beam delivery system constraints and output variations, and several investigations have to be accomplished before a clinical integration of this tracking technique. Two types of delivery were investigated in this study I) a single beam perpendicular to a target with a one dimensional motion parallel to the MLC moving direction, and II) an intensity modulated arc delivery (RapidArc®) with a target motion diagonal to the MLC moving direction. The feasibility study (I) was made using an 2D ionisation chamber array and a true 3D polymer gel. The arc delivery (II) was verified using polymer gel and a biplanar diode array. Good agreement in absorbed dose was found between delivery to a static target and to a moving target with DMLC tracking using all three detector systems. However, due to the limited spatial resolution of the 2D array a detailed comparison was not possible. The RapidArc® plan delivery was successfully verified using the biplanar diode array and true 3D polymer gel, and both detector systems could verify that the DMLC-based tumor-tracking delivery system has a very good ability to account for respiratory target motion.
|
|
| 4. |
- Unkelbach, Jan, et al.
(författare)
-
The role of computational methods for automating and improving clinical target volume definition
- 2020
-
Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 153, s. 15-25
-
Tidskriftsartikel (refereegranskat)abstract
- Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume(GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definitionand PTV margins have improved through the development of novel imaging techniques and better imageguidance, respectively. CTV definition is sometimes considered the weakest element in the planning pro-cess. CTV definition is particularly complex since the extension of microscopic disease cannot be seenusing currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowl-edge of the patterns of tumor progression. While CTV delineation has largely been considered the domainof radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses thecontributions that medical physics and computer science can make by developing computational meth-ods to support CTV definition. First, we overview the role of image segmentation algorithms, which mayin part automate CTV delineation through segmentation of lymph node stations or normal tissues repre-senting anatomical boundaries of microscopic tumor progression. The recent success of deep convolu-tional neural networks has also enabled learning entire CTV delineations from examples. Second, wediscuss the use of mathematical models of tumor progression for CTV definition, using as example theapplication of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consis-tent with neuroanatomy. We further consider statistical machine learning models to quantify lymphaticmetastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we dis-cuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well asgeneral limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.
|
|
