CERR: A computational environment for radiotherapy research

The feasibility of accounting of the dose from kilovoltage cone‐beam CT in treatment planning has been discussed previously for a single cone‐beam CT (CBCT) beam from one manufacturer. Modeling the beams and computing the dose from the full set of beams produced by a kilovoltage cone‐beam CT system requires extensive beam data collection and verification, and is the purpose of this work. The beams generated by Elekta X‐ray volume imaging (XVI) kilovoltage CBCT (kV CBCT) system for various cassettes and filters have been modeled in the Philips Pinnacle treatment planning system (TPS) and used to compute dose to stack and anthropomorphic phantoms. The results were then compared to measurements made using thermoluminescent dosimeters (TLDs) and Monte Carlo (MC) simulations. The agreement between modeled and measured depth‐dose and cross profiles is within 2% at depths beyond 1 cm for depth‐dose curves, and for regions within the beam (excluding penumbra) for cross profiles. The agreements between TPS‐calculated doses, TLD measurements, and Monte Carlo simulations are generally within 5% in the stack phantom and 10% in the anthropomorphic phantom, with larger variations observed for some of the measurement/calculation points. Dose computation using modeled beams is reasonably accurate, except for regions that include bony anatomy. Inclusion of this dose in treatment plans can lead to more accurate dose prediction, especially when the doses to organs at risk are of importance.PACS numbers: 87.55.D, 87.55.K, 87.56.bd

Section: Methodsmentioning

confidence: 99%

Commissioning kilovoltage cone‐beam CT beams in a radiation therapy treatment planning system

Alaei

Spezi

2012

“…( 14 , 15 ) For each voxel, dose was converted to the normalized total dose (NTD), (16) which is the biologically equivalent dose in 2 Gy/fraction. The formula used to convert dose to one voxel for the i th fraction to NTD is

{NTD}_{i} MJX-TeXAtom-ORD \frac{d_{MJX-TeXAtom-ORD i} [α / β + d_{MJX-TeXAtom-ORD i}]}{α / β + 2 Gy},

where α and β are the parameters in the linear‐quadratic model.…”

Section: Methodsmentioning

confidence: 99%

PTV margin for dose‐escalated radiation therapy of prostate cancer with daily online realignment using internal fiducial markers: Monte Carlo approach and dose population histogram (DPH) analysis

Zhang

Moiseenko

Liu

2006

Using internal fiducial markers and electronic portal imaging (EPI) to realign patients has been shown to significantly reduce positioning uncertainties in prostate radiation treatment. This creates the possibility of decreasing the planning target volume (PTV) margin added on the clinical target volume (CTV), which in turn may allow for dose escalation. We compared the outcome of two plans: 70 Gy/35 fx, 10‐mm PTV margin without patient realignment (Reference Plan) and 78 Gy/39 fx, 5‐mm PTV margin with patient realignment (Escalated Plan). Four‐field‐oblique (gantry angles 35°, 90°, 270°, 325°) beam arrangement was used. Monte Carlo code was used to simulate the daily organ motion. Dose to each organ was calculated. Tumor control probability (TCP) and the effective dose to critical organs false(Defffalse) were calculated using the biologically normalized dose‐volume histograms. By comparing the biological factors, we found that the prescription dose can be escalated to 78 Gy/39 fx with a 5‐mm PTV margin when using internal fiducial markers and EPI. Based on the available dose‐response data for intermediate risk prostate patients, this will result in a 20% increase of local control and significantly reduced rectal complications provided that less serial dose‐volume behavior of rectum is proven.PACS number: 87.50.‐a

“…The Matlab‐based software known as CERR (Computational Environment for Radiotherapy Research) provides an open‐source platform that is effective for prototype treatment planning and evaluation, especially for research purposes. Furthermore, CERR is capable of working with patient files from DICOM or AAPM/RTOG archives, which makes data transfer between multiple platforms straightforward 3. For analysis across multiple patients, the Matlab‐based software DREES (dose–response explorer system) is an open‐source extension of CERR which provides a data‐driven analysis of treatment outcomes; DREES provides analytical tools such as fitting tumor control probability (TCP) and normal tissue complication probability (NTCP) curves, modeling and visualizing dose‐volume and plan metrics, and estimating uncertainty in planning parameters 4.…”

Section: Introductionmentioning

confidence: 99%

DVH Analytics: A DVH database for clinicians and researchers

Cutright

Gopalakrishnan

Roy

et al. 2018

In this study, we build a vendor‐agnostic software application capable of importing and analyzing non‐image‐based DICOM files for various radiation treatment modalities (i.e., DICOM RT Dose, RT Structure, and RT Plan files). Dose‐volume histogram (DVH) and planning data are imported into a SQL database, and methods are provided to manage, edit, view, and download data. Furthermore, the software provides various analytical tools for plan evaluations, plan comparisons, benchmarking, and plan outcome predictions. DVH Analytics is developed using Python, including libraries such as pydicom, dicompyler, psycopg2, SciPy, Statsmodels, and Bokeh for parsing DICOM files, computing DVHs, communicating with a PostgreSQL database, performing statistical analyses, and creating a web‐based user interface. This software is open‐source and compatible with Windows, Mac OS, and Linux. For proof‐of‐concept, a database with over 3,000 DVHs from a single physician's head & neck practice was built. From these data, differences in means, correlations, and temporal trends in dose to multiple organs‐at‐risk (OARs) were observed. Furthermore, an example of the predictive regression tool is reported, where a model was constructed to predict maximum dose to brainstem based on minimum distance from planning target volume (PTV) and treatment beam source‐to‐skin distance (SSD). With DVH Analytics, we have developed a free, open‐source software program to parse, organize, and analyze non‐image‐based DICOM data for use in a radiation oncology setting. Furthermore, this software can be used to generate statistical models for the purposes of quality control or outcome predictions and correlations.