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Journal ArticleDOI

CERR: A computational environment for radiotherapy research

Joseph O. Deasy, +2 more
- 01 May 2003 - 
- Vol. 30, Iss: 5, pp 979-985
TLDR
CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.
Abstract
A software environment is described, called the computational environment for radiotherapy research (CERR, pronounced "sir"). CERR partially addresses four broad needs in treatment planning research: (a) it provides a convenient and powerful software environment to develop and prototype treatment planning concepts, (b) it serves as a software integration environment to combine treatment planning software written in multiple languages (MATLAB, FORTRAN, C/C++, JAVA, etc.), together with treatment plan information (computed tomography scans, outlined structures, dose distributions, digital films, etc.), (c) it provides the ability to extract treatment plans from disparate planning systems using the widely available AAPM/RTOG archiving mechanism, and (d) it provides a convenient and powerful tool for sharing and reproducing treatment planning research results. The functional components currently being distributed, including source code, include: (1) an import program which converts the widely available AAPM/RTOG treatment planning format into a MATLAB cell-array data object, facilitating manipulation; (2) viewers which display axial, coronal, and sagittal computed tomography images, structure contours, digital films, and isodose lines or dose colorwash, (3) a suite of contouring tools to edit and/or create anatomical structures, (4) dose-volume and dose-surface histogram calculation and display tools, and (5) various predefined commands. CERR allows the user to retrieve any AAPM/RTOG key word information about the treatment plan archive. The code is relatively self-describing, because it relies on MATLAB structure field name definitions based on the AAPM/RTOG standard. New structure field names can be added dynamically or permanently. New components of arbitrary data type can be stored and accessed without disturbing system operation. CERR has been applied to aid research in dose-volume-outcome modeling, Monte Carlo dose calculation, and treatment planning optimization. In summary, CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.

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Citations
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Dissertation

Generalized Inverse Optimization with Application to Cancer Therapy

Taewoo Lee
TL;DR: New inverse optimization methodologies are studied that generalize the traditional method of solving inverse optimization problems and accommodate data that makes the standard method ill-posed and establish a new connection between inverse optimization and existing multiobjective optimization techniques.
Journal ArticleDOI

Defining and assessing an anisotropic delineation margin for modern radiotherapy.

TL;DR: The anisotropic approach was found to be superior to a conventional approach for target volumes >1400 cm3 only with significantly greater encompassment of interobserver variation, less missed malignant tissue and less included healthy tissue.
Journal ArticleDOI

A novel mathematical model to generate semi-automated optimal IMRT treatment plan based on predicted 3D dose distribution and prescribed dose.

TL;DR: In this paper , the QuadLin model has been used to predict the 3D distribution dose (3D3) of a new patient based on the treatment plans of similar recent patients.
Journal ArticleDOI

High-grade glioma RT and reRT treatment planning utilizing TSPO PET with 18F-GE-180

TL;DR: In this paper , the authors evaluated the possible benefit of using 18F-GE-180 PET in primary radiotherapy and re-radiation (reRT) treatment planning for high grade glioma (HGG) patients.
References
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Book ChapterDOI

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Journal ArticleDOI

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Journal ArticleDOI

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Journal ArticleDOI

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Journal Article

Extending python with Fortran

TL;DR: Pyfort as mentioned in this paper is a tool for connecting Fortran routines to Python, using a syntax that is close to a subset of the Fortran 95 interface syntax, which can produce one or more Python extension modules which can then be loaded into Python, either statically or dynamically, as desired.
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