SU‐FF‐T‐362: PLanUNC as An Open‐Source Radiotherapy Planning System for Research and Education

Schreiber, Eric; Xu, Zhiyong; Lorenzen, Ann-Christin; Foskey, Mark; Cullip, T; Tracton, Gregg; Chaney, Edward L.

doi:10.1118/1.2241282

Cited by 6 publications

(3 citation statements)

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“…CT intensities are based on the absorptivity of tissue to radiation, so it is possible to calculate the amount of energy a given beam deposits in each portion of tissue it passes through or near, taking into account the attenuation of the beam and the scattering of radiation into neighboring tissue. We use the treatment planning system known as PlanUNC [16] to calculate dose. Dose calculations for treatment planning are expected to be ±3% compared to measurements.…”

Section: Methodsmentioning

confidence: 99%

Image Estimation from Marker Locations for Dose Calculation in Prostate Radiation Therapy

Lee¹,

Foskey²,

Levy

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. Tracking implanted markers in the prostate during each radiation treatment delivery provides an accurate approximation of prostate location, which enables the use of higher daily doses with tighter margins of the treatment beams and thus improves the efficiency of the radiotherapy. However, the lack of 3D image data with such a technique prevents calculation of delivered dose as required for adaptive planning. We propose to use a reference statistical shape model generated from the planning image and a deformed version of the reference model fitted to the implanted marker locations during treatment to estimate a regionally dense deformation from the planning space to the treatment space. Our method provides a means of estimating the treatment image by mapping planning image data to treatment space via the deformation field and therefore enables the calculation of dose distributions with marker tracking techniques during each treatment delivery.

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Section: Methodsmentioning

confidence: 99%

Image Estimation from Marker Locations for Dose Calculation in Prostate Radiation Therapy

Lee¹,

Foskey²,

Levy

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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“…The tool is designed with adaptive radiation therapy in mind. For manual segmentation, it extends functionality found in in the treatment planning system PlanUNC [5]. In turn, the segmentation component of PlanUNC was a development of two earlier tools, IMEX [6] and MASK [7].…”

mentioning

confidence: 99%

SU‐FF‐I‐58: A Software Toolkit for Multi‐Image Registration and Segmentation in IGRT and ART

Foskey¹,

Gash²,

Han³

et al. 2007

Medical Physics

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Purpose: Extracting information from images for image‐guided and adaptive radiation therapy usually involves segmentation and/or registration with a reference image. The purpose of this research was to develop and make freely available a software toolkit for research use that combines several registration and segmentation methods and is applicable to problems involving multiple sequential images. Method and Materials: Two automatic approaches have been integrated with standard interactive methods. One automatic approach solves PDEs describing visco‐elastic flow to register a set of target images with a reference image. Segmentations can then be transferred from the reference image to target images via corresponding deformation fields. The second method segments images via Bayesian posterior optimization of a 3D deformable shape model called an m‐rep. User efficiency has been improved by the ability to queue, view, work with, and compare multiple images and segmentations during a single session. The software was developed in C++ and runs on Windows and Linux. Results: The GUI, including a window with panes for standard orthogonal 2D views linked to a second 3D window, preserves a common look and feel for all three approaches. A set of treatment images of the same patient initially are rigidly registered to a reference image. The images are then segmented by the methods of choice. Unsatisfactory automatic results are edited with conventional methods. Registered images and segmentations are output in DICOM RT format. Conclusion: The toolkit combines multiple approaches for image registration and segmentation in a system specifically designed to support research on IGRT and ART. A cost‐free license to download and use the software is available at http://planunc.radonc.unc.edu. Research supported by NCI/NCRR R01 RR 01861501.

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“…The main purpose of this paper is to compare the results obtained from the deterministic calculations of PLanUNC (PLUNC), 2) a set of software tools for radiotherapy treatment planning (RTP), with those obtained by means of MCNP5 (Monte Carlo N-Particle transport code) 3) utilizing the RANDO ® phantom 4) as the patient model and the MultiLeaf Collimated (MLC) Linear Accelerator (LinAc) Elekta Precise as the irradiation source.…”

Section: Introductionmentioning

confidence: 99%

Comparison of MCNP5 Dose Calculations inside the RANDO® Phantom Irradiated with a MLC LinAc Photon Beam against Treatment Planning System PLUNC

Aranda

Herrero

Vidal

et al. 2011

Progress in Nuclear Science and Technology

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MC treatment planning techniques provide a very accurate dose calculation compared to 'conventional' deterministic treatment planning systems. In the present work, PLanUNC (PLUNC), a set of software tools for radiotherapy treatment planning (RTP), is compared with MCNP5 (Monte Carlo N-Particle transport code) by calculating dose maps inside the RANDO ® phantom, utilized as the patient model, irradiated with different field sizes with the MultiLeaf Collimated (MLC) Linear Accelerator (LinAc) Elekta Precise. PLUNC was initially coupled with MCNP5 and so exactly the same patient and plan parameters can be utilized in both dose calculation processes. A MLC Linear Accelerator was commissioned for PLUNC and a MCNP5 model used in the calculations. The coupling of MCNP5 with PLUNC has been achieved via a series of Matlab interfaces, which extract patient and beam information created with PLUNC during the treatment plan and write it in MCNP5 input deck format. A set of Computer Tomography images of the RANDO ® phantom was obtained and formatted. The CT slices are input in PLUNC, which performs the segmentation by defining anatomical structures. The Matlab algorithm developed by the authors, validated in previous works writes the phantom information in MCNP5 input deck format. Both calculations result in mapping of dose distribution inside the phantom. MCNP5 utilizes the FMESH tool, superimposed mesh tally, which allows registering the results over the problem geometry. Resulting dose maps are compared.

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SU‐FF‐T‐362: PLanUNC as An Open‐Source Radiotherapy Planning System for Research and Education

Cited by 6 publications

References 0 publications

Image Estimation from Marker Locations for Dose Calculation in Prostate Radiation Therapy

Image Estimation from Marker Locations for Dose Calculation in Prostate Radiation Therapy

SU‐FF‐I‐58: A Software Toolkit for Multi‐Image Registration and Segmentation in IGRT and ART

Comparison of MCNP5 Dose Calculations inside the RANDO® Phantom Irradiated with a MLC LinAc Photon Beam against Treatment Planning System PLUNC

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