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A protocol to extend the longitudinal coverage of on-board cone-beam CT.

Dandan Zheng | Jun Lu | Ariel Jefferson | Cheng Zhang | Jian Wu | William Sleeman | Elisabeth Weiss | Nesrin Dogan | Shiyu Song | Jeffrey Williamson
Journal of applied clinical medical physics | 2012

The longitudinal coverage of a LINAC-mounted CBCT scan is limited to the corresponding dimensional limits of its flat panel detector, which is often shorter than the length of the treatment field. These limits become apparent when fields are designed to encompass wide regions, as when providing nodal coverage. Therefore, we developed a novel protocol to acquire double orbit CBCT images using a commercial system, and combine the images to extend the longitudinal coverage for image-guided adaptive radiotherapy (IGART). The protocol acquires two CBCT scans with a couch shift similar to the "step-and-shoot" cine CT acquisition, allowing a small longitudinal overlap of the two reconstructed volumes. An in-house DICOM reading/writing software was developed to combine the two image sets into one. Three different approaches were explored to handle the possible misalignment between the two image subsets: simple stacking, averaging the overlapped volumes, and a 3D-3D image registration with the three translational degrees of freedom. Using thermoluminescent dosimeters and custom-designed holders for a CTDI phantom set, dose measurements were carried out to assess the resultant imaging dose of the technique and its geometric distribution. Deformable registration was tested on patient images generated with the double-orbit protocol, using both the planning FBCT and the artificially deformed CBCT as source images. The protocol was validated on phantoms and has been employed clinically for IRB-approved IGART studies for head and neck and prostate cancer patients.

Pubmed ID: 22766950

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Insight Segmentation and Registration Toolkit (tool)

RRID:SCR_001149

Open source, cross platform library that provides developers with extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK builds on proven, spatially oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.

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