Motion-compensated estimation of delivered dose during external beam radiation therapy: implementation in Philips' Pinnacle(3) treatment planning system.

Purpose: Recent research efforts investigating dose escalation techniques for three-dimensional conformal radiation therapy (3D CRT) and intensity modulated radiation therapy (IMRT) have demonstrated great benefit when high-dose hypofractionated treatment schemes are implemented. The use of these paradigms emphasizes the importance of smaller treatment margins to avoid high dose to surrounding normal tissue or organs at risk (OARs). However, tighter margins may lead to underdosage of the target due to the presence of organ motion.

Carbon fiber couch effects on skin dose for volumetric modulated arcs.

Purpose: The purpose of this study is to evaluate the dosimetric effect of carbon fiber couches (CFCs) on delivered skin dose as well as to explore potential venues for its minimization for volumetric modulated arc (VMAT) treatments.

Methods: A carbon fiber couch (BrainLab) was incorporated in Pinnacle treatment planning system (TPS) by autocontouring. A retrospective investigation on five lung and five prostate patient plans was performed.

Development and evaluation of an efficient approach to volumetric arc therapy planning.

An efficient method for volumetric intensity modulated arc therapy (VMAT) planning was developed, where a single arc (360 degrees or less) is delivered under continuous variation of multileaf collimator (MLC) segments, dose rate, and gantry speed. Plans can be generated for any current linear accelerator that supports these degrees of freedom. MLC segments are derived from fluence maps at relatively coarsely sampled angular positions.

Potential dosimetric benefits of four-dimensional radiation treatment planning.

Purpose: To determine the extent of dosimetric differences between conventional three-dimensional (3D) dose calculations and four-dimensional (4D) dose calculations based on deformation of organ models.

Methods And Materials: Four-dimensional dose calculations were retrospectively performed on computed tomography data sets for 15 patients with Stage III non-small-cell lung cancer, using a model-based deformable registration algorithm on a research version of a commercial radiation treatment planning system. Target volume coverage and doses to critical structures calculated using the 4D methodology were compared with those calculated using conventional 3D methodology.

What CTV-to-PTV margins should be applied for prostate irradiation? Four-dimensional quantitative assessment using model-based deformable image registration techniques.

Purpose: To quantify adequate anisotropic clinical target volume (CTV)-to-planning target volume (PTV) margins for three different setup strategies used during prostate irradiation: (1) no setup corrections, (2) on-line corrections determined from bony anatomy, and (3) on-line corrections determined from gold markers.

Method And Materials: Three radiation oncologists independently delineated the CTV on computed tomography images of 30 prostate cancer patients. Eight repeat scans were acquired to allow simulation of the delivered dose distributions in changing geometry.

Semiautomatic vertebrae visualization, detection, and identification for online palliative radiotherapy of bone metastases of the spine.

A treatment process which integrates simulation, planning, and delivery in one single session of < or =30 min on a treatment unit capable of cone-beam CT imaging (CBCT) is under development in our institution for palliation of spinal metastases. The objective of this work is to develop and validate a semiautomatic vertebra detection and identification algorithm to streamline the target definition process and improve the consistency of online planning on cone-beam CT data sets while the patient is on the treatment couch. Key issues pertaining to this work are the limited field of view and image quality of CBCT, the inter- and intrapatient variation of vertebra morphology, and the spine curvature.

Assessment of a model-based deformable image registration approach for radiation therapy planning.

Purpose: The aim of this study is to develop a surface-based deformable image registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, image-guided radiation therapy, and assessment of geometrical change during and after therapy.

Methods And Materials: A surface-model-based deformable image registration system has been developed that enables quantitative description of geometrical change in multimodal images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling.

Semiautomated four-dimensional computed tomography segmentation using deformable models.

The purpose of this work is to demonstrate a proof of feasibility of the application of a commercial prototype deformable model algorithm to the problem of delineation of anatomic structures on four-dimensional (4D) computed tomography (CT) image data sets. We acquired a 4D CT image data set of a patient's thorax that consisted of three-dimensional (3D) image data sets from eight phases in the respiratory cycle. The contours of the right and left lungs, cord, heart, and esophagus were manually delineated on the end inspiration data set.

Semiautomated four-dimensional computed tomography segmentation using deformable models.

The purpose of this work is to demonstrate a proof of feasibility of the application of a commercial prototype deformable model algorithm to the problem of delineation of anatomic structures on four-dimensional (4D) computed tomography (CT) image data sets. We acquired a 4D CT image data set of a patient's thorax that consisted of three-dimensional (3D) image data sets from eight phases in the respiratory cycle. The contours of the right and left lungs, cord, heart, and esophagus were manually delineated on the end inspiration data set.

Automated model-based organ delineation for radiotherapy planning in prostatic region.

Purpose: Organ delineation is one of the most tedious and time-consuming parts of radiotherapy planning. It is usually performed by manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional (3D) data set used for planning. In this paper, a 3D model-based approach to automated organ delineation is introduced that allows for a significant reduction of the time required for contouring.

Automated segmentation of the left ventricle in cardiac MRI.

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model.

Statistical validation of image segmentation quality based on a spatial overlap index.

Rationale And Objectives: To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy.

Materials And Methods: The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.

Statistical validation based on parametric receiver operating characteristic analysis of continuous classification data.

Rationale And Objectives: The accuracy of diagnostic test and imaging segmentation is important in clinical practice because it has a direct impact on therapeutic planning. Statistical validations of classification accuracy was conducted based on parametric receiver operating characteristic analysis, illustrated on three radiologic examples,

Materials And Methods: Two parametric models were developed for diagnostic or imaging data. Example 1: A semi-automated fractional segmentation algorithm was applied to magnetic resonance imaging of nine cases of brain tumors.

Automated 3-D PDM construction from segmented images using deformable models.

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem.