SU-E-J-102: The Impact of the Number of Subjects for Atlas-Based Automatic Segmentation.

PURPOSE: To determine the impact of atlas size on the performance of atlas-based automatic segmentation (ABAS) in delineation of organs at risk for adaptive radiation therapy. METHODS: A total of 25 patients who had undergone intensity modulated radiation therapy for various head and neck cancers were retrospectively selected for inclusion in a library to be used for ABAS with the MIM VISTA software package (MIM Software, Cleveland OH). Treatment planning computed tomography (CT) scans and subsequent organ at risk (OAR) contours generated as part of the treatment planning process for these patients were added to the library. This library of 25 patients was then successively pruned to generate 5 atlases with 25, 20, 15, 10, and 5 patient subjects respectively. Atlas based segmentation was performed on 10 retrospectively selected treatment planning CT scans to automatically generate right and left parotid glands and brainstem contours. These planning CT scans belonged to a unique set of 10 patient subjects different from the ones used for generating the atlases. One physician (JW), who was blinded to the ABAS results, manually delineated gold-standard contours for the right and left parotid glands and brainstem. Dice similarity coefficients were calculated and analyzed as a function of atlas subject size. RESULTS: For the sites selected in this study, the performance of ABAS was relatively insensitive to atlas size. Furthermore, some patient subjects were repeatedly selected implying that the adoption of a single standard patient for ABAS may be of benefit. CONCLUSIONS: Our preliminary results indicate that the performance of the atlas based segmentation module in MIM VISTA Version 5.2 for the organs studied here may be relatively insensitive to the atlas size.

Imaging of nanoparticles with dual-energy computed tomography.

A simulation study was performed to determine the feasibility and performance of imaging nanoparticles as contrast agents in dual-energy computed tomography. An analytical simulation model was used to model the relevant signal-to-noise ratio (SNR) in dual-energy imaging for the specific case of a three-material patient phantom consisting of water, calcium hydroxyapatite and contrast agent. Elemental gold and iodine were both considered as contrast agents. Simulations were performed for a range of monoenergetic (20-150 keV) and polyenergetic (20-150 kVp) beam spectra. A reference configuration was defined with beam energies of 80 and 140 kVp to match current clinical practice. The effect of adding a silver filter to the high-energy beam was also studied. A figure of merit (FOM), which normalized the dual-energy SNR to the square root of the patient integral dose, was calculated for all cases. The units of the FOM were keV(-1/2). A simple Rose model of detectability was used to estimate the minimum concentration of either elements needed to be detected (SNR > 5). For monoenergetic beams, the peak FOM of gold was 6.4 × 10(-6) keV(-1/2), while the peak FOM of iodine was 3.1 × 10(-6) keV(-1/2), a factor of approximately 2 greater for gold. For polyenergetic spectra, at the reference energies of 80 and 140 kVp, the FOM for gold and iodine was 1.65 × 10(-6) and 5.0 × 10(-7) keV(-1/2), respectively, a factor of approximately 3.3 greater. Also at these energies, the minimum detectable concentration of gold was estimated to be 58.5 mg mL(-1), while iodine was estimated to be 117.5 mg mL(-1). The results suggest that the imaging of a gold nanoparticle contrast agent is well suited to current conditions used in clinical imaging. The addition of a silver filter of 800 µm further increased the image quality of the gold signal by approximately 50% for the same absorbed dose to the patient.

Scatter correction in digital mammography based on image deconvolution.

X-ray scatter is a major cause of nonlinearity in densitometry measurements using digital mammography. Previous scatter correction techniques have primarily used a single scatter point spread function to estimate x-ray scatter. In this study, a new algorithm to correct x-ray scatter based on image convolution was implemented using a spatially variant scatter point spread function which is energy and thickness dependent. The scatter kernel was characterized in terms of its scattering fraction (SF) and scatter radial extent (k) on uniform Lucite phantoms with thickness of 0.8-8.0 cm. The algorithm operates on a pixel-by-pixel basis by grouping pixels of similar thicknesses into a series of mask images that are individually deconvolved using Fourier image analysis with a distinct kernel for each image. The algorithm was evaluated with three Lucite step phantoms and one anthropomorphic breast phantom using a full-field digital mammography system at energies of 24, 28, 31 and 49 kVp. The true primary signal was measured with a multi-hole collimator. The effect on image quality was also evaluated. For all 16 studies, the average mean percentage error in estimating the true primary signal was found to be -2.13% and the average rms percentage error was 2.60%. The image quality was seen to improve at every energy up to 25% at 49 kVp. The results indicate that a technique based on a spatially variant scatter point spread function can accurately estimate x-ray scatter.