No abstract

We aimed to design and fabricate synthetic lung nodules with patient-informed internal heterogeneity to assess the variability and accuracy of measured texture features in CT. To that end, 190 lung nodules from a publicly available database of chest CT images (Lung Image Database Consortium) were selected based on size (>3 mm) and malignancy. The texture features of the nodules were used to train a statistical texture synthesis model based on clustered lumpy background. The model parameters were ascertained based on a genetic optimization of a Mahalanobis distance objective function. The resulting texture model defined internal heterogeneity within 24 anthropomorphic lesion models which were subsequently fabricated into physical phantoms using a multimaterial three-dimensional (3-D) printer. The 3-D-printed lesions were imbedded in an anthropomorphic chest phantom and imaged with a clinical scanner using different acquisition parameters including slice thickness, dose level, and reconstruction kernel. The imaged lesions were analyzed in terms of texture features to ascertain the impact of CT imaging on lesion texture quantification. The texture modeling method produced lesion models with low and stable Mahalanobis distance between real and synthetic textures. The virtual lesions were successfully printed into 3-D phantoms. The accuracy and variability of the measured features extracted from the CT images of the phantoms showed notable influence from the imaging acquisition parameters with contrast, energy, and texture entropy exhibiting most sensitivity in terms of accuracy, and contrast, dissimilarity, and texture entropy most variability. Thinner slice thicknesses yielded more accurate and edge reconstruction kernels more stable results. We conclude that printed textured models of lesions can be developed using a method that can target and minimize the mathematical distance between real and synthetic lesions. The synthetic lesions can be used as the basis to investigate how CT imaging conditions might affect radiomics features derived from CT images.

The automatic patient positioning system and its alignment is critical and specified to be less than 0.35 mm for a radiosurgical treatment with the latest robotized Gamma Knife Perfexion (GKPFX). In this study, we developed a quantitative QA procedure to verify the accuracy and robustness of such a system. In particular, we applied the test to a unit that has performed >1000 procedures at our institution. For the test, a radiochromic film was first placed inside a spherical film phantom and then irradiated with a sequence of linearly placed shots of equal collimator size (e.g. 4 mm) via the Leksell Gamma Knife Perfexion system (PFX). The shots were positioned with either equal or unequal gaps of approximately 8 mm both at center and off-center positions of the patient positioning system. Two independent methods of localizing the irradiation shot center coordinates were employed to measure the gap spacing between adjacent shots. The measured distance was then compared with the initial preset values for the test. On average, the positioning uncertainty for the PFX delivery system was found to be 0.03 ± 0.2 mm (2σ). No significant difference in the positioning uncertainty was noted among measurements in the x-, y- and z-axis orientations. In conclusion, a simple, fast, and quantitative test was developed and demonstrated for routine QA of the submillimeter PFX patient positioning system. This test also enables independent verification of any patient-specific shot positioning for a critical treatment such as a tumor in the brainstem.

The purpose of this study was to investigate relationships between patient attributes and organ dose for a population of computational phantoms for 20 tomosynthesis and radiography protocols. Organ dose was estimated from 54 adult computational phantoms (age: 18 to 78 years, weight 52 to 117 kg) using a validated Monte-Carlo simulation (PENELOPE) of a system capable of performing tomosynthesis and radiography. The geometry and field of view for each exam were modeled to match clinical protocols. For each protocol, the energy deposited in each organ was estimated by the simulations, converted to dose units, and then normalized by exposure in air. Dose to radiosensitive organs was studied as a function of average patient thickness in the region of interest and as a function of body mass index. For tomosynthesis, organ doses were also studied as a function of x-ray tube position. This work developed comprehensive information for organ dose dependencies across a range of tomosynthesis and radiography protocols. The results showed a protocol-dependent exponential decrease with an increasing patient size. There was a variability in organ dose across the patient population, which should be incorporated in the metrology of organ dose. The results can be used to prospectively and retrospectively estimate organ dose for tomosynthesis and radiography.