PURPOSE
Recent work has shown that current TCM profile designs boost detection of low-contrast lung lesions in the lung apices, but yield reduced detection performance in the mid and lower lung regions relative to fixed tube current cases. This observed imbalance suggests that the TCM scheme might be tailored in new ways to maximize nodule detection throughout the entire lung. In this work, we begin a preliminary investigation into custom TCM profiles in an attempt to achieve uniform lesion detection throughout the extent of the lung.
METHODS
Low-contrast (25HU), 6mm nodules representing ground glass opacities were simulated at 1mm intervals over the length the lungs in a voxelized model of the XCAT phantom, one nodule per lung, per simulated scan. Voxel values represented attenuation values at 80keV. CT projection data was created by simulating a finite focal spot and using Joseph's method for forward projection; scanner geometry was that of the Siemens Sensation 64 and the X-ray source was simulated as an 80keV monochromatic beam. Noise realizations were created using Poisson statistics, a realistic bowtie filter and varying tube current. 500 noise realizations were created for the custom TCM designs. All reconstruction was done with FreeCT_wFBP. An SKE/BKE task was used in conjunction with a 2D Hotelling Observer to calculate area-under-the-curve (AUC) as a proxy for "detectability." AUC was plotted as a function of nodule Z-location to create a "detectability map." The detectability map for the custom TCM curve was qualitatively assessed relative to previous results for the fixed TC and clinical TCM cases for uniformity.
RESULTS
Detection uniformity was improved throughout the mid and lower lungs, however detection remained disproportionately high in the upper lung region.
CONCLUSION
Detection uniformity was improved with a custom TC profile. Future work will incorporate an analytic, task-specific approach to optimize the TC scheme for nodule detection. J. Hoffman: Part-time intern, Toshiba Medical Research Institute; M. McNitt-Gray: Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; F. Noo: Institutional research agreement, Siemens Healthcare; Receives research funding from Siemens Healthcare.