Physical Evaluation of Focal Spot Size and Phantom Thickness in Coronary CT Angiography Using Super-Resolution Deep Learning Reconstruction

Objective: To compare images with small and large focal spot sizes under clinical tube current limits across different subject thicknesses in the heart region using super-resolution deep learning reconstruction (SRDLR) to elucidate image quality behavior. Methods: We conducted quantitative assessments to compare small and large focal spot size images. The quantitative assessment involved imaging phantoms with 5 different diameters under coronary computed tomography angiography (CCTA) conditions, followed by SRDLR reconstruction. We calculated the noise power spectrum (NPS) to evaluate the frequency response of image noise in uniform phantom material. Spatial resolution was analyzed by measuring the task-based transfer function (TTF) for sections simulating iodine within the phantom. Results: In the NPS, for phantom diameters between 16 and 31 cm, small focal spot size images exhibited higher noise than large focal spot size images. However, for a 36 cm-diameter phantom, the small focal spot size image had less noise in the high-frequency region. TTF values were better for small focal spot size images, but the difference decreased with increasing phantom diameter, becoming negligible at 31 cm. Conclusions: The study clarified how focal spot size affects image quality in CCTA under maximum dose constraints, suggesting that optimal image quality can be achieved by appropriately selecting the focal spot size according to subject thickness.