Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging.

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The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization. To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction. A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49kV with 1.5mm aluminum filter and at 65kV and 0.257mm copper filter. A 10mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5mm iodine disk detection task at 49, 55, 60, and 65kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4mm. At 49kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min=3.7%; max=8.5%). At 65kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min=11.4%; max=16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2=0.993) and 19.14µGy/mA (R2=0.996) at 49 and 65kV, respectively. For iodine detection, a 0.25mm-thick copper filter at 65kV was found optimal, outperforming the default technique by 90%. The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.