A novel computed tomography image synthesis method for correcting the spectrum dependence of CT numbers

The quantitative evaluation of computed tomography (CT) images is widely investigated and applied in clinical diagnosis. However, the CT number of tissue can vary with scanners or applied tube voltages because of the x-ray spectrum dependence of measured linear attenuation coefficients that degrades evaluation accuracy and limits multicenter or multimodality research. This study proposed a novel CT image synthesis method to correct the spectrum dependence of CT numbers by normalizing them to the same spectrum condition. Stoichiometric calibration was performed to derive the spectrum characteristic parameters (SCPs) of six spectra from two CT scanners with different applied tube voltages. Subsequently, conversion relationships between CT numbers and tissue parameters (TPs) were determined using the SCPs and standard tissue data. The CT number of a tissue measured from a spectrum condition was converted to TPs using these relationships, and the results were used to estimate the CT number of the tissue in another spectrum condition using the corresponding SCPs. Phantom, cadaver, and patient studies were performed to evaluate the proposed method. In the phantom study, image synthesis reduced the mean difference between the CT numbers of tissue-equivalent phantoms measured using different spectra from 57.96 to 33.94 HU. In the cadaveric study, the mean difference between the CT numbers of a temporal bone flap measured using different spectra was lowered by over 57%. In the patient image study, a significant difference of 81.5 HU was observed between the mean CT numbers of femoral shafts obtained from the two scanners; this difference was reduced to less than 17 HU, which was nonsignificant, when the proposed method was used. The proposed image synthesis method could reduce the spectrum dependence of CT numbers measured with different spectra and could be applied clinically to improve the accuracy of multicenter and multimodality evaluation and research.