Abstract
Computed tomography (CT) can rapidly provide high resolution cross-section images. It has become one of the powerful tools in clinical and has been widely applied to achieve a variety of diagnostic and therapeutic purposes. In recent years, number of CT procedures increase year after year with an annual growth rate higher than 10% in Taiwan and in the U.S. Comparing to the other radiological examinations, CT scan delivers relatively high radiation dose to patients. From the view point of radiation protection, medical exposure is justified as long as it follows the ALARA principle. Currently, CT images are mainly used in visually diagnosis of various diseases but have no other way to effective utilize. In addition to the visual observation, tissue parameters, such as physical electron densities, effective atomic numbers and bone mineral densities, obtained from CT image-based quantification are also useful for several physical correction and diagnosis in clinical. However, common CT scanners employ polychromatic X-ray spectrum and cumulative detector, causes the composition and attenuation information of scanning objects are difficult to estimate from acquired projection data or reconstructed images. Therefore, present CT image-based quantification is mainly performed through various tissue equivalent materials (TEMs). Nevertheless, the differences between the elemental composition of tissue equivalent materials and actual human tissues results that the estimated parameters are a reference equivalent. In view of the above, a novel computer-aided quantification (CAQ) method was proposed to achieve fast and accurate tissue parameter quantification. In this method, a stoichiometric calibration was performed to acquire spectrum characteristic parameters (SCPs) that describe the energy spectrum of a specific CT scanner. The acquired SCPs were then used to convert CT number into clinically valuable physical and physiological tissue parameters (PTPs and PoTPs). This study was divided in to two parts. In the first part, the CT number was converted into the PTPs by using conversion relationships. In addition, these parameters were further used to calculate the mass attenuation coefficients (MACs) and mass energy transfer coefficients (MEACs) with physical models. In the second part, the CT numbers were converted into bone physiological parameters (BPPs) by using a novel mixture model. The results show that the proposed CAQ method can accurately convert the CT images into PTP and BPP maps. Moreover, the proposed method also reduce the influences of energy spectrum that is helpful in image exchanging and comparing between scanners. We conclude that the proposed CAQ method could be applied in the clinical to estimate several tissue parameters from CT image for various diagnostic and therapeutic purposes, whereby benefits for patients from CT examinations can be increased.