Quantitative Tissue Classification via Dual Energy Computed Tomography for Brachytherapy Treatment Planning : Accuracy of the Three Material Decomposition Method

Dual Energy Computed Tomography (DECT) is an emerging technique that offers new possibilities to determine composition of tissues in clinical applications. Accurate knowledge of tissue composition is important for instance for brachytherapy (BT) treatment planning. However, the accuracy of CT numbers measured with contemporary clinical CT scanners is relatively low since CT numbers are affected by image artifacts. The aim of this work was to estimate the accuracy of CT numbers measured with the Siemens SOMATOM Definition Flash DECT scanner and the accuracy of the resulting volume or mass fractions calculated via the three material decomposition method. CT numbers of water, gelatin and a 3rd component (salt, hydroxyapatite or protein powder) mixtures were measured using Siemens SOMATOM Definition Flash DECT scanner. The accuracy of CT numbers was determined by (i) a comparison with theoretical (true) values and (ii) using different measurement conditions (configurations) and assessing the resulting variations in CT numbers. The accuracy of mass fractions determined via the three material decomposition method was estimated by a comparison with mass fractions measured with calibrated scales. The latter method was assumed to provide highly accurate results. It was found that (i) axial scanning biased CT numbers for some detector rows. (ii) large volume of air surrounding the measured region shifted CT numbers compared to a configuration where the region was surrounded by water. (iii) highly attenuating object shifted CT numbers of surrounding voxels. (iv) some image kernels caused overshooting and undershooting of CT numbers close to edges. The three material decomposition method produced mass fractions differing from true values by 8% and 15% for the salt and hydroxyapatite mixtures respectively. In this case, the analyzed CT numbers were averaged over a volumetric region. For individual voxels, the volume fractions were affected by statistical noise. The method failed when statistical noise was high or CT numbers of the decomposition triplet were similar. Contemporary clinical DECT scanners produced image artifacts that strongly affected the accuracy of the three material decomposition method; the Siemens’ image reconstruction algorithm is not well suited for quantitative CT. The three material decomposition method worked relatively well for averages of CT numbers taken from volumetric regions as these averages lowered statistical noise in the analyzed data.