A comparison of material decomposition techniques for dual-energy CT colonography.

In recent years, dual-energy computed tomography (DECT) has been widely used in the clinical routine due to improved diagnostics capability from additional spectral information. One promising application for DECT is CT colonography (CTC) in combination with computer-aided diagnosis (CAD) for detection of lesions and polyps. While CAD has demonstrated in the past that it is able to detect small polyps, its performance is highly dependent on the quality of the input data. The presence of artifacts such as beam-hardening and noise in ultra-low-dose CTC may severely degrade detection performances of small polyps. In this work, we investigate and compare virtual monochromatic images, generated by image-based decomposition and projection-based decomposition, with respect to CAD performance. In the image-based method, reconstructed images are firstly decomposed into water and iodine before the virtual monochromatic images are calculated. On the contrary, in the projection-based method, the projection data are firstdecomposed before calculation of virtual monochromatic projection and reconstruction. Both material decomposition methods are evaluated with regards to the accuracy of iodine detection. Further, the performance of the virtual monochromatic images is qualitatively and quantitatively assessed. Preliminary results show that the projection-based method does not only have a more accurate detection of iodine, but also delivers virtual monochromatic images with reduced beam hardening artifacts in comparison with the image-based method. With regards to the CAD performance, the projection-based method yields an improved detection performance of polyps in comparison with that of the image-based method.     «

In recent years, dual-energy computed tomography (DECT) has been widely used in the clinical routine due to improved diagnostics capability from additional spectral information. One promising application for DECT is CT colonography (CTC) in combination with computer-aided diagnosis (CAD) for detection of lesions and polyps. While CAD has demonstrated in the past that it is able to detect small polyps, its performance is highly dependent on the quality of the input data. The presence of artifacts...     »