| Abstract: |
Based on well-established X-ray physics in computed tomography (CT) imaging, the spectral responses of different materials contained in lesions are different, which brings richer contrast information at various energy bins. Hence, obtaining the material decomposition of different tissue types and exploring its spectral information for lesion diagnosis becomes extremely valuable. The lungs are housed within the torso and consist of three natural materials, i.e., soft tissue, bone, and lung tissue. To benefit the lung nodule differentiation, this study innovatively proposed to use lung tissue as one basis material along with soft tissue and bone. This set of basis materials will yield a more accurate composition analysis of lung nodules and benefit the following differentiation. Moreover, a corresponding machine learning (ML)-based computer-aided diagnosis framework for lung nodule classification is also proposed and used for evaluation. Experimental results show the advantages of the virtual monoenergetic images (VMIs) generated with lung tissue material over the VMIs without lung tissue and conventional CT images in differentiating the malignancy from benign lung nodules. The gain of 9.63% in area under the receiver operating characteristic curve (AUC) scores indicated that the energy-enhanced tissue features from lung tissue have a great potential to improve lung nodule diagnosis performance. © 2022 SPIE. |
