An investigation on energy spectral information of computed tomography for machine learning in lesion classification

Recent advancement of spectral computed tomography (SpCT) technologies by either multi-energy spectral data acquisition with energy-integration detector or single-energy spectral data acquisition with photon counting detector has enabled the reconstruction of virtual monochromatic images (VMIs) at any energy values within and outside the energy spectral ranges of current CTs’ X-ray tubes, resulting in the possibility of not only visualizing the tissue contrast variation characteristics along the X-ray energy dimension, but also quantifying the variation characteristics by machine learning (ML) for prediction of lesion malignancy or computer-aided diagnosis (CADx). This study explored the energy spectral information of SpCT, i.e., the contrast variation characteristics along the X-ray energy dimension, for ML-CADx of lesion type of colorectal polyps. Particularly, the tissue contrast variation patterns, called energy spectral features, along the Xray energy dimension in the VMIs is investigated. A figure of merit (FOM) for the task of ML-CADx is proposed, which ranks the series of VMIs along the X-ray energy dimension by inputting each VMI into a single channel deep learning (DL) pipeline and generating a corresponding a score of AUC (area under the curve of receiver operating characteristics). Then the FOM selects different numbers of the most highly ranked VMIs as the inputs to a multi-channel DL pipeline to generate the corresponding of AUC scores until all VMIs are selected. It is hypothesized that the AUC scores from the multi-channel DL pipeline will increase to reach the highest score and then drop along the ranking order, because all VMIs have the same anatomic structure and, therefore, the strong data redundancy. The FOM reaches the highest AUC score by minimizing the redundancy. We tested the hypothesis by comparing the proposed FOM-rank ML-CADx with the widely used Karhunen-Loève (KL) transform-based ranking method where the principal components are ordered automatically by the KL transform. The lesion data include the CT images of colorectal polyps and the pathological reports after they were resected. The proposed FOM-rank method outperformed the KL-based ranking method with an optimal gain of 4.7%, showing its effectiveness in prediction of lesion malignancy.