Colorectal cancer (CRC) is the third most common cancer type and the second most common cause of cancer deaths. CT colonography is a nearly ideal safe and accurate method for effective colorectal screening and prevention of CRCs, but the ionizing radiation of CT has been cited as a risk for population screening by CT colonography. Photon-counting CT (PCCT) can be used to address that risk. However, there have been no studies on the performance of automated polyp detection in PCCT colonography. In this preliminary study, we investigated the feasibility of the automated detection of clinically significant polyps from a PCCT colonography dataset. A laxative-free CT colonography examination that was simulated on an anthropomorphic colon phantom was scanned by use of a 16-slice PCCT scanner at 120 kVp and 40 mA. Our previously developed computer-aided detection (CADe) system was used to detect polyps from the PCCT dataset. The polyp detection performance was evaluated by use of 10-fold cross-validation. Our preliminary results show that the CADe system was able to detect the clinically significant polyps ≥6 mm in size from the PCCT colonography dataset at a high accuracy. This indicates that PCCT colonography is indeed a very promising approach for addressing the remaining obstacles of CT colonography in the population screening for CRC.
Photon-counting CT is an emerging technology with several advantages over conventional CT technology, such as the ability to reduce radiation exposure to CT. In this study, we evaluated the effect of the use of photon-counting CT colonography on the performance of our self-supervised 3D generative adversarial learning (GAN)-based electronic cleansing (EC) scheme. We simulated a fecal-tagging CT colonography case by use of an anthropomorphic colon phantom. The empty phantom served as the ground truth for the EC. Both the empty and fecal-tagging versions of the phantom were scanned by use of a photon-counting CT and a conventional CT scanner. We evaluated the performance of the EC scheme by using 100 paired volumes of interest extracted from the corresponding locations on the empty and fecal-tagging phantoms that had not been used for the training of the EC scheme. The peak signal-to-noise ratio was used as the metric for the quality of the EC images generated. Our preliminary results indicate that using photon-counting CT colonography at a low dose generates higher-quality EC images than those obtained by using conventional CT colonography. The results also demonstrate that our self-supervised training scheme generates images of higher quality than those obtained by use of conventional supervised training. Therefore, photon-counting CT colonography combined with our self-supervised 3DGAN EC scheme is expected to provide EC images of the highest quality in low-dose fecal-tagging CT colonography.
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