Purpose: A videofluoroscopic swallowing study (VFSS) is the gold standard for the examination of swallowing function. A fluoroscopic unit and contrast medium are used to record an X-ray video of the patient's swallowing dynamics. This requires clinicians to observe three-dimensional swallowing movements on a two-dimensional video. In addition, the VFSS lacks facial surface information. In this study, we developed a method to synchronize the VFSS video with the three-dimensional movement of the facial surface. Approach: A 44-year-old man with no dysphagia was studied. Five smooth and one textured iron ball with a diameter of 3 mm were attached to the facial surface as markers at the tip of the nose, the upper and lower lips, the left and right corners of the mouth, and the chin. Using an X-ray fluoroscopic unit, the swallowing movements of gelatin jelly containing an iodine-based contrast media were recorded. The patient swallowed the jelly with mastication (right side, left side, both sides) and without mastication (open-mouth swallowing). At the same time, the movements of the facial surface were recorded using three video cameras. The four obtained videos were synchronized in terms of their start point, end point, and playback speed using a computer. Results: We created a synchronized video of the VFSS video and the three-dimensional video of the facial surface. This video could be observed from any viewpoint. In addition, we could analyze the velocity, distance, and angle of each point. Conclusion: This method can be used to objectively analyze mastication using numerical values.

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 3D-GAN EC scheme is expected to provide EC images of the highest quality in low-dose fecal-tagging CT colonography.

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.