Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

OBJECTIVE: Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computed tomography (SPECT). In this study, we proposed a novel deep learning approach referring to positron emission tomography (PET). The aims of this study were to analyze the agreement of representative voxel values and perfusion scores of SPECT-to-PET translation model-generated SPECT (SPECTSPT) against PET in 17 segments according to the American Heart Association (AHA). METHODS: This retrospective study evaluated the patient-to-patient stress, resting SPECT, and PET datasets of 71 patients. The SPECTSPT generation model was trained (stress: 979 image pairs, rest: 987 image pairs) and validated (stress: 421 image pairs, rest: 425 image pairs) using 31 cases of SPECT and PET image pairs using an image-to-image translation network. Forty of 71 cases of left ventricular base-to-apex short-axis images were translated to SPECTSPT in the stress and resting state (stress: 1830 images, rest: 1856 images). Representative voxel values of SPECT and SPECTSPT in the 17 AHA segments against PET were compared. The stress, resting, and difference scores of 40 cases of SPECT and SPECTSPT were also compared in each of the 17 segments. RESULTS: For AHA 17-segment-wise analysis, stressed SPECT but not SPECTSPT voxel values showed significant error from PET at basal anterior regions (segments #1, #6), and at mid inferoseptal regions (segments #8, #9, and #10). SPECT, but not SPECTSPT, voxel values at resting state showed significant error at basal anterior regions (segments #1, #2, and #6), and at mid inferior regions (segments #8, #9, and #11). Significant SPECT overscoring was observed against PET in basal-to-apical inferior regions (segments #4, #10, and #15) during stress. No significant overscoring was observed in SPECTSPT at stress, and only moderate over and underscoring in the basal inferior region (segment #4) was found in the resting and difference states. CONCLUSIONS: Our PET-supervised deep learning model is a new approach to correct well-known inferior wall attenuation in SPECT myocardial perfusion imaging. As standalone SPECT systems are used worldwide, the SPECTSPT generation model may be applied as a low-cost and practical clinical tool that provides powerful auxiliary information for the diagnosis of myocardial blood flow.

Programmable Non-Epipolar Indirect Light Transport: Capture and Analysis.

The decomposition of light transport into direct and global components, diffuse and specular interreflections, and subsurface scattering allows for new visualizations of light in everyday scenes. In particular, indirect light contains a myriad of information about the complex appearance of materials useful for computer vision and inverse rendering applications. In this paper, we present a new imaging technique that captures and analyzes components of indirect light via light transport using a synchronized projector-camera system. The rectified system illuminates the scene with epipolar planes corresponding to projector rows, and we vary two key parameters to capture plane-to-ray light transport between projector row and camera pixel: (1) the offset between projector row and camera row in the rolling shutter (implemented as synchronization delay), and (2) the exposure of the camera row. We describe how this synchronized rolling shutter performs illumination multiplexing, and develop a nonlinear optimization algorithm to demultiplex the resulting 3D light transport operator. Using our system, we are able to capture live short and long-range non-epipolar indirect light transport, disambiguate subsurface scattering, diffuse and specular interreflections, and distinguish materials according to their subsurface scattering properties. In particular, we show the utility of indirect imaging for capturing and analyzing the hidden structure of veins in human skin.

[Carcinoma of the gallbladder which progress on the mucosa of choledochocolonic fistula].

A 70-year-old man was admitted to our hospital for further examination of pneumobilia and atrophy in the gallbladder. Abdominal CT scan and EUS revealed that the atrophic gallbladder was occupied by a tumor lesion. In addition, ERCP showed choledochocolonic fistula. Colonoscopy revealed an elevated lesion in the colonic side of fistula, and biopsy of the elevated lesion revealed adenocarcinoma. Cholecystectomy and right hemicolectomy was performed under a preoperative diagnosis of gallbladder carcinoma with choledochocolonic fistula. Pathologically, most of the tumor was localized in the gallbladder, and grew along the mucosa of choledchocolonic fistula. This case was of interest with regard to the relationship between the choledochocolonic fistula and gallbladder carcinoma.