羽石 秀昭

Abstract Positron emission tomography (PET) is a valuable tool for diagnosing malignant tumors. Intraoperative PET imaging is expected to allow the more accurate localization of tumors that need resections. However, conventional devices feature a large detector ring that obstructs surgical procedures, preventing their intraoperative application. This paper proposes a new PET device, Scratch-PET, for image-guided tumor resection. The key feature of Scratch-PET is its use of a hand-held detector to scan the surgical field, ensuring open space for surgery while measuring annihilation radiation with a fixed detector array placed below the patient. We developed a prototype device using two detectors: the hand-held detector and a fixed detector, to demonstrate the feasibility of the proposed concept. Both detectors consisted of 16 × 16 arrays of lutetium yttrium orthosilicates (3 × 3 × 15 mm³) coupled one-to-one with 16 × 16 silicon photomultiplier arrays. The position and orientation of the hand-held detector are tracked using an optical tracking sensor that detects attached markers. We measured a ²²Na multi-rod phantom and two ²²Na point sources separately for 180 s while moving the hand-held detector. The rod diameters were 6.0, 5.0, 4.0, 3.0, 2.2, and 1.6 mm. Each point source was placed at the field-of-view center and 35 mm off-center which was outside the sensitive area when the hand-held detector was positioned facing the fixed detector. The 2.2 mm rods were partially resolved, and both point sources were successfully visualized. The potential of the proposed device to visualize small tumors was validated.

Abstract Purpose Despite a global decrease in the number of COVID-19 patients, early prediction of the clinical course for optimal patient care remains challenging. Recently, the usefulness of image generation for medical images has been investigated. This study aimed to generate short-term follow-up chest CT images using a latent diffusion model in patients with COVID-19. Materials and methods We retrospectively enrolled 505 patients with COVID-19 for whom the clinical parameters (patient background, clinical symptoms, and blood test results) upon admission were available and chest CT imaging was performed. Subject datasets (n = 505) were allocated for training (n = 403), and the remaining (n = 102) were reserved for evaluation. The image underwent variational autoencoder (VAE) encoding, resulting in latent vectors. The information consisting of initial clinical parameters and radiomic features were formatted as a table data encoder. Initial and follow-up latent vectors and the initial table data encoders were utilized for training the diffusion model. The evaluation data were used to generate prognostic images. Then, similarity of the prognostic images (generated images) and the follow-up images (real images) was evaluated by zero-mean normalized cross-correlation (ZNCC), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Visual assessment was also performed using a numerical rating scale. Results Prognostic chest CT images were generated using the diffusion model. Image similarity showed reasonable values of 0.973 ± 0.028 for the ZNCC, 24.48 ± 3.46 for the PSNR, and 0.844 ± 0.075 for the SSIM. Visual evaluation of the images by two pulmonologists and one radiologist yielded a reasonable mean score. Conclusions The similarity and validity of generated predictive images for the course of COVID-19-associated pneumonia using a diffusion model were reasonable. The generation of prognostic images may suggest potential utility for early prediction of the clinical course in COVID-19-associated pneumonia and other respiratory diseases.

RATIONALE AND OBJECTIVES: To analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician. MATERIALS AND METHODS: First, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist. RESULTS: The proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region. CONCLUSION: The developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.