In medical imaging, precise and reliable images are very important. However, the quality of medical images is sometimes limited by low-event statistics owing to the low sensitivity of the detectors commonly used in radiology. On the other hand, long exposure to radiation and long inspection duration can become a burden for patients. In this paper, we propose a method for generating high-quality images of gamma ray sources from low statistic data by using machine learning methods based on dictionary learning and sparse coding. As the first application, we generated a high-quality image of 137Cs, which emits 662-keV gamma rays, from low-event statistics measured using a Compton camera. We simulated with Geant4 various geometries of the gamma-ray source (137Cs; 662 keV) as measured with a Compton camera by Geant4. Then, complete sets of low-resolution and high-resolution dictionaries were prepared. We generated super-resolution images from low-resolution test images obtained from actual measurements. The convergence of the gamma-ray images was similar for both the ground truth and predicted images, as supported by the improvements in the structural similarity (SSIM), peak signal-to-noise (PSNR) ratio, and root mean square error (RMSE) in the corresponding images. We also discuss future plans to use the super-resolution technique for visualizing radium chloride (223RaCl2) in the patient's body, which will make it possible to achieve in-vivo imaging of alpha-particle internal therapy for the first time.
|ジャーナル||Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment|
|出版ステータス||Published - 2020 7月 21|
ASJC Scopus subject areas