First application of super-resolution imaging technique using a Compton camera
First application of super-resolution imaging technique using a Compton camera
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康普顿相机超分辨率成像技术的首次应用
DOI：
10.1016/j.nima.2020.164034
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sato,S.; Kataoka,J.; Kotoku,J.; Taki,M.; Oyama,A.; Tagawa,L.; Fujieda,K.; Nishi,F.; Toyoda,T.;
中科院分区：
文献类型：
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作者：
Yamanishi Hirokuni;Ito Tetsuo;Hosono Makoto;Sato,S.; Kataoka,J.; Kotoku,J.; Taki,M.; Oyama,A.; Tagawa,L.; Fujieda,K.; Nishi,F.; Toyoda,T.;
在医学成像中，精确和可靠的图像是非常重要的。然而，由于放射学中常用的检测器的低灵敏度，医学图像的质量有时受到低事件统计的限制。另一方面，长时间暴露于辐射和长时间检查可能成为患者的负担。本文提出了一种基于字典学习和稀疏编码的机器学习方法，从低统计数据中生成高质量的伽马射线源图像的方法。作为第一个应用程序，我们生成了一个高质量的图像137 Cs，它发出662 keV的伽马射线，从低事件统计测量使用康普顿相机。我们使用Geant 4模拟了γ射线源（137 Cs; 662 keV）的各种几何形状，这些几何形状是由Geant 4使用康普顿相机测量的。然后，准备了一整套低分辨率和高分辨率字典。我们从实际测量获得的低分辨率测试图像生成超分辨率图像。伽马射线图像的收敛对于地面实况和预测图像都是相似的，这得到了相应图像中的结构相似性（SSIM）、峰值信噪比（PSNR）和均方根误差（RMSE）的改进的支持。我们还讨论了未来的计划，使用超分辨率技术可视化氯化镭（223RaCl2）在病人的身体，这将使人们有可能实现在体内成像的α粒子内部治疗的第一次。
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 of137Cs, 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.