Imaging Diagnostic Technology Department, East Nagoya Imaging Diagnosis Center, 3-4-26 Jiyugaoka, Chikusa-ku, Nagoya, Aichi, Japan.
Graduate School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, Japan.
Phys Eng Sci Med. 2024 Mar;47(1):73-85. doi: 10.1007/s13246-023-01343-3. Epub 2023 Oct 23.
Dedicated breast positron emission tomography (db-PET) is more sensitive than whole-body positron emission tomography and is thus expected to detect early stage breast cancer and determine treatment efficacy. However, it is challenging to decrease the sensitivity of the chest wall side at the edge of the detector, resulting in a relative increase in noise and a decrease in detectability. Longer acquisition times and injection of larger amounts of tracer improve image quality but increase the burden on the patient. Therefore, this study aimed to improve image quality via reconstruction with shorter acquisition time data using deep learning, which has recently been widely used as a noise reduction technique. In our proposed method, a multi-adaptive denoising filter bank structure was introduced by training the training data separately for each detector area because the noise characteristics of db-PET images vary at different locations. Input and ideal images were reconstructed based on 1- and 7-min collection data, respectively, using list mode data. The deep learning model used residual learning with an encoder-decoder structure. The image quality of the proposed method was superior to that of existing noise reduction filters such as Gaussian filters and nonlocal mean filters. Furthermore, there was no significant difference between the maximum standardized uptake values before and after filtering using the proposed method. Taken together, the proposed method is useful as a noise reduction filter for db-PET images, as it can reduce the patient burden, scan time, and radiotracer amount in db-PET examinations.
专用乳腺正电子发射断层扫描（db-PET）比全身正电子发射断层扫描更敏感，因此有望检测早期乳腺癌并确定治疗效果。然而，降低探测器边缘胸壁侧的灵敏度具有挑战性，导致噪声相对增加，检测能力下降。增加采集时间和注射更多示踪剂可以改善图像质量，但会增加患者的负担。因此，本研究旨在通过使用深度学习对较短采集时间的数据进行重建来提高图像质量，深度学习最近已广泛用作降噪技术。在我们提出的方法中，由于 db-PET 图像的噪声特征在不同位置有所不同，因此通过分别为每个探测器区域的训练数据进行训练，引入了多自适应去噪滤波器组结构。使用列表模式数据，基于 1 分钟和 7 分钟采集数据分别重建输入和理想图像。所使用的深度学习模型具有带编码器-解码器结构的残差学习。与高斯滤波器和非局部均值滤波器等现有降噪滤波器相比，所提出方法的图像质量更优。此外，使用所提出的方法进行滤波前后的最大标准化摄取值没有显著差异。综上所述，该方法可作为 db-PET 图像的降噪滤波器，有助于降低 db-PET 检查中的患者负担、扫描时间和放射性示踪剂用量。
