Kwon Kyounghyoun, Oh Dongkyu, Kim Ji Hye, Yoo Jihyung, Lee Won Woo
Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Gwanggyo-ro 145, Yeongtong-gu, Suwon, Gyeonggi-do, 16229, Republic of Korea.
Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea.
EJNMMI Phys. 2024 Oct 12;11(1):84. doi: 10.1186/s40658-024-00686-4.
Accurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (μ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans.
A dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal μ-map generation. The problem of checkerboard artifacts, unique to μ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L) and three times the absolute gradient difference loss (3 × L), and the nearest-neighbor interpolation significantly enhanced AI performance in μ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced μ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%.
The study successfully developed and optimized a deep learning algorithm for generating synthetic μ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine.
准确的衰减校正（AC）在核医学中至关重要，特别是对于定量单光子发射计算机断层扫描/计算机断层扫描（SPECT/CT）成像。本研究旨在利用深度学习从SPECT数据生成合成衰减图（μ图），在肾脏SPECT成像中建立一种无需CT的定量技术，从而减少辐射暴露并消除CT扫描的需求。
使用改进的3D U-Net进行深度学习，分析了1000例Tc-99m DTPA SPECT/CT扫描数据集，用于训练（n = 800）、验证（n = 100）和测试（n = 100）。该研究调查了使用原始发射和散射SPECT数据、归一化方法、损失函数优化以及上采样技术以生成最佳μ图。评估了从SPECT信号生成μ图时特有的棋盘格伪影问题以及碘造影剂的影响。在原始发射SPECT成像中添加散射SPECT、对数最大归一化、绝对差损失（L）与三倍绝对梯度差损失（3×L）的组合以及最近邻插值显著提高了AI在μ图生成中的性能（p < 0.00001）。使用最近邻插值技术有效消除了棋盘格伪影。所开发的AI算法生成的μ图对碘造影剂的存在呈中性，并且在定量SPECT测量（如肾小球滤过率（GFR））中显示出可忽略不计的造影剂影响。转向基于AI的无CT SPECT成像潜在的辐射暴露减少范围为45.3%至78.8%。
该研究成功开发并优化了一种用于在肾脏SPECT图像中生成合成μ图的深度学习算法，证明了从传统SPECT/CT过渡到用于GFR测量的无CT SPECT成像的潜力。这一进展代表了在提高核医学患者安全性和效率方面迈出的重要一步。
