Improved Low-Count Quantitative PET Reconstruction With an Iterative Neural Network.
Improved Low-Count Quantitative PET Reconstruction With an Iterative Neural Network.
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DOI：
10.1109/tmi.2020.2998480
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发表时间：
2020-11
影响因子：
10.6
通讯作者：
Fessler JA
中科院分区：
文献类型：
--
作者：
Lim H;Chun IY;Dewaraja YK;Fessler JA
低计数PET中的图像重建特别具有挑战性，因为来自基于Lu的晶体中的天然放射性的伽马引起降低测量信噪比（SNR）的高随机分数。在基于模型的图像重建（MBIR）中，使用非正则化方法的更多迭代可能会增加噪声，因此将正则化结合到图像重建中以控制噪声是期望的。基于学习卷积算子的新正则化方法正在MBIR中出现。我们修改了迭代神经网络BCD-Net的架构，用于PET MBIR，并使用XCAT体模数据证明了训练的BCD-Net的有效性，该数据模拟了Y-90微球放射性栓塞后Y-90 PET患者成像的低真实符合计数率和高随机分数。数值结果表明，提出的BCD-Net显着提高CNR和RMSE的重建图像相比，MBIR方法使用非训练的正则化，总变差（TV）和非局部均值（NLM）。此外，BCD-Net成功地概括了与训练数据不同的测试数据。还证明了临床相关体模测量数据的改进，其中我们使用具有非常不同的活动分布和计数水平的训练和测试数据集。
Image reconstruction in low-count PET is particularly challenging because gammas from natural radioactivity in Lu-based crystals cause high random fractions that lower the measurement signal-to-noise-ratio (SNR). In model-based image reconstruction (MBIR), using more iterations of an unregularized method may increase the noise, so incorporating regularization into the image reconstruction is desirable to control the noise. New regularization methods based on learned convolutional operators are emerging in MBIR. We modify the architecture of an iterative neural network, BCD-Net, for PET MBIR, and demonstrate the efficacy of the trained BCD-Net using XCAT phantom data that simulates the low true coincidence count-rates with high random fractions typical for Y-90 PET patient imaging after Y-90 microsphere radioembolization. Numerical results show that the proposed BCD-Net significantly improves CNR and RMSE of the reconstructed images compared to MBIR methods using non-trained regularizers, total variation (TV) and non-local means (NLM). Moreover, BCD-Net successfully generalizes to test data that differs from the training data. Improvements were also demonstrated for the clinically relevant phantom measurement data where we used training and testing datasets having very different activity distributions and count-levels.