Shao Wenyi, Rowe Steven P, Du Yong
Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Ann Transl Med. 2021 May;9(9):819. doi: 10.21037/atm-20-3345.
Single photon emission computed tomography (SPECT) is an important functional tool for clinical diagnosis and scientific research of brain disorders, but suffers from limited spatial resolution and high noise due to hardware design and imaging physics. The present study is to develop a deep learning technique for SPECT image reconstruction that directly converts raw projection data to image with high resolution and low noise, while an efficient training method specifically applicable to medical image reconstruction is presented.
A custom software was developed to generate 20,000 2-D brain phantoms, of which 16,000 were used to train the neural network, 2,000 for validation, and the final 2,000 for testing. To reduce development difficulty, a two-step training strategy for network design was adopted. We first compressed full-size activity image (128×128 pixels) to a one-D vector consisting of 256×1 pixels, accomplished by an autoencoder (AE) consisting of an encoder and a decoder. The vector is a good representation of the full-size image in a lower-dimensional space and was used as a compact label to develop the second network that maps between the projection-data domain and the vector domain. Since the label had 256 pixels only, the second network was compact and easy to converge. The second network, when successfully developed, was connected to the decoder (a portion of AE) to decompress the vector to a regular 128×128 image. Therefore, a complex network was essentially divided into two compact neural networks trained separately in sequence but eventually connectable.
A total of 2,000 test examples, a synthetic brain phantom, and de-identified patient data were used to validate SPECTnet. Results obtained from SPECTnet were compared with those obtained from our clinic OS-EM method. Images with lower noise and more accurate information in the uptake areas were obtained by SPECTnet.
The challenge of developing a complex deep neural network is reduced by training two separate compact connectable networks. The combination of the two networks forms the full version of SPECTnet. Results show that the developed neural network can produce more accurate SPECT images.
单光子发射计算机断层扫描（SPECT）是用于脑部疾病临床诊断和科研的重要功能工具，但由于硬件设计和成像物理因素，其空间分辨率有限且噪声较高。本研究旨在开发一种用于SPECT图像重建的深度学习技术，可直接将原始投影数据转换为高分辨率、低噪声的图像，同时提出一种专门适用于医学图像重建的高效训练方法。
开发了一个定制软件来生成20,000个二维脑部体模，其中16,000个用于训练神经网络，2,000个用于验证，最后2,000个用于测试。为降低开发难度，采用了网络设计的两步训练策略。我们首先通过由编码器和解码器组成的自动编码器（AE）将全尺寸活动图像（128×128像素）压缩为一个由256×1像素组成的一维向量。该向量在低维空间中是全尺寸图像的良好表示，并用作紧凑标签来开发第二个在投影数据域和向量域之间进行映射的网络。由于标签仅具有256个像素，第二个网络紧凑且易于收敛。第二个网络成功开发后，连接到解码器（AE的一部分）以将向量解压缩为常规的128×128图像。因此，一个复杂的网络本质上被分为两个紧凑的神经网络，它们按顺序分别训练，但最终可连接。
总共2,000个测试示例、一个合成脑部体模和去识别的患者数据用于验证SPECTnet。将SPECTnet获得的结果与我们临床的OS-EM方法获得的结果进行比较。SPECTnet获得了噪声更低且摄取区域信息更准确的图像。
通过训练两个单独的紧凑可连接网络，降低了开发复杂深度神经网络的挑战。这两个网络的组合形成了完整版本的SPECTnet。结果表明，所开发的神经网络可以生成更准确的SPECT图像。
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