Datasheet1_Modular GAN: positron emission tomography image reconstruction using two generative adversarial networks.docx

IntroductionThe reconstruction of PET images involves converting sinograms, which represent the measured counts of radioactive emissions using detector rings encircling the patient, into meaningful images. However, the quality of PET data acquisition is impacted by physical factors, photon count statistics and detector characteristics, which affect the signal-to-noise ratio, resolution and quantitative accuracy of the resulting images. To address these influences, correction methods have been developed to mitigate each of these issues separately. Recently, generative adversarial networks (GANs) based on machine learning have shown promise in learning the complex mapping between acquired PET data and reconstructed tomographic images. This study aims to investigate the properties of training images that contribute to GAN performance when non-clinical images are used for training. Additionally, we describe a method to correct common PET imaging artefacts without relying on patient-specific anatomical images.MethodsThe modular GAN framework includes two GANs. Module 1, resembling Pix2pix architecture, is trained on non-clinical sinogram-image pairs. Training data are optimised by considering image properties defined by metrics. The second module utilises adaptive instance normalisation and style embedding to enhance the quality of images from Module 1. Additional perceptual and patch-based loss functions are employed in training both modules. The performance of the new framework was compared with that of existing methods, (filtered backprojection (FBP) and ordered subset expectation maximisation (OSEM) without and with point spread function (OSEM-PSF)) with respect to correction for attenuation, patient motion and noise in simulated, NEMA phantom and human imaging data. Evaluation metrics included structural similarity (SSIM), peak-signal-to-noise ratio (PSNR), relative root mean squared error (rRMSE) for simulated data, and contrast-to-noise ratio (CNR) for NEMA phantom and human data.ResultsFor simulated test data, the performance of the proposed framework was both qualitatively and quantitatively superior to that of FBP and OSEM. In the presence of noise, Module 1 generated images with a SSIM of 0.48 and higher. These images exhibited coarse structures that were subsequently refined by Module 2, yielding images with an SSIM higher than 0.71 (at least 22% higher than OSEM). The proposed method was robust against noise and motion. For NEMA phantoms, it achieved higher CNR values than OSEM. For human images, the CNR in brain regions was significantly higher than that of FBP and OSEM (p

引言：正电子发射断层扫描（PET）图像的重建涉及将环绕患者并测量放射性发射计数的探测器环所代表的正弦图转换为有意义的图像。然而，PET数据采集的质量受到物理因素、光子计数统计和探测器特性的影响，这些因素影响了最终图像的信噪比、分辨率和定量精度。为了解决这些问题，已经开发了校正方法以分别缓解这些问题。最近，基于机器学习的生成对抗网络（GANs）在学习和获取的PET数据与重建的断层扫描图像之间的复杂映射方面显示出巨大潜力。本研究旨在探讨训练图像的性质，这些性质有助于GAN在非临床图像用于训练时的性能。此外，我们描述了一种无需依赖患者特定解剖图像的方法来校正常见的PET成像伪影。方法：模块化GAN框架包括两个GAN。模块1，类似于Pix2pix架构，在非临床正弦图-图像对上进行训练。通过考虑由指标定义的图像属性来优化训练数据。第二个模块利用自适应实例归一化和风格嵌入来提高模块1生成的图像质量。在训练两个模块时，还使用了额外的感知和基于块段的损失函数。将新框架的性能与现有方法（滤波反投影（FBP）和有序子集期望最大化（OSEM）以及带有和未带有点扩散函数（OSEM-PSF））在模拟、NEMA假体和人体成像数据中校正衰减、患者运动和噪声方面的性能进行了比较。评估指标包括结构相似性（SSIM）、峰值信噪比（PSNR）、相对均方根误差（rRMSE）（针对模拟数据），以及对比噪声比（CNR）（针对NEMA假体和人体数据）。结果：对于模拟测试数据，所提出框架的性能在定性和定量方面均优于FBP和OSEM。在存在噪声的情况下，模块1生成的图像的SSIM值达到0.48以上。这些图像显示出粗糙的结构，随后由模块2进行细化，产生的图像的SSIM值高于0.71（至少比OSEM高22%）。所提出的方法对噪声和运动具有鲁棒性。对于NEMA假体，它达到了比OSEM更高的CNR值。对于人体图像，大脑区域的CNR显著高于FBP和OSEM（p值未给出）。