Texture transformer super-resolution for low-dose computed tomography.
Texture transformer super-resolution for low-dose computed tomography.
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DOI：
10.1088/2057-1976/ac9da7
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发表时间：
2022-11-04
影响因子：
1.4
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计算机断层扫描（CT）被广泛用于诊断许多疾病。低剂量CT已被积极追求，以降低电离辐射的风险。在低剂量CT中通常使用相对平滑的内核来抑制图像噪声，这可能会牺牲空间分辨率。在这项工作中，我们提出了一个纹理Transformer网络，同时减少图像噪声，提高CT图像的空间分辨率。该网络被称为超分辨率纹理Transformer（TTSR），是一种基于生成对抗网络（GAN）的基于参考的深度学习图像超分辨率方法。该算法将含噪低分辨率CT（LRCT）图像和常规剂量高分辨率CT（HRCT）图像分别作为Transformer中的查询和关键字。通过深度神经网络（DNN）纹理提取、相关嵌入以及基于注意力的纹理转移和合成来优化图像平移，以实现LRCT和HRCT图像之间的联合特征学习，从而获得超分辨率CT（SRCT）图像。为了评价SRCT的性能，我们使用了XCAT体模程序的模拟数据和真实的患者数据。峰值信噪比（PSNR），结构相似性指数测度（SSIM）和特征相似性（FSIM）指数被用作定量度量。为了比较SRCT性能，使用了三次样条插值、SRGAN（具有额外内容损失的GAN超分辨率）和GAN-CIRCLE（具有循环一致性的GAN超分辨率）。与其他两种方法相比，TTSR可以恢复SRCT图像中更多的细节，并获得更好的PSNR，SSIM和FSIM模拟和真实患者数据。此外，我们表明，TTSR可以产生更好的图像质量，需要更少的计算时间比高分辨率低剂量CT图像去噪块匹配和三维滤波（BM 3D）和GAN-CIRCLE。综上所述，本文提出的基于纹理Transformer和注意力机制的TTSR方法为提高低剂量CT图像的空间分辨率和抑制噪声提供了一种有效的工具。
Computed tomography (CT) is widely used to diagnose many diseases. Low-dose CT has been actively pursued to lower the ionization radiation risk. A relatively smoother kernel is typically used in low-dose CT to suppress image noise, which may sacrifice spatial resolution. In this work, we propose a texture transformer network to simultaneously reduce image noise and improve spatial resolution in CT images. This network, referred to as Texture Transformer for Super Resolution (TTSR), is a reference-based deep-learning image super-resolution method built upon a generative adversarial network (GAN). The noisy low-resolution CT (LRCT) image and the routine-dose high-resolution (HRCT) image are severed as the query and key in a transformer, respectively. Image translation is optimized through deep neural network (DNN) texture extraction, correlation embedding, and attention-based texture transfer and synthesis to achieve joint feature learning between LRCT and HRCT images for super-resolution CT (SRCT) images. To evaluate SRCT performance, we use the data from both simulations of the XCAT phantom program and the real patient data. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity (FSIM) index are used as quantitative metrics. For comparison of SRCT performance, the cubic spline interpolation, SRGAN (a GAN super-resolution with an additional content loss), and GAN-CIRCLE (a GAN super-resolution with cycle consistency) were used. Compared to the other two methods, TTSR can restore more details in SRCT images and achieve better PSNR, SSIM, and FSIM for both simulation and real-patient data. In addition, we show that TTSR can yield better image quality and demand much less computation time than high-resolution low-dose CT images denoised by block-matching and 3D filtering (BM3D) and GAN-CIRCLE. In summary, the proposed TTSR method based on texture transformer and attention mechanism provides an effective and efficient tool to improve spatial resolution and suppress noise of low-dose CT images.