约束CycleGAN用于有效生成空间分辨率改善的超声扇形图像
Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution
AI总结：
该研究提出约束CycleGAN（CCycleGAN），利用无配对超声图像生成空间分辨率改善的扇形图像，通过恒等损失和相关系数损失保持结构一致性与背向散射模式，实验证明其优于基准方法。
摘要：
目的：相控阵或凸阵探头产生的超声（US）图像具有扇形视野（FOV），其图像分辨率在空间上变化，在远场区域和方位角两侧质量较差。具有改善空间分辨率的扇形超声图像有利于对大型动态器官（如心脏）进行准确的定量分析。因此，本研究旨在将空间分辨率变化的超声图像转换为空间分辨率变化较小的图像。CycleGAN一直是无配对医学图像翻译的突出选择；然而，它既不能保证结构一致性，也不能保持无配对超声图像输入与生成图像之间的背向散射模式。方法：为规避这一局限，我们提出了一种约束CycleGAN（CCycleGAN），它直接利用不同超声阵列探头获取的无配对图像进行超声图像生成。除了CycleGAN传统的对抗损失和循环一致性损失外，CCycleGAN还引入了基于超声背向散射信号内在特性的恒等损失和相关系数损失，分别约束结构一致性和背向散射模式。CCycleGAN不使用后处理的B模式图像，而是直接使用从波束形成射频信号中获得的包络数据，无需任何其他非线性后处理。主要结果：体外体模实验结果表明，与基准方法相比，CCycleGAN成功生成了具有改善空间分辨率以及更高峰值信噪比（PSNR）和结构相似性（SSIM）的图像。意义：CCycleGAN生成的体内人体跳动心脏超声图像进一步促进了比基准方法生成图像更高质量的心壁运动估计，特别是在深部区域。
英文摘要：
Objective. A phased or a curvilinear array produces ultrasound (US) images with a sector field of view (FOV), which inherently exhibits spatially-varying image resolution with inferior quality in the far zone and towards the two sides azimuthally. Sector US images with improved spatial resolutions are favorable for accurate quantitative analysis of large and dynamic organs, such as the heart. Therefore, this study aims to translate US images with spatially-varying resolution to ones with less spatially-varying resolution. CycleGAN has been a prominent choice for unpaired medical image translation; however, it neither guarantees structural consistency nor preserves backscattering patterns between input and generated images for unpaired US images. Approach. To circumvent this limitation, we propose a constrained CycleGAN (CCycleGAN), which directly performs US image generation with unpaired images acquired by different ultrasound array probes. In addition to conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN introduces an identical loss and a correlation coefficient loss based on intrinsic US backscattered signal properties to constrain structural consistency and backscattering patterns, respectively. Instead of post-processed B-mode images, CCycleGAN uses envelope data directly obtained from beamformed radio-frequency signals without any other non-linear postprocessing. Main Results. In vitro phantom results demonstrate that CCycleGAN successfully generates images with improved spatial resolution as well as higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with benchmarks. Significance. CCycleGAN-generated US images of the in vivo human beating heart further facilitate higher quality heart wall motion estimation than benchmarks-generated ones, particularly in deep regions.
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