生成对抗网络加速超分辨率超声定位显微成像方法研究

Accelerating super-resolution ultrasound localization microscopy using generative adversarial net

超快超声定位显微成像(uULM),突破了传统超声衍射极限,可实现分辨率远小于发射波长的在体深层微血管精准成像.通过对微血管中数以万计的运动微泡进行中心点定位和轨迹追踪, uULM技术可重建微血管图像.通常一张uULM图像需要数十秒甚至数百秒的连续长程图像采集,这在一定程度上限制了其更广泛的临床应用.针对这一挑战,本研究在阐明了超声衍射极限、超分辨率定位理论方法的基础上,给出了基于傅里叶环相关的分辨率测定原理和实现方法,并结合传统uULM重建技术,发展了一种基于生成对抗网络的深度学习超分辨超声成像方法,以缩减uULM对图像采集时长的依赖,提高成像速度和成像分辨率.针对大鼠脑的在体数据分析结果表明,基于生成对抗网络的超声定位显微技术微血管分辨达到10μm,在保持较高超声成像空间分辨率和图像饱和度的同时,数据采集时间缩减一半,从而显著降低了uULM对图像数据采集时长的依赖.相关深度学习模型连接轨迹的计算复杂度较小,且避免了人工调参以及轨迹筛选,为加速超分辨率uULM微血流成像和提升uULM成像分辨率提供了一种有效的工具.相关思路与方法对促进超分辨率uULM成像技术发展具有一定的借鉴意义.

Ultrafast ultrasound localization microscopy(uULM) has broken through the fundamental acoustic diffraction limit by accumulating thousands of sub-wavelength microbubble localisation points and improved the spatial resolution by more than one order of magnitude, which is conducive to clinical diagnosis. By localizing individually injected microbubbles and tracking their movement with a subwavelength resolution, the vasculature microscopy can be achieved with micrometer scale. However, the reconstruction of a uULM image often requires tens or even hundreds of seconds of continuous long-range image acquisition, which limits its clinical application. In order to solve this problem, a generative adversarial network(GAN) based deep learning method is proposed to reconstruct the super-resolution ultrasound localization microscopy. In vivo uULM ultrasound datasets are used to train the network to reconstruct dense vascular networks via localized microbubbles. This approach is validated by using another in-vivo dataset obtained in a rat brain. Results show that GAN based ultrafast ultrasound localization microscopy(GAN-uULM) can resolve micro vessels smaller than 10 μm. Besides, GAN-uULM is able to distinguish small vessels that cannot be continuously reconstructed by using a standard uULM reconstruction method. Saturation parameter based on counting the number of explored pixels is used to evaluate the reconstruction quality. The proposed reconstruction approach reduces the data requirement by half and thus significantly accelerates the uULM imaging. It is illustrasted that for a dataset of 292 s ultrafast acquisition, the saturation of standard uULM image is 33%, while that of GAN-uULM can reach 46%. Fourier ring correlation(FRC) method is utilized to measure the spatial resolution in uULM.Resolutions of the images obtained by standard uULM and GAN-ULM are 7.8 μm and 8.9 μm, respectively.In conclusion, the developed deep learning model is able to connect trajectories with less computational complexity and avoids manual tuning and trajectory screening, providing an effective solution for accelerating ultrasound localization microscopy.