Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning.
Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning.
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DOI：
10.1088/1361-6501/ad1671
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发表时间：
2024-04-01
影响因子：
2.4
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中科院分区：
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超声定位显微镜（ULM）具有数十微米的空间分辨率，它通过追踪对比增强超声（CEUS）图像中的微气泡（1 - 5μm）来重建微血管结构并测量血管内血流。由于CEUS气泡轨迹的大小（例如，对于波长λ = 280μm的超声为0.5 - 1mm）通常比气泡直径大两个数量级，因此在有噪声的CEUS数据中准确定位微气泡对于ULM结果的准确性至关重要。在本文中，我们引入了一种基于残差学习的有监督超分辨率盲去卷积网络（SupBD - net），以及一种用于自监督盲去卷积网络（SelfBD - net）的新损失函数，用于以优于λ/10的空间分辨率检测气泡中心。我们的最终目的是提高区分紧密相邻微血管的能力以及大血管中速度剖面测量的准确性。利用真实的合成数据，对这些方法的性能进行了校准，并与几种近期引入的深度学习和盲去卷积技术进行了比较。对于气泡检测，气泡中心位置的误差随着轨迹大小、噪声水平和气泡浓度的增加而增大。在所有情况下，SupBD - net产生的误差最小，使其保持在0.1λ以下。对于未知的气泡轨迹形态（所有有监督学习方法在此情况下均失效），SelfBD - net仍能将误差保持在小于0.15λ。SupBD - net在分离紧密相邻的气泡和平行微血管方面也优于其他方法。在大血管中，在引入一种纠正因轨迹重叠而导致的轨迹终止的程序后，SupBD - net在血管半径和速度剖面方面保持最小的误差。通过绘制新生仔猪的脑微血管图展示了这些方法的应用，其中被0.15λ分隔的相邻微血管可以很容易地被SupBD - net和SelfBD - net区分，但其他技术无法做到。因此，新提出的基于残差学习的方法提高了ULM在微血管和大血管中的空间分辨率和准确性。
With a spatial resolution of tens of microns, ultrasound localization microscopy (ULM) reconstructs microvascular structures and measures intravascular flows by tracking microbubbles (1–5 μm) in contrast enhanced ultrasound (CEUS) images. Since the size of CEUS bubble traces, e.g. 0.5–1 mm for ultrasound with a wavelength λ = 280 μm, is typically two orders of magnitude larger than the bubble diameter, accurately localizing microbubbles in noisy CEUS data is vital to the fidelity of the ULM results. In this paper, we introduce a residual learning based supervised super-resolution blind deconvolution network (SupBD-net), and a new loss function for a self-supervised blind deconvolution network (SelfBD-net), for detecting bubble centers at a spatial resolution finer than λ/10. Our ultimate purpose is to improve the ability to distinguish closely located microvessels and the accuracy of the velocity profile measurements in macrovessels. Using realistic synthetic data, the performance of these methods is calibrated and compared against several recently introduced deep learning and blind deconvolution techniques. For bubble detection, errors in bubble center location increase with the trace size, noise level, and bubble concentration. For all cases, SupBD-net yields the least error, keeping it below 0.1 λ. For unknown bubble trace morphology, where all the supervised learning methods fail, SelfBD-net can still maintain an error of less than 0.15 λ. SupBD-net also outperforms the other methods in separating closely located bubbles and parallel microvessels. In macrovessels, SupBD-net maintains the least errors in the vessel radius and velocity profile after introducing a procedure that corrects for terminated tracks caused by overlapping traces. Application of these methods is demonstrated by mapping the cerebral microvasculature of a neonatal pig, where neighboring microvessels separated by 0.15 λ can be readily distinguished by SupBD-net and SelfBD-net, but not by the other techniques. Hence, the newly proposed residual learning based methods improve the spatial resolution and accuracy of ULM in micro- and macro-vessels.
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