Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for
non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound
Localization Microscopy (ULM) has enabled a revolutionary breakthrough by
offering ten times higher resolution. To date, Delay-And-Sum (DAS) beamformers
are used to render ULM frames, ultimately determining the image resolution
capability. To take full advantage of ULM, this study questions whether
beamforming is the most effective processing step for ULM, suggesting an
alternative approach that relies solely on Time-Difference-of-Arrival (TDoA)
information. To this end, a novel geometric framework for micro bubble
localization via ellipse intersections is proposed to overcome existing
beamforming limitations. We present a benchmark comparison based on a public
dataset for which our geometric ULM outperforms existing baseline methods in
terms of accuracy and reliability while only utilizing a portion of the
available transducer data.

该研究提出了一种新颖的基于几何框架的微泡定位方法，该方法利用时差到达信息，克服了现有波束成形的限制，并在可用传感器数据的一部分情况下，比现有基准方法更准确，更可靠。

几何超声定位显微镜

Geometric Ultrasound Localization Microscopy

We present an ultra-fast, precise, parameter-free method, which we term
Deep-STORM, for obtaining super-resolution images from stochastically-blinking
emitters, such as fluorescent molecules used for localization microscopy.
Deep-STORM uses a deep convolutional neural network that can be trained on
simulated data or experimental measurements, both of which are demonstrated.
The method achieves state-of-the-art resolution under challenging
signal-to-noise conditions and high emitter densities, and is significantly
faster than existing approaches. Additionally, no prior information on the
shape of the underlying structure is required, making the method applicable to
any blinking data-set. We validate our approach by super-resolution image
reconstruction of simulated and experimentally obtained data.

提出了一种超快、精确的、无参数方法 Deep-STORM，用于从随机闪烁的发射器（如用于定位显微镜的荧光分子）中获取超分辨率图像。该方法使用了深度卷积神经网络，可以在模拟数据或实验测量上进行训练，可以在挑战性的信噪比条件和高发射器密度下实现最先进的分辨率，并且比现有方法快得多。此外，它不需要关于基础结构形状的先验信息，使得该方法适用于任何闪烁数据集。通过对模拟和实验数据的超分辨率图像重建，验证了我们的方法。