Ultrasound Localization Microscopy (ULM) is a non-invasive technique that
allows for the imaging of micro-vessels in vivo, at depth and with a resolution
on the order of ten microns. ULM is based on the sub-resolution localization of
individual microbubbles injected in the bloodstream. Mapping the whole
angioarchitecture requires the accumulation of microbubbles trajectories from
thousands of frames, typically acquired over a few minutes. ULM acquisition
times can be reduced by increasing the microbubble concentration, but requires
more advanced algorithms to detect them individually. Several deep learning
approaches have been proposed for this task, but they remain limited to 2D
imaging, in part due to the associated large memory requirements. Herein, we
propose to use sparse tensor neural networks to reduce memory usage in 2D and
to improve the scaling of the memory requirement for the extension of deep
learning architecture to 3D. We study several approaches to efficiently convert
ultrasound data into a sparse format and study the impact of the associated
loss of information. When applied in 2D, the sparse formulation reduces the
memory requirements by a factor 2 at the cost of a small reduction of
performance when compared against dense networks. In 3D, the proposed approach
reduces memory requirements by two order of magnitude while largely
outperforming conventional ULM in high concentration settings. We show that
Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense
deep learning based method in 2D ULM i.e. the use of higher concentration in
silico and reduced acquisition time.

用稀疏张量神经网络可以减少超声局部显微镜在三维成像中的存储需求，并在高浓度情况下明显优于传统的 ULM 方法。

稀疏张量神经网络修剪用于 3D 超声定位显微镜的深度学习

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D  Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) enables imaging of vascular
structures in the micrometer range by accumulating contrast agent particle
locations over time. Precise and efficient target localization accuracy remains
an active research topic in the ULM field to further push the boundaries of
this promising medical imaging technology. Existing work incorporates
Delay-And-Sum (DAS) beamforming into particle localization pipelines, which
ultimately determines the ULM image resolution capability. In this paper we
propose to feed unprocessed Radio-Frequency (RF) data into a super-resolution
network while bypassing DAS beamforming and its limitations. To facilitate
this, we demonstrate label projection and inverse point transformation between
B-mode and RF coordinate space as required by our approach. We assess our
method against state-of-the-art techniques based on a public dataset featuring
in silico and in vivo data. Results from our RF-trained network suggest that
excluding DAS beamforming offers a great potential to optimize on the ULM
resolution performance.

通过将未经处理的射频数据输入到超分辨率网络中，本文提出了绕过 DAS 波束成形及其限制的方法，以提高超声定位显微镜（ULM）的分辨率性能。通过对公开数据集的评估，我们的结果表明，排除 DAS 波束成形具有优化 ULM 分辨率性能的巨大潜力。

从射频数据学习超分辨率超声定位显微镜

Learning Super-Resolution Ultrasound Localization Microscopy from  Radio-Frequency Data

Ultrasound Localization Microscopy can resolve the microvascular bed down to
a few micrometers. To achieve such performance microbubble contrast agents must
perfuse the entire microvascular network. Microbubbles are then located
individually and tracked over time to sample individual vessels, typically over
hundreds of thousands of images. To overcome the fundamental limit of
diffraction and achieve a dense reconstruction of the network, low microbubble
concentrations must be used, which lead to acquisitions lasting several
minutes. Conventional processing pipelines are currently unable to deal with
interference from multiple nearby microbubbles, further reducing achievable
concentrations. This work overcomes this problem by proposing a Deep Learning
approach to recover dense vascular networks from ultrasound acquisitions with
high microbubble concentrations. A realistic mouse brain microvascular network,
segmented from 2-photon microscopy, was used to train a three-dimensional
convolutional neural network based on a V-net architecture. Ultrasound data
sets from multiple microbubbles flowing through the microvascular network were
simulated and used as ground truth to train the 3D CNN to track microbubbles.
The 3D-CNN approach was validated in silico using a subset of the data and in
vivo on a rat brain acquisition. In silico, the CNN reconstructed vascular
networks with higher precision (81%) than a conventional ULM framework (70%).
In vivo, the CNN could resolve micro vessels as small as 10 $\mu$m with an
increase in resolution when compared against a conventional approach.

通过深度学习中的三维卷积神经网络，从具有高泡沫浓度的超声数据中恢复密集的血管网络。

时空超声定位显微镜的深度学习框架

A Deep Learning Framework for Spatiotemporal Ultrasound Localization  Microscopy

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images
relies on the precise localization of contrast agent particles across
consecutive beamformed frames. However, our study uncovers an enormous
potential: The process of delay-and-sum beamforming leads to an irreversible
reduction of Radio-Frequency (RF) data, while its implications for localization
remain largely unexplored. The rich contextual information embedded within RF
wavefronts, including their hyperbolic shape and phase, offers great promise
for guiding Deep Neural Networks (DNNs) in challenging localization scenarios.
To fully exploit this data, we propose to directly localize scatterers in RF
signals. Our approach involves a custom super-resolution DNN using learned
feature channel shuffling and a novel semi-global convolutional sampling block
tailored for reliable and accurate localization in RF input data. Additionally,
we introduce a geometric point transformation that facilitates seamless mapping
between B-mode and RF spaces. To validate the effectiveness of our method and
understand the impact of beamforming, we conduct an extensive comparison with
State-Of-The-Art (SOTA) techniques in ULM. We present the inaugural in vivo
results from an RF-trained DNN, highlighting its real-world practicality. Our
findings show that RF-ULM bridges the domain gap between synthetic and real
datasets, offering a considerable advantage in terms of precision and
complexity. To enable the broader research community to benefit from our
findings, our code and the associated SOTA methods are made available at
this https URL

采用超分辨率深度神经网络和几何点变换的射频超声成像技术在定位过程中发现有巨大潜力，可以有效地提高精度和复杂性，弥合了合成和真实数据集之间的领域差距。