RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts

TL;DR

RF-ULM addresses the irreversible loss of RF wavefront information caused by DAS beamforming by localizing scatterers directly from RF I/Q data using a custom Semi-Global Sub-Pixel Convolutional Network. The method employs learned wavefront cues, non-maximum suppression, and an affine coordinate transformation to map RF localizations into B-mode space, enabling sub-wavelength precision without traditional beamforming. Benchmark results on in silico and in vivo datasets show >20% improvement in localization accuracy over state-of-the-art beamformed baselines, with favorable processing times and robust generalization from synthetic to real data. The approach reduces computational complexity and holds promise for real-time 3-D ULM, with code made publicly available to foster broader adoption.

Abstract

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of 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) channel 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 channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift 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 https://github.com/hahnec/rf-ulm.

Figures (26)

• Figure 1: Overview of the RF-ULM framework: We leverage RF channel data by feeding In-phase and Quadrature (I/Q) components into a super-resolution neural network. This enables microbubble localization through Non-Maximum Suppression (NMS) without relying on Delay-And-Sum (DAS) beamforming. The resulting sub-wavelength localizations are then mapped to the B-mode coordinate space using an affine transformation. The final ULM rendering step involves the accumulation of all detections over time .
• Figure 2: Our Semi-Global-SPCN architecture employs multiple convolutional layers, residual skip connections and channel shuffling to predict a map upsampled by factor $R$. The model takes as input a 2-D signal with $C$ channels for optional feature concatenation. The initial layer applies a 2-D convolution (opaque pink) with $F$ filters and a kernel size of 9 followed by a Rectified Linear Unit (ReLU) (dark orange). Layer 2 and 3 represent our proposed semi-global bottleneck block consisting of 2-D convolutions with a kernel size of 5, $S=\max(1,G/10)$, LeakyReLUs, and down- as well as upsampling blocks (purple and blue) with scale $G=16$, respectively. The subsequent layers (4 to 14) consist of 2-D convolutions with $F$ filters and a kernel size of 7. Residual connections are added after every other layer, whereas a ReLU follows convolutions without residual connections. The second last layer uses a 2-D convolution with $F$ filters and a kernel size of 3, followed by an element-wise addition with the third layer residual output. The final output is obtained by applying a 2-D convolution with the specified upsampling factor $R$ and a kernel size of 3, followed by a channel to pixel shuffle operation (green).
• Figure 3: RS heiles2022pala
• Figure 4: G-ULM gulm:2023
• Figure 5: U-Net van2020super