CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy

TL;DR

CycleULM is presented, the first unified label-free deep learning framework for ULM, which provides a practical pathway toward robust, real-time ULM and accelerates its translation to clinical applications.

Abstract

Super-resolution ultrasound via microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy (ULM), can resolve microvasculature beyond the acoustic diffraction limit. However, significant challenges remain in localisation performance and data acquisition and processing time. Deep learning methods for ULM have shown promise to address these challenges, however, they remain limited by in vivo label scarcity and the simulation-to-reality domain gap. We present CycleULM, the first unified label-free deep learning framework for ULM. CycleULM learns a physics-emulating translation between the real contrast-enhanced ultrasound (CEUS) data domain and a simplified MB-only domain, leveraging the power of CycleGAN without requiring paired ground truth data. With this translation, CycleULM removes dependence on high-fidelity simulators or labelled data, and makes MB localisation and tracking substantially easier. Deployed as modular plug-and-play components within existing pipelines or as an end-to-end processing framework, CycleULM delivers substantial performance gains across both in silico and in vivo datasets. Specifically, CycleULM improves image contrast (contrast-to-noise ratio) by up to 15.3 dB and sharpens CEUS resolution with a 2.5{\times} reduction in the full width at half maximum of the point spread function. CycleULM also improves MB localisation performance, with up to +40% recall, +46% precision, and a -14.0 μm mean localisation error, yielding more faithful vascular reconstructions. Importantly, CycleULM achieves real-time processing throughput at 18.3 frames per second with order-of-magnitude speed-ups (up to ~14.5{\times}). By combining label-free learning, performance enhancement, and computational efficiency, CycleULM provides a practical pathway toward robust, real-time ULM and accelerates its translation to clinical applications.

Figures (14)

• Figure 1: Overview of the CycleULM framework.a, CycleULM learns a bidirectional physics-emulating translation between the CEUS frames and a simplified simulated MB-only domain via self-supervised cycle-consistent adversarial training. The translation by MB-DT effectively bridges the domain gap between simulated data and real acquisitions, enabling the downstream processing or model training within a simplified, well-controlled domain without requiring manual annotations or complex simulators. b, The variant CycleULM methods. The modular design of CycleULM offers the flexibility that three networks can be integrated as plug-and-play modules within conventional ULM pipelines (CycleULM-NCC, CycleULM-Decon and CycleULM-Loc), or combined into an end-to-end DL pipeline for fully automated ULM post-processing (CycleULM-E2E). c, MBL-Net takes the MB-only image translated by MB-DT as input and produces four complementary outputs, including a probability map denoting the likelihood of a MB at each pixel, an intensity map estimating the bubble intensities, two sub-pixel coordinate maps that refine the accuracy of MB locations, and three uncertainty maps that quantify confidence in these predictions. d, MBT-Net takes a short sequence of 8 consecutive MB-only frames translated by the MB-DT as input and outputs three maps, including a trajectory-probability map indicating the likelihood of a MB path present at every pixel, a velocity map showing the horizontal velocity components, and a velocity map showing the vertical velocity components, respectively.
• Figure 2: CycleULM learns a physics-emulating translation from the CEUS domain into a simplified MB-only domain. Demonstrations on the in silico ULTRA-SR Challenge data in a and the in vivo rabbit kidney data in b show that, by leveraging the temporal consistency from three consecutive CEUS frames, the MB-DT can effectively distinguish MB signals from background clutter. This physics-emulating translation bridges the domain gap between simulated data and real acquisitions, enabling the downstream processing or model training within a simplified, well-controlled domain without requiring manual annotations or complex simulators.
• Figure 3: CycleULM improves image contrast and resolution on in silico ULTRA-SR Challenge dataset.a, An example CEUS frame and b, the corresponding MB-only frame translated by the MB-DT. In the MB-only frame, background clutter is significantly suppressed and the PSF size is visibly reduced, resulting in the improved separation of overlapping MBs (yellow arrows). c, MIPs of the raw CEUS dataset and d, the MB-DT output sequence. The MIP after MB-DT preserves the vascular pattern in the raw MIP but exhibits markedly lower background, higher contrast, and finer structural detail. e,f,g, Zoomed-in MIPs and the accumulated GT MB image show a substantial CNR improvement of 9.2 dB after MB-DT. h, Cross-sectional analyses across a single MB and i, two adjacent vessels demonstrate enhanced resolution. The measured FWHM decreased from 355 µm to 178 µm (a 2-fold resolution improvement), and overlapping MB signals became clearly separable after MB-DT.
• Figure 4: CycleULM improves image contrast and resolution in an in vivo rabbit kidney dataset.a, An example CEUS frame and b, the corresponding MB-only frame translated by the MB-DT. The MB-only shows markedly reduced background clutter. c, MIPs of the raw CEUS dataset and d, the sequence output by MB-DT. The MIP after MB-DT preserved the vascular architecture in the raw MIP while achieving substantially higher contrast and resolution. e,f, Zoomed-in MIPs show an extraordinary CNR improvement of 15.3 dB after MB-DT g, Cross-sectional analyses across a single MB and h, several adjacent vessels. The measured FWHM decreased from 725 µm to 294 µm after MB-DT, corresponding to a 2.5-fold resolution improvement. Adjacent vascular structures also appear more clearly separated in the MB-DT–translated MIP.
• Figure 5: CycleULM significantly improves MB localisation performance on in silico ULTRA-SR Challenge dataset.a, Precision–recall curves. b, Mean localisation error versus recall curves. c, Accumulated GT MB image. d–h, Accumulated super-localised MB maps generated by different methods, each with the same number of detected MBs. i,j, Zoomed-in comparisons of two selected regions from the super-localised MB maps, highlighting differences in localisation accuracy and background suppression.