arXiv subjects

Antoine Malescot

Publications and source records attributed to Antoine Malescot.

4 recordsLinked to original sources

arXiv2026

ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging

Ultrasound Localization Microscopy (ULM) enables microscopic imaging of the cerebral microvasculature in vivo, but relies on a multi-stage processing pipeline in which acquisition settings and reconstruction processes strongly influence the final output. Existing public datasets remain sparse, restricting rigorous evaluation and slowing progress in algorithm development, including emerging machine-learning approaches, which by design require large quantities of data to be robust and reliable. We introduce \textbf{ULMShare}, an open-access dataset of 99 whole-brain transcranial ULM acquisitions from 61 healthy mice (36 females, 22 males, 3 unknown; mean age: $8.2 \pm 5.5$ weeks; mean weight: $17.7 \pm 4.2$ g), for a total of 30TB of raw data. The dataset spans three experimental procedures, multiple injection and anesthesia protocols, two ultrasound probes, and different imaging planes and orientations. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction and the corresponding microbubble trajectories. Alongside the data, we report vascular saturation, Fourier Ring Correlation, and track-length statistics, plus expert visual gradings. ULMShare provides a broad, standardized and publicly available resource for method development, validation, and benchmarking. The full dataset is available on the Federated Research Data Repository and additional resources are hosted on the ULMShare Github repository.

physics.med-ph↗

arXiv2026

Automatic Aberration Correction for Transcranial Functional and Super-Resolution Ultrasound Imaging in Rodents and Nonhuman Primates

Skull-induced aberrations remain a major drawback of transcranial ultrasound localization microscopy (ULM), degrading sensitivity and spatial accuracy through microbubble mislocalization, false detections, and imaging artifacts, such as disconnected or duplicated vessels. Here, we present a differentiable beamforming framework for automatic aberration correction in transcranial Doppler and ULM. Our approach uses spatially distributed delay-based parameterization of the aberration that is optimized in a closed-loop manner using angular coherence as an objective function. We demonstrate robust improvements of transcranial ULM, in vivo, with enhanced resolution of both mouse and nonhuman primate (NHP) brains. We also extended differentiable beamforming to functional measurements, with improvements in the sensitivity of transcranial functional ultrasound (fUS) and ULM based hemodynamic quantification. Extending this approach to 3D transcranial ULM imaging in NHPs, we show efficient correction of skull induced aberrations and removal of artifacts, such as vessel duplications. By providing a fully automated and generalizable solution for aberration correction, this work lowers a major technical barrier to transcranial ultrasound imaging, enabling broader adoption of non-invasive, super-resolution and functional neuroimaging across laboratories and across species.

physics.med-ph↗

arXiv2024

Inverse Problem Approach to Aberration Correction for in vivo Transcranial Imaging Based on a Sparse Representation of Contrast-enhanced Ultrasound Data

Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals. We propose to use the \textit{a priori} knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC \textit{in vivo} in 5 mouse brains. We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 $\mu$m to 18.3 $\mu$m. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction.

eess.IV↗

arXiv2023

Phase Aberration Correction for in vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network

Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers (μm). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration function rather than corrected images also confers enhanced explainability to the network. In addition, 3D spatiotemporal convolutions were used for the network to leverage entire microbubble tracks. For training and validation, we used an anatomically and hemodynamically realistic mouse brain microvascular network model to simulate the flow of microbubbles in presence of aberration. The proposed CV-CNN performance was compared to the coherence-based method by using microbubble tracks. We then confirmed the capability of the proposed network to generalize to transcranial \textit{in vivo} data in the mouse brain (n=3). Vascular reconstructions using a locally predicted aberration function included additional and sharper vessels. The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse. After correction, we measured a resolution of 15.6 μm for younger mice, representing an improvement of 25.8 $\%$, while the resolution was improved by 13.9 $\%$ for the 6-month-old mouse. This work leads to different applications for complex-valued convolutions in biomedical imaging and strategies to perform transcranial ULM.

eess.IV↗