arXiv subjects

Olivier Couture

Publications and source records attributed to Olivier Couture.

6 recordsLinked to original sources

arXiv2026

Simultaneous 3D co-registered perfusion and oxygenation with ULM, photoacoustic imaging, and a planar matrix array

Objective. Joint assessment of tissue oxygenation and microvascular perfusion could offer valuable insights into vascular function across a wide range of biomedical applications. Multispectral photoacoustic imaging enables the evaluation of blood oxygenation, while ultrasound localization microscopy provides sub-diffraction visualization of the microvasculature and blood perfusion. Here, we combine these two complementary modalities to simultaneously generate co-registered, volumetric maps of blood oxygenation and perfusion. Approach. Photoacoustic imaging and ultrasound localization microscopy are both ultrasound-based techniques. We developed an imaging platform that integrates the two modalities using a single planar ultrasonic matrix array, a state-of-the-art array for 3D ultrasound localization microscopy. The bimodal platform was validated in vitro using vessel-mimicking phantoms, then in vivo in mice. Main results. In vitro bimodal images of tubes injected with contrast agents demonstrated a coregistration accuracy of 20 $μ$m and revealed complementary structural and functional information. Multispectral photoacoustic imaging achieved oxygen saturation measurements spanning the physiological range (60-95 %) with 5 % accuracy using only five optical wavelengths. In vivo imaging of healthy mouse tissues with known vascular anatomy further demonstrated the ability of the proposed platform to jointly characterize blood oxygenation and microvascular perfusion. Significance. This work experimentally validates a bimodal photoacoustic imaging-ultrasound localization microscopy approach using a planar ultrasound array. We characterized the functional imaging performance of this platform and identified limited-view artifacts inherent to this array configuration in photoacoustic imaging. These findings establish a foundation for adopting the platform in future studies of murine models and for advancing this promising bimodal approach.

physics.med-ph↗

arXiv2025

Ultrasound matrix imaging for 3D transcranial in vivo localization microscopy

Transcranial ultrasound imaging is usually limited by skull-induced attenuation and high-order aberrations. By using contrast agents such as microbubbles in combination with ultrafast imaging, not only can the signal-to-noise ratio be improved, but super-resolution images down to the micrometer scale of the brain vessels can also be obtained. However, ultrasound localization microscopy (ULM) remains affected by wavefront distortions that limit the microbubble detection rate and hamper their localization. In this work, we show how ultrasound matrix imaging, which relies on the prior recording of the reflection matrix, can provide a solution to these fundamental issues. As an experimental proof of concept, an in vivo reconstruction of deep brain microvessels is performed on three anesthetized sheep. The compensation of wave distortions is shown to markedly enhance the contrast and resolution of ULM. This experimental study thus opens up promising perspectives for a transcranial and nonionizing observation of human cerebral microvascular pathologies, such as stroke.

physics.med-ph↗

arXiv2024

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

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.

cs.CG↗

arXiv2023

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

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.

eess.IV↗

arXiv2021

Freeze-dried microfluidic monodisperse microbubbles as a new generation of ultrasound contrast agents

In the paper, we succeeded to freeze-dry monodisperse microbubbles without degrading their size and acoustic properties. We used microfluidic technology to generate highly monodisperse (coefficient of variation, CV<5%) microbubbles and optimized their formulation along with a cryoprotectant. By using a specific technique of retrieval of the bubble, we showed that freeze-drying the microbubbles does not alter their size distribution. To compare the fundamental resonance properties of the bubbles, we performed backscattered acoustic characterization measurements. Our experimental results revealed that the freeze-drying process conserved the acoustic properties of the bubbles. The maximum backscattering power amplitude of fresh and freeze-dried monodisperse PVA bubbles was around ten eight times higher than that of SonoVue at a similar concentration in vitro. By solving the question of storage and transportation of monodisperse bubbles, our work facilitates their penetration in the domain of UCAs, for performing new tasks and developing novel non-invasive measurements, such as pressure, unaccessible to the existing commercialized bubbles.

physics.med-ph↗

arXiv2009

Khovanov homology for signed divides

The purpose of this paper is to interpret polynomial invariants of strongly invertible links in terms of Khovanov homology theory. To a divide, that is a proper generic immersion of a finite number of copies of the unit interval and circles in a 2-disc, one can associate a strongly invertible link in the 3-sphere. This can be generalized to signed divides : divides with + or - sign assignment to each crossing point. Conversely, to any link $L$ that is strongly invertible for an involution $j$, one can associate a signed divide. Two strongly invertible links that are isotopic through an isotopy respecting the involution are called strongly equivalent. Such isotopies give rise to moves on divides. In a previous paper of the author, one can find an exhaustive list of moves that preserves strong equivalence, together with a polynomial invariant for these moves, giving therefore an invariant for strong equivalence of the associated strongly invertible links. We prove in this paper that this polynomial can be seen as the graded Euler characteristic of a graded complex of vector spaces. Homology of such complexes is invariant for the moves on divides and so is invariant through strong equivalence of strongly invertible links.

math.AT↗