Dynamic Ultrasound Localization Microscopy Without ECG-Gating.

OBJECTIVE: Dynamic Ultrasound Localization Microscopy (DULM) has first been developed for non-invasive Pulsatility measurements in the rodent brain. DULM relies on the localization and tracking of microbubbles (MBs) injected into the bloodstream, to obtain highly resolved velocity and density cine-loops. Previous DULM techniques required ECG-gating, limiting its application to specific datasets, and increasing acquisition time. The objective of this study is to eliminate the need for ECG-gating in DULM experiments by introducing a motion-matching method for time registration. METHODS: We developed a motion-matching algorithm based on tissue Doppler that leverages the cyclic tissue motion within the brain. Tissue Doppler was estimated for each group of frames in the acquisitions, at multiple locations identified as local maxima in the skin above the skull. Subsequently, each group of frames was time-registered to a reference group by delaying it based on the maximum correlation value between their respective tissue Doppler signals. This synchronization ensured that each group of frames aligned with the brain tissue motion of the reference group, and consequently, with its cardiac cycle. As a result, velocities of MBs could be averaged to retrieve flow velocity variations over time. RESULTS: Initially validated in ECG-gated acquisitions in a rat model (n = 1), the proposed method was successfully applied in a mice model in 2D (n = 3) and in a feline model in 3D (n = 1). Performing time-registration with the proposed motion-matching method or by using ECG-gating leads to similar results. For the first time, dynamic velocity and density cine-loops were extracted without the need for any information on the animal ECG, and complex dynamic markers such as the Pulsatility index were estimated. CONCLUSION: Results suggest that DULM can be performed without external gating, enabling the use of DULM on any ULM dataset where enough MBs are detectable. Time registration by motion-matching represents a significant advancement in DULM techniques, making DULM more accessible by simplifying its experimental complexity.

Boosting Cardiac Color Doppler Frame Rates with Deep Learning.

Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ventricular filling. Concurrently, deep learning is demonstrating promising outcomes in post-processing of echocardiographic data for various applications. This work explores the use of deep learning models for intracardiac Doppler velocity estimation from a reduced number of filtered I/Q signals. We used a supervised learning approach by simulating patient-based cardiac color Doppler acquisitions and proposed data augmentation strategies to enlarge the training dataset. We implemented architectures based on convolutional neural networks. In particular, we focused on comparing the U-Net model and the recent ConvNeXt models, alongside assessing real-valued versus complex-valued representations. We found that both models outperformed the state-of-the-art autocorrelator method, effectively mitigating aliasing and noise. We did not observe significant differences between the use of real and complex data. Finally, we validated the models on in vitro and in vivo experiments. All models produced quantitatively comparable results to the baseline and were more robust to noise. ConvNeXt emerged as the sole model to achieve high-quality results on in vivo aliased samples. These results demonstrate the interest of supervised deep learning methods for Doppler velocity estimation from a reduced number of acquisitions.

Functional Assessment of Cerebral Capillaries using Single Capillary Reporters in Ultrasound Localization Microscopy.

The brain's microvascular cerebral capillary network plays a vital role in maintaining neuronal health, yet capillary dynamics are still not well understood due to limitations in existing imaging techniques. Here, we present Single Capillary Reporters (SCaRe) for transcranial Ultrasound Localization Microscopy (ULM), a novel approach enabling non-invasive, whole-brain mapping of single capillaries and estimates of their transit-time as a neurovascular biomarker. We accomplish this first through computational Monte Carlo and ultrasound simulations of microbubbles flowing through a fully-connected capillary network. We unveil distinct capillary flow behaviors which informs methodological changes to ULM acquisitions to better capture capillaries in vivo. Subsequently, applying SCaRe-ULM in vivo, we achieve unprecedented visualization of single capillary tracks across brain regions, analysis of layer-specific capillary heterogeneous transit times (CHT), and characterization of whole microbubble trajectories from arterioles to venules. Lastly, we evaluate capillary biomarkers using injected lipopolysaccharide to induce systemic neuroinflammation and track the increase in SCaRe-ULM CHT, demonstrating the capability to detect subtle capillary functional changes. SCaRe-ULM represents a significant advance in studying microvascular dynamics, offering novel avenues for investigating capillary patterns in neurological disorders and potential diagnostic applications.

Utility of PSMA PET/CT in Staging and Restaging of Renal Cell Carcinoma: A Systematic Review and Metaanalysis.

Prostate-specific membrane antigen (PSMA) is expressed in the neovasculature of multiple solid tumors, including renal cell carcinoma (RCC). Studies have demonstrated promising results on the utility of PSMA-targeted PET/CT imaging in RCC. This report aims to provide a systematic review and metaanalysis on the utility and detection rate of PSMA PET/CT imaging in staging or evaluation of primary RCC and restaging of metastatic or recurrent RCC. Methods: Searches were performed in PubMed, Embase, and abstract proceedings (last updated, August 2023). Studies that provided a lesion-level detection rate of PSMA radiotracers in staging or restaging of RCC were included in the metaanalysis. The overall pooled detection rate with a 95% CI was estimated, and subgroup analysis was performed when feasible. Results: Nine studies comprising 152 patients (133 clear cell RCC [ccRCC], 19 other RCC subtypes) were included in the metaanalysis. The pooled detection rate of PSMA PET/CT in evaluation of primary or metastatic RCC was estimated to be 0.83 (95% CI, 0.67-0.92). Subgroup analysis showed a pooled PSMA detection rate of 0.74 (95% CI, 0.57-0.86) in staging or evaluation of primary RCC lesions and 0.87 (95% CI, 0.73-0.95) in restaging of metastatic or recurrent RCC. Analysis based on the type of radiotracer showed a pooled detection rate of 0.85 (95% CI, 0.62-0.95) for 68Ga-based PSMA tracers and 0.92 (95% CI, 0.76-0.97) for 18F-DCFPyL PET/CT. Furthermore, in metastatic ccRCC, the available data support a significantly higher detection rate for 18F-DCFPyL PET/CT than for conventional imaging modalities (2 studies). Conclusion: Our preliminary results show that PSMA PET/CT could be a promising alternative imaging modality for evaluating RCC, particularly metastatic ccRCC. Large prospective studies are warranted to confirm clinical utility in the staging and restaging of RCC.

[PAL-PRAT study: Healthcare workers' knowledge and perception of palliative practices in a Cancer Center]. / Quelle connaissance et quelle perception ont les professionnels de santé d'un Centre de lutte contre le cancer à propos des pratiques palliatives encadrées par la loi Claeys-Leonetti : étude PAL PRAT.

INTRODUCTION: The issue of end-of-life care is the subject of a sensitive debate in French society, particularly regarding the possibility for certain patients to have access to medical assistance in dying. The aim of this study was to assess the knowledge and opinion of healthcare providers on the care practices for patients at the end of life, as well as to highlight any specificities in their discourse. METHOD: A survey of healthcare providers' opinions, composed of closed and open questions, that were analyzed using a lexicometric approach, was distributed in a cancer center. RESULTS: The results of the study reveal a good knowledge of the different procedures. Professionals considered that advance directives should be systematically collected; a majority of them differentiated euthanasia from deep continuous sedation and perceived the latter as a means of relieving patients' suffering without inducing death. The different procedures related to the active assistance in dying were known by a majority of professionals and the survey did not identify a dominant trend concerning the will to practice euthanasia if the legal framework allowed it. Half of the participants considered their training insufficient, indicating the need to fill this gap. DISCUSSION: This survey underlines the importance of training and support for the professionals caring for patients in palliative situation and their relatives in France.

Supportive Care Needs of Newly Diagnosed Cancer Patients in a Comprehensive Cancer Center: Identifying Care Profiles and Future Perspectives.

The prompt introduction of supportive care for patients with cancer leads to a better quality of life, potential survival benefits, and improvements in treatment safety. Considering that patients' needs vary, descriptive assessments could serve as a compass for an efficient and prompt healthcare response. The aim of this study was to identify supportive care needs in newly diagnosed patients according to cancer type. A retrospective study was conducted by collecting data from the case consultation and medical records of a comprehensive cancer center in France. Patients' needs were divided into twelve domains: nutrition, psychological support, psychiatric support, social care, physiotherapy, addictology, pain management, palliative care, pharmacology, complementary and alternative practice (CAM), sexual health, and speech therapy. Out of 6217 newly diagnosed patients of various cancer types who sought medical care at Gustave Roussy in 2021, 2541 (41%) required supportive cancer care (SCC), and of them, 1331 patients (52%) required two or more different SCC specialist interventions. The top five interventions were dietary (for 60% of patients), physiotherapy (33%), psychology (29%), social care (28%), and pain management (16%). Subgroup analysis according to cancer department highlighted additional specific needs: CAM for breast cancer patients (11%), speech specialist (27%) and addictologist (22%) interventions for ENT patients, psychiatry consultations for neurological patients (16%), and palliative care for dermatology patients (23%). The aforementioned data suggest that an early, multidisciplinary supportive care intervention should be required. Assembling human resources at the time of diagnosis within a dedicated day unit would be the next appropriate step in developing personalized care pathways related to the highlighted needs.

Quantitative pulsatility measurements using 3D dynamic ultrasound localization microscopy.

A rise in blood flow velocity variations (i.e. pulsatility) in the brain, caused by the stiffening of upstream arteries, is associated with cognitive impairment and neurodegenerative diseases. The study of this phenomenon requires brain-wide pulsatility measurements, with large penetration depth and high spatiotemporal resolution. The development of dynamic ultrasound localization microscopy (DULM), based on ULM, has enabled pulsatility measurements in the rodent brain in 2D. However, 2D imaging accesses only one slice of the brain and measures only 2D-projected and hence biased velocities . Herein, we present 3D DULM: using a single ultrasound scanner at high frame rate (1000-2000 Hz), this method can produce dynamic maps of microbubbles flowing in the bloodstream and extract quantitative pulsatility measurements in the cat brain with craniotomy and in the mouse brain through the skull, showing a wide range of flow hemodynamics in both large and small vessels. We highlighted a decrease in pulsatility along the vascular tree in the cat brain, which could be mapped with ultrasound down to a few tens of micrometers for the first time. We also performed an intra-animal validation of the method by showing consistent measurements between the two sides of the Willis circle in the mouse brain. Our study provides the first step towards a new biomarker that would allow the detection of dynamic abnormalities in microvessels in the brain, which could be linked to early signs of neurodegenerative diseases.

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 ( [Formula: see text]). 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 recently introduced 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 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 [Formula: see text] 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.

Equivalent-time-active-cavitation-imaging enables vascular-resolution blood-brain-barrier-opening-therapy planning.

Objective. Linking cavitation and anatomy was found to be important for predictable outcomes in focused-ultrasound blood-brain-barrier-opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either (1) do not leverage the full resolution capabilities of ultrasound imaging or (2) place constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map.Approach. Herein, we develop BandPass-sampled-equivalent-time-active-cavitation-imaging (BP-ETACI), derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood-brain barrier opening with long therapeutic bursts using a 1.5 MHz focused transducer in the brain of C57BL/6 mice.Main results. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for (1) monitoring focused-ultrasound blood-brain-barrier-opening (FUS-BBBO), but also for (2) therapy planning and target verification.Significance. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.

Ultrafast Cardiac Imaging Using Deep Learning for Speckle-Tracking Echocardiography.

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this article, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle-tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e., high-quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on nonsimulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

Implementing a PROACTive Care Pathway to Empower and Support Survivors of Breast Cancer.

PURPOSE: Optimal comprehensive survivorship care is insufficiently delivered. To increase patient empowerment and maximize the uptake of multidisciplinary supportive care strategies to serve all survivorship needs, we implemented a proactive survivorship care pathway for patients with early breast cancer at the end of primary treatment phase. METHODS: Pathway components included (1) a personalized survivorship care plan (SCP), (2) face-to-face survivorship education seminars and personalized consultation for supportive care referrals (Transition Day), (3) a mobile app delivering personalized education and self-management advice, and (4) decision aids for physicians focused on supportive care needs. A mixed-methods process evaluation was performed according to the Reach, Effectiveness, Adoption, Implementation and Maintenance framework including administrative data review, pathway experience survey (patient, physician, and organization), and focus group. The primary objective was patient-perceived satisfaction with the pathway (predefined progression criteria for pathway continuation ≥70%). RESULTS: Over 6 months, 321 patients were eligible for the pathway and received a SCP and 98 (30%) attended the Transition Day. Among 126 patients surveyed, 77 (66.1%) responded. 70.1% received the SCP, 51.9% attended the Transition Day, and 59.7% accessed the mobile app. 96.1% of patients were very or completely satisfied with the overall pathway, whereas perceived usefulness was 64.8% for the SCP, 90% for the Transition Day, and 65.2% for the mobile app. Pathway implementation seemed to be positively experienced by physicians and the organization. CONCLUSION: Patients were satisfied with a proactive survivorship care pathway, and the majority reported that its components were useful in supporting their needs. This study can inform the implementation of survivorship care pathways in other centers.

Facing points of view: Representations on adjuvant endocrine therapy of premenopausal patients after breast cancer and their healthcare providers in France. The FOR-AD study.

PURPOSE: Adjuvant endocrine therapy (ET) for 5-10 years is the backbone of the therapeutic strategy in patients with hormone receptor positive (HR+) early breast cancer (BC). However, long-term adherence to adjuvant ET represents a major challenge for most patients. According to prior studies, side effects of adjuvant ET are an important reason for poor adherence. In contrast, better communication and relational bond between patients and healthcare providers (HCPs) may improve adherence. The FOR-AD (Focus on non-adherence) study aimed at better understanding the representation of adjuvant ET by patients and their HCPs, in order to improve the care process. METHODS: Three focus groups of premenopausal women (receiving adjuvant ET for variable amount of time) and two focus groups of HCPs (including oncologists, pharmacists, and nurses) were conducted, each including around ten participants. Thematic analyses using a general inductive approach were constructed to report participants' representations. RESULTS: Two main themes emerged across groups, and appeared of major importance. Representations on adjuvant ET were often homogenous within each group, but differed between patients and their HCPs. The relationship between both groups was considerably discussed, particularly its importance in facilitating adherence to adjuvant ET. Suggestions on improving the care process were also given, such as systematically including psychologists in follow-up care paths and having a nurse navigator follow patients under treatment with adjuvant ET. CONCLUSION: The present qualitative exploration may help buildi future tailored interventions to improve adherence to adjuvant ET, in particular regarding the role of nurse navigators.

A pragmatic randomized controlled trial of a group self-management support program versus treatment-as-usual for anxiety disorders: study protocol.

BACKGROUND: The integration of a personal recovery-oriented practice in mental health services is an emerging principle in policy planning. Self-management support (SMS) is an intervention promoting recovery that aims at educating patients on the nature of their mental disorder, improving their strategies to manage their day-to-day symptoms, fostering self-efficacy and empowerment, preventing relapse, and promoting well-being. While SMS is well established for chronic physical conditions, there is a lack of evidence to support the implementation of structured SMS programs for common mental disorders, and particularly for anxiety disorders. This study aims to examine the effectiveness of a group-based self-management support program for anxiety disorders as an add-on to treatment-as-usual in community-based care settings. METHODS/DESIGN: We will conduct a multicentre pragmatic randomized controlled trial with a pre-treatment, post-treatment (4-month post-randomization), and follow-ups at 8, 12 and 24-months. TREATMENT AND CONTROL GROUPS: a) group self-management support (10 weekly 2.5-h group web-based sessions with 10-15 patients with two trained facilitators); b) treatment-as-usual. Participants will include adults meeting DSM-5 criteria for Panic Disorder, Agoraphobia, Social Anxiety Disorder, and/or Generalized Anxiety Disorder. The primary outcome measure will be the Beck Anxiety Inventory; secondary outcome measures will comprise self-reported instruments for anxiety and depressive symptoms, recovery, self-management, quality of life, and service utilisation. STATISTICAL ANALYSIS: Data will be analysed based on intention-to-treat with a mixed effects regression model accounting for between and within-subject variations in the effects of the intervention. DISCUSSION: This study will contribute to the limited knowledge base regarding the effectiveness of structured group self-management support for anxiety disorders. It is expected that changes in patients' self-management behaviour will lead to better anxiety management and, consequently, to improved patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05124639 . Prospectively registered 18 November 2021.

In Vivo Pulsatility Measurement of Cerebral Microcirculation in Rodents Using Dynamic Ultrasound Localization Microscopy.

An increased pulse pressure, due to arteries stiffening with age and cardiovascular disease, may lead to downstream brain damage in microvessels and cognitive decline. Brain-wide imaging of the pulsatility propagation from main feeding arteries to capillaries in small animals could improve our understanding of the link between pulsatility and cognitive decline. However, it requires higher spatiotemporal resolution and penetration depth than currently available with existing brain imaging techniques. Herein, we show the feasibility of performing Dynamic Ultrasound Localization Microscopy (DULM), a novel imaging approach to capture hemodynamics with a subwavelength resolution. By producing cine-loops of flowing microbubbles in 2D in the whole rodent brain lasting several cardiac cycles, DULM performed pulsatility measurements in microvessels in-depth, in vivo, with and without craniotomy. Cortical veins and arteries were shown to have a significatively different pulsatility index and the method was compared against Contrast Enhanced Ultrafast Ultrasound Doppler (CEUFD) pulsatility measurements.

Social Isolation in Dementia: The Effects of COVID-19.

The aim of this literature review is to identify the effects of social isolation and lonliness on persons with dementia and to highlight interventions for private homes and long-term care facilities. It includes articles published in the last 5 years for a total of 45 articles. Social isolation and/or lonliness is linked to reduced quality of life, neuropsychiatric symptoms, and psychotropic drug use in people living with demential. Interventions, including physical activity, should be individualized and patient centered.

Equivalent time active cavitation imaging.

Rationale. Despite the development of a large number of neurologically active drugs, brain diseases are difficult to treat due to the inability of many drugs to penetrate the blood-brain barrier. High-intensity focused ultrasound (HIFU) blood-brain barrier opening in a site-specific manner could significantly expand the spectrum of available drug treatments. However, without monitoring, brain damage and off-target effects can occur during these treatments. While some methods can monitor inertial cavitation, temperature increase, or passively monitor cavitation events, to the best of our knowledge none of them can actively and spatiotemporally map the HIFU pressure field during treatment.Methods. Here we detail the development of a novel ultrasound imaging modality called equivalent time active cavitation imaging (ETACI) capable of characterizing the HIFU pressure field through stable cavitation events across the field of view with an ultrafast active imaging setup. This work introduces (1) a novel plane wave sequence whose transmit delays increase linearly with transmit events enabling the sampling of high-frequency cavitation events, and (2) an algorithm allowing the processing of the microbubble signal for pressure field mapping. The pressure measurements with our modality were first carried outin vitrofor hydrophone comparison and thenin vivoduring blood-brain barrier opening treatment in mice.Results. This study demonstrates the capability of ETACI to spatiotemporally characterize a modulation pressure field with an active imaging setup. The resulting pressure field mapping reveals a good correlation with hydrophone measurements. Further results iareprovided experimentallyin vivowith promising results.Conclusion. This proof of concept establishes the first steps towards a novel ultrasound modality for monitoring focused ultrasound blood-brain barrier opening, allowing new possibilities for a safe and precise monitoring method.

Dynamic Myocardial Ultrasound Localization Angiography.

Dynamic Myocardial Ultrasound Localization Angiography (MULA) is an ultrasound-based imaging modality destined to enhance the diagnosis and treatment monitoring of coronary pathologies. Current diagnosis methods of coronary artery disease focus on the observation of vessel narrowing in the coronary vasculature to assess the organ's condition. However, we would strongly benefit from mapping and measuring flow from intramyocardial arterioles and capillaries as they are the direct vehicle of the myocardium blood income. With the advent of ultrafast ultrasound scanners, imaging modalities based on the localization and tracking of injected microbubbles allow for the subwavelength resolution imaging of an organ's vasculature. Yet, the application of these vascular imaging modalities relies on an accumulation of cine loops of a region of interest undergoing no or minimal tissue motion. This work introduces the MULA framework that combines 1) the mapping of the dynamics of the microvascular flow using an ultrasound sequence triggered by the electrocardiogram with a 2) novel Lagrangian beamformer based on non-rigid motion registration algorithm to form images directly in the myocardium's material coordinates and thus correcting for the large myocardial motion and deformation. Specifically, we show that this framework enables the non-invasive imaging of the angioarchitecture and dynamics of intramyocardial flow in vessels as small as a few tens of microns in the rat's beating heart in vivo.

Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM).

Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculaturein vivowith a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conductedin silicousing the SIMUS simulation software in an anatomically realistic phantom and then compared toin vivoacquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6µm and 9.93µm for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

Ultrasound Localization Microscopy (ULM) 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 leads 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 (CNN) 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 in a rat brain. 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 µ m with an improvement in resolution when compared against a conventional approach.

Broadly and finely tunable hybrid silicon laser with nanosecond-scale switching speed.

We demonstrate a hybrid silicon tunable laser with wide tunability and rapid switching speed for applications in sensing and optical networks. By implementing an optimized carrier injection phase shifter design, the filters of the silicon laser cavity may be efficiently controlled, enabling both fine and broad wavelength tuning across a 56 nm range, in addition to a rapid 10 ns switching time. The laser emits up to 10 dBm output power, and the linewidth is near 200 kHz. The fast wavelength switching demonstrated here may be employed in data center and access networks, while the potential for rapid wavelength sweeping is attractive for optical sensing and imaging applications.