Retention of Stellar-Mass Black Holes in Globular Clusters

Globular clusters should be born with significant numbers of stellar-mass black holes (BHs). It has been thought for two decades that very few of these BHs could be retained through the cluster lifetime. With masses ~10 MSun, BHs are ~20 times more massive than an average cluster star. They segregate into the cluster core, where they may eventually decouple from the remainder of the cluster. The small-N core then evaporates on a short timescale. This is the so-called Spitzer instability. Here we present the results of a full dynamical simulation of a globular cluster containing many stellar-mass BHs with a realistic mass spectrum. Our Monte Carlo simulation code includes detailed treatments of all relevant stellar evolution and dynamical processes. Our main finding is that old globular clusters could still contain many BHs at present. In our simulation, we find no evidence for the Spitzer instability. Instead, most of the BHs remain well-mixed with the rest of the cluster, with only the innermost few tens of BHs segregating significantly. Over the 12 Gyr evolution, fewer than half of the BHs are dynamically ejected through strong binary interactions in the cluster core. The presence of BHs leads to long-term heating of the cluster, ultimately producing a core radius on the high end of the distribution for Milky Way globular clusters (and those of other galaxies). A crude extrapolation from our model suggests that the BH--BH merger rate from globular clusters could be comparable to the rate in the field.Comment: 5 pages, 4 figures, 1 table, published in Astrophysical Journal Letter

Towards Quantitative Evaluation of Tissue Absorption Coefficients Using Light Fluence Correction in Optoacoustic Tomography.

Optoacoustic tomography is a fast developing imaging modality, combining the high contrast available from optical excitation of tissue with the high resolution and penetration depth of ultrasound detection. Light is subject to both absorption and scattering when traveling through tissue; adequate knowledge of tissue optical properties and hence the spatial fluence distribution is required to create an optoacoustic image that is directly proportional to chromophore concentrations at all depths. Using data from a commercial multispectral optoacoustic tomography (MSOT) system, we implemented an iterative optimization for fluence correction based on a finite-element implementation of the delta-Eddington approximation to the Radiative Transfer Equation (RTE). We demonstrate a linear relationship between the image intensity and absorption coefficients across multiple wavelengths and depths in phantoms. We also demonstrate improved feature visibility and spectral recovery at depth in phantoms and with in vivo measurements, suggesting our approach could in the future enable quantitative extraction of tissue absorption coefficients in biological tissue.This work was funded by the EPSRC-CRUK Cancer Imaging Centre in Cambridge and Manchester (C197/A16465); CRUK (C47594/A16267, C14303/A17197); EU FP7 framework programme (FP7-PEOPLE-2013-CIG-630729) and the University of Cambridge EPSRC Impact Acceleration Account via a Partnership Development Award.This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via http://dx.doi.org/10.1109/TMI.2016.260719

Fast Multispectral Optoacoustic Tomography (MSOT) for Dynamic Imaging of Pharmacokinetics and Biodistribution in Multiple Organs

The characterization of pharmacokinetic and biodistribution profiles is an essential step in the development process of new candidate drugs or imaging agents. Simultaneously, the assessment of organ function related to the uptake and clearance of drugs is of great importance. To this end, we demonstrate an imaging platform capable of high-rate characterization of the dynamics of fluorescent agents in multiple organs using multispectral optoacoustic tomography (MSOT). A spatial resolution of approximately 150 µm through mouse cross-sections allowed us to image blood vessels, the kidneys, the liver and the gall bladder. In particular, MSOT was employed to characterize the removal of indocyanine green from the systemic circulation and its time-resolved uptake in the liver and gallbladder. Furthermore, it was possible to track the uptake of a carboxylate dye in separate regions of the kidneys. The results demonstrate the acquisition of agent concentration metrics at rates of 10 samples per second at a single wavelength and 17 s per multispectral sample with 10 signal averages at each of 5 wavelengths. Overall, such imaging performance introduces previously undocumented capabilities of fast, high resolution in vivo imaging of the fate of optical agents for drug discovery and basic biological research

A detached stellar-mass black hole candidate in the globular cluster NGC 3201

Large scale structure and cosmolog

Efficient segmentation of multi-modal optoacoustic and ultrasound images using convolutional neural networks

Multispectral optoacoustic tomography (MSOT) offers the unique capability to map the distribution of spectrally distinctive endogenous and exogenous substances in heterogeneous biological tissues by exciting the sample at various wavelengths and detecting the optoacoustically-induced ultrasound waves. This powerful functional and molecular imaging capability can greatly benefit from hybridization with pulse-echo ultrasound (US), which provides additional information on tissue anatomy and blood flow. However, speed of sound variations and acoustic mismatches in the imaged object generally lead to errors in the coregistration of compounded images and loss of spatial resolution in both imaging modalities. The spatially- and wavelength-dependent light fluence attenuation further limits the quantitative capabilities of MSOT. Proper segmentation of different regions and assignment of corresponding acoustic and optical properties turns then essential for maximizing the performance of hybrid optoacoustic and ultrasound (OPUS) imaging. Particularly, accurate segmentation of the boundary of the sample can significantly improve the images rendered. Herein, we propose an automatic segmentation method based on a convolutional neural network (CNN) for segmenting the mouse boundary in a pre-clinical OPUS system. The experimental performance of the method, as characterized with the Dice coefficient metric between the network output and the ground truth (manually segmented) images, is shown to be superior than that of a state-of-the-art active contour segmentation method in a series of two-dimensional (cross-sectional) OPUS images of the mouse brain, liver and kidney regions

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images

The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid optoacoustic ultrasound (OPUS) combination, images retrieved by one modality may further be used to improve the reconstruction accuracy of the other. In this regard, image segmentation plays a major role as it can aid improving the image quality and quantification abilities by facilitating modeling of light and sound propagation through the imaged tissues and surrounding coupling medium. Here, we propose an automated approach for surface segmentation in whole-body mouse OPUS imaging using a deep convolutional neural network (CNN). The method has shown robust performance, attaining accurate segmentation of the animal boundary in both optoacoustic and pulse-echo ultrasound images, as evinced by quantitative performance evaluation using Dice coefficient metrics

IPASC:A community-driven consensus-based initiative towards standardisation in photoacoustic imaging

Photoacoustic Imaging (PAI) is a rapidly emerging imaging modality that is based on the conversion from light into ultrasound. One of the most promising applications of PAI is in diagnosis and longitudinal monitoring of solid tumours. By visualizing differences in the optical energy absorbed by endogenous chromophores, such as haemoglobin or melanin, PAI has made rapid advances over the past decade. In particular, PAI has been successfully demonstrated in early stage human trials in cancer, for delineating benign and malignant lesions as well as monitoring treatment. The promise of these early pilot studies has led to the development of several commercial clinical PAI instruments. Furthermore, qualitative image interpretation is increasingly being replaced by calculation of quantitative imaging biomarkers (IBs) as both research tools and as putative clinical decision-making tools. Considering the recently reported roadmaps for clinical translation of IBs, it is clear that acceleration of PAI biomarkers into clinical use requires parallel technical, biological and clinical validation, as well as assessment of cost-effectiveness. The International Photoacoustic Standardisation Consortium (IPASC) has been founded to address this unmet need. The overall objective of IPASC is to reach an international consensus on PAI standardization to improve the quality of preclinical studies and to accelerate efforts in clinical translation