Image processing improvements afford second-generation handheld optoacoustic imaging of breast cancer patients.

Background: Since the initial breast transillumination almost a century ago, breast cancer imaging using light has been considered in different implementations aiming to improve diagnostics, minimize the number of available biopsies, or monitor treatment. However, due to strong photon scattering, conventional optical imaging yields low resolution images, challenging quantification and interpretation. Optoacoustic imaging addresses the scattering limitation and yields high-resolution visualization of optical contrast, offering great potential value for breast cancer imaging. Nevertheless, the image quality of experimental systems remains limited due to a number of factors, including signal attenuation with depth and partial view angle and motion effects, particularly in multi-wavelength measurements. Methods: We developed data analytics methods to improve the accuracy of handheld optoacoustic breast cancer imaging, yielding second-generation optoacoustic imaging performance operating in tandem with ultrasonography. Results: We produced the most advanced images yet with handheld optoacoustic examinations of the human breast and breast cancer, in terms of resolution and contrast. Using these advances, we examined optoacoustic markers of malignancy, including vasculature abnormalities, hypoxia, and inflammation, on images obtained from breast cancer patients. Conclusions: We achieved a new level of quality for optoacoustic images from a handheld examination of the human breast, advancing the diagnostic and theranostic potential of the hybrid optoacoustic-ultrasound (OPUS) examination over routine ultrasonography.

Cardiovascular optoacoustics: From mice to men - A review.

Imaging has become an indispensable tool in the research and clinical management of cardiovascular disease (CVD). An array of imaging technologies is considered for CVD diagnostics and therapeutic assessment, ranging from ultrasonography, X-ray computed tomography and magnetic resonance imaging to nuclear and optical imaging methods. Each method has different operational characteristics and assesses different aspects of CVD pathophysiology; nevertheless, more information is desirable for achieving a comprehensive view of the disease. Optoacoustic (photoacoustic) imaging is an emerging modality promising to offer novel information on CVD parameters by allowing high-resolution imaging of optical contrast several centimeters deep inside tissue. Implemented with illumination at several wavelengths, multi-spectral optoacoustic tomography (MSOT) in particular, is sensitive to oxygenated and deoxygenated hemoglobin, water and lipids allowing imaging of the vasculature, tissue oxygen saturation and metabolic or inflammatory parameters. Progress with fast-tuning lasers, parallel detection and advanced image reconstruction and data-processing algorithms have recently transformed optoacoustics from a laboratory tool to a promising modality for small animal and clinical imaging. We review progress with optoacoustic CVD imaging, highlight the research and diagnostic potential and current applications and discuss the advantages, limitations and possibilities for integration into clinical routine.

Multispectral Optoacoustic Tomography of Brown Adipose Tissue.

MSOT has revolutionized biomedical imaging because it allows anatomical, functional, and molecular imaging of deep tissues in vivo in an entirely noninvasive, label-free, and real-time manner. This imaging modality works by pulsing light onto tissue, triggering the production of acoustic waves, which can be collected and reconstructed to provide high-resolution images of features as deep as several centimeters below the body surface. Advances in hardware and software continue to bring MSOT closer to clinical translation. Most recently, a clinical handheld MSOT system has been used to image brown fat tissue (BAT) and its metabolic activity by directly resolving the spectral signatures of hemoglobin and lipids. This opens up new possibilities for studying BAT physiology and its role in metabolic disease without the need to inject animals or humans with contrast agents. In this chapter, we overview how MSOT works and how it has been implemented in preclinical and clinical contexts. We focus on our recent work using MSOT to image BAT in resting and activated states both in mice and humans.

Non-invasive Measurement of Brown Fat Metabolism Based on Optoacoustic Imaging of Hemoglobin Gradients.

Metabolism is a fundamental process of life. However, non-invasive measurement of local tissue metabolism is limited today by a deficiency in adequate tools for in vivo observations. We designed a multi-modular platform that explored the relation between local tissue oxygen consumption, determined by label-free optoacoustic measurements of hemoglobin, and concurrent indirect calorimetry obtained during metabolic activation of brown adipose tissue (BAT). By studying mice and humans, we show how video-rate handheld multi-spectral optoacoustic tomography (MSOT) in the 700-970 nm spectral range enables non-invasive imaging of BAT activation, consistent with positron emission tomography findings. Moreover, we observe BAT composition differences between healthy and diabetic tissues. The study consolidates hemoglobin as a principal label-free biomarker for longitudinal non-invasive imaging of BAT morphology and bioenergetics in situ. We also resolve water and fat components in volunteers, and contrast MSOT readouts with magnetic resonance imaging data.

Energy-based scheme for reconstruction of piecewise constant signals observed in the movement of molecular machines.

Analyzing the physical and chemical properties of single DNA-based molecular machines such as polymerases and helicases requires to track stepping motion on the length scale of base pairs. Although high-resolution instruments have been developed that are capable of reaching that limit, individual steps are oftentimes hidden by experimental noise which complicates data processing. Here we present an effective two-step algorithm which detects steps in a high-bandwidth signal by minimizing an energy-based model (energy-based step finder, EBS). First, an efficient convex denoising scheme is applied which allows compression to tuples of amplitudes and plateau lengths. Second, a combinatorial clustering algorithm formulated on a graph is used to assign steps to the tuple data while accounting for prior information. Performance of the algorithm was tested on Poissonian stepping data simulated based on published kinetics data of RNA polymerase II (pol II). Comparison to existing step-finding methods shows that EBS is superior in speed while providing competitive step-detection results, especially in challenging situations. Moreover, the capability to detect backtracked intervals in experimental data of pol II as well as to detect stepping behavior of the Phi29 DNA packaging motor is demonstrated.