A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data.

Multispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. Furthermore, such tools are inflexible, often locking users into a restricted set of processing motifs, which may not be able to accommodate the demands of diverse experiments. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. We demonstrate various use cases, including dynamic imaging challenges and quantification of drug effect, and describe the ability of the toolbox to be parallelized on a high performance computing cluster.

Non-Invasive Evaluation of Acute Effects of Tubulin Binding Agents: A Review of Imaging Vascular Disruption in Tumors.

Tumor vasculature proliferates rapidly, generally lacks pericyte coverage, and is uniquely fragile making it an attractive therapeutic target. A subset of small-molecule tubulin binding agents cause disaggregation of the endothelial cytoskeleton leading to enhanced vascular permeability generating increased interstitial pressure. The resulting vascular collapse and ischemia cause downstream hypoxia, ultimately leading to cell death and necrosis. Thus, local damage generates massive amplification and tumor destruction. The tumor vasculature is readily accessed and potentially a common target irrespective of disease site in the body. Development of a therapeutic approach and particularly next generation agents benefits from effective non-invasive assays. Imaging technologies offer varying degrees of sophistication and ease of implementation. This review considers technological strengths and weaknesses with examples from our own laboratory. Methods reveal vascular extent and patency, as well as insights into tissue viability, proliferation and necrosis. Spatiotemporal resolution ranges from cellular microscopy to single slice tomography and full three-dimensional views of whole tumors and measurements can be sufficiently rapid to reveal acute changes or long-term outcomes. Since imaging is non-invasive, each tumor may serve as its own control making investigations particularly efficient and rigorous. The concept of tumor vascular disruption was proposed over 30 years ago and it remains an active area of research.

Evaluating online filtering algorithms to enhance dynamic multispectral optoacoustic tomography.

Multispectral optoacoustic tomography (MSOT) is an emerging imaging modality, which is able to capture data at high spatiotemporal resolution using rapid tuning of the excitation laser wavelength. However, owing to the necessity of imaging one wavelength at a time to the exclusion of others, forming a complete multispectral image requires multiple excitations over time, which may introduce aliasing due to underlying spectral dynamics or noise in the data. In order to mitigate this limitation, we have applied kinematic α and α ß filters to multispectral time series, providing an estimate of the underlying multispectral image at every point in time throughout data acquisition. We demonstrate the efficacy of these methods in suppressing the inter-frame noise present in dynamic multispectral image time courses using a multispectral Shepp-Logan phantom and mice bearing distinct renal cell carcinoma tumors. The gains in signal to noise ratio provided by these filters enable higher-fidelity downstream analysis such as spectral unmixing and improved hypothesis testing in quantifying the onset of signal changes during an oxygen gas challenge.

Tomographic breathing detection: a method to noninvasively assess in situ respiratory dynamics.

Physiological monitoring is a critical aspect of in vivo experimentation, particularly imaging studies. Physiological monitoring facilitates gated acquisition of imaging data and more robust experimental interpretation but has historically required additional instrumentation that may be cumbersome. As frame rates have increased, imaging methods have been able to capture ever more rapid dynamics, passing the Nyquist sampling rate of most physiological processes and allowing the capture of motion, such as breathing. With this transition, image artifacts have also changed their nature; rather than intraframe motion causing blurring and deteriorating resolution, interframe motion does not affect individual frames and may be recovered as useful information from an image time series. We demonstrate a method that takes advantage of interframe movement for detection of gross physiological motion in real-time image sequences. We further demonstrate the ability of the method, dubbed tomographic breathing detection to quantify the dynamics of respiration, allowing the capture of respiratory information pertinent to anesthetic depth monitoring. Our example uses multispectral optoacoustic tomography, but it will be widely relevant to other technologies.