To address these issues, we have created an MSOT Reconstruction, Analysis, and Filtering Toolbox (RAFT), enabling users of various technical skill levels to benefit from the rich information present in MSOT data, and to share and compare analyses between sites in a verifiable manner. Due to the high spatiotemporal resolution, MSOT captures vast quantities of information, creating potential challenges: How does one effectively and efficiently analyze such data? Moreover, how does one ensure that the analysis is done in a manner, which is transparent and verifiable, and thus trustworthy, while allowing the flexibility to adjust the manner of processing to accommodate the differential needs of a wide variety of experiments and data acquisition conditions? Multispectral optoacoustic tomography (MSOT) is a relatively new imaging modality, which combines optical contrast and ultrasonic resolution in order to provide a highly parametric view of an imaged sample. 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. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data.
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. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. Multispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution.