Motion Rejection and Spectral Unmixing for Accurate Estimation of In Vivo Oxygen Saturation Using Multispectral Optoacoustic Tomography

Multispectral optoacoustic tomography (MSOT) uniquely enables spatial mapping in high resolution of oxygen saturation (SO2), with potential applications in studying pathological complications and therapy efficacy. MSOT offers seamless integration with ultrasonography, by using a common ultrasound (US) detector array. However, MSOT relies on multiple successive acquisitions of optoacoustic (OA) images at different optical wavelengths and the low frame rate of OA imaging makes the MSOT acquisition sensitive to body/respiratory motion. Moreover, the estimation of SO2 is highly sensitive to noise, and artifacts related to the respiratory motion of the animal were identified as the primary source of noise in MSOT. In this work, we propose a two-step image processing method for SO2 estimation in deep tissues. First, to mitigate motion artifacts, we propose a method of selection of OA images acquired only during the respiratory pause of the animal, using ultrafast ultrasound (US) images acquired immediately after each OA acquisition (US image acquisition duration of 1.4 ms and a total delay of 7 ms). We show that gating is more effective using US images than OA images at different optical wavelengths. Second, we propose a novel method that can estimate directly the SO2 value of a pixel and at the same time evaluate the amount of noise present in that pixel. Hence, the method can efficiently eliminate the pixels dominated by noise from the final SO2 map. Our postprocessing method is shown to outperform conventional methods for SO2 estimation, and the method was validated by in vivo oxygen challenge experiments.