Motion rejection and spectral unmixing for accurate estimation of in vivo oxygen saturation using multispectral optoacoustic tomography

TL;DR

This paper introduces a two-step processing approach for accurate in vivo oxygen saturation estimation using multispectral optoacoustic tomography, addressing motion artefacts and noise sensitivity issues.

Contribution

It presents a novel motion gating technique using ultrafast ultrasound imaging and a pixel-wise noise estimation method for improved SO₂ mapping in deep tissues.

Findings

Motion gating with USIs improves image quality.

Pixel-wise noise estimation enhances SO₂ accuracy.

Method outperforms conventional SO₂ estimation techniques.

Abstract

Multispectral Optoacoustic Tomography (MSOT) uniquely enables spatial mapping in high resolution of oxygen saturation (SO$_2$), with potential applications in studying pathological complications and therapy efficacy. MSOT offers seamless integration with ultrasonography, by using a common ultrasound 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, estimation of SO$_2$ is highly sensitive to noise, and artefacts 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 SO$_2$ estimation in deep tissues. First, to mitigate motion artefacts, we propose a method of selection of OA images acquired only during the respiratory pause of the animal, using ultrafast ultrasound images (USIs) acquired immediately after each OA acquisition (USI acquisition duration of 1.4 ms and a total delay of 7 ms). We show that gating is more effective using USIs than OA images at different optical wavelengths. Secondly, we propose a novel method which can estimate directly the SO$_2$ 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 SO$_2$ map. Our post-processing method is shown to outperform conventional methods for SO$_2$ estimation, and the method was validated by in vivo oxygen challenge experiments.