Evaluating online filtering algorithms to enhance dynamic multispectral optoacoustic tomography

Highlights

• Simple $\alpha$ and $\alpha \beta$ filters effectively suppress noise in dynamic spectral images.

• A multispectral extension of the Shepp-Logan phantom was developed.

• Practical application is demonstrated in distinct renal cell carcinoma models.

• $\alpha \beta$ filters enabled effective separation of gas breathing challenge response.

Abstract

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 $\alpha$ and $\alpha \beta$ 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.