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.

多光谱光声层析成像（MSOT）是一种新兴的成像方式，它能够通过快速调整激发激光波长来以高时空分辨率捕获数据。但是，由于必须一次成像一个波长而排除其他波长，因此形成完整的多光谱图像需要随时间推移进行多次激发，这可能会由于数据中的潜在光谱动力学或噪声而引起混叠。为了减轻这种限制，我们应用了运动学$\alpha$ 和 $\alpha \beta$过滤器过滤多光谱时间序列，从而在整个数据采集过程中的每个时间点提供对基础多光谱图像的估计。我们证明了这些方法在抑制动态多光谱图像时间过程中使用多光谱Shepp-Logan幻像和携带不同肾细胞癌肿瘤的小鼠中出现的帧间噪声的功效。这些滤波器提供的信噪比增益可实现更高保真度的下游分析（例如频谱分解）和改进的假设测试，以量化氧气挑战期间信号变化的开始。