No abstract

The impulse response of optoacoustic (photoacoustic) tomographic imaging system depends on several system components, the characteristics of which can influence the quality of reconstructed images. The effect of these system components on reconstruction quality have not been considered in detail so far. Here we combine sparse measurements of the total impulse response (TIR) with a geometric acoustic model to obtain a full characterization of the TIR of a handheld optoacoustic tomography system with concave limited-view acquisition geometry. We then use this synthetic TIR to reconstruct data from phantoms and healthy human volunteers, demonstrating improvements in image resolution and fidelity. The higher accuracy of optoacoustic tomographic reconstruction with TIR correction further improves the diagnostic capability of handheld optoacoustic tomographic systems.

The physical properties of each transducer element play a vital role in the quality of images generated in optoacoustic (photoacoustic) tomography using transducer arrays. Thorough experimental characterization of such systems is often laborious and impractical. A shortcoming of the existing impulse response correction methods, however, is the assumption that all transducers in the array are identical and therefore share one electrical impulse response (EIR). In practice, the EIRs of the transducer elements in the array vary, and the effect of this element-to-element variability on image quality has not been investigated so far, to the best of our knowledge. We hereby propose a robust EIR derivation for individual transducer elements in an array using sparse measurements of the total impulse response (TIR) and by solving the linear system for temporal convolution. Thereafter, we combine a simulated spatial impulse response with the derived individual EIRs to obtain a full characterization of the TIR, which we call individual synthetic TIR. Correcting for individual transducer responses, we demonstrate significant improvement in isotropic resolution, which further enhances the clinical potential of array-based handheld transducers.

Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture complex characteristics of signal and noise. Moreover, they often involve time-consuming processing steps that are unsuited for real-time imaging applications. In this work, we develop and demonstrate a discriminative deep learning approach to separate electrical noise from optoacoustic signals prior to image reconstruction. The proposed deep learning algorithm is based on two key features. First, it learns spatiotemporal correlations in both noise and signal by using the entire optoacoustic sinogram as input. Second, it employs training on a large dataset of experimentally acquired pure noise and synthetic optoacoustic signals. We validated the ability of the trained model to accurately remove electrical noise on synthetic data and on optoacoustic images of a phantom and the human breast. We demonstrate significant enhancements of morphological and spectral optoacoustic images reaching 19% higher blood vessel contrast and localized spectral contrast at depths of more than 2 cm for images acquired in vivo. We discuss how the proposed denoising framework Manuscript