The characteristics of the transducer, such as the transducer shape, have a significant impact on the image performance in optoacoustic (photoacoustic) imaging. Several reconstruction algorithms have considered the shape of the transducer in the optoacoustic reconstruction process, showing the improvement in image quality compared to reconstruction procedures with the point detector approximation. One flexible approach assumes the surface of transducer that consists of a set of surface elements. However, this approach suffers from long computation time and excessive memory consumption, especially for model-based reconstruction strategies. Herein, we present a modified model-based reconstruction algorithm using a virtual parallel-projection method, for the optoacoustic imaging system with flat detector. In this case, the sum of the surface elements' model matrixes can be replaced by a virtual parallel-projection model matrix, in order to reduce the reconstruction time and memory consumption. The proposed method has been performed on numerical simulations, phantom experiments of microspheres with the diameter of 200 μm and in vivo experiments in mice. The reconstruction results of proposed method show the similar image quality as the results of the traditional reconstruction method setting surface elements, while the computation time and memory requirements have been efficiently decreased.
Quantitative photoacoustic tomography (q-PAT) is a nontrivial technique can be used to reconstruct the absorption image with a high spatial resolution. Several attempts have been investigated by setting point sources or fixed-angle illuminations. However, in practical applications, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification especially for large-size domains, due to the limitation of the ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and a calibrated iterative multi-angle reconstruction. The approach can acquire more complete information on the intrinsic absorption and SNR-boosted photoacoustic signals at selected planes from the multi-angle wide-field excitations of light-sheet. Therefore, the sliced absorption maps over whole body can be recovered in a measurementflexible, noise-robust and computation-economic way. The proposed approach is validated by the phantom experiment, exhibiting promising performances in image fidelity and quantitative accuracy.
To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both of the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme where a virtual parallel-projection concept matching for the proposed measurement condition is introduced to help to achieve the “compressive sensing” procedure of the reconstruction, and meanwhile the spatially adaptive filtering fully considering the a priori information of the mutually similar blocks existing in natural images is introduced to effectively recover the partial unknown coefficients in the transformed domain. Therefore, the sparse-view PAT images can be reconstructed with higher quality compared with the results obtained by the universal back-projection (UBP) algorithm in the same sparse-view cases. The proposed approach has been validated by simulation experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.
Multispectral optoacoustic mesoscopy (MSOM) has been recently introduced for cancer imaging, it has the potential for high resolution imaging of cancer development in vivo, at depths beyond the diffusion limit. Based on spectral features, optoacoustic imaging is capable of visualizing angiogenesis and imaging cancer heterogeneity of malignant tumors through endogenous hemoglobin. However, high-resolution structural and functional imaging of whole tumor mass is limited by modest penetration and image quality, due to the insufficient capability of ultrasound detectors and the twodimensional scan geometry. In this study, we introduce a novel multi-spectral optoacoustic mesoscopy (MSOM) for imaging subcutaneous or orthotopic tumors implanted in lab mice, with the high-frequency ultrasound linear array and a conical scanning geometry. Detailed volumetric images of vasculature and oxygen saturation of tissue in the entire tumors are obtained in vivo, at depths up to 10 mm with the desirable spatial resolutions approaching 70μm. This unprecedented performance enables the visualization of vasculature morphology and hypoxia conditions has been verified with ex vivo studies. These findings demonstrate the potential of MSOM for preclinical oncological studies in deep solid tumors to facilitate the characterization of tumor’s angiogenesis and the evaluation of treatment strategies.
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