One of the key limitations for the clinical translation of photoacoustic imaging is its penetration depth, which is linked to the tissue maximum permissible exposures (MPE) recommended by the American National Standards Institute (ANSI). Here, we propose a method based on deep learning in order to enhance the signal-to-noise ratio of deep structures in the brain tissue. The proposed method is evaluated in an in vivo sheep brain imaging experiment. We believe this method can facilitate clinical translation of photoacoustic technique in brain imaging, especially in neonates via transfontanelle imaging.
A co-planar, simultaneous, photoacoustic tomography guided, diffused optical tomography (CS-PAT-DOT) methodology has been presented in this paper. We detect the absorption of sub-regions with different absorption characteristics in deep tissue with a high spatial resolution. To this aim, we initially utilize compressed sensing (CS), time reversal (TR) and back projection (BP) reconstruction algorithms to reconstruct a priori information inside a heterogeneous phantom. Then the reconstructed images are used in DOT image reconstruction through the total variation method. Improvements obtained from such hybrid methodology are measured by comparing DOT and CS-PAT-DOT images. It will also show that each of the reconstructions based on the proposed method has a unique capability to accurately detect heterogeneities in the tissue at different depths; significantly improving spatial resolution in DOT images. The focus of this study is directed towards quantifying the concentrations of endogenous chromophores, e.g., oxyhemoglobin, deoxyhemoglobin and cytochrome-c-oxidase etc., which are significant indices in detecting tissue abnormalities.
Photoacoustic imaging (PAI) is an imaging modality for obtaining absorption coefficient at every location inside the tissue based on the detected photoacoustic signals. PA image reconstruction aims to determine the initial PA pressure everywhere inside the tissue. The pressure is proportional to both absorption coefficient and light fluence. Provided that fluence is homogenous, the reconstructed image will be an accurate mapping of the absorption coefficient of the tissue. Here we presented a method for obtaining uniform fluence inside the region of interest. We created a large dataset of fluence maps for different source locations, diameters and numerical apertures with Monte Carlo simulations, then used this dataset to solve an optimization problem for finding the source configuration which results in the best fluence distribution.
Photoacoustic imaging (PAI) has proved to be a promising non-invasive technique for diagnosis, prognosis and treatment monitoring of neurological disorders in small and large animals. The conventional illumination method for photoacoustic is a called top-illumination were diffused light hit the target from one side. However, this method is not suitable for all target shapes and body parts like breast. To overcome this problem we proposed a novel side-illumination scheme where light comes from a set of fibers all around the object. We showed that this method can improve the obtained images with phantom experiments and simulations. This method is particularly useful for in-plane imaging where the ring of fibers scan long objects.
The accurate quantification of lesions located in deep tissue is an important challenge in diffuse optical tomography (DOT), while photoacoustic tomography (PAT) as a non-invasive optical imaging provides high-resolution imaging of optical contrast in deep tissue that can be served as a complementary modality to improve the accuracy of DOT. Here, we coupled advantages of photoacoustic tomography (PAT) to diffuse optical tomography (DOT) for enhancing reconstructed DOT images. Using a priori information provided by PAT was used to reasonably regularize the DOT inversion procedure.. The results show that hybrid DOT-PAT can provide high-resolution image in deep tissue.
With the growing application of photoacoustic imaging (PAI) in medical fields, there is a need to make them more compact, portable, and affordable. Therefore, we designed very low-cost PAI systems by replacing the expensive and sophisticated laser with a very low-energy laser diode. We implemented photoacoustic (PA) microscopy, both reflection and transmission modes, as well as PA computed tomography systems. The images obtained from tissue-mimicking phantoms and biological samples determine the feasibility of using a very low-energy laser diode in these configurations.
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