Photoacoustic imaging modality is a new biomedical imaging which provides images with high resolution and contrast from different parts of body. In this paper, we have designed a new optical system by using a fiber bundle in order to imaging of a hemorrhage inside of the infant's head. We used Monte Carlo algorithm to simulate light propagation in the infant's head, an acoustic k-space method to simulate photoacoustic signal propagation in it, and time reversal image reconstruction algorithm to get 3D image of the hemorrhage. According to our simulation, this new optical system can provides homogeneous illumination on the infant's head Leads to more accurate images. Furthermore, we have designed and optimized an optical system in order to coupling light from laser source into a fiber bundle with more than 94% efficiency.
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 (PA) signal experiences excessive background noise when generated using cost-effective, low-energy laser diodes. A denoising technique is essential in this case. Averaging is a common approach to increase the Signal-to-Noise Ratio (SNR) of PA signals. This technique requires numbers of data acquisition in hundreds and thousands and hence, demands more hardware and time consuming at the same time. Here, an adaptive method based on Adaptive Line Enhancers (ALE) algorithm to improve the SNR of PA signals has been presented. Our results validate the feasibility of the usage of an adaptive method and also indicate excellent improvement in terms of increasing the SNR of the PA signals. Additionally, this proposed algorithm requires way less number of acquisitions as compared to the conventional averaging techniques that leads to faster PA image processing.
Photoacoustic tomography (PAT) is an emerging modality for imaging living biological tissue. Being label free, non-invasive, and having comparable resolution to ultrasound, PAT has many medical translations. This paper demonstrates our development of a low-cost 16 element transducer array for rapid imaging (1 frame per second) of biological samples. For the first time we demonstrate quality images obtained with a completely low-cost system. A rotatable platform houses our 16 equidistant Technisonic transducers, which is rotated 22.5° to acquire a full 360° field of view. We use Ekspla NL200 series Q-switched laser at 532 nm illumination wavelength with coupled optical fiber for overhead illumination. Our transducers send data to a National Instrument data acquisition system, triggered by the previously mentioned laser for efficient detection of photoacoustic signal. We have characterized this system through the imaging of complex optical absorbing lead phantoms. Thin lead has been imaged to demonstrate the spatial resolution of the system using the point spread function. Characterization of this system will allow us to move to ex-vivo imaging. We aim to develop this system as a platform for quality small animal functional brain imaging.
In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.