Photoacoustic (PA) imaging is an emerging imaging technique for many clinical applications. One of the challenges posed by clinical translation is that imaging systems often rely on a finite-aperture transducer rather than a full tomography system. This results in imaging artifacts arising from an underdetermined reconstruction of the initial pressure distribution (IPD). Furthermore, clinical applications often require deep imaging, resulting in a low-signal-to-noise ratio for the acquired signal because of strong light attenuation in tissue. Conventional approaches to reconstruct the IPD, such as back projection and time-reversal, do not adequately suppress the artifacts and noise. We propose a sparsity-based optimization approach that improves the reconstruction of IPD in PA imaging with a linear array ultrasound transducer. In simulation studies, the forward model matrix was measured from k-Wave simulations, and the approach was applied to reconstruct simulated point objects and the Shepp-Logan phantom. The results were compared with the conventional back projection, time-reversal approach, frequency-domain reconstruction, and the iterative least-squares approaches. In experimental studies, the forward model of our imaging system is directly measured by scanning a graphite point source through the imaging field of view. Experimental images of graphite inclusions in tissue-mimicking phantoms are reconstructed and compared with the back projection and iterative least-squares approaches. Overall these results show that our proposed optimization approach can leverage the sparsity of the PA images to improve the reconstruction of the IPD and outperform the existing popular reconstruction approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Abstract:We propose a spectral-domain interferometric technique, termed spectral modulation interferometry (SMI), and present its application to high-sensitivity, high-speed, and speckle-free quantitative phase imaging. In SMI, one-dimensional complex field of an object is interferometrically modulated onto a broadband spectrum. Full-field phase and intensity images are obtained by scanning along the orthogonal direction. SMI integrates the high sensitivity of spectral-domain interferometry with the high speed of spectral modulation to quantify fast phase dynamics, and its dispersive and confocal nature eliminates laser speckles. The principle and implementation of SMI are discussed. Its performance is evaluated using static and dynamic objects.

Deep learning (DL) is a powerful tool in computational imaging for many applications. A common strategy is to reconstruct a preliminary image as the input of a neural network to achieve an optimized image. Usually, the preliminary image is acquired with the prior knowledge of the model. One outstanding challenge, however, is that the model is sometimes difficult to acquire with high accuracy. Another main challenge is that the process to reconstruct the preliminary image is time consuming, which cannot achieve real-time imaging. In this work, a two-step-training DL (TST-DL) framework is proposed for real-time computational imaging without prior knowledge of the model. A single fully-connected layer (FCL) is trained to directly learn the model with the raw measurement data as input and the image as output. Then, this pre-trained FCL is fixed and connected with an un-trained deep convolutional network for a second-step training to improve the output image fidelity. This approach has three main advantages. First, no prior knowledge of the model is required since the first-step training is to directly learn the model. Second, real-time imaging can be achieved since the raw measurement data is directly used as the input to the model. Third, it can handle any dimension of the network input and solve the input-output dimension mismatch issues which arise in convolutional neural networks. We demonstrate this framework in the applications of single-pixel imaging and photoacoustic imaging for linear model cases. The results are quantitatively compared with those from other DL frameworks and model-based optimization approaches. Noise robustness and the required size of the training dataset are studied for this framework. We further extend this concept to nonlinear models in the application of image de-autocorrelation by using multiple FCLs in the first-step training. Overall, this TST-DL framework is widely applicable to many computational imaging techniques for real-time image reconstruction without the physics priors.

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