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Photoacoustic (PA) imaging technology is of some value in medical diagnoses such as breast cancer detection, vasculature imaging, and surgery navigating. While as most imaging objects are bounded, the received RF signals consist of the direct-arrived signals (DAS) from the PA sources and the boundary-reflected signals (BRS). The undesired BRS will severely impair the quality during the image reconstruction. They will bring in many artifacts and confuse the actual shape and location of the PA sources. We improved the reconstruction procedure by removing the BRS before the regular reconstruction process to suppress those artifacts. To verify our proposed method, we compared the results of the conventional and optimized procedures experimentally. In terms of qualitative observation, the reconstructed images by the optimized procedure illustrate fewer artifacts and more accurate shapes of the PA sources. To quantitatively evaluate the traditional and the optimized imaging procedure, we calculated the Distribution Relative Error (DRE) between each experiment result and its standard drawing of the phantoms. For both phantoms and the ex-vivo sample, the DREs of reconstruction result by the optimized reconstruction procedure decrease significantly. The results suggest that the optimized reconstruction process can effectively suppress the reflection artifacts and improve the shape accuracy of the PA sources. [ABSTRACT FROM AUTHOR]

Photo-Acoustic Tomography (PAT) is an emerging non-invasive hybrid modality driven by a constant yearning for superior imaging performance. The image quality, however, hinges on the acoustic reflection, which may compromise the diagnostic performance. To address this challenge, we propose to incorporate a deep neural network into conventional iterative algorithms to accelerate and improve the correction of reflection artifacts. Based on the simulated PAT dataset from computed tomography (CT) scans, this network-accelerated reconstruction approach is shown to outperform two state-of-the-art iterative algorithms in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) in the presence of noise. The proposed network also demonstrates considerably higher computational efficiency than conventional iterative algorithms, which are time-consuming and cumbersome. [ABSTRACT FROM AUTHOR]

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these types of noise artifacts for removal in experimental photoacoustic data. To achieve this goal, a convolutional neural network (CNN) was first trained to locate and classify sources and artifacts in pre-beamformed data simulated with $k$ -Wave. Simulations initially contained one source and one artifact with various medium sound speeds and 2-D target locations. Based on 3,468 test images, we achieved a 100% success rate in classifying both sources and artifacts. After adding noise to assess potential performance in more realistic imaging environments, we achieved at least 98% success rates for channel signal-to-noise ratios (SNRs) of −9dB or greater, with a severe decrease in performance below −21dB channel SNR. We then explored training with multiple sources and two types of acoustic receivers and achieved similar success with detecting point sources. Networks trained with simulated data were then transferred to experimental waterbath and phantom data with 100% and 96.67% source classification accuracy, respectively (particularly when networks were tested at depths that were included during training). The corresponding mean ± one standard deviation of the point source location error was 0.40 ± 0.22 mm and 0.38 ± 0.25 mm for waterbath and phantom experimental data, respectively, which provides some indication of the resolution limits of our new CNN-based imaging system. We finally show that the CNN-based information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming. [ABSTRACT FROM AUTHOR]

Photo-Acoustic Tomography (PAT) is an emerging non-invasive hybrid modality driven by a constant yearning for superior imaging performance. The image quality, however, hinges on the acoustic reflection, which may compromise the diagnostic performance. To address this challenge, we propose to incorporate a deep neural network into conventional iterative algorithms to accelerate and improve the correction of reflection artifacts. Based on the simulated PAT dataset from computed tomography (CT) scans, this network-accelerated reconstruction approach is shown to outperform two state-of-the-art iterative algorithms in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) in the presence of noise. The proposed network also demonstrates considerably higher computational efficiency than conventional iterative algorithms, which are time-consuming and cumbersome.

An important problem in minimally invasive photoacoustic (PA) imaging of brachytherapy seeds is reflection artifacts caused by the high signal from the optical fiber/needle tip reflecting off the seed. The presence of these artifacts confounds interpretation of images. In this letter, we demonstrate a recently developed concept called photoacoustic-guided focused ultrasound (PAFUSion) for the first time in the context of interstitial illumination PA imaging to identify and remove reflection artifacts. In this method, ultrasound (US) from the transducer is focused on the region of the optical fiber/needle tip identified in a first step using PA imaging. The image developed from the US diverging from the focus zone at the tip region visualizes only the reflections from seeds and other acoustic inhomogeneities, allowing identification of the reflection artifacts of the first step. These artifacts can then be removed from the PA image. Using PAFUSion, we demonstrate reduction of reflection artifacts and thereby improved interstitial PA visualization of brachytherapy seeds in phantom and ex vivo measurements on porcine tissue. [ABSTRACT FROM AUTHOR]