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      Photoacoustic imaging for surgical guidance: Principles, applications, and outlook

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      a)
      Journal of Applied Physics
      AIP Publishing LLC

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          Abstract

          Minimally invasive surgeries often require complicated maneuvers and delicate hand–eye coordination and ideally would incorporate “x-ray vision” to see beyond tool tips and underneath tissues prior to making incisions. Photoacoustic imaging has the potential to offer this feature but not with ionizing x-rays. Instead, optical fibers and acoustic receivers enable photoacoustic sensing of major structures—such as blood vessels and nerves—that are otherwise hidden from view. This imaging process is initiated by transmitting laser pulses that illuminate regions of interest, causing thermal expansion and the generation of sound waves that are detectable with conventional ultrasound transducers. The recorded signals are then converted to images through the beamforming process. Photoacoustic imaging may be implemented to both target and avoid blood-rich surgical contents (and in some cases simultaneously or independently visualize optical fiber tips or metallic surgical tool tips) in order to prevent accidental injury and assist device operators during minimally invasive surgeries and interventional procedures. Novel light delivery systems, counterintuitive findings, and robotic integration methods introduced by the Photoacoustic & Ultrasonic Systems Engineering Lab are summarized in this invited Perspective, setting the foundation and rationale for the subsequent discussion of the author’s views on possible future directions for this exciting frontier known as photoacoustic-guided surgery.

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          Most cited references74

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          Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography

          Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.
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            Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal

            Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an image of the initial pressure distribution. A common challenge faced in PAT is that the measured acoustic waves can only be sparsely sampled. Reconstructing sparsely sampled data using standard methods results in severe artifacts that obscure information within the image. We propose a modified convolutional neural network (CNN) architecture termed fully dense UNet (FD-UNet) for removing artifacts from two-dimensional PAT images reconstructed from sparse data and compare the proposed CNN with the standard UNet in terms of reconstructed image quality.
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              Accuracy of pedicle screw placement in lumbar vertebrae.

              The location of pedicle screws (n = 42) in four human specimens of the lumbar spine and in 30 patients (n = 131 screws) after lumbar spinal fusion was assessed using computed tomography. To determine the accuracy of pedicle screw placement in lumbar vertebrae and the reproducibility and repeatability of the computed tomography examination. Failures in the placement of transpedicular screws for lumbar fusion are reported. The evaluation of such screws using computed tomography examination has not been investigated. After surgery, the specimens were dissected in transversal slices to observe macroscopically the location of the pedicle screw and to correlate these observations with the computed tomography images. All patients were examined by one observer. To determine the reproducibility and repeatability of the computed tomography examination, two observers studied computed tomography images of 12 patients (n = 58 screws) twice within 3 months. In the specimens, 10 screws were observed to penetrate the medial wall of the pedicle. This correlated fully with the images. In the patients' group, 40% of all screws penetrated the cortex of the vertebra. Of all screws, 29% penetrated the medial wall of the pedicle. From the computed tomography images, it appeared that a deviation of more than 6 mm medially was a high risk for nerve root damage. Three months after his first examination, Observer 1 documented a different position in three of 58 screws (kappa = 0.90). Observer 2 found a different position in eight screws (kappa = 0.65). The comparison between the reviews of the two observers showed a different opinion for the first evaluation, four disagreements (2-4 mm) and 17 disagreements (0-2 mm; kappa = 0.34), and for the second evaluation, four disagreements (2-4 mm) and 12 disagreements (0-2 mm; kappa = 0.43). Correct placement of transpedicular screws for spinal fusion seems to be more difficult than it looks. The computed tomography scanning is useful for differential diagnosis of postoperative radicular syndromes after lumbar transpedicular fixation.
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                Author and article information

                Contributors
                Journal
                J Appl Phys
                J Appl Phys
                JAPIAU
                Journal of Applied Physics
                AIP Publishing LLC
                0021-8979
                1089-7550
                14 August 2020
                13 August 2020
                13 August 2020
                : 128
                : 6
                : 060904
                Affiliations
                Department of Electrical and Computer Engineering, Johns Hopkins University , Baltimore, Maryland 21218, USA
                Author notes
                [a) ] Author to whom correspondence should be addressed: mledijubell@ 123456jhu.edu . Also at: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA and Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
                Author information
                http://orcid.org/0000-0002-8394-4482
                Article
                5.0018190 JAP20-PS-03744
                10.1063/5.0018190
                7428347
                32817994
                9811954d-f7eb-4924-a690-d9d3ad332214
                © 2020 Author(s).

                0021-8979/2020/128(7)/060904/13

                All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 12 June 2020
                : 30 July 2020
                Page count
                Pages: 13
                Funding
                Funded by: National Science Foundation https://doi.org/10.13039/100000001
                Award ID: ECCS-175152
                Funded by: National Science Foundation https://doi.org/10.13039/100000001
                Award ID: EEC-1852155
                Funded by: Alfred P. Sloan Foundation https://doi.org/10.13039/100000879
                Award ID: Sloan Research Fellowship in Physics
                Funded by: National Institutes of Health https://doi.org/10.13039/100000002
                Award ID: R00-EB018994
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