Investigation of photoacoustic tomography reconstruction with a limited view from linear array

Ai, Min; Cheng, Jiujun; Karimi, Davood; Salcudean, Septimiu E.; Rohling, Robert; Abolmaesumi, Purang; Tang, Shuo

doi:10.1117/1.jbo.26.9.096009

Cited by 11 publications

(1 citation statement)

References 26 publications

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“…The critical hurdles are still low image quality and inaccurate quantitative measures. Specifically, PA image reconstruction from raw sensor data represents a severely ill-posed problem due to the limited view and narrow bandwidth of clinical US transducers [ 37 ], [ 38 ]. As a result, the PA image is far from a one-to-one map of optical absorbers, and a range of diverse artifacts complicates image interpretation.…”

Section: Introductionmentioning

confidence: 99%

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Kim,

Pelivanov,

O’Donnell

2023

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.

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Section: Introductionmentioning

confidence: 99%

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Kim,

Pelivanov,

O’Donnell

2023

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Enhancing Ischemic Stroke Evaluation by a Model‐Based Photoacoustic Tomography Algorithm

Li,

Lin,

et al. 2024

Journal of Biophotonics

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Ischemic stroke (IS) is characterized by the sudden interruption of blood supply to the brain, resulting in neurological impairments and even mortality. Photoacoustic computed tomography (PACT) integrates the high contrast of optical imaging and the penetration of ultrasound imaging, enabling non‐invasive IS evaluation. However, the image reconstruction quality significantly affects the oxyhemoglobin saturation (sO2) estimation. This study investigates a model‐based with total variation minimized by augmented Lagrangian and alternating direction (MB‐TVAL3) approach and compared it with the widely used back‐projection (BP) and delay‐and‐sum (DAS) algorithms. Both simulations and in vivo experiments are conducted to validate the performance of the MB‐TVAL3 algorithm, showing a higher sO2 estimation accuracy and sensitivity in detecting infarct area compared to BP and DAS. The findings of this study emphasize the impact of acoustic inverse problem on the accuracy of sO2 estimation and the proposed approach offers valuable support for IS evaluation and cerebrovascular diagnosis.

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Adaptive complementary neighboring sub‐aperture beamforming for thermoacoustic imaging

Yang,

Wang,

Peng

et al. 2024

Medical Physics

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BackgroundWhen applied to thermoacoustic imaging (TAI), the delay‐and‐sum (DAS) algorithm produces strong sidelobes due to its disadvantages of uniform aperture weighting. As a result, the quality of TAI images recovered by DAS is often severely degraded by strong non‐coherent clutter, which restricts the development and application of TAI.PurposeTo address this issue, we propose an adaptive complementary neighboring sub‐aperture (NSA) beamforming algorithm for TAI.MethodsIn NSA, we introduce a coordinate system transformation when calculating the normalized cross‐correlation (NCC) matrix. This approach enables the computation of the NCC coefficient within the specified kernel without complex coordinate calculations. We first conducted the numerical simulation experiment to validate NSA using a tree branch phantom. In addition, we also conducted phantom (five sauce tubes), ex vivo (ablation needle in ex vivo porcine liver), and in vivo (human arm) TAI experiments using our TAI system with a center frequency of 3 GHz.ResultsIn the numerical simulation experiment, the structural similarity index (SSIM) value for NSA is increased from 0.37828 for DAS to 0.75492. In the point target phantom TAI experiment, the generalized contrast‐to‐noise ratio (gCNR) value for NSA is increased from 0.936 for DAS to 0.962. The experimental results show that NSA can recover clearer thermoacoustic images compared to DAS. In the ex vivo TAI experiment, the full width at half maxima (FWHM) of an ablation needle (diameter = 1.5 mm) for coherence factor (CF) weighted DAS and NSA are 0.9 and 1.3 mm, respectively. Furthermore, in the in vivo TAI experiment, CF reduces the signals within the arm compared to NSA. Therefore, compared with CF, NSA can maintain the integrity of target information in TAI while effectively suppressing non‐coherent background clutter.ConclusionsNSA can effectively reduce non‐coherent background noise while ensuring the completeness of the target information. So, NSA offers the potential to provide high‐quality thermoacoustic images and further advance their clinical application.

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Investigation of photoacoustic tomography reconstruction with a limited view from linear array

Cited by 11 publications

References 26 publications

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Enhancing Ischemic Stroke Evaluation by a Model‐Based Photoacoustic Tomography Algorithm

Adaptive complementary neighboring sub‐aperture beamforming for thermoacoustic imaging

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