Needle insertion is a vital procedure in both clinical diagnosis and therapeutical treatment. To ensure the accurate placement of needle, ultrasound (US) imaging is generally used to guide the needle insertion. However, due to depthdependent attenuation and angular dependency, US imaging always face the challenge in consistently and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. Deep learning, an advanced tool that has proven effective and efficient in addressing imaging challenges, has shown promise in enhancing needle visibility in US images. But the existing approaches often rely on manual annotation or simulated data as ground truth, leading to heavy human workload and bias or difficulties in generalizing to real US images. Recently, photoacoustic (PA) imaging has shown the capability of high-contrast needle visualization. In this study, we explore the potential of PA imaging as reliable ground truth for training deep learning networks, eliminating the need for expert annotation. Our network, trained on ex vivo image datasets, demonstrated the abilities of precise needle localization in US images. This research represents a significant advancement in the application of deep learning and PA imaging in clinical settings, with the potential to enhance the accuracy and safety of needle-based procedures.
KEYWORDS: Image restoration, Image quality, Image processing, Acquisition tracking and pointing, Reconstruction algorithms, Signal to noise ratio, Photoacoustic tomography, In vivo imaging, Spatial filtering, Brain
SignificanceIn photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications.AimTo implement PAT image reconstruction in Android-based mobile platforms.ApproachFor implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones.ResultsThe performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4 s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation.ConclusionsWe proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4 s is achieved without hampering the quality of the reconstructed image.
Photoacoustic tomography (PAT) is a non-invasive imaging modality showing great potential in medical diagnosis and research due to its high optical contrast and high-resolution deep imaging. After laser irradiation on the tissue surface, energy absorption leads to the generation of acoustic waves (also known as PA waves), which can be collected by ultrasound detectors such as single-element ultrasound transducers (SUTs). A variety of image reconstruction algorithms can be employed to obtain the initial pressure distribution map. Previously, desktops or workstations are widely used for performing image-forming processes owing to their high computation power. But with the upgrade of mobile phones, they possess more and more powerful CPU or GPU, sometimes comparable to desktop computers. The capability of PAT can be further enhanced with the use of the mobile platform. In this work, we explored the usage of mobile platforms to reconstruct PAT images without sacrificing image quality. A mobile application was developed based on Python, implementing a simple delay-and-sum (DAS) beamformer for generating PAT images. HUAWEI P20 was employed to test the application performance, which spent less than 30 seconds to form a well-reconstructed PAT image with the SNR value more than 40 dB. Downsampling process can be performed, leading to much less reconstruction time while the photoacoustic target structure was still reconstructed properly, especially for two-fold downsampling operation. These results indicated that mobile platforms could support fast PAT image reconstruction and at the same time support good image quality.
Significance: Deep tissue noninvasive high-resolution imaging with light is challenging due to the high degree of light absorption and scattering in biological tissue. Photoacoustic imaging (PAI) can overcome some of the challenges of pure optical or ultrasound imaging to provide high-resolution deep tissue imaging. However, label-free PAI signals from light absorbing chromophores within the tissue are nonspecific. The use of exogeneous contrast agents (probes) not only enhances the imaging contrast (and imaging depth) but also increases the specificity of PAI by binding only to targeted molecules and often providing signals distinct from the background.Aim: We aim to review the current development and future progression of photoacoustic molecular probes/contrast agents.Approach: First, PAI and the need for using contrast agents are briefly introduced. Then, the recent development of contrast agents in terms of materials used to construct them is discussed. Then, various probes are discussed based on targeting mechanisms, in vivo molecular imaging applications, multimodal uses, and use in theranostic applications.Results: Material combinations are being used to develop highly specific contrast agents. In addition to passive accumulation, probes utilizing activation mechanisms show promise for greater controllability. Several probes also enable concurrent multimodal use with fluorescence, ultrasound, Raman, magnetic resonance imaging, and computed tomography. Finally, targeted probes are also shown to aid localized and molecularly specific photo-induced therapy.Conclusions: The development of contrast agents provides a promising prospect for increased contrast, higher imaging depth, and molecularly specific information. Of note are agents that allow for controlled activation, explore other optical windows, and enable multimodal use to overcome some of the shortcomings of label-free PAI.
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