Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Farnia, Parastoo; Najafzadeh, Ebrahim; Hariri, Ali; Lavasani, Saeedeh Navaei; Makkiabadi, Bahador; Ahmadian, Alireza; Jokerst, Jesse V.

doi:10.1364/boe.387364

Cited by 26 publications

(16 citation statements)

References 56 publications

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“…Improving the LED array packaging, pulse repetition rate, and coded-excitation schemes may be some aspects to be considered in this direction [ 45 ]. Further, novel deep learning and image processing methods are also expected to have an impact in improving spatial resolution and imaging depth of LED-PAI [ 46 , 47 , 48 ]. In this work, we used a combination LED array of 750 nm and 850 nm with a pulse energy of around 200

J and 100

J per pulse, respectively.…”

Section: Discussionmentioning

confidence: 99%

Oxygen Saturation Imaging Using LED-Based Photoacoustic System

Bulsink

Singh²,

Xavierselvan

et al. 2021

Sensors

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Oxygen saturation imaging has potential in several preclinical and clinical applications. Dual-wavelength LED array-based photoacoustic oxygen saturation imaging can be an affordable solution in this case. For the translation of this technology, there is a need to improve its accuracy and validate it against ground truth methods. We propose a fluence compensated oxygen saturation imaging method, utilizing structural information from the ultrasound image, and prior knowledge of the optical properties of the tissue with a Monte-Carlo based light propagation model for the dual-wavelength LED array configuration. We then validate the proposed method with oximeter measurements in tissue-mimicking phantoms. Further, we demonstrate in vivo imaging on small animal and a human subject. We conclude that the proposed oxygen saturation imaging can be used to image tissue at a depth of 6–8 mm in both preclinical and clinical applications.

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J and 100

J per pulse, respectively.…”

Section: Discussionmentioning

confidence: 99%

Oxygen Saturation Imaging Using LED-Based Photoacoustic System

Bulsink

Singh²,

Xavierselvan

et al. 2021

Sensors

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“…However, averaging N frames can improve SNR by only √ N, and thus this approach has its limitations. Recently several developments in beamforming methodologies are made to improve the CNR and SNR of the LED-based PA systems [55,56]. We believe that the improvement of optical pulse energy and the development of novel image reconstruction and enhancement algorithms will be critical to accelerate the clinical translation of LED-based PAI [54].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System

Kuriakose

Nguyen

Singh³

et al. 2020

Sensors

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Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes (LED) are being explored as an alternative to bulky and expensive laser systems used in PA imaging for their portability and low-cost. Due to the large beam divergence of LEDs compared to traditional laser beams, it is imperative to quantify the angular dependence of LED-based illumination and optimize its performance for imaging superficial or deep-seated lesions. A custom-built modular 3-D printed hinge system and tissue-mimicking phantoms with various absorption and scattering properties were used in this study to quantify the angular dependence of LED-based illumination. We also experimentally calculated the source divergence of the pulsed-LED arrays to be 58° ± 8°. Our results from point sources (pencil lead phantom) in non-scattering medium obey the cotangential relationship between the angle of irradiation and maximum PA intensity obtained at various imaging depths, as expected. Strong dependence on the angle of illumination at superficial depths (−5°/mm at 10 mm) was observed that becomes weaker at intermediate depths (−2.5°/mm at 20 mm) and negligible at deeper locations (−1.1°/mm at 30 mm). The results from the tissue-mimicking phantom in scattering media indicate that angles between 30–75° could be used for imaging lesions at various depths (12 mm–28 mm) where lower LED illumination angles (closer to being parallel to the imaging plane) are preferable for deep tissue imaging and superficial lesion imaging is possible with higher LED illumination angles (closer to being perpendicular to the imaging plane). Our results can serve as a priori knowledge for the future LED-based PA system designs employed for both preclinical and clinical applications.

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“…Recently, deep learning is also being actively studied for PAI applications [45][46][47]. However, with PAI still in its early stage of clinical translation, the scarcity of clinical PAI data remains a major challenge in optimally training the deep learning models for a given task.…”

Section: E Uspa Simulations Aided Deep Learning Enhanced Paimentioning

confidence: 99%

Modeling Combined Ultrasound and Photoacoustic Imaging: Simulations aiding Device Development and Deep Learning

Agrawal

Dangi

Kothapalli

2020

Preprint

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Combined ultrasound and photoacoustic (USPA) imaging has attracted several clinical applications due to its ability to simultaneously display structural and molecular information of deep biological tissue in real time. However, the depth dependent optical attenuation and the unknown optical and acoustic heterogeneities, limit the USPA imaging performance, especially from deeper tissue regions. Novel instrumentation, image reconstruction and deep learning methods are currently being explored to improve the USPA image quality. Effective implementation of these approaches requires a reliable USPA simulation tool capable of generating US based anatomical and PA based molecular information. Here, we developed a hybrid USPA simulation platform by integrating finite element models of light and ultrasound propagation. The feasibility of modeling US combined with optical fluence dependent multispectral PA imaging is demonstrated using in silico homogeneous and heterogeneous prostate tissue. The platform allows optimization of device design parameters, such as the aperture size and frequency of light source and ultrasound detector arrays. In addition, the potential of this simulation platform to generative massive USPA datasets aiding the data driven deep-learning enhanced USPA imaging has been validated using both simulations and experiments.

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Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Cited by 26 publications

References 56 publications

Oxygen Saturation Imaging Using LED-Based Photoacoustic System

Oxygen Saturation Imaging Using LED-Based Photoacoustic System

Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System

Modeling Combined Ultrasound and Photoacoustic Imaging: Simulations aiding Device Development and Deep Learning

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