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Book ; Online ; E-Book: Imaging technologies and transdermal delivery in skin disorders

Xu, Chenjie / Wang, Xiaomeng / Pramanik, Manojit

2020

Author's details	edited by Chenjie Xu, Xiaomeng Wang, Manojit Pramanik
Keywords	Electronic books
Language	English
Size	1 Online-Ressource (xviii, 480 Seiten), Illustrationen, Diagramme
Publisher	Wiley-VCH
Publishing place	Newark
Publishing country	United States
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
HBZ-ID	HT020403827
ISBN	978-3-527-81466-4 ; 978-3-527-81464-0 ; 978-3-527-81463-3 ; 9783527344604 ; 3-527-81466-3 ; 3-527-81464-7 ; 3-527-81463-9 ; 3527344608
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Physics-guided neural network for tissue optical properties estimation.

Chong, Kian Chee / Pramanik, Manojit

Biomedical optics express

2023 Volume 14, Issue 6, Page(s) 2576–2590

Abstract: Finding the optical properties of tissue is essential for various biomedical diagnostic/therapeutic applications such as monitoring of blood oxygenation, tissue metabolism, skin imaging, photodynamic therapy, low-level laser therapy, and photo-thermal ... ...

Abstract	Finding the optical properties of tissue is essential for various biomedical diagnostic/therapeutic applications such as monitoring of blood oxygenation, tissue metabolism, skin imaging, photodynamic therapy, low-level laser therapy, and photo-thermal therapy. Hence, the research for more accurate and versatile optical properties estimation techniques has always been a primary interest of researchers, especially in the field of bioimaging and bio-optics. In the past, most of the prediction methods were based on physics-based models such as the pronounced diffusion approximation method. In more recent years, with the advancement and growing popularity of machine learning techniques, most of the prediction methods are data-driven. While both methods have been proven to be useful, each of them suffers from several shortcomings that could be complemented by their counterparts. Thus, there is a need to bring the two domains together to obtain superior prediction accuracy and generalizability. In this work, we proposed a physics-guided neural network (PGNN) for tissue optical properties regression which integrates physics prior and constraint into the artificial neural network (ANN) model. With this method, we have demonstrated superior generalizability of PGNN compared to its pure ANN counterpart. The prediction accuracy and generalizability of the network were evaluated on single-layered tissue samples simulated with Monte Carlo simulation. Two different test datasets, the in-domain test dataset and out-domain dataset were used to evaluate in-domain generalizability and out-domain generalizability, respectively. The physics-guided neural network (PGNN) showed superior generalizability for both in-domain and out-domain prediction compared to pure ANN.
Language	English
Publishing date	2023-05-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.487179
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Article ; Online: High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.

Rajendran, Praveenbalaji / Pramanik, Manojit

Journal of biomedical optics

2022 Volume 27, Issue 6, Page(s) 66005

Abstract: Significance: In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning ( ... ...

Abstract	Significance: In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning (with high repetition rate light sources) and using multiple-USTs. However, artifacts arising from the sparse signal acquisition and low signal-to-noise ratio at higher scanning speeds limit the imaging speed. Thus, there is a need to improve the imaging speed of the PAT systems without hampering the quality of the PAT image. Aim: To improve the frame rate (or imaging speed) of the PAT system by using deep learning (DL). Approach: For improving the frame rate (or imaging speed) of the PAT system, we propose a novel U-Net-based DL framework to reconstruct PAT images from fast scanning data. Results: The efficiency of the network was evaluated on both single- and multiple-UST-based PAT systems. Both phantom and Conclusions: We proposed an innovative method to improve the frame rate (or imaging speed) by using DL and with this method, the fastest frame rate of
MeSH term(s)	Deep Learning ; Tomography, X-Ray Computed ; Phantoms, Imaging ; Artifacts ; Transducers
Language	English
Publishing date	2022-06-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1309154-2
ISSN	1560-2281 ; 1083-3668
ISSN (online)	1560-2281
ISSN	1083-3668
DOI	10.1117/1.JBO.27.6.066005
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Article ; Online: Special Section Guest Editorial: Seeing Inside Tissue with Optical Molecular Probes.

Boustany, Nada N / Niedre, Mark / Pramanik, Manojit

Journal of biomedical optics

2023 Volume 28, Issue 8, Page(s) 82801

Abstract: The editorial introduces the Special Section on Seeing Inside Tissue with Optical Molecular Probes. ...

Abstract	The editorial introduces the Special Section on Seeing Inside Tissue with Optical Molecular Probes.
MeSH term(s)	Molecular Probes
Chemical Substances	Molecular Probes
Language	English
Publishing date	2023-08-30
Publishing country	United States
Document type	Editorial ; Introductory Journal Article
ZDB-ID	1309154-2
ISSN	1560-2281 ; 1083-3668
ISSN (online)	1560-2281
ISSN	1083-3668
DOI	10.1117/1.JBO.28.8.082801
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Article ; Online: Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration.

Rajendran, Praveenbalaji / Pramanik, Manojit

Optics letters

2021 Volume 46, Issue 18, Page(s) 4510–4513

Abstract: Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of ... ...

Abstract	Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of PAT can be improved. However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Here we developed a novel deep learning approach to alleviate the need for radius calibration. We used a convolutional neural network (fully dense U-Net) aided with a convolutional long short-term memory block to reconstruct the PAT images. Our analysis on the test set demonstrates that the proposed network eliminates the need for radius calibration and improves the peak signal-to-noise ratio by ∼73
Language	English
Publishing date	2021-09-15
Publishing country	United States
Document type	Journal Article
ISSN	1539-4794
ISSN (online)	1539-4794
DOI	10.1364/OL.434513
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Article ; Online: Looking deep inside tissue with photoacoustic molecular probes: a review.

Hui, Xie / Malik, Mohammad O A / Pramanik, Manojit

Journal of biomedical optics

2022 Volume 27, Issue 7, Page(s) 70901

Abstract: 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 ... ...

Abstract	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, 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.
MeSH term(s)	Molecular Probes ; Contrast Media ; Spectrum Analysis ; Tomography, X-Ray Computed
Chemical Substances	Molecular Probes ; Contrast Media
Language	English
Publishing date	2022-07-22
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	1309154-2
ISSN	1560-2281 ; 1083-3668
ISSN (online)	1560-2281
ISSN	1083-3668
DOI	10.1117/1.JBO.27.7.070901
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Article ; Online: Recent advances in photoacoustic contrast agents for in vivo imaging.

Upputuri, Paul Kumar / Pramanik, Manojit

Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology

2020 Volume 12, Issue 4, Page(s) e1618

Abstract: Photoacoustic imaging (PAI) is a noninvasive hybrid imaging modality offering rich optical contrast and high depth-to-resolution ratio deep-tissue imaging. Endogenous chromophores present in the body such as hemoglobin, lipid, melanin, and so on provide ... ...

Abstract	Photoacoustic imaging (PAI) is a noninvasive hybrid imaging modality offering rich optical contrast and high depth-to-resolution ratio deep-tissue imaging. Endogenous chromophores present in the body such as hemoglobin, lipid, melanin, and so on provide strong photoacoustic contrast due to their strong light absorption in certain optical window. To enhance the performance of PAI further, researchers have developed several exogenous contrast agents such as metallic nanoparticles, carbon-based nanomaterials, quantum dots, organic small molecules, semiconducting polymer nanoparticles, and so on. These exogenous contrast agents not only help improving the imaging contrast, but also make targeted molecular imaging possible. In this review article, we first discuss the state-of-the-art PAI techniques with endogenous contrast mechanism. Later, we provide an overview of recent progress in the development of exogenous photoacoustic contrast agents for in vivo imaging applications. Finally, we present the pros/cons of the existing PA contrast agents along with future challenges of contrast agent-based PAI for biomedical applications. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.
MeSH term(s)	Animals ; Contrast Media/chemistry ; Diagnostic Imaging ; Humans ; Nanoparticles/chemistry ; Photoacoustic Techniques ; Polymers/chemistry ; Quantum Dots/chemistry
Chemical Substances	Contrast Media ; Polymers
Language	English
Publishing date	2020-02-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2502698-7
ISSN	1939-0041 ; 1939-5116
ISSN (online)	1939-0041
ISSN	1939-5116
DOI	10.1002/wnan.1618
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Article: Deep learning approach to improve tangential resolution in photoacoustic tomography.

Rajendran, Praveenbalaji / Pramanik, Manojit

Biomedical optics express

2020 Volume 11, Issue 12, Page(s) 7311–7323

Abstract: In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. ... ...

Abstract	In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental
Language	English
Publishing date	2020-11-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.410145
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Article: Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy.

Sharma, Arunima / Pramanik, Manojit

Biomedical optics express

2020 Volume 11, Issue 12, Page(s) 6826–6839

Abstract: In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of- ... ...

Abstract	In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of-focus region. Improvement in out-of-focus resolution without degrading the image quality remains a challenge. In this work, we propose a deep learning-based method to improve the resolution of AR-PAM images, especially at the out of focus plane. A modified fully dense U-Net based architecture was trained on simulated AR-PAM images. Applying the trained model on experimental images showed that the variation in resolution is ∼10% across the entire imaging depth (∼4 mm) in the deep learning-based method, compared to ∼180% variation in the original PAM images. Performance of the trained network on
Language	English
Publishing date	2020-11-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.411257
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Article ; Online: Improving tangential resolution with a modified delay-and-sum reconstruction algorithm in photoacoustic and thermoacoustic tomography.

Pramanik, Manojit

Journal of the Optical Society of America. A, Optics, image science, and vision

2014 Volume 31, Issue 3, Page(s) 621–627

Abstract: Spatial resolution in photoacoustic and thermoacoustic tomography is ultrasound transducer (detector) bandwidth limited. For a circular scanning geometry the axial (radial) resolution is not affected by the detector aperture, but the tangential (lateral) ...

Abstract	Spatial resolution in photoacoustic and thermoacoustic tomography is ultrasound transducer (detector) bandwidth limited. For a circular scanning geometry the axial (radial) resolution is not affected by the detector aperture, but the tangential (lateral) resolution is highly dependent on the aperture size, and it is also spatially varying (depending on the location relative to the scanning center). Several approaches have been reported to counter this problem by physically attaching a negative acoustic lens in front of the nonfocused transducer or by using virtual point detectors. Here, we have implemented a modified delay-and-sum reconstruction method, which takes into account the large aperture of the detector, leading to more than fivefold improvement in the tangential resolution in photoacoustic (and thermoacoustic) tomography. Three different types of numerical phantoms were used to validate our reconstruction method. It is also shown that we were able to preserve the shape of the reconstructed objects with the modified algorithm.
MeSH term(s)	Acoustics ; Algorithms ; Image Processing, Computer-Assisted/methods ; Phantoms, Imaging ; Photoacoustic Techniques/methods ; Pressure ; Temperature ; Tomography/methods
Language	English
Publishing date	2014-03-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	283633-6
ISSN	1520-8532 ; 1084-7529 ; 0740-3232
ISSN (online)	1520-8532
ISSN	1084-7529 ; 0740-3232
DOI	10.1364/JOSAA.31.000621
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