Evaluation of Mobile Phone Performance for Near-Infrared Fluorescence Imaging

Ghassemi, Pejhman; Wang, Bohan; Wang, Jianting; Wang, Quanzeng; Chen, Yu; Pfefer, T. Joshua

doi:10.1109/tbme.2016.2601014

Cited by 26 publications

(21 citation statements)

References 18 publications

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“…This capacity, has in addition, been applied to characterizing the colour of soils, in order to achieve objective Munsell type soil classifications [ 9 ]. Smartphone imaging has also been applied in fluorescence measurements in the visible [ 10 , 11 ], particularly in terms of point of care diagnostics [ 12 ], and in the infrared [ 13 ], as well as in flame emission diagnostics [ 14 ], in profiling laser beams [ 15 ] and for characterizing food volumes via an image based segmentation algorithm as a potential aid in diet monitoring [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Smartphone Spectrometers

McGonigle

Wilkes

Pering

et al. 2018

Sensors

134

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Smartphones are playing an increasing role in the sciences, owing to the ubiquitous proliferation of these devices, their relatively low cost, increasing processing power and their suitability for integrated data acquisition and processing in a ‘lab in a phone’ capacity. There is furthermore the potential to deploy these units as nodes within Internet of Things architectures, enabling massive networked data capture. Hitherto, considerable attention has been focused on imaging applications of these devices. However, within just the last few years, another possibility has emerged: to use smartphones as a means of capturing spectra, mostly by coupling various classes of fore-optics to these units with data capture achieved using the smartphone camera. These highly novel approaches have the potential to become widely adopted across a broad range of scientific e.g., biomedical, chemical and agricultural application areas. In this review, we detail the exciting recent development of smartphone spectrometer hardware, in addition to covering applications to which these units have been deployed, hitherto. The paper also points forward to the potentially highly influential impacts that such units could have on the sciences in the coming decades.

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Section: Introductionmentioning

confidence: 99%

Smartphone Spectrometers

McGonigle

Wilkes

Pering

et al. 2018

Sensors

134

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“…Additionally, Fig. 7 shows that the CTF curves for [35][36][37] May require disabling of autofocus routines Sensitivity Graph signal vs concentration, LOD, LOQ Multiwell phantom 7,11,[15][16][17]22,24,27 (e.g., 12well) Fluorophore properties may be environmentdependent Linearity Concentration range, R 2 Multiwell phantom 11,13,17,32,33 Signal uniformity Graph, % variation Wide-field 16,24,34 Includes illumination and imaging nonuniformity…”

Section: B Depth Of Field (Dof)mentioning

confidence: 99%

“…Prior studies on medical imaging, [8][9][10] NIRF, [1][2][3][4][11][12][13][14][15][16][17][18] and other optical modalities (e.g., hyperspectral) 19,20 identify key characteristics relevant for en-face imaging. Several key commonly cited metrics are spatial resolution, 15,[21][22][23][24][25][26][27][28][29] sensitivity/ detectability, 1,7,[11][12][13][15][16][17][21][22][23][24][25][26][27][28][29][30][31] linearity, 11,13,17,32,3...…”

Section: Introductionmentioning

confidence: 99%

Performance test methods for near‐infrared fluorescence imaging

et al. 2020

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Purpose: Near-infrared fluorescence (NIRF) imaging using exogenous contrast has gained much attention as a technique for enhancing visualization of vasculature using untargeted agents, as well as for the detection and localization of cancer with targeted agents. In order to address the emerging need for standardization of NIRF imaging technologies, it is necessary to identify the best practices suitable for objective, quantitative testing of key image quality characteristics. Toward the development of a battery of test methods that are rigorous yet applicable to a wide variety of devices, we have evaluated techniques for phantom design, measurement, and calculation of specific performance metrics. Methods: Using a NIRF imaging system for indocyanine green imaging, providing excitation at 780 nm and detection above 830 nm, we explored methods to evaluate uniformity, field of view, spectral crosstalk, spatial resolution, depth of field, sensitivity, linearity, and penetration depth. These measurements were performed using fluorophore-doped multiwell plate and high turbidity planar phantoms, as well as a 3D-printed multichannel phantom and a USAF 1951 resolution target. Results and Conclusions: Based on a wide range of approaches described in medical and fluorescence imaging literature, we have developed and demonstrated a cohesive battery of test methods for evaluation of fluorescence image quality in wide-field imagers. We also propose a number of key metrics that can facilitate direct, quantitative comparison of device performance. These methods have the potential to facilitate more uniform evaluation and inter-comparison of clinical and preclinical imaging systems than is typically achieved, with the long-term goal of establishing international standards for fluorescence image quality assessment.

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“…Previously, we have fabricated 3D-printed phantoms incorporating morphology derived from human retinal vasculature, and imaged them with a hyperspectral reflectance oximetry system [22] and a NIRF imaging device using ICG contrast [23]. However, the vascular network used in these studies was semi-planar.…”

Section: Introductionmentioning

confidence: 99%

Biomimetic 3D-printed neurovascular phantoms for near-infrared fluorescence imaging

Liu¹,

Ghassemi²,

Depkon³

et al. 2018

Biomed. Opt. Express

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Emerging three-dimensional (3D) printing technology enables the fabrication of optically realistic and morphologically complex tissue-simulating phantoms for the development and evaluation of novel optical imaging products. In this study, we assess the potential to print image-defined neurovascular phantoms with patent channels for contrast-enhanced near-infrared fluorescence (NIRF) imaging. An anatomical map defined from clinical magnetic resonance imaging (MRI) was segmented and processed into files suitable for printing a forebrain vessel network in rectangular and curved-surface biomimetic phantoms. Methods for effectively cleaning samples with complex vasculature were determined. A final set of phantoms were imaged with a custom NIRF system at 785 nm excitation using two NIRF contrast agents. In addition to demonstrating the strong potential of 3D printing for creating highly realistic, patient-specific biophotonic phantoms, our work provides insight into optimal methods for accomplishing this goal and elucidates current limitations of this approach.

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Evaluation of Mobile Phone Performance for Near-Infrared Fluorescence Imaging

Cited by 26 publications

References 18 publications

Smartphone Spectrometers

Smartphone Spectrometers

Performance test methods for near‐infrared fluorescence imaging

Biomimetic 3D-printed neurovascular phantoms for near-infrared fluorescence imaging

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