# Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Principles and Methods

#### 2.1. Typical SFDI System

^{®}(https://modulim.com/, accessed on 23 March 2021), has also been used in some studies [35,36,37,38].

_{1}and L

_{2}are used to expand and collimate the beam onto a mask M of a sinusoidal pattern. The image of the illuminated pattern is then collimated by L

_{3}and polarized by linear polarizer P

_{1}as it is sent through the projection channel of the endoscope and onto the sample. The reflected light is imaged through the collection channel of the endoscope. The collimated output is cross-polarized with respect to P

_{1}by linear polarizer P

_{2}and then imaged by objective lens L

_{4}onto the CCD. This design combines the endoscope with SFDI, which makes it possible to measure optical properties of endoscope in real-time with a large field-of-view.

#### 2.2. Principle of SFDI for Estimating Optical Properties

## 3. Applications

^{−1}). The results indicated that SFDI was capable of measuring the absorption and reduced scattering coefficients noninvasively. However, the value of their work was not recognized by the scientific community at that time. Thanks to the rapid advances in digital technology and computing technology, great progress has been made in the development of spatially resolved and time-domain techniques for measuring optical properties of biological tissues, which, in turn, can be routinely used for chemical composition prediction and functional analysis [57]. Therefore, the researchers began to renew their interest in spatial-frequency domain imaging after the arrival of the twenty-first century. In 2005, Cuccia et al. applied the SFDI technique for the measurement and analysis of wide-field mapping of tissue optical properties [30]. They used a modulation pattern with the frequency of 0–0.6 mm

^{−1}at 640 nm, demonstrating that SFDI was a fast and inexpensive method for tomographic imaging and quantitative optical property mapping in a wide field-of-view. The conceptual framework, hardware composition, and software algorithm proposed in their study have been widely used for optical property estimation by other researchers. The estimated optical properties can be used in the field of biomedical optics for inspecting breast tumors and non-melanoma tumor lesions, as well as in the food and agricultural domain for apple internal browning and early bruise detection. The following sections present more details regarding the practical applications of SFDI.

#### 3.1. Burn Assessment

^{−1}were frequently used, because low-frequency illumination has larger light penetration depth, which is suitable for detecting the subsurface burns. Visible lighting is still the popular illumination and wavelengths beyond the visible range are lower than 1000 nm. Near-infrared lighting may have abilities in penetrating deeper tissues, but requires more expensive instrumentation, such as imaging and wavelength dispersion devices. Most of the research in Table 2 was conducted on pigs and mice to create artificial burn wounds of different levels. Both ${\mu}_{a}$ and ${\mu}_{s}\prime $ could be used to examine the skin burns by comparing the differences of measured optical property between healthy and burned tissues. Relative changes in oxygenated hemoglobin concentration (HbO

_{2}), deoxygenated hemoglobin concentration (Hb), total hemoglobin concentration (HbT), and blood oxygen saturation (StO

_{2}) could be used to present the skin condition. StO

_{2}was more frequently used as an index of burn assessment due to its ability in revealing vascular damage and patency. Figure 3 shows typical results of burn assessment for porcine dorsal skin with three levels (i.e., superficial partial, deep partial, and full). It was found that the reduced scattering coefficients of porcine dorsal skin with burns were smaller than those without burns, indicating that the reduced scattering coefficient mappings estimated by the SFDI were capable of burn detection.

#### 3.2. Skin Tissue Evaluation

#### 3.3. Tumor Tissue Detection

#### 3.4. Brain Tissue Monitoring

#### 3.5. Quality Evaluation of Agro-Products

## 4. Challenges and Future Perspectives

^{−1}), three phases (e.g., 0, $\frac{2\pi}{3}$ and $\frac{4\pi}{3}$), and one wavelength takes about five to twenty seconds or even longer, which cannot meet the requirements of high real-time applications. Many efforts have thus been made to accelerate the speed, such as SSOP, which reduces the number of phase-images from three to one, improving the efficiency by approximately three times [89,104,105,106,107,108,109,110,111]. Development of hardware configurations, like the use of single-pixel camera, instead of industrial CCD camera, could further speed up the optical property measurement using the SFDI [54]. Now, the SFDI has been applied for real-time applications in the field of biomedicine optics, such as visualization of lateral spatial distribution of tissue chromophores over a contoured surface [112], and detection of early plantar ulcer of the patients [113]. However, in the field of food and agricultural engineering, the real-time application of SFDI is still challenging, because the speed requirement is higher than that in biomedical detection. For example, a real-time inspecting and sorting production line of apple quality works at a speed of 5–10 apples/second, which is rather fast and difficult to meet with the current development of SFDI. Therefore, the efficiency of SFDI needs further research in the future, especially for the food and agricultural application.

^{3}. Due to the single wavelength of LED, the frequency choice is relatively narrow. It carries processors without higher power and better performance, so that the data processing is time consuming. Sager et al. designed and manufactured a handheld SFDI device, which could conduct imaging with 1-D spatial resolution [67]. Since they changed the plane imaging to line imaging, the scanning and data processing speed was greatly improved. The instrument is compact, easy to use, and can collect data from in vivo skin at relatively fast speed. At present, there are two ways to optimize and improve the handheld SFDI device. One is to replace all components with compact parts and compress the space between components to achieve a smaller volume of the whole system. However, due to the smaller component size, there may be some loss in the imaging size, wavelength, and frequency selection of the modulated images. Another idea is to separate the detection part from the light source and camera, and connect them with a light guide. This method can make each part be directly connected to each other, i.e., relatively small detection part, while the light source and the camera part are relatively large, so as to avoid the loss of wavelength and frequency of the modulation patterns. The disadvantage is that the two still need to be connected together, and the reliability and flexibility of the connection are potential issues. Since the handheld SFDI device has no sample table for placing samples, the distance between the camera and the measured object cannot be controlled, which provides more challenges for accurate optical property estimation with SFDI.

_{2}were measured in vivo [81], while Gioux et al. applied the combination of these two techniques for real-time acquisition of optical properties of a hand in motion [39]. SFDI was also combined with the technique of fluorescence imaging to acquire maps co-registered in space and time of tissue optical properties and raw fluorescence emissions followed by a model-based correction to estimate the quantitative fluorescence. They provided a means to correct the emitted fluorescence with a quantitative fluorescence model [115]. These combinations integrating the advantages of two or multiple techniques can certainly expand the applications of the SFDI, but it should be mentioned that the system complexity was also increased with more components, which is a new challenge for real-time application. Moreover, multiple cameras were used to acquire images at different wavelengths simultaneously, which can accelerate the speed of image acquisition, at the expense of increasing system cost and calculation amount of image processing. Very recently, deep learning algorithms (e.g., generative adversarial networks, random forest, etc.) have evolved rapidly, which provide new means for image recognition, defect detection, and object classification [116,117,118,119]. What makes such methods attractive is their capacity to perform particularly well in learning nonlinear properties. In the future, deep learning algorithms are expected to be combined with SFDI for rapid and accurate optical property estimations of biological tissues.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Chen, B.; Tong, C.M. Modified physical optics algorithm for near field scattering. Chin. Phys. B
**2018**, 27, 114102–114106. [Google Scholar] [CrossRef] - Shi, R.; Hellmann, C.; Wyrowski, F. Physics optics propagation through curved surfaces. J. Opt. Soc. Am. A
**2019**, 36, 1252–1260. [Google Scholar] [CrossRef] [PubMed] - Liemert, A.; Reitzle, D.; Kienle, A. Analytical solutions of the radiative transport equation for turbid and fluorescent layered media. Sci. Rep.
**2017**, 7, 3819. [Google Scholar] [CrossRef] - Liemert, A.; Kienle, A. Green’s function of the time-dependent radiative transport equation in terms of rotated spherical harmonics. Phys. Rev. E
**2012**, 86, 036603. [Google Scholar] [CrossRef] [Green Version] - Liemert, A.; Kienle, A. Spatially modulated light source obliquely incident on a semi-infinite scattering medium. Opt. Lett.
**2012**, 37, 4158–4160. [Google Scholar] [CrossRef] [PubMed] - Liemert, A.; Kienle, A. Exact and efficient solution of the radiative transport equation for the semi-infinite medium. Sci. Rep.
**2013**, 3, 2018. [Google Scholar] [CrossRef] [PubMed] - Liemert, A.; Geiger, S.; Kienle, A. Solutions for single-scattered radiance in the semi-infinite medium based on radiative transport theory. J. Opt. Soc. Am. A
**2021**, 38, 405–411. [Google Scholar] [CrossRef] [PubMed] - Ostermeyer, M.R.; Jacques, S.L. Perturbation theory for diffuse light transport in complex biological tissues. J. Opt. Soc. Am. A
**1997**, 14, 255–261. [Google Scholar] [CrossRef] - Fan, S.X.; Li, C.Y.; Huang, W.Q.; Chen, L.P. Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths. Postharvest Biol. Technol.
**2017**, 134, 55–66. [Google Scholar] [CrossRef] - Anderson, E.R.; Cuccia, D.J.; Durkin, A.J. Detection of bruises on golden delicious apples using spatial- frequency-domain imaging. Proc. SPIE Int. Soc. Opt. Eng.
**2007**, 36, 6430. [Google Scholar] [CrossRef] - Vanoli, M.; Van Beers, R.; Sadar, N.; Rizzolo, A.; Buccheri, M.; Grassi, M.; Lovati, F.; Nicolaï, B.; Aernouts, B.; Watté, R.; et al. Time- and spatially-resolved spectroscopy to determine the bulk optical properties of ‘Braeburn’ apples after ripening in shelf life. Postharvest Biol. Technol.
**2020**, 168. [Google Scholar] [CrossRef] - Wang, L.V.; Wu, H.-I.; Masters, B.R. Biomedical Optics, Principles and Imaging. J. Biomed. Opt.
**2008**. [Google Scholar] [CrossRef] - Wilson, B.C.; Patterson, M.S.; Flock, S.T. Indirect versus direct techniques for the measurement of the optical properties of tissues. Photochem. Photobiol.
**1987**, 46, 601–608. [Google Scholar] [CrossRef] - Bashkatov, A.N.; Genina, E.A.; Kochubey, V.I.; Tuchin, V.V. Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm. J. Phys. D Appl. Phys.
**2005**, 38, 2543. [Google Scholar] [CrossRef] - Cheong, W.F.; Prahl, S.A.; Welch, A.J. A review of the optical properties of biological tissues. IEEE J. Quantum. Elect.
**1990**, 26, 2166–2185. [Google Scholar] [CrossRef] [Green Version] - Rohrbach, D.J.; Muffoletto, D.; Huihui, J.; Saager, R.; Keymel, K.; Paquette, A.; Morgan, J.; Zeitouni, N.; Sunar, U. Preoperative Mapping of Nonmelanoma Skin Cancer Using Spatial Frequency Domain and Ultrasound Imaging. Acad. Radiol.
**2014**, 21, 263–270. [Google Scholar] [CrossRef] [Green Version] - Lin, A.J.; Koike, M.A.; Green, K.N.; Kim, J.G.; Mazhar, A.; Rice, T.B.; LaFerla, F.M.; Tromberg, B.J. Spatial Frequency Domain Imaging of Intrinsic Optical Property Contrast in a Mouse Model of Alzheimer’s Disease. Ann. Biomed. Eng.
**2011**, 39, 1349–1357. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mazhar, A.; Sharif, S.A.; Cuccia, J.D.; Nelson, J.S.; Kelly, K.M.; Durkin, A.J. Spatial frequency domain imaging of port wine stain biochemical composition in response to laser therapy: A pilot study. Laser. Surg. Med.
**2012**, 44, 611–621. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ponticorvo, A.; Burmeister, D.M.; Rowland, R.; Baldado, M.; Kennedy, G.T.; Saager, R.; Bernal, N.; Choi, B.; Durkin, A.J. Quantitative long-term measurements of burns in a rat model using Spatial Frequency Domain Imaging (SFDI) and Laser Speckle Imaging (LSI). Laser. Surg. Med.
**2017**, 49, 293–304. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Cuccia, D.J.; Bevilacqua, F.; Durkin, A.J.; Ayers, F.R.; Tromberg, B.J. Quantitation and mapping of tissue optical properties using modulated imaging. J. Biomed. Opt.
**2009**, 14, 024012. [Google Scholar] [CrossRef] [PubMed] - Lu, Y.Z.; Li, R.; Lu, R.F. Gram–Schmidt orthonormalization for retrieval of amplitude images under sinusoidal patterns of illumination. Appl. Opt.
**2016**, 55, 6866–6873. [Google Scholar] [CrossRef] - Lu, Y.Z.; Li, R.; Lu, R.F. Fast demodulation of pattern images by spiral phase transform in structured-illumination reflectance imaging for detection of bruises in apples. Comput. Electron. Agric.
**2016**, 127, 652–658. [Google Scholar] [CrossRef] - Bassi, A.; D’Andrea, C.; Valentini, G.; Cubeddu, R.; Arridge, S. Temporal propagation of spatial information in turbid media. Opt. Lett.
**2008**, 33, 2836. [Google Scholar] [CrossRef] - Wang, L.V.; Wu, H.-I. Biomedical Optics: Principles and Imaging; John Wiley & Sons: Hoboken, NJ, USA, 2007. [Google Scholar]
- Prahl, S.A.; Vangemert, M.J.C.; Welch, A.J. Determining the optical properties of turbid media by using the adding-doubling method. Appl. Opt.
**1993**, 32, 559–568. [Google Scholar] [CrossRef] [PubMed] - Patterson, M.S.; Chance, B.; Wilson, B.C. Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties. Appl. Opt.
**1989**, 28, 2331–2336. [Google Scholar] [CrossRef] [PubMed] - Patterson, M.S.; Moulton, J.D.; Wilson, B.C.; Berndt, K.W.; Lakowicz, J.R. Frequency-domain reflectance for the determination of the scattering and absorption properties of tissue. Appl. Opt.
**1991**, 30, 4474–4476. [Google Scholar] [CrossRef] - Farrell, T.J.; Patterson, M.S.; Wilson, B. A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. Med. Phys.
**1992**, 19, 879–888. [Google Scholar] [CrossRef] [PubMed] - Kienle, A.; Patterson, M.S. Improved solutions of the steady-state and the time-resolved diffusion equations for reflectance from a semi-infinite turbid medium. J. Opt. Soc. Am. A
**1997**, 14, 246–254. [Google Scholar] [CrossRef] - Cuccia, D.J.; Bevilacqua, F.; Durkin, A.J.; Tromberg, B.J. Modulated imaging: Quantitative analysis and tomography of turbid media in the spatial-frequency domain. Opt. Lett.
**2005**, 30, 1354–1359. [Google Scholar] [CrossRef] - Wirth, D.; Sibai, M.; Olson, J.; Wilson, B.C.; Roberts, D.W.; Paulsen, K. Feasibility of using spatial frequency-domain imaging intraoperatively during tumor resection. J. Biomed. Opt.
**2019**, 24, 1–6. [Google Scholar] [CrossRef] - Nothelfer, S.; Bergmann, F.; Liemert, A.; Reitzle, D.; Kienle, A. Spatial frequency domain imaging using an analytical model for separation of surface and volume scattering. J. Biomed. Opt.
**2019**, 24, 1–10. [Google Scholar] [CrossRef] [PubMed] - Kennedy, G.T.; Stone, R.I.; Kowalczewski, A.C.; Rowland, R.; Chen, J.H.; Baldado, M.L.; Ponticorvo, A.; Bernal, N.; Christy, R.J.; Durkin, A.J. Spatial frequency domain imaging: A quantitative, noninvasive tool for in vivo monitoring of burn wound and skin graft healing. J. Biomed. Opt.
**2019**, 24, 1–9. [Google Scholar] [CrossRef] [PubMed] - Laughney, A.M.; Krishnaswamy, V.; Rice, T.B.; Cuccia, D.J.; Barth, R.J.; Tromberg, B.J.; Paulsen, K.D.; Pogue, B.W.; Wells, W.A. System analysis of spatial frequency domain imaging for quantitative mapping of surgically resected breast tissues. J. Biomed. Opt.
**2013**, 18, 036012–036022. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Gioux, S.; Mazhar, A.; Lee, B.T.; Lin, S.J.; Tobias, A.M.; Cuccia, D.J.; Stockdale, A.; Oketokoun, R.; Ashitate, Y.; Kelly, E.; et al. First-in-human pilot study of a spatial frequency domain oxygenation imaging system. J. Biomed. Opt.
**2011**, 16, 086015. [Google Scholar] [CrossRef] [Green Version] - Balu, M.; Mazhar, A.; Hayakawa, C.K.; Mittal, R.; Krasieva, T.B.; Konig, K.; Venugopalan, V.; Tromberg, B.J. In vivo multiphoton NADH fluorescence reveals depth-dependent keratinocyte metabolism in human skin. Biophys. J.
**2013**, 104, 258–267. [Google Scholar] [CrossRef] [Green Version] - Mazhar, A.; Saggese, S.; Pollins, A.C.; Cardwell, N.L.; Nanney, L.; Cuccia, D.J. Noncontact imaging of burn depth and extent in a porcine model using spatial frequency domain imaging. J. Biomed. Opt.
**2014**, 19, 086019–086028. [Google Scholar] [CrossRef] [PubMed] - Gioux, S.; Mazhar, A.; Cuccia, D.J.; Durkin, A.J.; Tromberg, B.J.; Frangioni, J.V. Three-dimensional surface profile intensity correction for spatially modulated imaging. J. Biomed. Opt.
**2009**, 14, 034045–034055. [Google Scholar] [CrossRef] [Green Version] - Angelo, J.P.; van de Giessen, M.; Gioux, S. Real-time endoscopic optical properties imaging. Biomed. Opt. Express
**2017**, 8, 5113–5126. [Google Scholar] [CrossRef] [Green Version] - Saager, R.B.; Cuccia, D.J.; Durkin, A.J. Determination of optical properties of turbid media spanning visible and near-infrared regimes via spatially modulated quantitative spectroscopy. J. Biomed. Opt.
**2015**, 15, 017012. [Google Scholar] [CrossRef] [Green Version] - Andrea, C.D.; Ducros, N.; Bassi, A.; Arridge, S.; Valentini, G.; Dipartimento, P.; Milano, P.; Leonardo, P. Fast 3D optical reconstruction in turbid media using spatially modulated light Abstract. Biomed. Opt. Express
**2010**, 1, 471–481. [Google Scholar] [CrossRef] [Green Version] - BeaLanger, S.; Abran, M.; Intes, X.; Casanova, C.; Lesage, F. Real-time diffuse optical tomography based on structured illumination. J. Biomed. Opt.
**2010**, 15, 016006. [Google Scholar] [CrossRef] [PubMed] - Konecky, S.D.; Owen, C.M.; Tyler, R.; Valdés, P.A.; Kolbein, K.; Wilson, B.C.; Frederic, L.; Roberts, D.W.; Paulsen, K.D.; Tromberg, B.J. Spatial frequency domain tomography of protoporphyrin IX fluorescence in preclinical glioma models. J. Biomed. Opt.
**2012**, 17, 056008. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Konecky, S.D.; Amaan, M.; Cuccia, D.; Durkin, A.J.; Schotland, J.C.; Tromberg, B.J. Quantitative optical tomography of sub-surface heterogeneities using spatially modulated structured light. Opt. Express
**2009**, 17, 14780–14790. [Google Scholar] [CrossRef] [Green Version] - Gardner, A.R.; Vasan, V. Accurate and efficient Monte Carlo solutions to the radiative transport equation in the spatial frequency domain. Opt. Lett.
**2011**, 36, 2269–2271. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Yao, R.Y.; Intes, X.; Fang, Q.Q. Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation. Biomed. Opt. Express
**2016**, 7, 171–184. [Google Scholar] [CrossRef] [Green Version] - He, X.M.; Jiang, X.; Fu, X.P.; Gao, Y.W.; Rao, X.Q. Least squares support vector machine regression combined with Monte Carlo simulation based on the spatial frequency domain imaging for the detection of optical properties of pear. Postharvest Biol. Technol.
**2018**, 145, 1–9. [Google Scholar] [CrossRef] - Regan, C.; Hayakawa, C.K.; Choi, B. Momentum transfer Monte Carlo for the simulation of laser speckle imaging and its application in the skin. Biomed. Opt. Express
**2017**, 8, 5708–5723. [Google Scholar] [CrossRef] [Green Version] - Kijanka, P.; Packo, P. Novel method for true guided waves spectral characteristics estimation using a logistic function fit and nonlinear least square algorithm. Struct. Control Health Monit.
**2019**, 26, 13. [Google Scholar] [CrossRef] - Song, J.W.; Lau, D.L.; Ho, Y.S.; Liu, K. Automatic look-up table based real-time phase unwrapping for phase measuring profilometry and optimal reference frequency selection. Opt. Express
**2019**, 27, 13357–13371. [Google Scholar] [CrossRef] [PubMed] - Vicent Servera, J.; Alonso, L.; Martino, L.; Sabater, N.; Verrelst, J.; Camps-Valls, G.; Moreno, J. Gradient-Based Automatic Lookup Table Generator for Radiative Transfer Models. IEEE Trans. Geosci. Remote Sens.
**2019**, 57, 1040–1048. [Google Scholar] [CrossRef] - Angelo, J.; Vargas, C.R.; Lee, B.T.; Bigio, I.J.; Gioux, S. Ultrafast optical property map generation using lookup tables. J. Biomed. Opt.
**2016**, 21, 110501. [Google Scholar] [CrossRef] - Vervandier, J.; Gioux, S. Single snapshot imaging of optical properties. Biomed. Opt. Express
**2013**, 4, 2938–2944. [Google Scholar] [CrossRef] [Green Version] - Aguenounon, E.; Dadouche, F.; Uhring, W.; Ducros, N.; Gioux, S. Single snapshot imaging of optical properties using a single-pixel camera: A simulation study. J. Biomed. Opt.
**2019**, 24, 1–6. [Google Scholar] [CrossRef] [PubMed] - Aguenounon, E.; Dadouche, F.; Uhring, W.; Gioux, S. Single snapshot of optical properties image quality improvement using anisotropic two-dimensional windows filtering. J. Biomed. Opt.
**2019**, 24, 1–21. [Google Scholar] [CrossRef] [Green Version] - Dognitz, N.; Wagnieres, G. Determination of tissue optical properties by steady-state spatial frequency-domain reflectometry. Lasers Med. Sci.
**1998**, 13, 55–65. [Google Scholar] [CrossRef] [Green Version] - Lu, R.F. Light Scattering Technology for Food Property, Quality and Safety Assessment; CRC Press: Boca Raton, FL, USA, 2017. [Google Scholar]
- Nguyen, T.T.A.; Ramella-Roman, J.C.; Moffatt, L.T.; Ortiz, R.T.; Jordan, M.H.; Shupp, J.W. Novel Application of a Spatial Frequency Domain Imaging System to Determine Signature Spectral Differences Between Infected and Noninfected Burn Wounds. J. Burn Care Res.
**2013**, 34, 44–50. [Google Scholar] [CrossRef] [Green Version] - Kennedy, G.T.; Stone, R.; Kowalczewski, A.C.; Chen, J.H.; Rowland, R.; Ponticorvo, A.; Christy, R.J.; Durkin, A.J. Characterization of debrided burn wounds using spatial frequency domain imaging. Photonics Dermatol. Plast. Surg.
**2019**, 108510. [Google Scholar] [CrossRef] - Ponticorvo, A.; Rowland, R.; Baldado, M.; Burmeister, D.M.; Christy, R.J.; Bernal, N.P.; Durkin, A.J. Evaluating clinical observation versus Spatial Frequency Domain Imaging (SFDI), Laser Speckle Imaging (LSI) and thermal imaging for the assessment of burn depth. Burns
**2019**, 45, 450–460. [Google Scholar] [CrossRef] [Green Version] - Rowland, R.; Ponticorvo, A.; Baldado, M.; Kennedy, G.T.; Burmeister, D.M.; Christy, R.J.; Bernal, N.P.; Durkin, A.J. A Simple Burn Wound Severity Assessment Classifier Based On Spatial Frequency Domain Imaging (SFDI) and Machine Learning. Photonics Dermatol. Plast. Surg.
**2019**, 1085109. [Google Scholar] [CrossRef] - Ponticorvo, A.; Burmeister, D.M.; Yang, B.; Choi, B.; Christy, R.J.; Durkin, A.J. Quantitative assessment of graded burn wounds in a porcine model using spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI). Biomed. Opt. Express
**2014**, 5, 3467–3481. [Google Scholar] [CrossRef] [Green Version] - Poon, C.; Sunar, U.; Rohrbach, D.J.; Krishnamurthy, S.; Olsen, T.; Kent, M.; Weir, N.M.; Simman, R.; Travers, J.B. Early assessment of burn severity in human tissue ex vivo with multi-wavelength spatial frequency domain imaging. Toxicol. Vitr.
**2018**, 52, 251–254. [Google Scholar] [CrossRef] - Nguyen, J.Q.; Crouzet, C.; Mai, T.; Riola, K.; Uchitel, D.; Liaw, L.H.; Bernal, N.; Ponticorvo, A.; Choi, B.; Durkin, A.J. Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity. J. Biomed. Opt.
**2013**, 18, 066010. [Google Scholar] [CrossRef] [Green Version] - Burmeister, D.M.; Ponticorvo, A.; Yang, B.; Becerra, S.C.; Choi, B.; Durkin, A.J.; Christy, R.J. Utility of spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) to non-invasively diagnose burn depth in a porcine model. Burns
**2015**, 41, 1242–1252. [Google Scholar] [CrossRef] [Green Version] - Chen, X.; Lin, W.; Wang, C.; Chen, S.; Sheng, J.; Zeng, B.; Xu, M. In vivo real-time imaging of cutaneous hemoglobin concentration, oxygen saturation, scattering properties, melanin content, and epidermal thickness with visible spatially modulated light. Biomed. Opt. Express
**2017**, 8, 5468–5482. [Google Scholar] [CrossRef] [Green Version] - Saager, R.B.; Dang, A.N.; Huang, S.S.; Kelly, K.M.; Durkin, A.J. Portable (handheld) clinical device for quantitative spectroscopy of skin, utilizing spatial frequency domain reflectance techniques. Rev. Sci. Instrum.
**2017**, 88, 094302. [Google Scholar] [CrossRef] - Travers, J.B.; Poon, C.; Rohrbach, D.J.; Weir, N.M.; Cates, E.; Hager, F.; Sunar, U. Noninvasive mesoscopic imaging of actinic skin damage using spatial frequency domain imaging. Biomed. Opt. Express
**2017**, 8, 3045–3052. [Google Scholar] [CrossRef] [Green Version] - Yafi, A.; Muakkassa, F.K.; Pasupneti, T.; Fulton, J.; Cuccia, D.J.; Mazhar, A.; Blasiole, K.N.; Mostow, E.N. Quantitative Skin Assessment Using Spatial Frequency Domain Imaging (SFDI) in Patients With or at High Risk for Pressure Ulcers. Lasers Surg. Med.
**2017**, 49, 827–834. [Google Scholar] [CrossRef] [PubMed] - Gevaux, L.; Cherel, M.; Seroul, P.; Clerc, R.; Tremeau, A.; Hebert, M. Hyperspectral imaging and spatial frequency domain imaging: Combined acquisition for full face skin analysis. Imaging Manip. Anal. Biomol. Cells Tissues Xvii
**2019**, 10881. [Google Scholar] [CrossRef] - Rohrbach, D.J.; Zeitouni, N.C.; Muffoletto, D.; Saager, R.; Tromberg, B.J.; Sunar, U. Characterization of nonmelanoma skin cancer for light therapy using spatial frequency domain imaging. Biomed. Opt. Express
**2015**, 6, 1761–1766. [Google Scholar] [CrossRef] [Green Version] - Nandy, S.; Mostafa, A.; Kumavor, P.D.; Sanders, M.; Brewer, M.; Zhu, Q. Characterizing optical properties and spatial heterogeneity of human ovarian tissue using spatial frequency domain imaging. J. Biomed. Opt.
**2016**, 21, 101402. [Google Scholar] [CrossRef] [PubMed] - Lin, W.H.; Zeng, B.X.; Cao, Z.L.; Chen, X.L.; Yang, K.Y.; Xu, M. Quantitative diagnosis of tissue microstructure with wide-field high spatial frequency domain imaging. Biomed. Opt. Express
**2018**, 9, 2905–2916. [Google Scholar] [CrossRef] [PubMed] - Lin, W.H.; Zeng, B.X.; Cao, Z.L.; Zhu, D.F.; Xu, M. Wide-field high spatial frequency domain imaging of tissue microstructure. Prog. Biomed. Opt. Imaging Proc. SPIE
**2018**, 10484. [Google Scholar] [CrossRef] - Laughney, A.M.; Krishnaswamy, V.; Rizzo, E.J.; Schwab, M.C.; Barth, R.J.; Cuccia, D.J.; Tromberg, B.J.; Paulsen, K.D.; Pogue, B.W.; Wells, W.A. Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging. Breast Cancer Res.
**2013**, 15, R61. [Google Scholar] [CrossRef] [Green Version] - Nguyen, J.T.; Lin, S.J.; Tobias, A.M.; Gioux, S.; Mazhar, A.; Cuccia, D.J.; Ashitate, Y.; Stockdale, A.; Oketokoun, R.; Durr, N.J.; et al. A Novel Pilot Study Using Spatial Frequency Domain Imaging to Assess Oxygenation of Perforator Flaps During Reconstructive Breast Surgery. Ann. Plast. Surg.
**2013**, 71, 308–315. [Google Scholar] [CrossRef] - McClatchy, D.M.; Rizzo, E.J.; Wells, W.A.; Cheney, P.P.; Hwang, J.C.; Paulsen, K.D.; Pogue, B.W.; Kanick, S.C. Wide-field quantitative imaging of tissue microstructure using sub-diffuse spatial frequency domain imaging. Optica
**2016**, 3, 613–621. [Google Scholar] [CrossRef] [Green Version] - McClatchy, D.M.; Rizzo, E.; Krishnaswamy, V.; Kanick, S.; Wells, W.; Paulsen, K.; Pogue, B. Combined multispectral spatial frequency domain imaging and computed tomography system for intraoperative breast tumor margin assessment. Prog. Biomed. Opt. Imaging Proc. SPIE
**2017**, 10057. [Google Scholar] [CrossRef] - Robbins, C.M.; Antaki, J.F.; Kainerstorfer, J.M. Spatial frequency domain imaging for monitoring palpable breast lesions. Prog. Biomed. Opt. Imaging Proc. SPIE
**2017**, 10059. [Google Scholar] [CrossRef] - Wei, R.L.; Leproux, A.; Laoui, S.; Kuo, J.V.; Daroui, P.; Farol, H.Y.; Ramsinghani, N.S.; Al-Ghazi, M.; Durkin, A.J.; Tromberg, B. Temporal and Spatial Quantification of Tissue Oxygen Saturation and Melanin Deposition During Whole Breast Radiation Using Noninvasive Spatial Frequency Domain Imaging. Int. J. Radiat. Oncol.
**2017**, 99, E54–E55. [Google Scholar] [CrossRef] - Nandy, S.; Erfanzadeh, M.; Zhou, F.F.; Zhu, Q. Feasibility study of spatial frequency domain imaging using a handheld miniaturized projector and rigid endoscope. Prog. Biomed. Opt. Imaging Proc. SPIE
**2017**, 10059. [Google Scholar] [CrossRef] - Tabassu, S.; Pera, V.; Greening, G.; Muldoon, T.J.; Roblyer, D. Two-layer inverse model for improved longitudinal preclinical tumor imaging in the spatial frequency domain. J. Biomed. Opt.
**2018**, 23, 076011. [Google Scholar] [CrossRef] [Green Version] - Zhao, Y.; Tabassum, S.; Piracha, S.; Nandhu, M.S.; Viapiano, M.; Roblyer, D. Angle correction for small animal tumor imaging with spatial frequency domain imaging (SFDI). Biomed. Opt. Express
**2016**, 7, 2373–2384. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Burns, J.M.; Schaefer, E.; Anvari, B. Near Infrared Spatial Frequency Domain Fluorescence Imaging of Tumor Phantoms Containing Erythrocyte-Derived Optical Nanoplatforms. Prog. Biomed. Opt. Imaging Proc. SPIE
**2018**, 105060. [Google Scholar] [CrossRef] - Lin, A.J.; Castello, N.A.; Lee, G.; Green, K.N.; Durkin, A.J.; Choi, B.; LaFerla, F.; Tromberg, B.J. In vivo optical signatures of neuronal death in a mouse model of Alzheimer’s disease. Lasers Surg. Med.
**2014**, 46, 27–33. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Singh-Moon, R.P.; Roblyer, D.M.; Bigio, I.J.; Joshi, S. Spatial mapping of drug delivery to brain tissue using hyperspectral spatial frequency-domain imaging. J. Biomed. Opt.
**2014**, 19, 096003. [Google Scholar] [CrossRef] - Wilson, R.H.; Crouzet, C.; Torabzadeh, M.; Bazrafkan, A.; Farahabadi, M.H.; Jamasian, B.; Donga, D.; Alcocer, J.; Zaher, S.M.; Choi, B.; et al. High-speed spatial frequency domain imaging of rat cortex detects dynamic optical and physiological properties following cardiac arrest and resuscitation. Neurophotonics
**2017**, 4, 045008. [Google Scholar] [CrossRef] [PubMed] - Sibai, M.; Fisher, C.; Veilleux, I.; Elliott, J.T.; Leblond, F.; Roberts, D.W.; Wilson, B.C. Preclinical evaluation of spatial frequency domain-enabled wide-field quantitative imaging for enhanced glioma resection. J. Biomed. Opt.
**2017**, 22, 76007. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Abookasis, D.; Meitav, O. Assessing mouse brain tissue refractive index in the NIR spectral range utilizing spatial frequency domain imaging technique combined with processing algorithms. Clin. Transl. Neurophotonics
**2019**, 10864. [Google Scholar] [CrossRef] - Li, T.W.; He, X.M.; Fu, X.P.; Rao, X.Q. LabVIEW Base Software for Spatial Frequency Domain Imaging System. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA, 16 July 2017. [Google Scholar]
- Hu, D.; Fu, X.P.; He, X.M.; Ying, Y.B. Noncontact and Wide-Field Characterization of the Absorption and Scattering Properties of Apple Fruit Using Spatial-Frequency Domain Imaging. Sci. Rep.
**2016**, 6, 37920–37930. [Google Scholar] [CrossRef] [Green Version] - He, X.M.; Fu, X.P.; Rao, X.Q.; Fu, F. Nondestructive determination of optical properties of a pear using spatial frequency domain imaging combined with phase-measuring profilometry. Appl. Opt.
**2017**, 56, 8207–8215. [Google Scholar] [CrossRef] - He, X.M.; Fu, X.P.; Li, T.W.; Rao, X.Q. Spatial frequency domain imaging for detecting bruises of pears. J. Food Meas. Charact.
**2018**, 12, 1266–1273. [Google Scholar] [CrossRef] - Hu, D.; Lu, R.F.; Ying, Y.B. Spatial-frequency domain imaging coupled with frequency optimization for estimating optical properties of two-layered food and agricultural products. J. Food. Eng.
**2020**, 277, 109909–109913. [Google Scholar] [CrossRef] - Hu, D.; Lu, R.F.; Ying, Y.B. Optimization of Spatial Frequency Domain Imaging Technique for Estimating Optical Properties of Food and Biological Materials. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA, 16 July 2017. [Google Scholar]
- Hu, D.; Lu, R.F.; Ying, Y.B. A two-step parameter optimization algorithm for improving estimation of optical properties using spatial frequency domain imaging. J. Quant. Spectrosc. Radiat. Transf.
**2018**, 207, 32–40. [Google Scholar] [CrossRef] - Lu, Y.Z.; Lu, R.F. Development of a Multispectral Structured Illumination Reflectance Imaging (SIRI) System and Its Application to Bruise Detection of Apples. Trans. ASABE
**2017**, 60, 1379–1389. [Google Scholar] [CrossRef] - Lu, Y.Z.; Lu, R.F. Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging. Biosyst. Eng.
**2017**, 160, 30–41. [Google Scholar] [CrossRef] - Lu, Y.Z.; Huang, Y.P.; Lu, R.F. Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review. Appl. Sci.
**2017**, 7, 189. [Google Scholar] [CrossRef] - Lu, Y.Z.; Lu, R.F. Using composite sinusoidal patterns in structured-illumination reflectance imaging (SIRI) for enhanced detection of apple bruise. J. Food Eng.
**2017**, 199, 54–64. [Google Scholar] [CrossRef] - Nguyen, T.T.A.; Le, H.N.D.; Vo, M.; Wang, Z.Y.; Luu, L.; Ramella-Roman, J.C. Three-dimensional phantoms for curvature correction in spatial frequency domain imaging. Biomed. Opt. Express
**2012**, 3, 1200–1214. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Nothelfer, S.; Liemert, A.; Reitzle, D.; Bergmann, F.; Kienle, A. A New method for correction of surface scattering in spatial frequency domain imaging for an accurate determination of volume scattering. Opt. InfoBase Conf. Pap.
**2017**, 10412. [Google Scholar] [CrossRef] - Hachadorian, R.; Bruza, P.; Jermyn, M.; Mazhar, A.; Cuccia, D.; Jarvis, L.; Gladstone, D.; Pogue, B. Correcting Cherenkov light attenuation in tissue using spatial frequency domain imaging for quantitative surface dosimetry during whole breast radiation therapy. J. Biomed. Opt.
**2019**, 24, 1–10. [Google Scholar] [CrossRef] - Fang, Q.Q.; Kaeli, D.R. Accelerating mesh-based Monte Carlo method on modern CPU architectures. Biomed. Opt. Express
**2012**, 3, 3223–3230. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Cai, F.H.; Lu, W. A Dynamic Accuracy Estimation for GPU-based Monte Carlo Simulation in Tissue Optics. Curr. Opt. Photonics
**2017**, 1, 551–555. [Google Scholar] [CrossRef] - Ren, N.; Liang, J.; Qu, X.C.; Li, J.F.; Lu, B.J.; Tian, J. GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues. Opt. Express
**2010**, 18, 6811–6823. [Google Scholar] [CrossRef] [PubMed] - Fang, Q.Q. Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates. Biomed. Opt. Express
**2010**, 1, 165–175. [Google Scholar] [CrossRef] [PubMed] - Alerstam, E.; Svensson, T.; Andersson-Engels, S. Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration. J. Biomed. Opt.
**2008**, 13, 060504. [Google Scholar] [CrossRef] [Green Version] - Cai, F.H. Using graphics processing units to accelerate perturbation Monte Carlo simulation in a turbid medium. J. Biomed. Opt.
**2012**, 17, 040502. [Google Scholar] [CrossRef] [Green Version] - Ismail, A.; Idris, M.; Ayub, M.; Por, L. Vision-Based Apple Classification for Smart Manufacturing. Sensors
**2018**, 18, 4353. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Alerstam, E.; Andersson-Engels, S.; Svensson, T. White Monte Carlo for time-resolved photon migration. J. Biomed. Opt.
**2008**, 13, 10. [Google Scholar] [CrossRef] [PubMed] - Zhao, Y.Y.; Deng, Y.; Yue, S.H.; Wang, M.; Song, B.; Fan, Y.B. Direct mapping from diffuse reflectance to chromophore concentrations in multi-fx spatial frequency domain imaging (SFDI) with a deep residual network (DRN). Biomed. Opt. Express
**2021**, 12, 433–443. [Google Scholar] [CrossRef] - Li, Y.; Guo, M.R.; Qian, X.F.; Lin, W.H.; Zheng, Y.; Yu, K.Y.; Zeng, B.X.; Xu, Z.; Zheng, C.; Xu, M. Single snapshot spatial frequency domain imaging for risk stratification of diabetes and diabetic foot. Biomed. Opt. Express
**2020**, 11, 4471–4483. [Google Scholar] [CrossRef] [PubMed] - Nadeau, K.P.; Khoury, P.; Mazhar, A.; Cuccia, D.; Durkin, A.J. Component and system evaluation for the development of a handheld point-of-care spatial frequency domain imaging (SFDI) device. Prog. Biomed. Opt. Imaging Proc. SPIE
**2013**, 8573. [Google Scholar] [CrossRef] [Green Version] - Valdes, P.A.; Angelo, J.P.; Choi, H.S.; Gioux, S. qF-SSOP: Real-time optical property corrected fluorescence imaging. Biomed. Opt. Express
**2017**, 8, 3597–3605. [Google Scholar] [CrossRef] [Green Version] - Wang, Z.D.; Hu, M.H.; Zhai, G.T. Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data. Sensors
**2018**, 18, 1126. [Google Scholar] [CrossRef] [Green Version] - Liu, R.X.; Yao, M.H.; Wang, X.B. Defects detection based on deep learning and transfer learning. Metall. Min. Ind.
**2015**, 7, 312–321. [Google Scholar] - Nayeli, V.R.; Juan, G.S.; Jorge, C.P.; Juan, J.C. Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst. Eng.
**2014**, 122, 91–98. [Google Scholar] [CrossRef] - Baranowski, P.; Mazurek, W.; Pastuszka, W. Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data. Postharvest Biol. Technol.
**2013**, 86, 249–258. [Google Scholar] [CrossRef]

**Figure 1.**(

**a**) Schematics of a typical SFDI system. QTH and LCTF denote quartz tungsten halogen and liquid crystal tunable filter, respectively. (

**b**) Schematic of an endoscopic imaging system: a laser source is expanded and collimated by lenses L

_{1}and L

_{2}, passes through a mask of a sinusoid printed onto a transparency and is collimated by L

_{3}into the projection channel of the endoscope. The polarizers P

_{1}and P

_{2}ensure specular light removal. The collection channel of the endoscope sends light through L

_{4}where it is imaged onto a CCD camera (adapted from Ref. [39]).

**Figure 2.**Flow chart of data processing for estimating optical properties of biological tissues by using the spatial-frequency domain imaging technique (adapted from Ref. [20]).

**Figure 3.**Typical results for burn assessment of porcine dorsal skin in three levels (i.e., superficial partial, deep partial, and full). The top row is for color digital images, and the bottom row is for maps of the reduced scattering coefficients (adapted from Ref. [62]).

**Figure 4.**(

**a**–

**c**) are absorption maps for three patients at 590 nm, and the red arrow highlights the visible lesion for P3; (

**d**) is histogram of the absorption coefficient for the three patients at 590 nm; P1, P2 (without actinic keratosis), and P3 (with actinic keratosis) in (

**e**) are three patients expressing various levels of photodamage, corresponding to (

**a**–

**c**), respectively (adapted from Ref. [68]).

**Figure 5.**Representative spectral parameter maps for tissue subtypes. Spectral parameter maps correspond to the pathology subtypes: normal (including fibrocystic disease) (red outline), fibroadenoma (blue outline), DCIS, invasive cancer and partially treated invasive cancer after neoadjuvant chemotherapy (all black outline), and fat (yellow outline or label). Row 1 is the tissue photograph of the cut face of one slice of the specimen with the lesion; row 2 is the corresponding histology; row 3 is the scattering-amplitude maps; row 4 is the scattering slope maps; row 5 is the hemoglobin concentration maps; row 6 is the percentage oxygenated hemoglobin maps; and row 7 is the percentage water maps (adapted from Ref. [75]).

**Figure 6.**A series of 2-D false-color spatial maps of the refractive index (RI) at different wavelengths for two extreme temperatures of 28 °C and 43 °C (adapted from Ref. [89]).

Classification | Measuring Method | Light Transfer Model | Optical Property | Ref. |
---|---|---|---|---|

Direct method | Collimated transmittance | Beer–Lambert Law | ${\mu}_{a}$, ${\mu}_{s}$ | [24] |

Indirect method | Integrating sphere | Adding-doubling | ${\mu}_{a}$, ${\mu}_{s}\prime $ | [25] |

Time-domain | Diffusion approximation equation, Monte Carlo or analytical solutions of radiative transfer equation | ${\mu}_{a}$, ${\mu}_{s}\prime $ | [26] | |

Frequency-domain | ${\mu}_{a}$, ${\mu}_{s}\prime $ | [27] | ||

Spatially resolved | ${\mu}_{a}$, ${\mu}_{s}\prime $ | [28,29] | ||

Spatial-frequency domain imaging | ${\mu}_{a}$, ${\mu}_{s}\prime $ | [5,20,30] |

Object | Optical Property | Indices | Frequency/mm^{−1} | Wavelength/nm | Ref. |
---|---|---|---|---|---|

Rat burn in vivo model | ${\mu}_{s}\prime $ | HbO_{2}, Hb, HbT, StO_{2} | 0, 0.10 | 650–970 nm with step length of 20 nm | [64] |

${\mu}_{a}$, ${\mu}_{s}\prime $ | StO_{2}, Hb | 0.20 | sixteen wavelengths in 500–700 nm | [58] | |

${\mu}_{s}\prime $ | HbO_{2}, Hb, H_{2}O, StO_{2} | 0.20 | seventeen equally spaced wavelengths in 650–970 nm | [19] | |

Pig burns in vivo model | ${\mu}_{a}$, ${\mu}_{s}\prime $ | StO_{2} | 0.20 | 658, 730, 850 | [62] |

${\mu}_{a}$, | StO_{2} | 0.20 | 658, 730, 850 | [65] | |

${\mu}_{s}\prime $ | - | 0, 0.05. 0.10, 0.15, 0.20 | nine wavelengths in 470–970 nm | [59] | |

${\mu}_{s}\prime $ | - | 0, 0.05. 0.10, 0.15, 0.20 | eight wavelengths in 471–850 nm | [60] | |

calibrated reflectance | - | 0, 0.05, 0.10, 0.20 | 471, 526, 591, 621, 659, 731, 851 | [61] | |

Heat burns skin | ${\mu}_{a}$, ${\mu}_{s}\prime $ | - | Eleven-frequencies in 0–0.44 | 490, 590, 660, 780 | [63] |

Object | Wavelength/nm | Optical Property of Normal Tissue/mm^{−1} | Optical Property of Tumor Tissue/mm^{−1} | Ref. | ||
---|---|---|---|---|---|---|

${\mu}_{a}$ | ${\mu}_{s}\prime $ | ${\mu}_{a}$ | ${\mu}_{s}\prime $ | |||

Breast tissue | 658 | - | - | - | 0.910 | [77] |

750 | - | - | - | 0.750 | [78] | |

Mouse tumor | 530 | 0.025 | 1.850 | 0.032 | 0.950 | [81] |

659 | - | - | 0.024 | 2.054 | [82] | |

Non-melanoma skin cancer | 630 | 0.021 ± 0.002 | 1.497 ± 0.097 | 0.027 ± 0.003 | 1.177 ± 0.120 | [16] |

630 | 0.025 | 1.670 | 0.059 | 1.070 | [71] | |

Human ovarian tissue | 730 | 0.015 | 3.370 | 0.049 | 1.050 | [72] |

Cervical tissue | 623 | 0.018 ± 0.001 | 0.900 ± 0.062 | 0.040 ± 0.004 | 1.412 ± 0.245 | [73] |

Bladder tumor tissue | 623 | 0.018 | 0.550 | 0.045 | 1.050 | [74] |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sun, Z.; Hu, D.; Wang, Z.; Xie, L.; Ying, Y.
Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues. *Photonics* **2021**, *8*, 162.
https://doi.org/10.3390/photonics8050162

**AMA Style**

Sun Z, Hu D, Wang Z, Xie L, Ying Y.
Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues. *Photonics*. 2021; 8(5):162.
https://doi.org/10.3390/photonics8050162

**Chicago/Turabian Style**

Sun, Zhizhong, Dong Hu, Zhong Wang, Lijuan Xie, and Yibin Ying.
2021. "Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues" *Photonics* 8, no. 5: 162.
https://doi.org/10.3390/photonics8050162