WO2024028877A1

WO2024028877A1 - Optical system and method for monitoring biological tissue condition

Info

Publication number: WO2024028877A1
Application number: PCT/IL2023/050807
Authority: WO
Inventors: Avihu Meir Gamliel; Genady Kostenich; Eyal Margalit; Noam Allon; Michael Aronov; Yuval SAVATEIV
Original assignee: Spring Vision Ltd.
Priority date: 2022-08-04
Filing date: 2023-08-03
Publication date: 2024-02-08
Also published as: IL295373A

Abstract

An imaging system and method are presented for inspection of a biological tissue to determine at least one predetermined tissue property. The system comprises an illumination unit producing a plurality of wavelengths of at least visual spectral range, an optical detector generating image data indicative of light response of the tissue to the illumination, an illumination controller, and a control system which analyzes the image data by determining a relation between responses of the tissue to different illumination wavelengths. The illumination controller is adapted to selectively operate the illumination source to generate a number M (M^2) of wavelengths forming at least one group of two or more illuminating wavelengths such that the relation between the light responses of the tissue to the illuminating wavelengths of the group is indicative of the predetermined tissue property(ies) at a corresponding depth in the tissue. Each group of wavelengths comprises first and second wavelengths satisfying the following conditions: (1) the wavelengths of the group are spectrally close to one another therefore being characterized by substantially the same penetration depth in the tissue, and (2) the wavelengths of the group comprise at least one first wavelength and at least one second wavelength whose interactions with the tissue are characterized by, respectively, first and second substantially opposite absorption properties, i.e. each first wavelength is substantially affected by said tissue property while each second wavelength is weakly affected by said tissue property.

Description

OPTICAL SYSTEM AND METHOD

FOR MONITORING BIOLOGICAL TISSUE CONDITION

TECHNOLOGICAL FIELD AND BACKGROUND

The invention relates to a system and method for optical investigation of biological tissues to detect abnormal tissue conditions and/or pathologies.

Biological tissues have complex optical properties due to their complex biological structure. Light propagation in biological tissues (e.g., scattering, absorption, penetration) depends on the bio-chemical/bio-physical tissue properties. Biological tissue inspection typically utilizes analysis of detected/measured reflectance and/or transmission of radiation from / through the biological tissue to obtain quantitative tissue absorption data. Data analysis has to take into account complicated photon transport theories (e.g. Monte Carlo modeling, Kubelka-Munk formula).

For example, known in the art techniques to monitor hypoxia and ischemia in biological tissues rely on illuminating the tissue with white light, separating the collected light by wavelengths, and resolving for the concentrations of major known absorbers (deoxyhemoglobin, oxyhemoglobin, melanin, lipids etc.) via matrix-based methods. This requires a priori knowledge of the optical properties of particular tissue content of the mentioned substances in vivo at any given wavelength, such data is practically not available.

US Patent No. 7,860,554 describes a non-invasive method of characterizing burn injuries using near infrared spectroscopy. In this method, a beam of light is emitted into the burnt tissue portion at two or more different tissue depths. The spectra are then compared using multivariate analysis to determine diagnostic regions of the spectra. JP patent publication No. 2012107942 discloses a technique aimed at obtaining a clear image of a specimen, where the image is in focus with both observation surfaces, when simultaneously imaging the two observation surfaces located at different depths. The imaging device operates to receive two observation lights of different wavelengths generated from these two observation surfaces and includes an optical path length adjusting means for adjusting the optical path length from at least one observation surface to the light-receiving surface according to a distance z in the depth direction between both observation surfaces.

WO 2020/044337, assigned to the assignee of the present application, describes data analysis for use in monitoring oxygenation in biological tissues. According to this technique, data indicative of light response from an illuminated region of the biological tissue is received and analyzed. This data includes the light response of two separate wavelengths in two selected wavelength ranges. The data processing includes comparing data indicative of the selected wavelength ranges to determine an oxygenated/deoxygenated status of the biological tissue. The two wavelength ranges comprise a first wavelength range in which the absorbance of the deoxyhemoglobin within the tissue is higher than the oxyhemoglobin, and a second wavelength range in which the absorbance of the oxyhemoglobin within the tissue is higher than the deoxyhemoglobin or vice versa. The two wavelengths in said two wavelength ranges comprise first and second wavelengths satisfying a predetermined condition of a ratio between the absorbance of the deoxyhemoglobin and the oxyhemoglobin for each of the first and second identified wavelengths.

GENERAL DESCRIPTION

There is a need in the art for a novel approach of optical inspection of biological tissues to determine one or more desired tissue properties. This can be crucial to optical diagnosis with regard to various tissue abnormalities / pathologies, e.g., in ophthalmology. Moreover, there is a need for such technique, as well as for enabling its implementation, e.g., in endoscopy procedures, as well as in screening and surgery procedures, and enabling to use data analysis results for photodynamic and photothermal therapy of various diseases, including cancer. According to the technique of the present invention, a tissue can be analyzed using wavelength penetration properties of illumination spectra, relevant for different materials and depths in the tissue. Specifically, a certain tissue property can be determined by detecting the tissue response to illumination of two or more different wavelengths (or wavelength ranges) characterized by their penetration properties at various tissue depths, satisfying the following conditions: these two or more wavelengths are selected in such a way that they include at least two close enough wavelengths which are characterized by substantially the same penetration depth in the tissue, and at least one of these at least two wavelengths is sensitive to a specific tissue property (i.e. the tissue property affects the tissue response to said wavelength), while at least one other of these wavelengths is substantially inert relative to that tissue property (the tissue property does not substantially affect the tissue response to the illumination by this wavelength).

It should be noted that the tissue response to illumination which is to be detected and which is informative of a tissue property may include reflection / scattering of illuminating wavelengths (selected as defined above) from the tissue and/or secondary emission excited by the illuminating wavelength (autofluorescence). In the latter case, the illumination wavelengths are selected to satisfy the above-described conditions (i.e., the exciting property of one of them is affected by the tissue property of interest and that of the other is not), and are also such that the secondary emission spectra excited by these wavelengths is substantially inert relative to that tissue property.

Thus, the technique of the present invention enables to derive the tissue property from the use of different illumination wavelengths in such a way that, for each layer in the tissue, detected tissue response to said illuminating wavelengths includes at least one first wavelength which is affected by the desired property of the tissue (i.e. holds the signal of the desired property) and at least one second, spectrally close enough wavelength which is not affected by said property (the desired property is inert to this wavelength).

The technique of the invention enables extracting information about the tissue property from a relation between the detected light responses to the illumination. This provides higher signal to noise performance. Such relation may be (but not limited to) a ratio between the detected light responses. Alternatively, or additionally, more sophisticated techniques can be used, such as image filtering (image processing method) before determining the ratio, or Deep Learning techniques that can be used in order to extract the desired properties more clearly. It should also be noted that machine learning is not limited to image data only.

It should be understood that generally, a group of wavelengths includes two or more spectrally close wavelengths, including wavelengths having opposite absorption properties with respect to a tissue property of interest. This means that the wavelengths of the group are such that each one or more wavelengths within the group constitute/s a “signal” (i.e., its interaction with the tissue is affected by the tissue property of interest, e.g., presence of a specific material/substance in the tissue), and each one or more of the other wavelengths within the group constitutes a normalizer/reference wavelength whose interaction with the tissue is substantially not affected by the tissue property of interest. In the description below, such group of wavelengths is referred to as a pair of wavelengths, but it should be understood that the invention is not limited to this specific example, and the term “pair” should be interpreted broadly covering also a pair of groups of more than two wavelengths.

The invention can, for example, be used for blood vessel mapping (angiography). In this case, for example, total Hb absorbance can be considered (using different wavelength pairs in order to analyze it at different layers/depths). The properties to be determined may include bio-chemical properties of blood vessel walls and/or auto- fluorescent properties of the blood vessels, etc.

For example, illuminating wavelengths of 400nm and 430nm can be used as a pair, where the 430nm wavelength is almost totally absorbed by Hb, while 400nm wavelength is minimally absorbed locally. Hence, angiography of the superficial tissue layer can be observed without deeper penetration and without having deeper interference effects.

The tissue inspection scheme may utilize excitation / induction of autofluorescence at different depths/layers in the tissue, and analysis of the optical response from each layer separately, in the same manner, i.e., using one (or more) first wavelengths that excite the required tissue property and another different one or more wavelength/s that is inert to said property. To this end, both acquired images (image data pieces) are used in order to extract better signal to noise of the specific property. The technique of the invention can also, for example, be used to determine the condition of optic nerve hypoplasia (ONH), utilizing different penetration properties of various wavelengths. This is because myelin (which is of high concentration in the optic nerve head) differently responds to different wavelengths.

Although it is known that oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) have different light absorption properties, assessment of the oxygenation level in biological tissues by optical measurements is not a straightforward step. As mentioned above, the known techniques of the type specified, suffer from a need for a priori knowledge regarding the optical properties of particular tissue contents (e.g., optical properties of muscle tissue cannot be compared to those of skin tissue, such as mouse skin flap vs muscle), as well as require complicated data processing. Also, known in the art spectroscopic methods based on the distinct differences in the absorption spectra between oxy- and deoxy -hemoglobin in the visible and infrared (IR) spectral regions, provide the oxygenation status of the tissue of interest, averaged over a relatively large volume of tissue. Assessment of in vivo tissue oxygenation with higher longitudinal (depth) spatial specificity, which is required for early diagnosis and monitoring of many diseases, including cancer of epithelial tissues, local inflammatory and infectious processes, retinopathy, choroidal eye disorders and stroke, is thus compromised.

It should be noted that differences in the absorption spectra between oxy- and deoxy-hemoglobin is mainly used to distinguish between oxygenated vs. deoxygenated blood in veins/arteries. However, inside the tissue, oxygen is not bound to Hb but rather is diffused into the tissue itself, depending on the tissue's metabolic properties / needs. Hence, measuring tissue oxygenation level is not a straightforward operation and cannot be derived directly from the above-described conventional approach.

For example, the state-of-the-art pulse oximetry indeed utilizes two wavelengths with co-similar penetration depth, e.g., wavelengths of 660nm and 850nm have close penetration depths in the retina. Both of these wavelengths can penetrate to the choroidal layer/tissue, while within this tissue, these wavelengths represent different depths, i.e., they would provide characteristic interactions with different layers in the tissue. However, pulse oximeter must use the pulse itself in order to calculate oxygenation. On the contrary, for the technique of the present invention there is no need to use the heartbeat rate, and in most cases the invention utilizes measurement of the relative oxygenation level at realtime.

Considering for example the human retina, it is only 200-300 pm thick and consists of many well defined physiological longitudinal (depth) layers, and has two independent vascular supplies (retinal and choroidal blood supply). Because the choroidal vascular supply provides a ten-fold higher blood flow than the retinal supply (choroid provides -70% of the blood to the retina while the superficial retinal blood system provides the rest), methods without longitudinal or depth-resolved specificity, provide oxygenation levels that are hard up to impossible to interpret.

It should be noted that although in the description below the technique of the invention is exemplified / explained in relation to monitoring of the retinal/ophthalmic condition, the principles of the invention are not limited to this specific application.

Thus, the technique of the present invention is based on the inventors' understanding that mapping of tissue properties (e.g., oxygenation) at different depths in the tissue can provide highly useful information for medical diagnostics. Further, for various medical applications, tissue inspection with visible light is desired, as this makes medical tools much simpler and more useful for non-invasive or minimally invasive medical procedures.

In particular, the present invention is based on the understanding that most critical parameters affecting depth penetration of light in soft tissues include wavelength dependent parameters, like tissue thickness, absorbance and reflectance of tissue properties (with or without mucous vs skin or bone or retina, etc.), transparency, pigmentation and autofluorescence.

Reference is made to Figs 1A to 1C, schematically illustrating wavelengthdependence of light interaction with a tissue, i.e., depth penetration of different wavelengths into the tissue and their scattering distribution from different tissues depths, affecting response of the tissue to said wavelengths (reflection / absorption properties).

In this regard, it should be noted that it is well known that any material responds to illumination by reflection, absorption (secondary emission) and transmission of the illuminating radiation. For purposes of the present invention, reflection properties of biological tissues are utilized, while the detected reflection itself is not originated at the same depth even for the same illuminating wavelength.

In Fig. 1A, illumination of wavelength i is incident on the tissue and while being highly absorbed by the tissue, providing some reflection from said tissue level at depth di, i.e., this wavelength has a short penetration depth in said tissue. Considering penetration properties of wavelengths of the blue spectrum, most of the detectable reflection is from the superficial tissue layer. Depending on the tissue type, it can represent shorter wavelengths, as well as longer wavelengths with deeper penetration (e.g., if the tissue is a bone). As shown, almost all of the reflection of this blue wavelength (about 80%) is returned from the tissue at depth di, while reflections from deeper layers at depths d2, da and ch form, respectively, 10%, 5% and 3% of the entire reflection of said wavelength from the tissue.

It is clear that short wavelengths, such as wavelengths in the blue part of the spectrum, have low penetration capabilities relative to longer wavelengths (e.g., of Green and Red spectra). Hence, in the schematic illustration of Fig. 1A, where a blue spectrum of illumination is exemplified, most of the light is reflected from the most superficial tissue depth. Figs. IB and 1C demonstrate the same principles for intermediate and longer wavelengths, respectively. As shown in Fig. IB, most of the reflectance comes from the intermediate tissue depth while the superficial layer / interface is relatively transparent (and this is the reason why only 10% is detected by the sensor from this tissue). It should, however, be understood that this is a specific and not-limiting example since different tissues are reacting in a similar manner at different wavelengths.

The above observations are supported, for example, by theoretical models described in the following article: Desjardins M, Sylvestre JP et al. Preliminary investigation of multispectral retinal tissue oximetry mapping using a hyperspectral retinal camera. Exp Eye Res. 2016;146:330-340. doi:10.1016/j.exer.2016.04.001. In this article, a simple simulation was performed based on the Beer-Lambert law of absorption, assuming different Hb concentrations in tissue capillaries, arterioles, and venules. Figs. 2A-2C (being Fig. 3 in said article) illustrate estimated maximal penetration depth as a function of wavelength in retinal tissue (Fig. 2A), arterioles (Fig. 2B) and venules (Fig. 2C). The computation is based on the Beer-Lambert law, assuming physiological values for oxygen saturation, hemoglobin concentration and melanin absorption. The present invention takes advantage of such wavelength- selective depth penetration of light into tissues for inspecting the tissues and determining its depth- resolved oxygenation map, to identify / evaluate tissue pathology, as well as to obtain structural features of the tissue with depth resolution in the order of tens and/or hundreds of microns. For example, in the human retina, two blue-spectrum wavelengths (e.g., 430nm and 460nm) have a penetration difference of about 15-20 microns, two greenspectrum wavelengths (e.g., 500nm an 535nm) have penetration difference of about 20- 25 microns. Thus, the present invention utilizes illumination of the tissue by visual and possibly also near infrared (NIR) light including selected pairs of spectrally close wavelengths.

Thus, according to a broad aspect of the present invention, there is provided an imaging system for inspection of a biological tissue to determine at least one predetermined property of the tissue, the system comprising: an illumination unit configured and operable to produce a plurality of illumination wavelengths of at least visual spectral range, a detection unit comprising at least one optical detector and being configured and operable to detect light responses of the tissue to the illumination wavelengths and generate image data indicative of the detected light responses, an illumination controller, and a control system configured and operable to analyze the image data by determining a relation between light responses of the tissue to different illumination wavelengths, wherein said illumination controller is configured and operable to selectively operate the illumination source to generate said plurality of the illumination wavelengths comprising a number M (M>2) of wavelengths defining at least one group of two or more illumination wavelengths such that the relation between the light responses of the tissue to the illumination wavelengths of the group is indicative of said at least one predetermined tissue property at a corresponding depth in the tissue, wherein each of said at least one group of the illumination wavelengths comprises first and second wavelengths satisfying the following conditions: (1) the first and second wavelengths of the group of the illumination wavelengths are spectrally close to one another being thereby characterized by substantially same penetration depth in the tissue, and

(2) the first and second wavelengths of the group of the illumination wavelengths comprise at least one first wavelength and at least one second wavelength whose interactions with the tissue are characterized by, respectively, first and second substantially opposite absorption properties, such that each of the at least one first wavelength is substantially affected by said tissue property while each of the at least one second wavelength is weakly affected by said tissue property.

With regard to the condition (1) above, it should be understood that the required degree of spectral closure depends on the tissue and the penetration properties of respective wavelengths therethrough, as well as on the tissue property being monitored. For example, in order to detect albumin in a tissue, the known spectral profile of excitation can be used to select from the spectrally close wavelengths (both of which penetrate up to the same layer/depth) while one wavelength having high excitation and the other having relatively low excitation.

The tissue property is associated with presence of a certain substance (material) in the tissue. Considering for example determination of oxygenation level, the condition (2), i.e., opposite absorption properties of the first wavelength(s) and the second wavelength(s) of the group can be characterized by at least one of the following: (i) the first wavelength and the second wavelength have opposite absorption properties with respect to oxyhemoglobin and deoxyhemoglobin; and (ii) one of the first and second wavelengths is relatively highly absorbable by either deoxyhemoglobin or oxyhemoglobin, and the other of the first and second wavelengths is substantially equally absorbable by both oxyhemoglobin and deoxyhemoglobin.

Thus, since the first and second wavelengths of the pair (generally of the group of one or more first wavelengths and one or more second wavelengths) are spectrally close to each other, they have substantially the same penetration depth for the inspected tissue. On the other hand, they either have opposite absorption properties with respect to oxyhemoglobin and deoxyhemoglobin (i.e. one of the first and second wavelengths is relatively highly absorbable by deoxyhemoglobin as compared to oxyhemoglobin, and the other of the first and second wavelengths is relatively highly absorbable by oxyhemoglobin than deoxyhemoglobin), or one of the first and second wavelengths is highly absorbable by either deoxyhemoglobin or oxyhemoglobin while the other is similarly absorbable by both oxyhemoglobin and deoxyhemoglobin.

Generally speaking, there are numerous examples of paired wavelengths in relation to the absorbance properties of oxyhemoglobin and deoxyhemoglobin, such as, but not limited to: 420nm and 460nm, 430nm and 460nm, and even better 420nm with 430-480nm; 480nm and 505nm, 480nm and 515nm, 480nm and 535nm, 660nm and 730nm, 660nm and 850nm, 590nm and 660nm (in these examples, light components of 660nm wavelength can be replaced by 645nm); 500nm and 540nm, 540nm and 570nm, 570nm and 590nm, 590nm and 645nm, 590nm and 660nm, 420nm and 435nm, 470nm and 500nm, and many other paired wavelengths.

The absorption properties of oxyhemoglobin and deoxyhemoglobin have spectrally close peaks (less than 50-70nm difference) and either contains orientation switching between the absorption peaks or one has relatively strong difference and the other one can be defined as an isosbestic point (i.e., no difference between the absorption graphs as shown in Fig. 2D).

It should be understood that, generally, illumination of the tissue by the first and second wavelengths of the pair / group may be performed simultaneously or sequentially. Also, in case multiple pairs / groups of wavelengths are used, this can be done with simultaneous or sequential illumination by one or more wavelengths. The optical detector may be properly provided with spectral filters whose position in the detection path may be controllably varied; or multiple detection units can be used, having different spectral properties; or a single detection unit having broad spectral properties can be used in a sequential illumination mode of different wavelengths.

Thus, using two or more pairs / groups of wavelengths satisfying the above conditions, provides for mapping the tissue property (e.g., oxygenation status/level) through the tissue.

The detector unit may include one or more of the following: a multispectral camera, a standard RGB color camera capable of generating a digital pixelated image data from each of the reflected back-scattered wavelengths separately. The image acquired at each wavelength of the pair / group can be analyzed using one or more of the following image analyses: pixelwise, area- wise, cluster- wise, obtaining tissue oxygenation data and deriving therefrom tissue pathology and/or structural information.

The technique of the present invention may be used for inspecting such biological tissues as retina, gastrointestinal tissue, cancer tissue in all bodily organs, pharyngeal or laryngeal tissue, and other soft tissues at different depths.

In some embodiments, the visualization of the tissue and its oxygenation map at different depths of the tissue may be related particularly to blood vessels.

According to another broad aspect of the invention, it provides method for inspecting a biological tissue to determine at least one predetermined tissue property, the method comprising: illuminating the tissue by a plurality of M (M^2) selected illumination wavelengths of at least visual spectral range, wherein said plurality of the selected illumination wavelengths defines at least one group of illumination wavelengths, the illumination wavelengths of each group comprise first and second wavelengths satisfy the following conditions:

(1) the first and second wavelengths of the group are spectrally close to one another being therefore characterized by substantially same penetration depth in the tissue, and

(2) the illumination wavelengths of the group comprise at least one first wavelength and at least one second wavelength whose interactions with the tissue are characterized by, respectively, first and second substantially opposite absorption properties, such that each of the at least one first wavelength is substantially affected by said tissue property while each of the at least one second wavelength is weakly affected by said tissue property; detecting a light response of the tissue to each of said plurality of the selected wavelengths, and generating corresponding image data, the image data being indicative of a relation between the light response of the tissue to the at least one first illumination wavelength and the light response of the tissue to the at least one second wavelength of the group of the illumination wavelengths, said relation being indicative of said at least one predetermined tissue property at a corresponding depth in the tissue. According to yet further broad aspect of the invention, it provides a control system for controlling inspection of a biological tissue being performed by an inspection system to determine at least one predetermined property tissue property of said biological tissue. The control system is a computerizes system configured and operable for data communication with an illumination source and a detection unit of said inspection system and comprises: an illumination controller configured and operable to selectively operate the illumination source to generate a plurality of illumination wavelengths of at least a visual spectral range comprising a number M (M>2) of the illumination wavelengths defining at least one group of two or more illumination wavelengths such that a relation between light responses of the tissue, detected by the detection unit, to the illumination wavelengths of the group is indicative of said at least one predetermined tissue property at a corresponding depth in the tissue, wherein each of said at least one group of the illumination wavelengths comprises first and second wavelengths satisfying the following conditions: (1) the first and second wavelengths of the group of the illumination wavelengths are spectrally close to one another being thereby characterized by substantially same penetration depth in the tissue, and (2) the first and second wavelengths of the group of the illumination wavelengths comprise at least one first wavelength and at least one second wavelength whose interactions with the tissue are characterized by, respectively, first and second substantially opposite absorption properties, such that each of the at least one first wavelength is substantially affected by said tissue property while each of the at least one second wavelength is weakly affected by said tissue property; a detection controller associated with the detection unit to control detection of the light responses and generation of corresponding image data; a synchronization circuitry configured and operable to synchronize generation of the illumination wavelengths and detection of the light responses of the tissue to the illumination wavelengths; a detection unit comprising at least one optical detector and being configured and operable to detect light responses of the tissue to the illumination wavelengths and generate image data indicative of the detected light responses; and a processor configured and operable to analyze the image data by determining a relation between the light responses of the tissue to different illumination wavelengths, and analyzing said relation and extracting data indicative of the at least one predetermined property of the tissue.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better understand the subject matter that is disclosed herein, and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting examples only, with reference to the accompanying drawings, in which:

Figs. 1A-1C schematically show the principles of wavelength-dependent interaction of light with a tissue, exemplified for relatively short wavelengths (Fig. 1A), intermediate wavelengths (Fig. IB) and longer wavelengths (Fig. 1C);

Figs. 2A-2C illustrate the prior art estimate for the penetration depth of various wavelengths in a retinal tissue (Fig. 2A), arterioles (Fig. 2B) and venules (Fig. 2C);

Fig. 2D illustrates the absorption spectra of oxyhemoglobin and deoxyhemoglobin ;

Fig. 3 is a block diagram exemplifying an imaging system of the present invention for inspection of a biological tissue;

Fig. 4 is a flow diagram exemplifying the technique of the invention;

Fig. 5A-5C exemplify images of mouse skin flap & muscle obtained using the technique of the invention, wherein Figs. 5A and 5B show images obtained with two different blue wavelengths, respectively, e.g., 430nm and 480nm; and Fig. 5C shows the image obtained after mathematical manipulation of the imaged data of Figs. 5A-5B;

Fig. 6A-6C show images of mouse skin flap and muscle, obtained using the technique of the invention, wherein Figs. 6A and 6B show images obtained with two different red wavelengths, e.g., 660nm and 595nm; and Fig. 6C shows the obtained image after mathematical manipulation of the imaged data of Figs. 6A-6B.

Fig. 7A-7D show images of a human retina with CRAO (Central Retinal Artery Occlusion), obtained using the technique of the invention with blue and red wavelengths, e.g. 436nm and 460nm; wherein Figs. 7A shows the retinal image obtained using a spectrally close pair of blue wavelengths (e.g. 430nm and 460-480nm) and mathematically combining the two images; Figs. 7B shows the retinal image obtained using a spectrally close pair of red wavelengths and mathematically combining both images; Figs. 7C and 7D show the retinal image obtained using two different, spectrally close blue wavelengths, used to produce the image data of Fig. 7A;

Figs. 8A-8E show images of an eye with a macroaneurysm obtained using the technique of the invention; wherein Figs. 8A, 8B and 8C show images of the retina's deep layer, intermediate layer and superficial layer, respectively, obtained after a mathematical combination of respective pairs of images; and Figs. 8D and 8E show images of the same retina obtained with two different red wavelengths; and

Figs. 9A-9D illustrate one more example of the invention demonstrating how the DNN-based data analysis can be applied to image data indicative of retina response to the paired wavelengths of blue and green spectral ranges.

DETAILED DESCRIPTION OF EMBODIMENTS

The present invention provides a novel tissue inspection technique. The principles underlying the invention are based on wavelength-dependent interactions of light with a tissue, as described above with reference to Figs. 1A-1C. This wavelength-dependent interaction is associated with known depth penetration properties of different wavelengths, as shown in Figs. 2A-2C.

Reference is now made to Fig. 3, showing by way of a block diagram, an inspection system 200 of the present invention. The system 200 includes an imaging system 210 and a control system 240 being in data communication between them (via wires or wireless communication of any known suitable type).

The imaging system 210 includes an illumination unit 220 which is operable to produce a plurality of wavelengths of at least the visual spectral range (and possibly also NIR spectrum), an optical detector unit 230 which is adapted to detect light response of the tissue to the illumination and generate image data indicative thereof. Also provided in the inspection system 200 is an illumination controller 252, which may be part of the imaging system 210 (e.g. of the illumination unit) or of the control system 240, or software utilities of the illumination controller may be distributed between the control system and the imaging system. The control system 240 is configured and operable to analyze the image data and to determine the relations between responses of the tissue to different illumination wavelengths indicative of oxygenation level(s) at one or more depth(s) in the tissue.

The illumination unit 220 is configured to be controllably operated to selectively produce number M (M^2) of wavelengths including a number N (N^l) of wavelength group(s) (e.g., pair(s)) such that the relation between the light responses to the wavelengths of the group/pair is indicative of the property of interest (e.g., oxygenation level) at the corresponding depth in the tissue.

As described above, a group of wavelengths includes two or more spectrally close wavelengths including wavelengths having opposite absorption properties with respect to a tissue property of interest. This means that the wavelengths of the group are such that each of one or more first wavelengths of the group constitute/s a “signal” (i.e., its interaction with the tissue is affected by the tissue property of interest, e.g., presence of a specific material/substance in the tissue), and each of one or more second wavelengths of the group constitute/s a normalizer/reference wavelength whose interaction with the tissue is not substantially affected by the tissue property of interest. In the description below, for simplicity, such group of wavelengths is referred to as a pair of wavelengths.

More specifically, the plurality of wavelengths includes number N of pair(s) of wavelengths

(i = 1,...,N, generally N>1) selected such that each pair of wavelengths is spectrally close, thus having substantially similar penetration depths in the illuminated tissue. Also, the wavelengths of the pair are characterized by having opposite absorption or inert response properties with respect to the tissue property of interest.

For example, the wavelengths of the pair have opposite absorption properties with respect to oxyhemoglobin and deoxyhemoglobin (generally, absorption peak orientation switching and isosbestic points as described above), and/or one of them is relatively highly absorbable by either deoxyhemoglobin or oxyhemoglobin, while the other one of them is substantially equally absorbable by both oxyhemoglobin and deoxyhemoglobin. Thus, data analysis of the image data includes determination of the relation(s) between the light responses to the wavelengths of the pair(s), each such relation being indicative of the oxygenation level at the corresponding depth in the tissue.

The above selection of the illumination wavelengths provides depth-resolved inspection of a biological tissue 280 enabling determination of the oxygenation level at two or more specific depths, each determined by the penetration depth of the respective i-th pair of wavelengths

in the investigated tissue, thus enabling mapping of oxygenation through the tissue. Also, this technique enables visualization of tissue properties at depth layer(s) or band(s) in the tissue.

Any known in the art technique can be implemented to provide each pair of wavelengths. Considering illumination by multiple (two or more) pairs of wavelengths, separate two or more light sources may be used to produce respective two or more spectrally close pairs of wavelengths (e.g., LED, Laser, etc.), or by utilizing a broad light source with narrow-band filters.

Illumination of the tissue by first and second wavelengths of the pair may be performed simultaneously or sequentially. The illumination can be coherent light (e.g., generated by a laser diode) and the inspection/measurement mode may utilize scanning. Similarly, in case of multiple pairs of wavelengths, simultaneous or sequential illumination by one or more wavelengths can be used. Collection/detection of different spectral responses of the illuminated region can be done simultaneously or sequentially (e.g., using multispectral camera).

Light returned (scattered) from the tissue in response to the illumination is collected by the detection system 230. The detection system may be configured to capture the various wavelengths scattered from the tissue, e.g., by using a multispectral camera or a standard RGB color camera. The detection system may include at least one detector properly provided with spectral filter(s) 282 whose position in the detection path may be controllably varied. The detection system may include multiple detection units having different spectral properties, or a single detection unit having broad spectral properties and operable to sequentially detect different spectral responses of the tissue.

It should be noted that in some embodiments, a selectively operable spectral filter (e.g., moving filter) is provided at the input of the detection system 230. The provision of the filter is optional and the respective lock in the figure is thus shown in dashed lines.

The inspection system 200 may also include a detection controller 254. Such controller may be part of the control system 240 (as exemplified in the figure), or may be part of the imaging system 210 (e.g., the detection system), or software utilities of the detection controller may be distributed between the control system and the imaging system.

Synchronization of the illumination (e.g., pulsed illumination) produced by light source(s) of the illumination unit 220 and spectral signals detection by the detection unit 230 may be properly provided. To this end, the control system 240 includes a synchronization circuitry 255 connectable to and controlling the time pattern of operation of the illumination and detection controllers 250 and 252. The detection system 230 is capable of generating a digital pixelated image data comprising image data pieces corresponding to each of the two reflected / back- scattered spectrally close light beams (A ₂) satisfying either one of the above conditions with respect to their interaction with oxyhemoglobin and deoxyhemoglobin (i.e., interaction associated with either one of absorbance, reflectance and autofluorescence properties).

As described above, the control system 240 is configured for data communication (via wires or wireless communication of any known suitable type) with the imaging system 210. The control system 240 includes / is configured as a computer system including inter alia such main functional utilities as input/output utility 270, storage 260, and data processor 250. The data processor 250 is configured and operable to process and analyze the image data of each pair of wavelengths ( , ^l ₂) to determine a relation between light responses of the wavelengths of the pair.

Such a relation between the light responses of “signal” and “reference” wavelengths may be determined as a ratio between intensities pixel-wise, or as a more complicated functional (mathematical formulas), pixel-wise. Machine learning data analysis can be used providing output data indicative of the relation between the light responses, and/or other manipulations such as statistical manipulations, area-wise analysis, etc. can be used.

The processor 250 may be preprogrammed to obtain a final image containing information about the tissue property (e.g., oxygenation level) and possibly also the structure of the tissue 280 at the depth(s) being the penetration depth(s) of the pair(s) of wavelengths.

Fig. 4 exemplifies, by way of a flow diagram 300, the operation of the tissue inspection system of the invention. As shown in the figure, in step 310, two or more wavelengths (number M>2 of wavelengths) are selected to define number N (N>1) of pairs of wavelengths ,A₂) (i = 1, ■ .,N are selected, wherein each pair of the wavelengths is chosen such that

and X₂ ^l are spectrally close, thus providing substantial penetration to the same depth (denoted by z ) by both wavelengths in the investigated biological tissue, and also satisfying the above condition(s) with respect the absorbance by oxyhemoglobin and deoxyhemoglobin. More specifically, first and second spectrally close wavelengths of each i-th pair

are selected such that: (i) absorbance of oxyhemoglobin and deoxyhemoglobin at wavelengths

and ₂ ^l in the tissue is opposite (one is highly absorbable by oxyhemoglobin and the other is highly absorbable by oxyhemoglobin), or (ii) one wavelength \ of the pair is highly absorbable by only one of oxyhemoglobin and deoxyhemoglobin, while the other wavelength ₂ ^l of the pair is similarly absorbable by both deoxyhemoglobin and oxyhemoglobin.

The inventors have found that either one of the constraints (i) or (ii) provides for an improved depth- specific contrast of the resulting final image of the investigated tissue.

In step 320, 2 x N images

) (t = 1, . . , IV) of the tissue (one for each wavelength) are obtained, and in step 330 a mathematical combination of each pair of images

) is performed, wherein this combination may be pixelwise, area-wise, cluster-wise or any other known in the art region-of-interest (ROI) selection method.

The following are some examples of a suitable mathematical combination:

• Standard pixelwise ratio between the images;

• A ratio F(m(2 ))/F(/m(2.2)) - where F represents image preprocessing operation performed on the respective image prior to the pixelwise division (e.g. noise reduction, illumination enhancements, smoothing, etc.);

• In case, a moving window-based processing technique is used with a window size of e.g. 5x5 pixels, one or more of the following options can be considered: o

- where F represents statistical operations, such as Standard Deviation, mean, median, etc.; o Ratio of image sub sampling; o Trained Deep Neural Network (DNN) mixing net such as Generative Adversarial Network (GAN); o Segmentation DNN such as UNET and SegNet or similar well known in the art nets, etc.

• Mixing procedure, where coefficient t is any value between 0 and 1.

Mixing close wavelength images with each other, enables to see the pathology in an accurate way, exampling the nature of the pathology depth and spread-out. It should be understood that "mixing" is the image processing operation, which can be mathematically presented as

where 0<t<l and I( i) and I( a) are the detected intensities of the spectrally close wavelengths.

Considering an imaging technique, the following simple basic image processing manipulation can be applied on each image: (x + aX)^b, where X is the illumination wavelength, x is the intensity value added to or subtracted from all the image pixels, a is the intensity stretching factor, and b is the image manipulation factor (image processing).

Some pre-processing can be performed, such as image registration, image smoothing, including Gaussian filters and Local Laplacian Filter (LLF) - edge preserving and Partial Differential Equation (PDE) filtering methods, which can handle a high variation of noise, etc.

Then, in step 340, a final series of images Im , (i = 1, . . , N') is obtained where Im contains information about the desired material, e.g., oxygenation level/map, of respective i-th layer (autofluorescence of a fluorophore/specific tissue property) of the tissue which enables to identify tissue pathology condition.

The following is an example of how the technique of the invention can be used to determine autofluorescence-related property of the tissue. According to the earlier technique (described in Gao Y. et al., "Imaging and Spectral Characteristics of Amyloid Plaque Autofluorescence in Brain Slices from the APP/PS 1 Mouse Model of Alzheimer's Disease", Neurosci Bull. 2019), P-Amyloids were visualized by autofluorescence elicited by both UV and visible light in brain sections from APP/PS 1 transgenic mice. It was found that autofluorescence signal of P-Amyloids can be elicited across a wide range of wavelengths, however, the shorter the wavelength used for generating autofluorescence of P-Amyloids, the stronger the emitted signal.

The P-Amyloid peak excitation is at 360-370nm, where longer wavelengths result in weaker emitted signal. For example, illumination of at 385nm can be used (in order to avoid UVA illumination), representing a "signal" wavelength, and its paired illumination wavelength is 430nm, which is more inert as compared to the "signal" wavelength, representing a "normalizer/reference" wavelength. The penetration difference between both wavelengths of the pair is less than 20 microns. Each of the wavelengths is filtered in its parallel emission filter, and combination of both wavelengths (via relation (e.g. ration) ratio), provides higher signal to noise result than that provided by each of the wavelengths separately.

Reference is made to Figs. 5A-C and Figs. 6A-C showing mouse skin flap & muscle imaged using the techniques of the present invention. Fig. 5A and 5B were obtained using two different and close blue wavelengths (e.g., 430nm and 480nm as exemplified in Figs. 5A-5C; and 645nm and 730nm as exemplified in Figs. 6A-6C), which penetrate to the same depth in the tissue, being in the superficial layer of the tissue.

It is difficult to see any pathological features on any one of Figs. 5A and/or 5B because of the scattered illumination. However, when the two figures are mathematically combined to obtain image data of Fig. 5C, a significant improvement in the contrast and feature detail of the tissue is obtained. In this example, blood vessels that are barely recognized at any of the discrete wavelengths are visible (and have better contrast) as shown in Fig. 5C, i.e., angiography property.

Moreover, since these wavelengths are a pair of wavelengths characterized by the above-described interactions with oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb), the darker areas in Fig. 5C represent slightly ischemic areas relative to the brighter areas, and the muscle is more oxygenated relative to the flap, in general, and it can be seen that each one of them has a different oxygenation map. In this example, two types of tissues are considered, each one of them having its own oxygenation properties.

Fig. 6A and 6B were obtained using two different and close red wavelengths which penetrate to the same depth in the tissue, being in the deeper layer of the tissue. Fig. 6C shows image data resulting from mathematically combined images of Figs. 6A and 6B/ correspondingly. As shown, it is difficult to see any pathological features on any one of Figs. 6A and6B. However, when the two figures are mathematically combined to obtain Fig. 6C, a significant improvement in the contrast and detail of the tissue is obtained. It can also be noted that details that can be seen in Fig 5C (vasculature on the upper part of the image) cannot be seen in Fig. 6C because Figs. 5C and 6C provide image data at different depth levels of the same tissue.

Since in this example the illumination includes two Red-spectrum wavelengths and their penetration is relatively deeper than in the previous example of Figs. 5A-5C, any features in the flap can be barely seen (since it is very thin), while in the muscle deeper view of the tissue is obtained.

In Fig. 6C, the deeper blood vessels can be seen which cannot be seen in each image of Figs. 6A and 6B separately.

In order to understand the second pathology, the operation done on the mouse is to be considered. The mouse skin was surgically exposed causing local trauma to the edges of the flap itself, as well as the tissue adjacent to the flap. As shown in Fig. 6C, darker and brighter areas can be identified. While brighter areas signify the fascia of the muscle that is still attached to the muscle itself, darker areas signify the muscle tissue without the fascia (the fascia was ripped and is attached to the skin flap).

Thus, the selection of two pairs of wavelengths, where each pair penetrates to a different depth, allows imaging of each layer of tissue selectively by a proper mathematical combination of the two images belonging to the same layer.

In the following figures, the advantages of the technique of the present invention will be demonstrated while imaging the eye retina.

Images of an eye with central retinal artery occlusion (CRAO) are shown in Figs. 7A-D. In addition to imaging different penetration depths, the specially selected wavelengths according to the technique of the present invention allowed the evaluation of tissue oxygenation status with depth selectivity. Fig. 7A shows the results of imaging the eye with a pair of blue wavelengths, probing the superficial retina, and mathematically combining the two images of Figs. 7C and 7D presenting the oxygenation of the superficial retina while Fig. 7C shows the oxygenation of the Choroidal tissue. In this case, the following mathematical operations are performed on both images: LLF smoothing, illumination balancing followed by pixelwise ratio.

It can be seen on Fig. 7A that the left corner of the superficial retina has signs S of an ischemic condition (darker), while there are no signs of ischemia on Fig. 7B which was identified using a pair of wavelengths in the red part of the visible spectrum for imaging the choroid, i.e. a deeper layer of the retina. Thus, it is clear from Figs. 7A and 7B that the ischemic area appears only at the superficial retina, probed by the pair of blue wavelengths. Figs. 7C and 7D show the separate images obtained each at a different bluespectrum wavelength (436nm and 460nm) and demonstrate that the ischemic region observed in Fig. 7A cannot be deduced from any one of Fig. 7C or 7D separately.

In another example of deducing depth-specific tissue pathology using the technique of the present invention, an eye retina affected with a macroaneurysm, was imaged with a selectivity of three different levels. Figs. 8A, 8B, 8C show images of the deep, intermediate and superficial layers, respectively. Each one of the figures was obtained by selecting a depth-specific pair of wavelengths comprising a first wavelength in which the absorbance of deoxyhemoglobin within the tissue is higher than that of oxyhemoglobin, and a second wavelength in which the absorbance of the oxyhemoglobin within the tissue is higher than that of deoxyhemoglobin or vice versa.

The result of the mathematical combination of the images of each pair of wavelengths allows to identify the pathology at different depths in Figs. 8A, 8B, 8C. In this example, the following mathematical operations were performed on both images: LLF smoothing, illumination balancing followed by pixelwise ratio.

In these examples, a ruptured retinal macroaneurysm, causing retinal bleeding, can be seen in the figures. The bleeding caused by the macroaneurysm is located at different depths of the retina. These differences are expressed in different colors of the blood in the image. In the superficial layer shown in Fig. 8C, the macroaneurysm can be seen, as well as the intra-retinal bleeding from it. In the intermediate layer shown in Fig. 8B the subretinal bleeding is seen and in the deep layers, the details of the superficial bleedings disappear (Fig. 8A). The superficial and intermediate layers also allow to better appreciate the size and architecture of the aneurysm as well as the bleeding.

However, when images obtained with the two different red wavelengths (Figs. 8D and 8E) are analyzed separately, the information obtained is unable to selectively indicate the ischemic area in the deep retinal tissue with the same clarity as it can be seen on Fig. 8A after the mathematical combination of Figs. 8D and 8E.

Reference is made to Figs. 9A-9D, there is illustrated one more example of the invention, where the tissue properties are extracted using the above-described pair (or pairs) of illumination wavelengths and analyzing and processing the image data using DNN. In this example, the retina is imaged using simultaneous illumination by blue and green wavelengths of, respectively, 470nm and green 535nm. The so-obtained colored image is shown in Fig. 9A. The image data pieces of separated spectral channels (blue and green), are analyzed using a DNN (UNET family variation) and a vascular tree was extracted (Fig. 9D). Using standard image processing manipulation would provide inferior results as compared to the DNN-based analysis), as well as by applying the DNN analysis on each channel separately (shown in Figs. 9B and 9C). Thus, the technique of the present invention can use DNN-based processing for layered/band data analysis to extract any one or more tissue properties (in this specific not limiting example - blood vessels segmentation).

Thus, the novel technique of the present invention may contribute to the increasing exploration and understanding of the morphological and functional correlations in, e.g., retinal diagnostics and to improve diagnosis of retinal ischemia. Non- invasive depth-resolved measurement of tissue oxygenation may have implications for diagnosis and treatment of various pathologies.

Claims

CLAIMS:

1. An imaging system for inspection of a biological tissue to determine at least one predetermined tissue property, the system comprising: an illumination unit configured and operable to produce a plurality of illumination wavelengths of at least visual spectral range, a detection unit comprising at least one optical detector and being configured and operable to detect light responses of the tissue to the illumination wavelengths and generate image data indicative of the detected light responses, an illumination controller, and a control system configured and operable to analyze the image data by determining a relation between light responses of the tissue to different illumination wavelengths, wherein said illumination controller is configured and operable to selectively operate the illumination source to generate said plurality of the illumination wavelengths comprising a number M (M>2) of wavelengths defining at least one group of two or more illumination wavelengths such that the relation between the light responses of the tissue to the illumination wavelengths of the group is indicative of said at least one predetermined tissue property at a corresponding depth in the tissue, wherein each of said at least one group of the illumination wavelengths comprises first and second wavelengths satisfying the following conditions:

(1) the first and second wavelengths of the group of the illumination wavelengths are spectrally close to one another being thereby characterized by substantially same penetration depth in the tissue, and

2. The imaging system according to claim 1, wherein said at least one predetermined tissue property is associated with presence of a certain substance in the tissue.

3. The system according to claim 1 or 2, wherein said plurality of the illumination wavelengths defines at least one additional group of the illumination wavelengths, the wavelengths of each group of the illumination wavelengths being spectrally close to one another and being different from the wavelengths of at least one other group, such that the wavelengths of at least two different groups are characterized by at least two different penetration depths, respectively, in the tissue.

4. The system according to any one of the preceding claims, wherein said control system is further configured and operable to analyze the image data and generate data indicative of a structure of a capillary system in at least one depth in the tissue.

5. The system according to any one of the preceding, wherein said control system is further configured and operable to analyze the image data and generate data indicative of an autofluorescence map of the tissue.

6. The imaging system according to any one of the preceding, wherein said at least one predetermined tissue property comprises oxygenation level, said first and second substantially opposite absorption properties of the at least one first wavelengths and at least one second wavelength of the group of the illumination wavelengths are characterized by at least one of the following:

(i) the at least one first wavelength and the at least one second wavelength have the opposite absorption properties with respect to oxyhemoglobin and deoxyhemoglobin; and

(ii) each of the at least one first wavelength is relatively highly absorbable by either one of deoxyhemoglobin and oxyhemoglobin, and each of the at least one second wavelength is substantially equally absorbable by both oxyhemoglobin and deoxyhemoglobin .

7. The system according to any one of the preceding claims, wherein said illumination unit is configured and operable by said illumination controller to sequentially illuminate the tissue by the illumination wavelengths of the at least one group.

8. The system according to any one of claims 1 to 7, wherein said illumination unit is configured and operable by said illumination controller to simultaneously illuminate the tissue by the illumination wavelengths of the at least one group.

9. The system according to claim 7 or 8, wherein the detection unit is configured and operable to detect the light responses of the tissue in synchronization with the illumination of the tissue by the illumination wavelengths of respective spectral ranges of the illumination wavelengths.

10. The system according to any one of the preceding claims, wherein the illumination unit comprises at least one broadband light source equipped with one or more spectral filters.

11. The system according to any one of the preceding claims, wherein the illumination unit comprises two or more light sources of respective different spectral properties.

12. The system according to any one of the preceding claims, wherein the detection unit comprises at least one spectrometer.

13. The system according to any one of the preceding claims, wherein the detection unit comprises two or more detectors having different spectral properties.

14. The system according to any one of the preceding claims, wherein the detection unit comprises at least one multispectral camera.

15. The system according to any one of the preceding claims, wherein the detection unit comprises at least one standard RGB color camera.

16. The system according to any one of the preceding claims, wherein the control system is configured and operable to analyze the image data by carrying out the following: analyzing the image data to identify N pairs of image data pieces, each of said N pairs comprising first and second image data pieces corresponding to said first and second wavelengths; processing the first and second image data pieces of each pair by applying a region of interest selection analysis to the first and second image data pieces, thereby obtaining series of image data pieces containing information about a predetermined substance; and processing said series of image data pieces to extract data indicative of the at least one predetermined tissue property.

17. The system according to claim 16, wherein said region of interest selection analysis comprises at least one mathematical combination of any one of the following types: pixelwise, area-wise, and cluster- wise.

18. The system according to any one of the preceding claims, wherein said control system is configured and operable to determine the relation between the light responses of the tissue to the illumination wavelengths of the group as a ratio between said light responses.

19. The system according to any one of the preceding claims, wherein said control system is configured and operable to apply deep learning processing to data indicative of said relation between the light responses of the tissue to the illumination wavelengths of the group to extract said at least one predetermined tissue property.

20. A method for inspecting a biological tissue to determine at least one predetermined tissue property, the method comprising: illuminating the tissue by a plurality of M (M^2) selected illumination wavelengths of at least visual spectral range, wherein said plurality of the selected illumination wavelengths defines at least one group of illumination wavelengths, the illumination wavelengths of each group comprise first and second wavelengths satisfy the following conditions:

(2) the illumination wavelengths of the group comprise at least one first wavelength and at least one second wavelength whose interactions with the tissue are characterized by, respectively, first and second substantially opposite absorption properties, such that each of the at least one first wavelength is substantially affected by said tissue property while each of the at least one second wavelength is weakly affected by said tissue property; detecting a light response of the tissue to each of said plurality of the selected wavelengths, and generating corresponding image data, the image data being indicative of a relation between the light response of the tissue to the at least one first illumination wavelength and the light response of the tissue to the at least one second wavelength of the group of the illumination wavelengths, said relation being indicative of said at least one predetermined tissue property at a corresponding depth in the tissue.

21. The method according to claim 20, wherein said at least one predetermined tissue property is associated with presence of a certain material / substance in the tissue.

22. The method according to claim 20 or 21, wherein said plurality of the selected illumination wavelengths defines at least one additional group of illumination wavelengths, the wavelengths of each group being spectrally close to one another and being different from the wavelengths of at least one other group, such that the wavelengths of at least two different groups are characterized by at least two different penetration depths, respectively, in the tissue.

23. The method according to any one of claims 20 to 22, further comprising analyzing the image data and generating data indicative of a structure of a capillary system in at least one depth in the tissue.

24. The system according to any one of claims 20 to 23, further comprising analyzing the image data and generating data indicative of an autofluorescence map of the tissue.

25. The method according to any one of claims 20 to 24, wherein said at least one predetermined tissue property comprises oxygenation level, said first and second substantially opposite absorption properties of the at least one first wavelength and at least one second wavelength are characterized by at least one of the following:

26. The method according to any one of claims 20 to 25, wherein the image data is indicative of said at least one tissue property at different depths of said tissue.

27. The method according to any one of claims 20 to 26, wherein the image data is indicative of visualization and oxygenation at different depths of said tissue relating to blood vessels.

28. The method according to any one of claims 25 to 27, wherein the image data is indicative of visualization and oxygenation maps through said tissue.

29. The method according to any one of claims 20 to 28, comprising processing the image data to determine the at least one predetermined tissue property, said processing comprising: identifying in the image data N pairs of image data pieces, each pair comprising first and second image data pieces corresponding to the light responses to said first and second illumination wavelengths; and processing the first and second image data pieces of each pair to determine said relation between the first and second image data pieces.

30. The method according to claim 29, wherein said processing comprises: applying a region of interest selection analysis to the first and second image data pieces, thereby obtaining series of image data pieces containing information about a predetermined material; and processing said series of image data pieces to extract data indicative of the at least one predetermined tissue property.

31. The method according to claim 30, wherein said region of interest selection analysis comprises at least one mathematical combination of any one of the following types: pixelwise, area-wise, and cluster- wise.

32. The method according to any one of claims 29 to 31, wherein said processing comprises determining said relation as a ratio between the first and second image data pieces.

33. The method according to any one of claims 29 to 32, wherein said processing comprises applying deep learning processing to data indicative of said relation between the first and second image data pieces to extract said at least one predetermined tissue property.

34. A control system for controlling inspection of a biological tissue being performed by an inspection system to determine at least one predetermined property tissue property of said biological tissue, the control system being configured and operable for data communication with an illumination source and a detection unit of said inspection system and comprising: an illumination controller configured and operable to selectively operate the illumination source to generate a plurality of illumination wavelengths of at least a visual spectral range comprising a number M (M>2) of the illumination wavelengths defining at least one group of two or more illumination wavelengths such that a relation between light responses of the tissue, detected by the detection unit, to the illumination wavelengths of the group is indicative of said at least one predetermined tissue property at a corresponding depth in the tissue, wherein each of said at least one group of the illumination wavelengths comprises first and second wavelengths satisfying the following conditions: (1) the first and second wavelengths of the group of the illumination wavelengths are spectrally close to one another being thereby characterized by substantially same penetration depth in the tissue, and (2) the first and second wavelengths of the group of the illumination wavelengths comprise at least one first wavelength and at least one second wavelength whose interactions with the tissue are characterized by, respectively, first and second substantially opposite absorption properties, such that each of the at least one first wavelength is substantially affected by said tissue property while each of the at least one second wavelength is weakly affected by said tissue property; a detection controller associated with the detection unit to control detection of the light responses and generation of corresponding image data; a synchronization circuitry configured and operable to synchronize generation of the illumination wavelengths and detection of the light responses of the tissue to the illumination wavelengths; a detection unit comprising at least one optical detector and being configured and operable to detect light responses of the tissue to the illumination wavelengths and generate image data indicative of the detected light responses; and a processor configured and operable to analyze the image data by determining a relation between the light responses of the tissue to different illumination wavelengths, and analyzing said relation and extracting data indicative of the at least one predetermined property of the tissue.

35. The control system according to claim 34, wherein said illumination controller is configured and operable to operate the illumination source to simultaneously illuminate the tissue by the illumination wavelengths of the at least one group.

36. The control system according to claim 34 or 35, wherein the synchronization controller is configured and operable to operate the detection controller to detect the light responses of the tissue in synchronization with the illumination of the tissue by the illumination wavelengths of respective spectral ranges of the illumination wavelengths.

37. The control system according to any one of claims 34 to 36, wherein said processor is configured and operable to analyze the image data by carrying out the following: analyzing the image data to identify N pairs of image data pieces, each of said N pairs comprising first and second image data pieces corresponding to said first and second wavelengths; processing the first and second image data pieces of each pair by applying a region of interest selection analysis to the first and second image data pieces, thereby obtaining series of image data pieces containing information about a predetermined substance; and processing said series of image data pieces to extract data indicative of the at least one predetermined tissue property.

38. The control system according to claim 37, wherein said region of interest selection analysis comprises at least one mathematical combination of any one of the following types: pixelwise, area-wise, and cluster- wise.

39. The control system according to any one of claims 34 to 38, wherein said processor is configured and operable to determine the relation between the light responses of the tissue to the illumination wavelengths of the group as a ratio between said light responses.

40. The control system according to any one of claims 34 to 39, wherein said processor is configured and operable to apply deep learning processing to data indicative of said relation between the light responses of the tissue to the illumination wavelengths of the group to extract said at least one predetermined tissue property.