METHOD AND DEVICE FOR MONITORING THE FLUORESCENCE EMISSED FROM THE SURFACE OF BIOLOGICAL TISSUE

Description

[0001] The invention relates to the field of medical imaging. More particularly, the invention relates to a method and device for tracking fluorescence in biological tissue.

[0002] An example of a fluorescence-based medical imaging technique is described in the document "The Use of In Vivo Fluorescence Image Sequences to Indicate the Occurrence and Propagation of Transient Focal Depolarizations in Cerebral Ischemia," by AJ Strong, et al., *Journal of Cerebral Blood Flow & Metabolism*, vol. 16, no. 3, 1 May 1996, pages 367-377. A marker-based fluorescence medical imaging system is described in document FR2989876A2. Documents US 8 743 241 B2 (LESIAK ET AL) and US 2009 / 238435 A1 (SHIELDS KEVIN ET AL) present high dynamic range (HDR) fluorescence imaging techniques.

[0003] Fluorescence imaging provides intraoperative information regarding perfusion in biological tissues, particularly human tissues. For certain medical indications, it can be useful to identify and locate areas of biological tissue where the fluorescence signal first appears, those where the rate of rise and fall of the fluorescent signal (i.e., the signal slope, or the rate of increase and decrease in signal intensity) is greater or lesser, as well as the amplitude of the fluorescence signal in different areas (which represents the level of perfusion in those areas). Indeed, this can allow for the evaluation of overall tissue perfusion and the identification of potential venous or arterial problems (plastic surgery, wounds and healing, etc.).

[0004] For these indications, it is necessary to have a good understanding of the dynamics of the fluorescence signal.

[0005] Methods have been proposed in which information relating to perfusion dynamics can be displayed and visualized. However, these require the intervention and expertise of a physician or surgeon to define reference areas in the observed tissues (for example, the surgeon must point to and designate healthy tissue on the image so that signal normalization can be performed relative to that tissue). Furthermore, calculations of the slope and amplitude of the fluorescence signal can be biased due to artifacts related to: a change in light in the room environment or a shift in the area of ​​interest over time (patient movements).

[0006] Similarly, providing a graphical representation (e.g., a fluorescence signal rise curve) without providing visual information, particularly regarding context, poses a risk of misinterpreting the results.

[0007] The invention aims to provide means to improve, at least partially, the knowledge of perfusion dynamics and to overcome, also at least partially, some of the drawbacks of prior art techniques.

[0008] This goal is at least partly achieved with a method for tracking the diffusion over time of a fluorescent marker within a biological tissue, as defined by claim 1.

[0009] In this document, the relative value of the signal measured on a pixel using the camera is called the "fluorescence signal," this signal being representative of the intensity of the fluorescence emission at a point in the tissue area (corresponding to this pixel).

[0010] Thanks to the invention, it is possible to directly visualize, on an image of the observed biological tissue (for example, on an image of a foot), the result of a calculation that allows for the automatic, pixel-by-pixel comparison of two images representing the diffusion of the fluorescent marker, observed at at least two successive time points. The resulting image shows the variation of the fluorescence signal between two measurement times, locally (but over the entire observed area of ​​the tissue, not just a restricted area or a few pixels, thus covering the entire foot, for example). In this way, it is possible to monitor the dynamics of perfusion by visualizing how this variation evolves over time and locally (by visualizing the variation of the signal on each pixel but across all the pixels of the image).In other words, the invention makes it possible to visualize the dynamics of perfusion in a given area and in the environment surrounding that area. For example, one can see if a signal increases around a certain area, but does not increase within that area, indicating that an artery supplying that area is blocked.

[0011] The images obtained using the method according to the invention can initially be interpreted by technicians (for example, for applications involving the analysis of potential chronic wounds – “wound care”), with a physician potentially intervening if the technician detects a problem requiring their expertise. And if a physician needs to intervene, they can do so more quickly.

[0012] The method according to the invention may include one or more of the features corresponding to claims 2 to 5 considered independently of each other or in combination with one or more others.

[0013] The invention also relates to a device for monitoring fluorescence emitted on the surface of biological tissue, comprising: an excitation source adapted to emit excitation radiation from a fluorescence marker, a camera including a sensor of the fluorescence light emitted on the surface of the biological tissue under the effect of the excitation radiation, a computer for recording and storing fluorescence images captured by the camera, and for performing fluorescence image processing, and a screen for displaying images resulting from the processing of fluorescence images by the computer, the computer including computing means for processing fluorescence images using software for implementing the process as mentioned above.

[0014] The device according to the invention optionally comprises a light source illuminating within a spectral band in which a marker is sensitive and in which the fluorescence of this marker is therefore excited. The fluorescence light sensor is not sensitive in this spectral excitation band, but is sensitive in the range of wavelengths corresponding to the emission of the marker's fluorescence.

[0015] Other features and advantages of the invention will become apparent from the detailed description that follows, as well as from the accompanying drawings. These drawings include: there figure 1 schematically represents one embodiment of a device according to the invention; the figure 2 schematically represents a set of successive steps corresponding to an example of implementing the process according to the invention; the figure 3 schematically represents the evolution over time of the average intensity of the fluorescence signal on an area of ​​biological tissue; the figure 4 schematically represents the dynamics of the fluorescence signal on an area of ​​biological tissue; the figure 5 schematically represents the evolution over time of the average intensity of the fluorescence signal on an area of ​​biological tissue, normalized by a threshold that corresponds to an arbitrary threshold assumed to correspond to a healthy foot; figure 6 schematically represents a synthesis of results of the type shown on the figures 3, 4 et 5 , but for acquisition times corresponding to unequal time intervals.

[0016] An example of an embodiment of a device 10 for monitoring the fluorescence emitted on the surface of biological tissue 20 is shown in the figure 1 .

[0017] This device 10 includes a probe 1 with a so-called "fluorescence" camera to capture fluorescence images (in the near-infrared or more generally in wavelengths detected by this "fluorescence" camera).

[0018] In other words, this camera is equipped with at least one sensor adapted to capture images in the near-infrared or, more generally, in the wavelengths emitted by fluorescent markers. This camera therefore makes it possible to create an image of the fluorescence light emitted by the fluorophore on the surface of an area of ​​biological tissue.20

[0019] In this document, a "fluorescence image" is an image of the fluorescence signal emitted on the surface of the biological tissue 20 to be observed, captured using a "fluorescence" camera, and a "current image" is an image extracted (directly, without integration or summation with one or more other images) from a video made using a camera of probe 1. The "current image" can be made, for example, by illuminating the biological tissue 20 to be observed using light-emitting diodes emitting in the near-infrared (or more generally in the wavelengths detected by the "fluorescence" camera), and / or by illuminating the biological tissue 20 to be observed using a light source adapted to excite the fluorescent marker(s).

[0020] In one variant, probe 1 includes a first fluorescence camera to capture fluorescence images (e.g., in the near-infrared) and a second camera to capture images in the visible spectrum. In this case, "standard images" can be acquired either in the wavelength range detected by the fluorescence camera or in the visible spectrum.

[0021] Device 10 also includes a computer 2 to record and store the images captured by each camera, as well as to perform processing of fluorescence images and possibly regular images.

[0022] Device 10 also includes means for viewing and displaying 3 (screen) images 4 after processing.

[0023] Probe 1, for example, is connected to computer 2.

[0024] Probe 1 also includes an excitation source (e.g., laser) adapted to emit excitation radiation from a fluorescence marker or fluorophore.

[0025] The fluorescence camera includes a sensor sensitive to the fluorescence light emitted by the fluorophore on the surface of this area of ​​biological tissue 20.

[0026] The probe 1 is advantageously held, using a support arm 5, in a stable manner and at a constant distance from the scene comprising the area of ​​biological tissue 20 to be observed and studied (there may however be a slight displacement of the scene due in particular to the breathing of the patient).

[0027] A fluorescent tracer, or fluorophore, is injected intravenously. The emission signal from the fluorophore is captured by the "fluorescence" camera of probe 1 and is recorded over time by leaving the excitation source on.

[0028] Fluorescence images are recorded starting at a time T1 greater than or equal to a time T0 corresponding to the intravenous injection time of the fluorophore. Recording continues until a final time TF considered sufficiently long to capture most of the perfusion dynamics.

[0029] Software controls the acquisition of standard and fluorescence image sequences. The time interval Δt The interval between each sequence can be predetermined. For example, this time interval Δt is regular and corresponds to twenty seconds. Therefore, in this case, every twenty seconds, for example, the software commands the acquisition of a sequence of current and fluorescence images. To increase the accuracy of the fluorescence signal measurements corresponding to a sequence, a sequence can be composed of fluorescence images acquired with different exposure times and / or different gains. The time interval between the first and last images of each of these sequences is chosen to be short enough to consider the fluorescence signal as remaining static relative to the overall dynamics of the perfusion. In other words, the acquisition time over which each sequence elapses is short enough to be considered negligible compared to the rate of change of the measured signal.

[0030] A sequence consists, for example, of the following sub-sequences or series: 1) N fluorescence images with an exposure time of X seconds, 2) M fluorescence images with an exposure time of 2X seconds, 3) P fluorescence images with an exposure time of 3X seconds, 4) A current image acquired with the laser excitation source off and the illumination, including infrared-emitting LEDs, on, to obtain a current image of the background, IC, alone, without fluorescence. Note that for this (for step 4), according to a variant, the excitation source remains on, but processing is then performed to recover a current image by subtracting an image obtained with only the excitation source on from an image obtained with both the excitation source and the illumination, including LEDs, on.In other words, the excitation source remains constantly illuminated, and we obtain an image without an excitation source by subtracting an image (or an average of images) without LED illumination from the image (or an average of images) where the LEDs are illuminated. It should also be noted that N, M, and P can be equal integers, or different in pairs, etc.

[0031] It should also be noted that instead of varying the exposure times in steps 1), 2) and 3), we could have varied the gains.

[0032] Increasing the exposure time – sub-sequences 2) and 3) above – allows the signal buried in noise to emerge for the exposure corresponding to sub-sequence 1). With a linear camera, doubling the exposure time is equivalent to doubling the average signal level. Therefore, using this type of camera, all images can easily be brought to identical exposure levels, even with different exposure times. This type of sequence increases the accuracy of the fluorescence signal measurement and reduces noise (averaging effect on Poissonian noise).

[0033] More specifically, the gray levels obtained for each exposure time are summed pixel by pixel. Before summation, the images are registered relative to each other, and pixels corresponding to signal saturation are eliminated. This image registration can be performed using a standard image registration algorithm. However, registration algorithms with at least six degrees of freedom (rotations + translations), incorporating singular points, optical flow, etc., are preferred.

[0034] The total exposure time per pixel is calculated (either without considering exposure times corresponding to saturation for the pixel in question, or by considering exposure times for these pixels but replacing the signal value for these pixels with an extrapolation from values ​​obtained on images in which the signal is not saturated). The sum of the gray levels per pixel is divided by the total exposure time corresponding to that pixel, in order to then obtain normalized images for the same exposure time. It should be noted that, depending on the method, weightings can be applied or a non-linear color conversion can be performed using color tables. This type of method can be particularly useful for handling signal variations with a wide dynamic range.This then amounts, for example, to slightly increasing weak signals and reducing stronger signals, so that the signal can be displayed over its full range of variation without generating saturation or underexposure.

[0035] For the images obtained in this way, the precision, particularly in weak signals, is greater. Indeed, as mentioned above, increasing the exposure time allows the signal to stand out against the noise. This is especially true with a fluorescence camera equipped with a charge-coupled device (CCD) sensor. Thus, weak signals stand out more clearly from the noise as exposure times increase. The software allows the Poissonian noise in the image to be averaged (and therefore reduced).

[0036] A more detailed example of how to implement the process according to the invention is described below.

[0037] The times T i for which fluorescence information must be captured (with i ranging from 0 to F; F being the number of images on which the practitioner wishes to perform the study; for the example described here F=6), are entered as parameters in the software.

[0038] This acquisition then includes, for example, the following steps: Step 1: A timer is started. At T0, the practitioner injects ICG (Indocyanine Green) and initiates image acquisition using the software. Step 2: At a time T1 later than or equal to T0, the software triggers the following operations: ∘ Acquisition and recording of a current background image, or background image I C1; during this acquisition the fluorescence excitation source is switched off; as mentioned above, this acquisition of the current background image I C1, depending on the type of probe, can be carried out, for example, using either a camera equipped with a visible light sensor, or a camera equipped with a near-infrared light sensor (in the latter case, the laser excitation source is preferably switched off and lighting including infrared-emitting diodes is switched on);∘ Acquisition and recording of a fluorescence image sequence (the laser excitation source is on and the lighting including the infrared-emitting LEDs is preferably off, if not, proceed as indicated above);This acquisition, as described above, may include image series corresponding to different exposure times (e.g., a series of N images at an exposure time X, then one or more series of M images corresponding to one or more exposure times Y greater than X (or different gains)), in order to create a high dynamic range (HDR) image and benefit from a better signal-to-noise ratio. Calculation of a fluorescence image I1 resulting from the sum of the fluorescence images processed as described above (image registration, removal or replacement of saturated pixels, normalization for a given exposure time). Instead of summing images, it is possible, according to various methods, to perform image stacking, with or without linear operations, weighting, etc.

[0039] This image I 1 therefore represents a "freeze frame" of the perfusion level at time T 1.

[0040] The operations of step 2 are repeated when each time Ti (with this time i=2 at F) is reached, until finally the acquisition at time TF.

[0041] Step 3: At the end, after the last acquisition at time TF the different fluorescence levels corresponding to fluorescence images, I i, as well as the current background images I Ci are recorded in memory in computer 2 (alternatively, images are recorded in memory in the computer being acquired).

[0042] Step 4: The software then determines a maximum value for the fluorescence signal intensity for all the I i fluorescence images. This maximum intensity value can be chosen by using an xth percentile, x% of the maximum value, and / or by smoothing the I i fluorescence images to avoid point artifacts on each of these I i fluorescence images.

[0043] Step 5: The software then normalizes each Ii fluorescence image against the maximum determined in the previous step. Next, the software colorizes the normalized Ii fluorescence images using a specific color conversion table. Finally, the software overlays this result onto the current context image corresponding to that time, displaying in grayscale the pixels of the current context image that do not exhibit fluorescence. An example of a series of Ii fluorescence images obtained using the above method is shown on the figure 3 . Such a series of I1 fluorescence images corresponds to a study of the vascularization of a healthy foot.

[0044] Step 6: However, it is also possible to highlight the dynamics of fluorescence signals over time even more effectively. To do this, the software offers the ability to compare the intensity levels of successive fluorescence images (Ii) over time. Thus, the intensity of the fluorescence image (Ii+1) taken at time Ti+1 can be subtracted (after registration), pixel by pixel, from the intensity of the fluorescence image (Ii) taken at time Ti. This difference allows us to see what happened between time Ti and time Ti+1.

[0045] It should be noted that simply subtracting the fluorescence signal intensities associated with each pixel is not the only way to highlight the dynamics of a signal. Generally speaking, the software can calculate the difference between the squares of the intensities, or the difference between logarithms of the intensities, etc., or any distance in the mathematical sense of the term, and more specifically an algebraic distance, between two successive fluorescence images.Thus, the calculation on which the comparison operation is based includes at least one operation chosen from the following: a subtraction between values ​​of a signal representative of the intensity of the fluorescence emission, a calculation of the norm of a quantity represented by values ​​of a signal representative of the intensity of the fluorescence emission, a calculation of the algebraic distance between values ​​of a signal representative of the intensity of the fluorescence emission, and a logical operation "or" or "nor" or "xor" on values ​​of a signal representative of the intensity of the fluorescence emission. These logical operations can indeed highlight the following phenomena: There is no fluorescence signal associated with a pixel or group of pixels in either the image resulting from the first or second acquisition sequence (NOR operation): this reveals areas that are never vascularized. There is a fluorescence signal associated with a pixel or group of pixels in either the image resulting from the first or second acquisition sequence (OR operation): this reveals areas that are vascularized at different times. There is a fluorescence signal associated with a pixel or group of pixels only in the image resulting from the first or second acquisition sequence (XOR operation): this reveals areas that are vascularized only during one of the sequences.

[0046] To compare Ii fluorescence images, they must be registered so that the pixels correspond between the different Ii fluorescence images. Thus, the difference between the images (or more generally, the operation that allows the images to be compared) can be calculated pixel by pixel.

[0047] It should be noted that these comparison operations do not involve a reference image (a "baseline image") acquired, for example, before the appearance of fluorescence, which would be subtracted from each image resulting from a sequence. Indeed, particularly when comparing two images resulting from two sequences, the use of such a reference image is unnecessary since the corresponding information disappears through subtraction during the comparison process.

[0048] Step 7: Once this calculation has been performed by the software, for successive pairs of images, the software determines the maximum and minimum values ​​obtained by calculating the distance (as defined above) between the successive fluorescence images. These maximum and minimum values ​​may be positive or negative. Pixels with a negative distance value correspond to areas where the fluorescence signal is weaker at time Ti+1 than at time Ti, and conversely, a positive value corresponds to an increase in fluorescence at that location during the corresponding time interval.

[0049] The software therefore normalizes the positive pixels of the images obtained by the previous distance calculation, using the maximum value obtained across all calculations performed on the fluorescence images of a sequence from T1 to TF. Similarly, the software normalizes the negative pixels of the images obtained by the previous distance calculation, using the minimum value obtained across all calculations performed on the images of a sequence from T1 to TF.

[0050] The software colorizes the normalized images with a specific false color for positive pixels (e.g., warm colors, from yellow to red) and a specific false color for negative pixels (e.g., cool colors, from blue to violet). The software allows you to display the result (see figure 4 ), by superimposing each colorized image onto the corresponding current context image. This presentation of results provides quick visual information about areas where the signal is increasing, when this signal increases in those areas (and therefore over what period of time), and so on. Similarly, this type of results presentation provides quick visual information about areas where the signal is decreasing, when it decreases, and over what period of time, etc.

[0051] This type of display can notably allow visualization of arterial or venous problems by identifying areas in which the intensity of the fluorescence signal decreases later than in others.

[0052] Step 8: In addition, the software allows the entire set of Ii fluorescence images to be normalized to a predefined threshold. The software then colorizes the normalized images using a color conversion table. For example, this conversion table is the same as the one used in step 5 above.

[0053] The software overlays each normalized image with the corresponding current background image I Ci. The result is displayed. This display allows for the comparison of perfusion dynamics between patients and their local characterization on the current image (obtained in the visible or near-infrared range, for example). Indeed, for instance, the threshold can be chosen to correspond to a standard average level for a healthy foot. A false-color display then allows for a quick visualization of whether the average fluorescence intensity level measured for a patient is standard, or whether it is higher or lower (see figure 5 ).

[0054] For example, we can choose the following convention: warm colors indicate that the area is adequately perfused compared to a standard level of perfusion. Conversely, cool colors indicate lower than average vascularization.

[0055] All the calculated images can be displayed simultaneously (the figures 3, 4 et 5 are displayed together, for example in a manner analogous to the figure 6 ).

[0056] According to one variant, the time interval Δt The interval between each sequence is not regular. For example, this time interval Δt can be equal to twenty seconds, then forty seconds, then sixty seconds. An example of a result obtained with intervals Δt variables is represented on the figure 6 In this case, advantageously, the normalization of the resulting image should take into account this variation in time intervals.

[0057] The method according to the invention therefore makes it possible to facilitate the interpretation of measurements of a fluorescence signal using a visual representation, with in particular precise information on the local dynamics of the fluorescence signals, and in particular the following parameters: fluorescence signal onset time, variation in fluorescence signal intensity over time, localization of fluorescence signal rises and falls over time.

[0058] The method according to the invention allows for comparisons between patients.

[0059] The method according to the invention provides an automatic analysis tool, particularly when used by normalizing against a reference threshold (see the example of the healthy foot above). This eliminates the need for intervention or arbitrary choices (such as selecting an incorrect reference tissue) by a practitioner, which could introduce biases and thus errors in interpreting the results. It also allows for the rapid identification of cases where there is a clear error in the calculation, which would not be possible on a curve (for example, at the edges of a foot if the scene has moved too much and / or can no longer be registered, for example, if the foot moves out of the field of view, or if an object enters the field and causes a measurement artifact). Furthermore, image registration allows for the easy and efficient management of scenes that may move over time (particularly the movement of a patient's feet).It therefore allows for working with deformable tissues and / or tissues that can move over time. Thanks in particular to the detection of saturated pixels and image normalization, it effectively manages overexposure. It also allows for the effective management of underexposure, notably through the combination of fluorescence images taken at different exposure times or gains. Finally, it enables rapid comparison of different areas within a tissue and between tissues of the same type.

Claims

1. Method for monitoring the diffusion over time of a fluorescent marker within a biological tissue (20), this method comprising: - exciting the fluorescent marker using an excitation light source, - acquiring, using a camera, fluorescence images of an area of the biological tissue, each fluorescence image corresponding to a set of pixels, a value of a signal representative of the intensity of the emission of fluorescence at a point in the area of the biological tissue being associated with each pixel, - processing at least one fluorescence image, and - displaying on a screen (3) at least one image resulting from the processing of a fluorescence image, characterized in that - the acquisition of the fluorescence images comprises at least two image acquisition sequences, each of these sequences starting respectively at different times Ti during a given perfusion of the fluorescent marker, with i comprised between 0 and F, T0 corresponding to the time at which the fluorescent marker is injected into the biological tissue and TF corresponding to the time at which the acquisition of the fluorescence images ends, - in that it comprises an operation of comparing two images each respectively resulting from the processing of images acquired during two successive acquisition sequences respectively starting at different times Ti, and - in that it comprises displaying on a screen the result of this comparing operation in the form of an image representative of the area of biological tissue.

2. Method according to Claim 1, wherein the comparing operation comprises at least one operation chosen from the following operations: a subtraction between values of a signal representative of the intensity of the fluorescence emission, a computation of a norm of a quantity represented by values of a signal representative of the intensity of the fluorescence emission, a computation of an algebraic distance between values of a signal representative of the intensity of the fluorescence emission, and an "or" or "nor" or "xor" logic operation on values of a signal representative of the intensity of the fluorescence emission.

3. Method according to one of the preceding claims, wherein processing of the fluorescence images is carried out for a given series of fluorescence images in order to align them with one another before summing them pixel by pixel.

4. Method according to one of the preceding claims, wherein an image acquisition sequence comprises acquiring a current background image, this current background image being used, when displaying the fluorescence images, to locate dynamic variations in the fluorescence signal in the area of biological tissue.

5. Method according to one of the preceding claims, wherein each fluorescence image acquisition sequence comprises acquiring at least two series of fluorescence images, the exposure time possibly being different for each of these series of fluorescence images.

6. Device for monitoring the fluorescence emitted from the surface of the biological tissue (20), comprising: - an excitation source suitable for emitting excitation radiation in order to excite a fluorescence marker, - a camera comprising a sensor of the fluorescence light emitted from the surface of the biological tissue (20), under the effect of the excitation radiation, - a computer (2) for recording and storing fluorescence images captured by the camera, and for processing the fluorescence images, and - a screen (3) for displaying images resulting from the processing of the fluorescence images by the computer, the computer comprising computing means configured for processing fluorescence images using a software package for implementing the method according to one of the preceding claims.

7. Device according to Claim 6, comprising a light source that generates illumination in a spectral band that excites the fluorescence of a marker but to which the fluorescence light sensor is not sensitive.