Tissue analysis apparatus and method

Abstract

To provide a concept for determining the reliability of tissue identification.SOLUTION: A tissue analysis device 10 having a light receiving device 16 and a spectrometer device 20 includes an evaluation device 22 connected with an assignment device 23. The evaluation device 22 serves for the determination of at least one tissue characteristic G of biological tissue, e.g. a type of biological tissue or an infection with a disease. The assignment device serves for the assignment of a suitable transmission curve model that models contamination of the light receiving device 16. For different degrees of contamination, different transmission curve models including reliability values R for each tissue characteristic G that can be determined respectively are provided. The concept can achieve not only the tissue analysis but also reliability with which the analysis has been performed, i.e. how reliable the display of the tissue characteristic G is.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an apparatus and method for tissue analysis, particularly for incorporation into a surgical device.

Background Art

[0002] It is known to act on living tissue with an electric spark or electrically generated plasma and analyze the light generated thereby to obtain results regarding the treated tissue. In this regard, Patent Document 1 discloses an excision catheter provided with a light receiving device in the form of an optical fiber disposed close to an excision electrode. The light received from the optical fiber is supplied to an analysis device, for example a spectrometer, and undergoes spectral analysis capable of discriminating whether an electric spark is acting on deposits, so-called plaque, and in particular whether spectral lines of phosphorus are observed (254 nm).

[0003] It is also known from Patent Document 2 to determine the type of characteristics of the treated tissue or other tissue, for example as non-malignant or malignant. Here, for tissue identification, the light of the spark acting on the living tissue is analyzed for its spectral composition. Also here, it is necessary to receive the light generated from the spark close to the spark.

[0004] It is known that deposits that affect tissue analysis can form on the light receiving window during the intervention of the spark on the living tissue. As an improvement measure, Patent Document 3 proposes forming the light receiving window with a stationary or flowing liquid. This suppresses the tendency of carbon black or other contaminants to deposit on the light receiving window. However, the liquid does not have a geometrically defined shape and cannot be used particularly for plasma applications involving excessive heat generation.

[0005] Due to these and other influencing factors, certain situation-dependent uncertainties occur during the optical measurement of tissue properties based on the light emitted from the spark or plasma.

Prior Art Documents

Patent Document

[0006]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0007] An object of the present invention is to provide a concept for determining the reliability of tissue identification.

Means for Solving the Problems

[0008] This object is solved by the device according to claim 1 and also by the method according to claim 13.

[0009] The tissue analysis device according to the present invention is useful for tissue identification. The spectral composition of the light generated by the action of a spark on a biological tissue is evaluated for tissue identification. For this purpose, the tissue analysis device comprises a light receiving device that receives the light generated by the action of an electrical spark or plasma on a biological tissue. The light receiving device can be, for example, the end face of an optical fiber, an objective lens attached to the optical fiber, etc. In particular, the light receiving device is preferably arranged in the vicinity of the electrode of each surgical instrument and thus near the resulting spark or plasma. For this reason, the light receiving device can be contaminated to some extent or even further deteriorated. This contamination can result from deposits of carbon black, tissue particles, dust, salt crystals or the like. The light received from the light receiving device is supplied to a spectrometer device, which determines the light intensity at at least one, preferably a plurality of wavelengths of the light and supplies a signal characterizing the light intensity to an evaluation device. The evaluation device determines data from the signal characterizing the light intensity, and the data characterizes at least one tissue property. The tissue property means any characteristic that characterizes a tissue, such as, for example, tissue type (bone, blood, connective tissue, muscle, nerve, organ tissue, etc., or even various tumor tissues). The tissue property to be discriminated can also be a property within the tissue type, for example, whether the tissue is a healthy tissue, a painful tissue, a tumor tissue, an infected tissue, a necrotic tissue, etc.

[0010] The assignment device that assigns a reliability value to the data determined by the evaluation device is part of the tissue analysis device. The assignment device determines the reliability value based on the contamination of the light receiving device. The determination of contamination is done indirectly by evaluating the spectrum generated by the spectrometer device. The spectrum can result from a light source with a known spectral composition or, additionally, from the light emitted by a spark acting on the tissue. When the light results from a light source with a known spectral composition, the evaluation is particularly simple. And since it is possible to directly determine an evaluation regarding the type and degree of contamination from the spectrum provided by the spectrometer device, the contamination can be classified. Different contamination classes can correspond to different transmission curves that are emitted by the light source and affect the light received by the light receiving device, like filter curves. Reliability values for different tissue characteristics, for example different tissue types, can be assigned to different transmission curves. For example, a certain tissue type can still be identified quite reliably even in the case of extreme contamination, while other tissue types cannot be identified as reliably even with little contamination.

[0011] However, in this approach for contamination determination, a test device sometimes has to be used to supply light with a known spectral composition to the light receiving device. The light source can be part of the test device and is arranged such that the light emitted from the light source is detected by the light receiving device. The light source can be, for example, a light source that is always lit when there is no spark at the electrodes of each instrument. When there is a spark, the light source can be turned off (extinguished).

[0012] As an alternative, the test device can use the surgical area lighting as a light source. This is particularly applicable when the surgical area lighting emits light in the wavelength range relevant for tissue identification. Furthermore, this is advantageous when the brightness of the surgical area lighting is sufficiently constant. When the instrument is not operating at all and (optionally) always in that case, it is possible to perform the test.

[0013] In another embodiment, such a light source is omitted or the surgical area illumination is not used for test purposes. Instead, the light emitted from the spark and received by the light receiving device is supplied to a spectrometer device that determines the assigned spectrum. In this case, the assignment device can be connected to a data block containing a plurality of transmission curve models specific to each contaminant. Next, the transmission curve model is a filter curve that can be discriminated for a qualitative shape and a wavelength-dependent attenuation value. In this case, the assignment device identifies the transmission curve model that matches the recorded spectrum.

[0014] In both embodiments, a data set that assigns different reliability values to different tissue characteristics is assigned to the transmission curve model. These data are used for further evaluation of the spectrum. If the manufacturer or user has defined a reliability value of at least 98%, for example, and the effective transmission curve model in that example has a reliability above this limit for only some of the tissue characteristics that can be determined in principle, the assignment device can indicate only the tissue types that provide sufficient reliability. As an alternative, the relevant reliability values can be indicated together with each determined tissue characteristic. Low reliability can be optically or audibly indicated to avoid treatment errors.

[0015] Further details of advantageous embodiments of the present invention are derived from the dependent claims, the figures, the drawings, or the respective descriptions.

Brief Description of the Drawings

[0016]

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Figure 2

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[0017] FIG. 1 shows a tissue analysis apparatus 10 that identifies a specific tissue characteristic G based on light generated from a spark 12 acting on a biological tissue 11. The spark 12 can be generated from an electrode 13 of a surgical instrument 14 powered by a device 15. For example, the electrode 13 is thus supplied with a high-frequency current flowing through the tissue 11 and respective opposing electrodes. For example, the instrument 14 is a monopolar instrument that requires a neutral electrode (not shown in FIG. 1) that must be attached to the patient to close the electrical circuit. The tissue analysis apparatus 10 can also cooperate with a bipolar instrument that includes two or more electrodes between which a spark occurs. As soon as the spark 12 or plasma jet generated by the electrode contacts the tissue 11, light appears, and the spectrum of the light can determine the type and state, i.e., the characteristics, of the tissue 11.

[0018] The tissue analysis apparatus 10, which can be part of the instrument 14 or configured as a separate unit, is useful for determining such tissue characteristics G. The tissue analysis apparatus 10 determines and indicates relevant characteristics of the tissue, such as what type of tissue the tissue in contact with the spark is (e.g., connective tissue or organ tissue).

[0019] For example, a light receiving device 16 in the form of a light guide 17 whose distal end 18 forms a light receiving window and is arranged close to the electrode 13 and / or the spark 12 is part of the tissue analysis apparatus 10. The light receiving window can also be formed by a lens or an objective lens or the like.

[0020] The light receiving device 16 is connected to the spectrometer device 20 and supplies the received light generated from the spark 12 to the spectrometer device 20. The spectrometer device 20 determines the spectrum of the light. The spectrum is characterized by the light intensity existing at different wavelengths of the light. Any type of spectrometer is suitable as the spectrometer device 20 suitable for outputting a signal characterizing different light intensities at different light wavelengths to the conductor 21.

[0021] The conductor 21 connects the spectrometer device 20 to an evaluation device 22 that determines the tissue characteristic G from the spectrum measured by the spectrometer device 20 (i.e., from the signal output from the spectrometer device 20). The determined tissue characteristic G can be the tissue type or even a specific characteristic of the tissue type. For example, the tissue type (muscle tissue, bone tissue, adipose tissue, blood, etc.) can be examined for specific characteristics (ion content, phosphorus content or other subtle characteristics). In this regard, the evaluation device can be trained based on a number of different tissue samples and can include respective learning algorithms or other learning structures. In this regard, the evaluation device 22 can also use a clearly defined calculation algorithm or other evaluation algorithm. The evaluation device 22 generates data D characterizing the characteristics of the tissue. For example, the data D may be suitable for indicating the tissue type or for discriminating malignant tissue from non-malignant tissue.

[0022] The tissue analysis device 10 further includes an assignment device 23 that assigns a reliability value R to the data D. The data D and the reliability value R can be provided to the display device 25 via the conductor 24. The reliability value is determined based on the transmission measurement and is applied to all subsequent data D until the next transmission measurement. In this way, the reliability of the tissue classification, and in some cases also the reduced reliability, is assigned to those measurements to a certain extent in advance.

[0023] Regarding the evaluation device 22, the allocation device 23 connected to the evaluation device 22, and their cooperation, it is clearer in more detail from FIG. 2. The allocation device 23 particularly functions to determine to what extent the determination of a specific tissue characteristic G can be trusted. Generally speaking, the reliability decreases as the contamination of the light receiving device 16 increases. However, this does not equally apply to all tissue characteristics G to be determined. For example, even when the light receiving device or its light receiving window is already significantly contaminated, adipose tissue can still be well discriminated from bone tissue. Or, for example, even when the degree of contamination is lower, the discrimination of more subtle tissue types or the discrimination between non-malignant and malignant tissues may already be unreliable.

[0024] When the light receiving window is contaminated, its transmission characteristics change. The deposits on the light receiving window have an effect similar to that of a filter, so there is an effect of distorting the spectrum. The allocation device can provide various transmission curve models 26 specific to each contamination V (V1, V2... Vn). For example, the transmission curve V1 without contamination has all-pass characteristics, while the transmission curves V2... Vn are transmission curves with low-pass characteristics or band-pass characteristics, or transmission curves with filter curves having a plurality of minimum values, maximum values, and / or inflection points. Regarding this, FIG. 3 shows a typical change in the transmission curve while the contamination increases from 1 to 9. The curve shows that the illustrated light intensity I depends on the light wavelength λ.

[0025] During the identification of tissue characteristic G, different reliability values R are obtained for each transmission curve model 26 having different contaminants V1 to Vn. This is shown in FIG. 5 for different tissue types of type A to type F. Also in this case, numbers 1 to 8 are assigned to the contaminants as in FIG. 3, and the numbers correspond to the increasing degree of contamination. Except for types C and E, a reliability of almost 100% is still obtained in the case of a contamination degree of 1 in almost all tissue types. As the degree of contamination increases (2, 3, 4, etc.), the reliability decreases depending on the tissue type, and the amount of decrease varies. This applies to tissue types A to F and other tissue characteristics G or tissue features. For example, when the contamination degrees of three certain tissue characteristics G are defined, for example, the characteristics of type B can still be determined reliably, type D is somewhat reliable, and other types or characteristics are, for example, no longer reliable. This information, that is, which tissue characteristic G can be determined with what reliability, is part of each transmission curve model 26 of V1 to Vn.

[0026] The evaluation device 22 first determines a desired tissue characteristic G, for example, a tissue type. Thereafter, the assignment device 23 assigns each reliability value R based on the respective effective transmission curve model 26 (V1, V2... or Vn) to this characteristic. Both pieces of data are provided to the display device 25 via the conductor 24 and can be displayed on the display device 25. Thereby, the data D characterizes, for example, the specified tissue type or another tissue characteristic G. Thereby, the reliability value R characterizes the reliability of determining the tissue characteristic G.

[0027] The determination of the reliability value R can be performed prior to actual application in at least one embodiment of the present invention. In this case, the reliability value R can also be subsequently assigned following the transmittance measured during the operation. For example, the spectrum can be recorded, and the tissue can be classified prior to application to the test tissue using a fiber having a transmittance of 100%. Subsequently, different transmittance curve models can be used to simulate different contaminations. Next, these transmittance curve models can be used to reclassify the tissue. By comparison with the tissue classified at a transmittance of 100%, it is possible to determine to what extent each transmittance curve model deteriorates in terms of the reliability of tissue analysis.

[0028] And a transmittance curve model can be used during the application to determine whether a fiber having a specific transmittance measured during the application is still sufficiently good for the current tissue classification.

[0029] Also, when the reliability value R falls below a specified or selected limit, it is possible not to display the tissue characteristic G (data D).

[0030] As is apparent from FIGS. 3 and 5, the transmittance curve model 26 can be a one-dimensional model that characterizes only increasing contamination. However, it is also possible to configure classifications of various contaminations so that the contamination type T and the size or degree K of the contamination can be discriminated. For example, the contamination type can depend on the type of contamination (carbon black deposit, tissue deposit, deposit of other gas). This can be particularly applicable when the respective particle sizes are different. And the degree of contamination K can characterize the thickness of the formed deposit. For example, the contamination type T can define different filter curves, such as low-pass or band-pass, or combinations of different basic characteristics, while the degree of contamination characterizes the cut-off frequency, slope or other parameters of the filter curve.

[0031] The allocation device 23 must select, from the provided transmission curve models 26, the model that best matches each contaminant. Regarding this, FIG. 2 is referred to. In the first spectrum 27, a spectrum such as that recorded for a specific tissue type, for example muscle tissue, in the case of the uncontaminated light receiving device 16 is shown. There are a plurality of spectral lines A, B, C occurring at different intensities I. The number of spectral lines and their intensities depend on each tissue type. They are merely metaphorically shown in FIG. 2 here. Now, when different spectra 28 are recorded for each tissue, for example muscle tissue, due to the contamination of the light receiving device 16, the spectral lines are deformed due to the contamination. Spectrum 27 shows spectral lines A, B, C, while spectrum 28 includes spectral lines a, B, C, that is, one or more spectral lines have an intensity I lower than the intensity they would have in the case of a clean light receiving device 16. However, when the surgeon knows from which tissue the spectrum is generated and this information is available to the evaluation device 22, the evaluation device 22 can determine, based on the deformation of spectrum 28 compared to the ideal spectrum 27, which of the transmission curve models 26 has clearly affected the deformation of the spectrum, and can select each transmission curve model from the group of available models V1 - Vn. When selecting the transmission curve model, the evaluation device 22 simultaneously obtains from the allocation device 23 an evaluation of the reliability that can identify specific tissue characteristics. Since the reliability value R for all other tissue characteristics G is known for the transmission curve model, while the display device 25 constantly shows the determined tissue characteristic G (for example, tissue type) and the assigned reliability value R that identifies the tissue characteristic G (for example, tissue type) to the surgeon, the surgeon can continuously work with his instrument and can act on different tissue types.

[0032] The selection of each transmission curve model can be confirmed after a predetermined time interval, for example, after 1 second or more. It is also possible to estimate the transmission model based on the startup time and the contamination rate so far. The estimation can be confirmed by transmission measurement at a specified time interval or on a given occasion, for example, during the operation of the instrument. The surgeon does not need to perform separate calibration.

[0033] In a modified form of the present invention, as shown in FIG. 6, it is also possible to provide a test device 30. The test device 30 includes a light source 31 that can supply light having a specified spectral composition to the light receiving window of the light receiving device 16. Thereby, the control device 34 adjusts the test process by sometimes operating the light source 31 so that the light enters the light receiving window of the light receiving device 16. The spectrometer 20 outputs its data to the transmission classifier 33 via the switch 32 under this test condition, and the transmission classifier 33 determines the contamination degree K and / or the contamination type T. The start of the test and the control of the progress of the test are under the management of the control device 34 that can be part of the test device 30.

[0034] Alternatively, the surgical area illumination can be used as the light source 31. Regarding this, a short operation pause in which the electrode 13 does not emit the spark 12 can be used. The control device 34 can process a routine for determining an appropriate transmission curve model using these operation pauses.

[0035] The contamination degree K and / or the contamination type T provided by the transmission classifier 33 are supplied to the assignment device 23, and then the assignment device 23 selects a matching transmission curve model 26 similar to that described above with reference to FIG. 2. The transmission measurement provides information regarding the contamination degree. The transmission classifier 33 and / or the assignment device 23 can determine a comparable transmission curve model (selected from the pool) and determine which tissue classification is possible with what reliability R. Thereby, the transmission classifier 33 and / or the assignment device 23 can perform interpolation between comparable transmission curve models based on the contamination degree and / or the contamination type.

[0036] When the test is completed, the control device 34 switches the switch 32 again so that the signal supplied by the spectrometer 20 is sent to the evaluation device 22. Also in this case, the evaluation device 22 determines the desired tissue characteristics from the spectrum obtained from the spark light. These tissue characteristics are provided in the form of data to a display device 25 indicating the tissue characteristics.

[0037] At that time, the transmission classifier 33 and the assignment device 23 can predict how good the result of the tissue classification is from the measured transmittance. For example, by tissue classification, a result with 92% accuracy is obtained using an actual contaminated fiber. In the case of a clean fiber, the tissue classifier provides, for example, a result with 96% accuracy. By the transmission classifier 33, the measured transmittance can here be subdivided into a transmittance that realizes, for example, an accuracy of 92% or more and a transmittance for which the expected quality of the tissue classification is less than 92%.

[0038] For tissue determination for determining the type of tissue, data D is provided to the tissue classifier 33. The type of tissue and the determined reliability value R are here supplied to the display device 25. The display device 25 can display the reliability value R. The display device 25 can also indicate whether the reliability value R falls below a threshold value. The threshold value can be fixed or defined variably.

[0039] The tissue analysis device 10 according to the invention having a light receiving device 16 and a spectrometer device 20 for determining tissue characteristics G comprises, for this purpose, an evaluation device 22 connected to the assignment device 23. The evaluation device 22 serves to determine at least one tissue characteristic G of the living tissue, for example the type of living tissue or the determination of a disease infection. The assignment device serves for the assignment of a suitable transmission curve model 26 which models the contamination of the light receiving device 16. Different transmission curve models are provided for different degrees of contamination, each containing a reliability value R which can be determined for each tissue characteristic G. Using the concept of the invention, it is possible to realize not only tissue analysis but also an indication of the reliability of the performed analysis, that is, how reliable the indication of the tissue characteristic G is.

Description of Symbols

[0040] 10 Tissue analyzer 11 Biological tissue 12 Spark 13 Electrode 14 Instrument 15 Device 16 Light receiving device 17 Light guide 18 Distal end of the light guide 17 20 Spectrometer device 21 Conductor 22 Evaluation device G Tissue characteristics D Data 23 Allocation device 24 Conductor 25 Display device 26 Transmission curve model V, V1...Vn Contamination / transmission curve I Light intensity λ Light wavelength T Contamination type K Degree of contamination R Reliability value 27 First spectrum 28 Different spectra 30 Test device 31 Light source 32 Switch 33 Transmission classifier 34 Control device 35 Fiber optic coupler

Claims

A tissue analysis device (10) incorporated in a surgical instrument (14) and / or a device (15) useful for providing the instrument, comprising: a light receiving device (16) that receives light generated by the action of an electric spark (12) or plasma on tissue (11); a spectrometer device (20) that determines the light intensity at different wavelengths of the light; an evaluation device (22) that determines data (D) characterizing at least one tissue property (G) from the light intensity; an assignment device (23) that classifies contamination (V) of the light receiving device (16) based on the light intensity determined by the spectrometer device (20), and assigns and / or determines a reliability value (R) for the tissue property (G) based on the contamination (V); A tissue analysis device.

2. A test device (30) for classifying the contamination is provided The tissue analysis device according to claim 1.

3. The classification includes assigning the contamination (V) to one or more contamination types (T) The tissue analysis device according to claim 1 or 2.

4. The test device (30) detects the degree (K) of the contamination (V) The tissue analysis device according to claim 2.

5. The assignment device (23) determines the reliability value (R) based on the contamination (V), and the reliability value (R) is specific to the tissue property (G) determined by the evaluation device (22) The tissue analysis device according to claim 3 or 4.

6. The assignment device (23) includes a model regarding the relationship between the reliability value (R) identifying a specific tissue property (G) and the contamination (V) The tissue analysis device according to any one of claims 1 to 5.

7. The model is a data aggregate The tissue analysis device according to claim 6.

8. The assignment device (23) includes a plurality of transmission curve models (26) specific to each contamination (V) The tissue analysis device according to any one of claims 1 to 7.

9. The assignment device (23) selects the matching transmission curve model (26) based on the spectrum (28) obtained by the spectrometer device (20) The tissue analysis device according to claim 8.

10. A reliability value (R) is assigned to each transmission curve model (26) of each tissue property (G) The tissue analysis device according to claim 8 or 9.

11. A test device (30) for determining the contamination (V) is provided, which includes a light source (31) that emits multi-spectral light. The tissue analysis device according to any one of claims 1 to 10.

12. The test device (30) determines the contamination (V) based on a plurality of light wavelengths. The tissue analysis device according to claim 11.

13. A method of tissue analysis, comprising: a light receiving device (16) receives light generated by the action of an electrical spark (12) or plasma on the tissue (11); light intensities (I) at different light wavelengths (λ) are determined from the received light; data (D) characterizing at least one tissue characteristic (G) is determined from the light intensity (I); the contamination (V) of the light receiving device (16) is classified based on the determined light intensity; a reliability value (R) is assigned to the determined data (D) based on the contamination (V); when the assigned reliability value (R) exceeds a threshold value, the tissue characteristic (G) and the assigned reliability value (R) are shown, or only the tissue characteristic (G) is shown. Method.

14. A test interval is defined according to the determined tissue characteristic (G). The method according to claim 13.

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