DE4420401A1 - Retro-reflective device - Google Patents

Description

The phenomenon lies with retro-reflective spectroscopic devices based on the fact that electromagnetic waves from irradiated Materials are partly reflected, partly absorbed and the affected materials for the emission of new elec can excite tromagnetic waves (so-called fluorescence). If one detects the intensity as a function of the waves length of the retroreflection of an examined sample, so obtained one a characteristic spectrum. It can also or alternatively the temporal development of the retroreflection be taken into account. With simultaneous consideration of intensity, wavelength and temporal development of the Retroreflection the spectrum could be represented as an area. Of the For the sake of simplicity, however, the Curves. The goal of retroreflective spectroscopy consists of obtaining unknown samples based on such spectra identify and analyze. Areas of application of the Retroreflect spectroscopy are mainly in the area of Medicine, especially laser-induced fluorescence spec tren (e.g. in the field of biopsy), but also in areas such as environmental protection (e.g. Air analysis) and recycling (e.g. material separation).

Include reflective spectroscopy devices basically a facility for generating and targeted  Forwarding electromagnetic waves to the under searching sample and a device for receiving the return beam spectrum. To illustrate the following Dar Unless otherwise stated, the position is increased in the further text as a source of electromagnetic radiation used a laser as an example, and as a retroreflect spec the fluorescence spectrum induced by the laser. The principle independence of those presented below Devices and solutions to problems of these exemplary However, restrictions are explicitly highlighted.

Identification methods known from medicine are based essentially on two different approaches:

(1) From the entire fluorescence spectrum one or selected several peaks, the specific elements, enzymes or atomic compounds can be assigned. The height of the peak and thus the intensity of the underlying Radiation depends on the concentration of the respective ele together etc. This can result in a significant increase of the peak in degenerate versus undegenerate tissue be determined. This is a yes / no decision like carcinoma / non-carcinoma in otherwise known tissue enables (see e.g. J. Beuthan et al., studies on NADH concentration determination using optical biopsy, in: Laser medicine Vol. 10, pp. 57-63, Stuttgart 1994). Frequently in similar processes, fluorescence is caused by appliqué tion of dyes reinforced. The - among other physi calic and biological disturbing factors - essential after parts of this process are that it is only yes / no- Allows decisions and laboratory conditions. So For example, the distance between the sample and the Laser emitter can be known because of the height of the peak here too depends on. Furthermore, the coloring of living tissue in the body due to possible side effects such as allergies only partially practical.  

(2) The entire fluorescence spectrum is used in the process selects areas of the pair comparison which differ significantly from each other after "ratio" formation. This in turn enables a differentiation between degenerate and non-degenerate tissue of the same type. The However, finding such places means a high time and Workload. A quick analysis is not possible. As a result, only distinctions like ent can be made degenerates / does not degenerate in otherwise known tissue (see e.g. Fluorescence Spectroscopy: Ramanujam et al., A Diagnostic Tool for Cervical Intraepithelial Neoplasia (CIN), in: Gynecologic Oncology 52, 31-38, Aus tin / Texas 1994).

It therefore corresponds to the state of the art, the re beam spectroscopy for "yes / no" examination of known Use tissue for pathological changes. The investigation must always be in an even treatment required separately. There is a constant or at least a known Ab was often a requirement between the laser emitter and the sample for a comparison of the spectra.

So far it has hardly been possible to determine from which Tissue types an unknown sample exists. And it is not possible in the "on-line" process under variable, unbe Known distances between exit emitter and sample for example to determine which type of fabric will wear, or to control the laser so that only the desired type of tissue is automatically removed. More generally speaking, it is therefore not possible to use the current medical and general technical field Processes using retroreflective spectroscopy, in particular laser-induced fluorescence spectroscopy, in sync with check and intervene if necessary. Des further it is not possible to have more than two dimensions like wavelength and time or wavelength and intensity  to be considered at the same time. This leads to loss of time most, limits the possible uses of the retro-beam spectroscopy and causes the elaborate investigation correspondingly high costs.

The aim of the present invention is now a to create retroreflective spectroscopic device which is in principle able to during any one set of a source for electromagnetic waves, such as of a laser to identify any unknown sample grace and analyze.

According to the invention, this goal is the subject of Claim 1 achieved, that is, by a retro-reflective spectroscope pische device, with a device for generating and targeted transmission of electromagnetic waves to a sample to be examined, means for recording and digitali sation of the resulting retroreflective spectrum and a Analysis device. The analyzer is like this placed so that it has access to one or more specific mean curves, each formed from nor mated reflectance spectra of known samples of the same type, one or more associated, on the deviations of the individual normalized spectra from their mean curve based weighting curves and associated ones, for each because of the type of sample characteristic limits for weighted Error. She evaluates her from the recording / digitizing digitized spectra supplied as follows from: it normalizes the spectra supplied to it in the same way Way, as with the specific ones available to her Mean curves happen and compare them with the previous ones existing mean value curves, it weights any deviations of the available mean value curves with the respective weighting curve and it determines whether the ge important deviation inside or outside of each associated characteristic limit for weighted Error and whether the digitized spectrum of the  associated sample type or not. The won It outputs this information via an output device.

Compared to the prior art, the invention offers Device according to claim 1 especially the advantage of poss Possibility of an "on line" fabric or material identification ornamentation and analysis. It can be of any size section of the respective measurement curves are taken into account. The Previously customary and only partially possible Assignment of individual curve sections to the underlying Elements or enzymes are viewed through a global perspective replaced. Here, the dimension of the Time, intensity and wavelength in the analysis ver drive down. This allows the reliability and Increase the informative value of the analysis. The entire evaluation the measurements are fully automatic and require from Users have no expertise in this regard. The so far usual scientific interpretation of the measured spectra is created by one, the human mind due to its enormous complexity inaccessible, comparative technical evaluation process replaced. The important ones for the dentist, for example Statements about the tissue to be treated, such as B. Type of Tissue, degenerate / non-degenerate, changed / not changed change, degree of a healing course, etc. B. by Specialist words or symbols are displayed or acoustically communicated be and allow a constant objective, from the practitioner independent and reproducible control over you ongoing intervention.

In an advantageous embodiment of the invention The device is the analysis device for a learning mode designed and has an input option for the desired characteristics of the sample types to be learned and a memory for the learned sample types (claim 2). In an advantageous embodiment of the device according to Claim 2 be in learning mode by test measurements on be  samples knew the required mean and wich tion curves, as well as the corresponding ones, for the respective Specimen-specific limits for the weighted Errors determined by the analyzer by the standardized test spectra for each sample type, averaged and based on the deviations of the individual nor weighted spectra from their mean value curve the test based on the weighted errors that occur measurements a characteristic of the respective sample type cal limit for the weighted error is determined and the results obtained are saved (claim 3).

Such self-taught learning ability of the inventor The device according to the invention also enables this technical laymen a problem-free adaptation to or expansion the desired possible area of application. Ins there is a special possibility, for example at the Removal of a tumor first of all the specifically intended tissue be in the "safe" area of the device according to the invention to teach in learning mode in order to then be as precise as possible To be able to remove marginal areas.

In a further advantageous embodiment of the Invention according device gives the output device for the Information obtained thereon also or exclusively as Control signals for one or more treatment devices the sample from and on to the devices (claim 4). At an advantageous embodiment of the invention Direction according to claim 4 is the device for generation and targeted transmission of electromagnetic waves a sample to be examined as a laser for treatment or Removal of tissue or other materials and the analyzer converts the identifications or analyzes of the respective sample in Steueri gnale for the corresponding continued operation of the laser (Claim 5).  

The subject of claim 4 or 5 allows, for example wise extensive automation of operational intervention fe. For example, there is the possibility of a straight laser to control over a tissue complex and to determine beforehand, that only a certain type of tissue is removed. To for this purpose the laser can be controlled so that it independently between weak, sufficient for analysis Starch and strength needed to remove tissue back and forth switched. With such a precise removal of Weaving z. B. an enormous advance in the field of minimally invasive surgery. For example in Field of angioplasty and other important medicin There is an urgent need for such areas Options. Bacteria could be present in highly sterile areas recognized and killed by the laser. The Flexibi are the application of the device according to the invention after all, none outside of the field of medicine Set limits. For example, one would be conceivable automated waste separation based on this method ba siert, or an automation of the meat inspection in Slaughterhouses, etc., z. B. devices for Ausor unwanted samples could be controlled.

The invention based on preferred embodiment Example and the attached schematic drawing explained in more detail. In turn, it will be a look ensure representation - as a source of electroma electromagnetic radiation uses a laser and the laserindu graced fluorescence spectrum evaluated. On the self permanent possibility of principle, also others electromagnetic radiation, such as burl, light emission emitting diodes, high pressure lamps or highly parallel radiant lamps, within the scope of the device according to the invention use, if this should be useful, has already been referred. The drawing shows:

Figure 1 shows a first embodiment with a pulsed laser.

2 shows a second embodiment with a non-pulsed laser.

Figure 3 shows a first embodiment of the corresponding third embodiment in which a device is controlled for the treatment of the sample. and

Fig. 4 shows a fourth embodiment corresponding to the first embodiment, which controls the laser.

In FIGS. 1-4, a laser beam in the block 1 is generated and fed via an output 3 to a conductor for electromagnetic waves, in this case, a first light guide 4 in the form of a glass fiber. Whose output 5 is placed such that it enables the irradiation of a sample 6 with the laser beam. The retroreflective light, in the present example the fluorescent light, is reflected in FIG. 1 via the light guide output 5 and the light guide 4 in the laser output 3 and, before it reaches the block 1 , through a beam splitter 2 via a second light guide 7 in a retroreflective spectroscopic system 8 headed. There the fluorescence spectrum is determined and digitized. In order to avoid interference of the fluorescent light by laser reflections, the laser beam is pulsed by a "trigger" device 11 in such a way that the path between the radiation splitter 2 and the light guide output 5 is never penetrated by laser and fluorescent light at the same time. Such an embodiment has the advantage that the diameter of the light guide output 5 can be minimized. If this is of no importance or if an uninterrupted laser beam is desired without interference effects occurring and having to be taken into account, one or more third light guides 12 can be seen as in FIG. 2, which direct the fluorescent light to the back beam spectroscopic system 8 hand off.

In FIGS. 1-4, the rückstrahlspektroskopische system 8 outputs the digitized spectrum to an analyzing device 9, for example a device for data processing for evaluation on. In the following, the processes of this evaluation and that of the learning process are shown schematically. Afterwards, a possible concrete mathematical formulation is added based on a specific test procedure.

In the first step of the evaluation process, the received spectrum is standardized. A suitable form of standardization is the area standardization. The normalized spectrum is then compared with the existing and equally normalized mean value curves. For this purpose, the distance curve of the spectrum is calculated from the mean curve (this gives the distance to each wavelength, preferably the relative distance, alternatively but also, for example, the absolute distance between the measured spectrum and mean curve), and im Connection determined on the basis of the weighting curve belonging to the respective mean value curve, the weighted error of the measurement spectrum compared to the mean value curve. The weighted error is obtained by multiplying the respective distance curve by the corresponding weighting curve. Then it is determined from the position of the weighted errors relative to the error limits characteristic of the individual sample types, which type of sample the examined sample corresponds to (or whether it does not correspond to any of the recorded sample types). In the experiment, a probability of error-free assignment of around 90% was achieved even with statistically poor mean value curves (based on ten test measurements only). The data thus obtained (type of tissue, condition of the tissue, etc.) are communicated to the practitioner with the aid of an output device 10 , such as a monitor or an acoustic system. For example, technical terms or symbols can be used.

In order to use an embodiment according to FIG. 1 or 2 in the learning mode, the analysis device 9 is switched over to the learning mode via a switching means and an input possibility for the desired characteristics for the samples to be entered. The actual learning process takes place according to the following sequence: Test measurements are carried out on known samples and the associated indicators for the respective sample type are entered. The resulting fluorescence spectra are normalized. An average curve is formed from all the normalized spectra belonging to a sample type. Then the standard deviation curves - taking into account the relative distances - to the respective mean value curves are preferably calculated. Weighting curves are determined from the deviation curves obtained in this way. A particularly suitable weighting is proportional to the square of the distance. The weighted errors are calculated from the weighting curve and the standard deviation curve. A lower error limit is established based on the position of the weighted errors. A suitable criterion for this is, for example, to allow one or a few "outliers" (relative to the number of test measurements x%) and to set the next weighted error as the characteristic error limit. The data obtained in this way are automatically stored in the Analyze device 9 and can be transferred to other data processing devices as desired using the usual data transmission means.

To illustrate the method in particular, a concrete test execution shown:  

TMJ samples from freshly slaughtered pigs were used as test objects used. The aim was to differentiate between six different types of tissue. It were the muscle, disc, capsule, cancellous bone, cortical bone and cartilage.

Experiment description. . . Excimer laser, 308 nm, 110 ns, 140 milli joules

60 measurement curves were recorded, that is to say 10 for each tissue type. All 60 curves were standardized, then, using these standard curves, the 10 characteristic ones Average curves for the different tissues determined.

In the next step, the related standard deviation curves were used for these mean value curves calculated. This resulted in a deviation curve for each test fabric. The weighting function could be determined with the deviation curve.

The weighting was carried out in proportion to the square of the distance:

with i = 1. . . 6; j = 1. . . 10th

Becomes the related standard deviation

greater than ε applies

w _i (λ) = 0

Now the weighted errors have been calculated:

This results in ten weighted errors for each of the six tissue types. Out the location of the errors, a lower error limit can be established. In this one case was said: an outlier is allowed, d. H. the second largest Error has been made the error limit.

The test then proceeded as follows:
A curve to be checked was read in and standardized. Then all six weighted errors were calculated for the curve:

where G (i) is the weighted error with respect to object i, and g (λ) is the normalized one curve to be checked.

The position of the weighted errors relative to the error limits F _i could then be determined with approximately 90 percent certainty which tissue was present. This means that 10 percent of the tissue was not recognized even though it was present, and that the other way around, the tissue was incorrectly mistaken for another with a 10 percent probability.

Three-dimensional

If you consider three dimensions at the same time, namely the intensity, the wavelength and the temporal development of the reflection spectrum, then the following changes are required. The weightings that related deviations etc. as areas over the wavelength and the time shown. The formulas then have the same form as above - only that as further dependence the time occurs.

The formulas in detail:
The weighting becomes:

The weighted errors of the test fabrics are calculated as:

The characteristic error limits F _i can thus be calculated again, which are then compared again with the weighted errors of an unknown sample with respect to an object to be tested. These weighted errors G (i) are calculated as follows:

The temporal influence during the fluorescence examination can also be considered as Include pattern recognition information.

f _ÿ - normalized measurement curve of a calibration sample (i: object number)
g _λ - normalized measurement curve of a sample
f _mi - mean value curve of calibration object i
Δf _ÿ - (f _ÿ -f _mi )
w _i (λ) - weight curve of object i
F _i - error limit with respect to object i
G (i) - weighted errors from g (λ) with respect to object i
ε - Significance barrier (0.05)
λ - wavelength
t - time
j - number of calibration samples of an object i
i - number of the object

In FIGS. 3 and 4, an embodiment according to FIG. 1, which is used for automatic control of a device 13 for treatment of the sample 6 and for controlling the sample under investigation and treated laser 1 via a return connection 14. The device or devices 13 can be created via the line 15, for example for mechanical, but nevertheless targeted removal of materials or for the targeted sorting out of undesired materials, for example in waste separation. Any device 13 thus receives a visual perception combined with a corresponding responsiveness by the device according to the invention. In the case of the self-control of the laser 1, there is, for example, the possibility already mentioned of switching back and forth between the pure analysis of useful, weak laser strength and, at the same time, the removal of useful, large laser strength. In this way, in medicine, for example, unintended injuries to tissue accidentally hit by laser can be avoided and the laser can thus be handled much more simply or automation of the intervention can be made possible. For these purposes, it is necessary for the analysis device to convert the data obtained into corresponding control signals and to transmit them to the devices to be controlled.

Claims (5)

1. reflective spectroscopic device, with

• a) a device for generating ( 1 ) and ge targeted forwarding ( 3-5 ) electromagnetic waves to a sample to be examined ( 6 );
• b) means ( 8 ) for recording and digitizing the resulting retroreflective spectrum; and
• c) an analysis device ( 9 ) which is designed such that it
• c.1) one has access to c.1.1) one or more specific mean value curves, each formed from standardized retroreflect spectra of known similar samples;
• c.1.2) one or more associated weighting curves based on the deviations of the individual standardized spectra from their mean value curve; and
• c.1.3) associated limits for weighted errors that are characteristic of the respective sample type;
• c.2) evaluates the digitized spectra supplied by the recording / digitizing means ( 8 ) as follows:
• c.2.1) it normalizes the spectra supplied to it in the same way as with the specific mean value curves available to it, and compares them with the existing mean value curves;
• c.2.2) it weights any deviations from the available mean value curves with the respective weighting curve; and
• c.2.3) it determines whether the weighted deviation lies within or outside the respective characteristic limit for weighted errors and whether the digitized spectrum is therefore to be assigned to the associated sample type or not; and
• c.3) outputs the information obtained via an output device ( 10 ).

2. Device according to claim 1, in which the analysis device ( 9 ) is designed for a learning mode and has an input option for the desired characteristics of the sample types to be learned and a memory for the sample types learned. 3. Apparatus according to claim 2, in which in the learning mode by means of test measurements on known samples, the required mean and weighting curves, as well as the associated limits characteristic of the respective sample type for the weighted error are determined by the analysis device ( 9 ) by

• a) the test spectra belonging to each sample type are standardized, averaged and weighted based on the deviations of the individual standardized spectra from their mean value curve;
• b) a characteristic limit for the weighted error, which is characteristic of the respective sample type, is determined on the basis of the weighted errors of the test measurements;
• c) the results obtained are saved.

4. Device according to one of the preceding claims, in which the output device ( 10 ) also or exclusively outputs the information obtained as control signals for one or more devices ( 13 ) for treating the sample ( 6 ) and forwards them to the devices ( 13 ) . 5. The device according to claim 4, wherein the device for generating ( 1 ) and targeted transmission ( 3-5 ) of electromagnetic waves to a sample to be examined ( 6 ) is designed as a laser for treatment or ablation of tissue or other materials is, and the Analyteinrich device ( 9 ) converts the identifications or analyzes of the respective sample ( 6 ) into control signals for the corresponding continued operation of the laser ( 1 ).