Blood-vessel recognizing method and blood-vessel recognizing device

A blood-vessel recognizing method for recognizing blood vessels present in biological tissue, the method including: obtaining real-time Doppler spectra on the basis of time waveforms data of intensities of scattered light generated in the biological tissue due to irradiation with laser light; calculating average frequencies of the real-time Doppler spectra; correcting the calculated average frequencies on the basis of peak intensities of the real-time Doppler spectra; and determining whether or not blood vessels are present in regions of the biological tissue irradiated with the laser light on the basis of the corrected average frequencies.

TECHNICAL FIELD

The present invention relates to a blood-vessel recognizing method and a blood-vessel recognizing device.

BACKGROUND ART

In performing a surgical procedure on biological tissue, it is important that the surgeon accurately recognizes blood vessels in the interior of the biological tissue. Thus, there is a known device equipped with a function for recognizing blood vessels present in biological tissue (for example, see Patent Literatures 1 and 2). In Patent Literatures 1 and 2, blood vessels are extracted from an image on the basis of differences between the blood vessels and other regions in terms of light reflection or absorption, and the extracted blood vessels are emphasized in the image.

CITATION LIST

Patent Literature

SUMMARY OF INVENTION

One aspect of the present invention is a blood-vessel recognizing method for recognizing blood vessels present in biological tissue, the method including: obtaining real-time Doppler spectra on the basis of time waveforms data of intensities of scattered light generated in the biological tissue due to irradiation with laser light; calculating average frequencies of the real-time Doppler spectra; correcting the calculated average frequencies on the basis of peak intensities of the real-time Doppler spectra; and determining whether or not blood vessels are present in regions of the biological tissue irradiated with the laser light on the basis of the corrected average frequencies.

Another aspect of the present invention is a blood-vessel recognizing device including: an optical fiber that radiates laser light onto biological tissue; and one or more processors comprising hardware, wherein the one or more processors are configured to: acquire real-time Doppler spectra on the basis of time waveforms data of intensities of scattered light generated in the biological tissue due to the irradiation with the laser light; calculate average frequencies of the acquired real-time Doppler spectra; correct the calculated average frequencies on the basis of peak intensities of the real-time Doppler spectra; and determine whether or not blood vessels are present in regions of the biological tissue irradiated with the laser light on the basis of the corrected average frequencies.

DESCRIPTION OF EMBODIMENT

A blood-vessel recognizing device according to an embodiment of the present invention will be described below with reference to the drawings.

As shown inFIG.1, a blood-vessel recognizing device100according to this embodiment comprises: a probe1that can be inserted into a living organism, that radiates laser light L toward tissue T in the living organism, and that receives scattered light S from the tissue T; a light-source unit2that supplies the probe1with the laser light L and visible light V; a light-detecting portion (spectrum acquisition portion)3that detects the scattered light S received by the probe1; and a control device4that applies analytical processing to data for the scattered light S detected by the light-detecting portion3and that controls the light-source unit2.

The probe1comprises: a long, thin probe body5; and an irradiation optical fiber (laser-light radiating portion)6and a light-receiving optical fiber (spectrum acquisition portion)7that are provided in the probe body5along the longitudinal direction thereof.

The probe1may be a treatment device with which the tissue T is treated, such as a high-frequency scalpel. In this case, a working portion (not shown) for treating the tissue T is provided at a distal end of the probe body5.

A distal end of the irradiation optical fiber6is disposed in the vicinity of the distal end of the probe body5, and a base end of the irradiation optical fiber6is connected to the light-source unit2. The laser light L and the visible light V supplied to the base end of the irradiation optical fiber6from the light-source unit2are emitted forward in the longitudinal direction of the probe body5from the distal end of the irradiation optical fiber6.

A distal end of the light-receiving optical fiber7is disposed in the vicinity of the distal end of the probe body5, and a base end of the light-receiving optical fiber7is connected to the light-detecting portion3. The scattered light S of the laser light L scattered by the tissue T is received by the light-receiving optical fiber7and is guided to the light-detecting portion3.

The light-source unit2comprises: a laser light source8that outputs the laser light L; a visible light source9that outputs the visible light V having a wavelength in the visible range; and an optical multiplexer (not shown) that combines the laser light L and the visible light V and makes the combined light enter the irradiation optical fiber6.

The laser light source8outputs the laser light L in a wavelength range in which absorption by blood is low and in which the laser light L is allowed to reach a deep portion from a surface layer of the tissue T (for example, the near infrared region or the infrared region). The surface layer refers to a region between the surface of the tissue T and a depth of about several tens of micrometers to several hundreds of micrometers. The deep portion refers to a region that is deeper than several hundreds of micrometers from the surface of the tissue T (for example, a region at a depth that is equal to or greater than 3 mm from the surface of the tissue T). Many small blood vessels B1are present in the surface layer, and many large blood vessels B2(for example, blood vessels having diameters that are equal to greater than 2 mm) are present in the deep portion. Therefore, the scattered light S that is received by the light-receiving optical fiber7could contain scattered light S generated by the small blood vessels B1and scattered light S generated by the large blood vessels B2.

It is preferable that the visible light source9be a laser light source. It is preferable that the color of the visible light V be a color that allows a surgeon to easily visually recognize the visible light V radiated onto the tissue T, for example, green or blue.

The light-detecting portion3comprises a photodetector such as a photodiode or a photomultiplier tube. The light-detecting portion3receives the scattered light S guided thereto by the light-receiving optical fiber7and converts the intensity of the received scattered light S to a digital value. The obtained digital value is transmitted to a storage portion10(described later) in the control device4.

The control device4comprises: the storage portion (spectrum acquisition portion)10that generates time waveforms by accumulating data about the intensities of the scattered light S detected by the light-detecting portion3; an analyzing portion (average-frequency calculating portion)11that calculates an average frequency<ω> of real-time Doppler spectra Freal (ω) by analyzing the time waveforms generated in the storage portion10; an average-frequency correcting portion12that corrects the average frequency<ω>; a blood-vessel determining portion13that determines the presence/absence of the large blood vessels B2on the basis of the corrected average frequency<ω>cal; and a control portion14that controls the laser light source8and the visible light source9.

The control device4is, for example, a computer, and comprises: a central processing unit (CPU); a main storage device such as a RAM; and an auxiliary storage device. The auxiliary storage device is a non-temporary storage medium, such as a hard disk drive, and stores programs for causing the CPU to execute processing (described later) performed by the analyzing portion11, the average-frequency correcting portion12, the blood-vessel determining portion13, and the control portion14. The processing performed by each of the portions11,12,13, and14is realized as a result of the programs being loaded into the main storage device from the auxiliary storage device and the CPU executing the processing in accordance with the programs. Alternatively, the processing performed by each of the portions11,12,13, and14may be realized by means of an FPGA (programmable logic device) or dedicated hardware such as an ASIC (application specific integrated circuit).

The storage portion10is formed from, for example, the main storage device or another type of storage. The storage portion10generates time-waveform data of the intensities of the scattered light S by time-sequentially storing the digital values received from the light-detecting portion3.

The analyzing portion11reads out the time waveform data from the storage portion10and obtains the real-time Doppler spectra Freal (ω) by applying high-speed Fourier transformation to the time waveform data. As shown inFIG.2, the real-time Doppler spectra Freal (ω) are decay curves in which the intensities decrease toward the high-frequency side, and exhibit the intensities at frequencies ω in accordance with the blood-flow velocities in the blood vessels B1and B2that generate the scattered light S. Specifically, because the Doppler shift of the scattered light S caused by blood decreases with a decrease in the blood-flow velocity, intensity distributions of the real-time Doppler spectra Freal (ω) are skewed toward the low-frequency side. On the other hand, because the Doppler shift of the scattered light S caused by blood increases with an increase in the blood-flow velocity, the intensity distributions of the real-time Doppler spectra Freal (ω) spread out toward the high-frequency side. The blood-flow velocity is known to be substantially proportional to the size of the blood vessel. Therefore, it is possible to estimate the sizes of the blood vessels B1and B2from the average frequencies<ω> of the real-time Doppler spectra Freal (ω).

Next, the analyzing portion11sets an integration interval for calculating the average frequencies<ω>. As shown inFIG.2, the integration interval is set to be an interval 0≤ω≤ωUon the basis of the spectral shapes of the real-time Doppler spectra Freal (ω) so as to exclude the high-frequency range in which the real-time Doppler spectra Freal (ω) decay to a background level, and thus the spectral intensities become the lowest.

Next, the analyzing portion11calculates the average frequencies<ω> in the integration interval 0≤ω≤⋅Uon the basis of equation (1) below.

FIG.2shows examples of the real-time Doppler spectra Freal (ω) obtained from blood vessels at different depths in biological tissue at the same blood-flow velocity.

Ideally, the average frequencies<ω> have a one-to-one correspondence with the blood-flow velocities. However, in reality, different average frequencies<ω> could be calculated even if the blood-flow velocities are the same. This is because, due to scattering or the like of the laser light L and scattered light S caused by the tissue T, the intensities of the scattered light S detected by the light-detecting portion3decrease with an increase in the depths at which the blood vessels B1and B2are located, as shown inFIG.2, the intensities of the real-time Doppler spectra Freal (ω) decrease as a whole, thus decreasing the average frequencies<ω>. Furthermore, as the intensities of the real-time Doppler spectra Freal (ω) approach the background level, the calculation precision of the average frequencies<ω> decreases as a result of being more strongly influenced by noise.

FIGS.3A and3Cshow the relationship between the average frequencies<ω> and intensities (log) of the real-time Doppler spectra Freal (ω) at the average frequencies<ω>.FIG.3Ashows the case in which the integration interval is 0≤ω≤ωU, andFIG.3Cshows the case in which the integration interval includes the high-frequency range in which the spectral intensities are at the background level. As shown inFIG.3C, in the case in which the integration interval also includes the high-frequency range, the average frequencies<ω> become variable, in particular, when the spectral intensities are low, and the relationship between the average frequencies<ω> and the intensities does not become constant. In contrast, as shown inFIG.3A, in the case in which integration interval is restricted to the range 0≤ω≤ωU, the variation in the average frequencies<ω> is suppressed, and, as a result, average frequencies<ω> that linearly increase with respect to the intensities (log) are calculated.

FIG.4shows an example of a method for setting the integration interval.

As shown inFIG.4, a real-time Doppler spectrum Freal (ω) is smoothed by using a moving average filter, a median filter, or the like (smoothing step). Next, as a result of subjecting a smoothed real-time Doppler spectrum Fmed (ω) to first-order differentiation first-order differentiation with respect to the frequency ω, a first-order differential spectrum|d1(ω)| that represents the slopes of spectral intensities at the respective frequencies co is obtained (first-order differentiating step). Next, the lower end of the integration interval is set to zero, and a frequency ωUat which the intensity of the first-order differential spectrum|d1(ω)| becomes equal to a prescribed value d1this set for the upper end of the integration interval.

Alternatively, a second-order differential spectrum|d2(ω)| may be obtained by additionally differentiating the first-order differential spectrum|d1(ω)| with respect to the frequency ω (second-order differentiation), and a frequency ωUthat gives a maximum value of a second-order differential spectrum|d2(ω)| may be set for the upper end of the integration interval (second-order differentiating step).

In this way, as a result of using the differential spectrum|d1(ω)| or |d2(ω)|, it is possible to detect, by means of calculation, the frequency ωU, which is a boundary between the interval in which the real-time Doppler spectrum Freal (ω) is decaying and the interval in which the real-time Doppler spectrum Freal (ω) does not decay any further and the intensities thereof have become the lowest. Note that, in the example inFIG.4, spectral intensities expressed as logarithms may be used.

FIG.5Ashows another example of the method for setting the integration interval.

As shown inFIG.5A, a real-time Doppler spectrum Freal (ω) is fitted by using an exponential function (fitting function) ffit(ω) represented by the equation below (fitting step), and a frequency ωUthat satisfies ffit(ω)=pthis set for the upper end of the integration interval. The peak intensity p′ of the exponential function ffit(ω) is A+C.
ffit(ω)=A×exp(−ω/B)+C

pthis set, for example, on the basis of the equation below:
pth=(A+C)×pfac+C,
where pfac=0.001.

As shown inFIG.5B, pthmay be set on the basis of the equation below:
pth=(ffit(ωp)+C)×pfac+C,
where pfac=0.001.

In other words, as a frequency ωpthat defines the peak intensity of the exponential function ffit(ω), a frequency other than zero may be set in advance, and ffit(ωp) may be used instead of p′.

The average-frequency correcting portion12converts the average frequencies<ω> to corrected average frequencies<ω>calby using a conversion equation G (F) and transmits the corrected average frequencies<ω>calto the blood-vessel determining portion13. The conversion equation G (F) is an equation in which the peak intensity p and the average frequency<ω> of a real-time Doppler spectrum Freal (ω) are variables, as represented in the equation below. In the equation below, F=<ω>, p is the intensity at a peak frequency ωpdefined in advance, and a1, a2, a3, b1, and b2are correction factors that are experimentally determined.
G(F)=a(F)×p+b(F)
a(F)=a1×F{circumflex over ( )}2+a2×F+a3
b(F)=b1×F+b2

The conversion equation G (F): is a linear function of the peak intensity p, which has a slope a (F) and an intercept b (F) that are determined by an average frequency F; increases the average frequency F by a greater increment with a decrease in the peak intensity p; and increases the average frequency F by a smaller increment with an increase in the peak intensity p. By using such a conversion equation G (F), the average frequency F is corrected so as to reach substantially the same level as that of the average frequency<ω> when the peak intensity p is high, as shown inFIGS.3A and3B. Preferably, the correction factors a1, a2, a3, b1, and b2of the conversion equation G (F) are determined so that the corrected average frequencies<ω>calare constant regardless of the peak intensities p, as shown inFIG.3B. By doing so, corrected average frequencies<ω>calthat have a one-to-one correspondence with the blood-flow velocities are obtained regardless of the peak intensities p of the real-time Doppler spectra Freal (ω).

FIGS.6A and7Aindividually show the relationship between the peak intensities p and the average frequencies<ω> of the real-time Doppler spectra Freal (ω) for small blood vessels (blood-vessel diameter D1) and large blood vessels (blood-vessel diameter D2, where D1<D2). At individual blood-flow velocities v1to v7and v1′ to v9′, with an increase in the peak intensities p, the average frequencies<ω> also increase. In other words, because the average frequencies<ω> differ in accordance with the peak intensities p even if the blood-flow velocities are the same, it is difficult to determine the blood-flow velocities and the sizes of the blood vessels only on the basis of the average frequencies<ω>.

Here, the peak intensities p and the average frequencies<ω> at the respective blood-flow velocities have a linear relationship, as indicated by broken lines inFIGS.6A and7A, and, at all of the blood-flow velocities, the slopes and the intercepts show increasing trends with an increase in the average frequencies<ω>. At the respective blood-flow velocities, the reliability of the average frequencies<ω> increases with an increase in the peak intensities p. Therefore, by correcting the average frequencies<ω> so as to reach the same levels as those of the average frequencies<ω> when the peak intensities p are high, it is possible to obtain corrected average frequencies<ω>calthat accurately correspond to the blood-flow velocities.

FIGS.6B and7Bindividually show the corrected average frequencies<ω>calobtained by converting the average frequencies<ω> inFIGS.6A and7Aby using the conversion equation G (F).

The correction factors a1, a2, a3, b1, and b2of the conversion equation G (F) used in calculating the corrected average frequencies<ω>calinFIG.6Bare as follows:
a1=−4.45E−07;
a2=2.21E−05;
a3=−7.47E−01;
b1=1.21E+00; and
b2=1.22E+00.

The correction factors a1, a2, a3, b1, and b2of the conversion equation G (F) used in calculating the corrected average frequencies<ω<calinFIG.7Bare as follows:
a1=−1.17E−06;
a2=2.21E−05;
a3=−7.47E−01;
b1=1.27E+00; and
b2=1.22E+00.

The above-described correction factors a1, a2, a3, b1, and b2are values optimized by using actually measured values of the peak intensities p and the average frequencies<ω> inFIGS.6A and7A. In this way, the correction factors a1, a2, a3, b1, and b2that are optimum with respect to the small blood vessels having the blood-vessel diameter D1and the correction factors a1, a2, a3, b1, and b2that are optimum with respect to the thick blood vessels having the blood-vessel diameter D2are substantially identical to each other. In other words, by optimizing the correction factors a1, a2, a3, b1, and b2, it is possible to correct the average frequencies<ω> by using the common conversion equation G (F) regardless of the blood-vessel diameters.

The blood-vessel determining portion13compares the corrected average frequencies<ω>calreceived from the average-frequency correcting portion12with a prescribed threshold. The prescribed threshold is an average frequency that corresponds to a minimum value of the diameters of the large blood vessels B2that serve as detection targets, and is set by, for example, the surgeon via an input means (not shown). The blood-vessel determining portion13determines that the large blood vessels B2are present when the corrected average frequencies<ω>calare equal to or greater than the threshold, and outputs TRUE signals to the control portion14. On the other hand, the blood-vessel determining portion13determines that the large blood vessels B2are not present in regions irradiated with the laser light L when the corrected average frequencies<ω>calare less than the threshold, and outputs FALSE signals to the control portion14.

The control portion14causes the visible light source9to output the visible light V when the TRUE signals are received from the blood-vessel determining portion13, and causes the visible light source9to stop outputting the visible light V when the FALSE signals are received from the blood-vessel determining portion13. By doing so, the visible light V is radiated only onto positions at which the large blood vessels B2are detected.

Next, the operation of the blood-vessel recognizing device100, thus configured, will be described with reference toFIG.8.

The blood-vessel recognizing device100according to this embodiment is used together with an endoscope for observing the interior of a living organism. First, the endoscope and the probe1of the blood-vessel recognizing device100are inserted into a body.

Next, outputting of the laser light L from the laser light source8is started, and the probe1is moved so that the laser light L radiated from the irradiation optical fiber6is scanned on the surface of the tissue T while the surface of the tissue T is observed with the endoscope.

The scattered light S generated in the regions irradiated with the laser light L is received by the light-receiving optical fiber7and is detected by the light-detecting portion3. Then, time waveform data of the intensities of the scattered light S are generated in the storage portion10.

Next, the time waveform data are read out into the analyzing portion11from the storage portion10, and the real-time Doppler spectra Freal (ω) are calculated from the time waveform data in the analyzing portion11. Next, in the analyzing portion11, the integration interval 0≤ω≤ωUis set on the basis of the shapes of the real-time Doppler spectra Freal (ω), as shown inFIG.8(integration-interval setting step S1), and the average frequencies<ω> in the integration interval 0≤ω≤ωUare calculated (average-frequency calculating step S2).

Next, in the average-frequency correcting portion12, the average frequencies<ω> are converted to the corrected average frequencies<ω>calin accordance with the conversion equation G (F) (average-frequency correcting step S3).

Next, in the blood-vessel determining portion13, on the basis of the corrected average frequencies<ω>cal, it is determined whether or not the large blood vessels B2are present in the regions irradiated with the laser light L (S4). When the corrected average frequencies<ω>calare equal to or greater than the threshold, the TRUE signals are output to the control portion14from the blood-vessel determining portion13. On the other hand, when the corrected average frequencies<ω>calare less than the threshold, the FALSE signals are output to the control portion14from the blood-vessel determining portion13.

When the TRUE signals are received from the blood-vessel determining portion13, the control portion14causes the visible light V to be emitted from the irradiation optical fiber6together with the laser light L. On the other hand, when the FALSE signals are received from the blood-vessel determining portion13, the control portion14does not cause the visible light V to be emitted. Therefore, it is possible for the surgeon to recognize the regions irradiated with the visible light V as regions in which the large blood vessels B2are present.

As has been described above, the intensities of the real-time Doppler spectra Freal(ω) differ in accordance with the environment in which the blood vessels B1and B2are placed, such as the depths of the blood vessels B1and B2in the tissue T and properties of the tissue T, even if the blood-flow velocities of the blood vessels B1and B2are the same, and thus, variation due to the difference in the spectral intensities occurs in the average frequencies<ω>. Therefore, in the case in which the sizes of the blood vessels B1and B2are determined only on the basis of the average frequencies<ω>, the presence of the large blood vessels B2may not be determined accurately as result of, for example, low average frequencies<ω> being calculated despite the presence of the large blood vessels B2at a deep position.

In contrast, with this embodiment, by correcting the variation in the average frequencies<ω> on the basis of the peak intensities p (or p′) of the real-time Doppler spectra Freal(ω), the corrected average frequencies<ω>calthat accurately correspond to the blood-flow velocities are obtained. It is possible to accurately recognize the sizes of the blood vessels B1and B2on the basis of such corrected average frequencies<ω>cal, and thus, there is an advantage in that it is possible to allow the surgeon to recognize the positions of the large blood vessels B2in a reliable manner.

In addition, by separately setting, on the basis of the shapes of the real-time Doppler spectra Freal (ω), the integration intervals for calculating the average frequencies<ω> of the real-time Doppler spectra Freal (ω), it is possible to enhance the calculation precision of the average frequencies<ω>, and thus, there is an advantage in that it is possible to further enhance the precision for determining the sizes of the blood vessels B1and B2.

In this embodiment, although the average frequencies<ω> of the real-time Doppler spectra Freal (ω) are corrected in one step by using the conversion equation G (F), alternatively, the average frequencies<ω> may be corrected in two steps, as shown inFIG.9. Specifically, before the correction by means of the conversion equation G (F), the average frequencies<ω> may be subjected to primary correction on the basis of the shapes of the real-time Doppler spectra Freal (ω) (S5), and the corrected average frequencies<ω>cal1that have been subjected to the primary correction may be subjected to secondary correction by using the conversion equation G (F).

Because the overall shapes of the real-time Doppler spectra Freal (ω) originating from the small blood vessels B1are upward-convex decay curves, there is a tendency to calculate average frequencies<ω> that are greater than the average frequencies<ω> accurately corresponding to the blood-flow velocities. On the other hand, because the overall shapes of the real-time Doppler spectra Freal (ω) originating from the large blood vessels B2are downward-convex decay curves, there is a tendency to calculate average frequencies<ω> that are less than the average frequencies<ω> accurately corresponding to the blood-flow velocities. Therefore, it is possible to correct the variation in the average frequencies<ω> caused by the blood-vessel diameters on the basis of a shape parameter m that indicates whether the decay curve representing the overall shape of a real-time Doppler spectrum Freal (ω) is upward convex or downward convex.

The shape parameter m is a parameter that increases when a real-time Doppler spectrum Freal (ω) is upward convex, and that decrease when a real-time Doppler spectrum Freal (ω) is downward convex.

FIG.10shows an example of calculation of the shape parameter m.

As shown inFIG.10, a real-time Doppler spectrum Freal (ω) is fitted by using a fitting function hfit(ω) below, and thus, coefficients A and B are calculated:
hfit(ω)=A×hfit-cvx(ω)+B×hfit-cav(ω)+C,
where hfit-cvx(ω) is a normalized upward-convex function, hfit-cav(ω) is a normalized downward-convex function, and C is a constant.

When the blood vessels are small, the coefficient A increases, and the coefficient B decreases. On the other hand, when the blood vessels are large, the coefficient A decreases, and the coefficient B increases. The parameter m is a value that varies in a range of values that are equal to or greater than the constant C and equal to or less than the peak intensity p′. The relationship between the coefficients A and B and the shape parameter m is set so that the shape parameter m increases with an increase in the coefficient A or with a decrease in the coefficient B.

Next, as shown inFIGS.11A and11B, the average frequencies<ω> are corrected by using a conversion function g (ωm) on the basis of the frequencies ωmat which the shape parameter m is applied. The conversion function g (ωm) is set so as to convert a straight line representing the relationship between the peak intensities p and the average frequencies<ω> to an ideal straight line. Therefore, the average frequencies<ω> of the real-time Doppler spectra Freal (ω) of the small blood vessels are corrected to lower values by means of the conversion function g (ωm), as shown inFIG.11A. On the other hand, the average frequencies<ω> of the real-time Doppler spectra Freal (ω) of the large blood vessels are corrected to greater values by means of the conversion function g (ωm), as shown inFIG.11B.

As has been described above, as a result of subjecting the variation in the average frequencies<ω> caused by the blood-vessel diameters to primary correction on the basis of the shape parameter m of the real-time Doppler spectra Freal (ω) and subjecting the corrected average frequencies<ω>cal1that have been subjected to the primary correction to secondary correction by using the conversion equation G (F), corrected average frequencies<ω>cal2that more accurately correspond to the blood-flow velocities are obtained. Therefore, it is possible to more accurately determine the sizes of blood vessels on the basis of the corrected average frequencies<ω>cal2.

As shown inFIG.10, the shape parameter m may be a central value between the peak intensity p′ and the background level C of the fitting function hfit(ω). Alternatively, the shape parameter m may be a central value between the peak intensity p′ and a prescribed intensity pth. In calculating the shape parameter m, the peak intensity p of a real-time Doppler spectrum Freal (ω) may be used instead of the peak intensity p′.

The above-described embodiment also leads to the following aspects.

A first aspect of the present invention is a blood-vessel recognizing method for recognizing blood vessels present in biological tissue on the basis of real-time Doppler spectra obtained by applying Fourier transformation to time waveforms of intensities of scattered light generated in the biological tissue due to irradiation with laser light, the method including: an average-frequency calculating step of calculating average frequencies of the real-time Doppler spectra; and an average-frequency correcting step of correcting the average frequencies calculated in the average-frequency calculating step on the basis of peak intensities of the real-time Doppler spectra.

With this aspect, the real-time Doppler spectra based on the scattered light generated in the blood vessels in the biological tissue due to irradiation with the laser light exhibit intensities at frequencies in accordance with the blood velocities (blood-flow velocities) of blood flowing in the blood vessels. The blood-flow velocities are correlated with the sizes of the blood vessels. Therefore, it is possible to recognize the sizes of the blood vessels present in the regions irradiated with the laser light on the basis of the average frequencies of the real-time Doppler spectra calculated in the average-frequency calculating step.

In this case, the intensities of the real-time Doppler spectra change as a whole in accordance with the environment in which the blood vessels are placed (for example, the depths of blood vessels in biological tissue and the properties of biological tissue). Therefore, the average frequencies calculated from the real-time Doppler spectra exhibit variation in accordance with the environment in which the blood vessels are placed even if the blood-flow velocities are the same. In the average-frequency correcting step, the variation in the average frequencies is corrected on the basis of the peak intensities of the real-time Doppler spectra. It is possible to accurately recognize the sizes of the blood vessels on the basis of the average frequencies that has been corrected in this way, regardless of the environment in which the blood vessels are placed.

In the above-described aspect, in the average-frequency correcting step, the average frequencies may be corrected by using a conversion equation that uses the average frequencies and the peak intensities as variables, and that increases the average frequencies with a greater increment as the peak intensities decrease. The conversion equation may correct the average frequencies so that the corrected average frequencies become constant regardless of the peak intensities.

The average frequencies of the real-time Doppler spectra decrease as the peak intensities decrease. Therefore, as a result of correcting the average frequencies with a greater increment as the peak intensities decrease by using the conversion equation, it is possible to more accurately correct a decrease in the average frequencies caused by a decrease in the intensities of the real-time Doppler spectra.

In the above-described aspect may include an integration-interval setting step of setting an integration interval for calculating the average frequencies on the basis of the intensities of the real-time Doppler spectra.

In the real-time Doppler spectra, a high-frequency range having low intensities is a cause of deterioration of calculation precision of the average frequencies. By setting the integration interval so as to exclude such a high-frequency range, it is possible to enhance the calculation precision of the average frequencies.

In the above-described aspect, the integration-interval setting step may include a smoothing step of applying smoothing processing to the real-time Doppler spectra.

The real-time Doppler spectra include numerous small peaks. By using real-time Doppler spectra in which such numerous small peaks are smoothed by means of the smoothing processing, it is possible to further enhance the calculation precision of the average frequencies.

In the above-described aspect, the integration-interval setting step may include a first-order differentiating step of performing, with respect to frequency, first-order differentiation of the real-time Doppler spectra that have been subjected to the smoothing processing in the smoothing step.

By doing so, it is possible to more accurately distinguish, on the basis of the intensities of a first-order differential spectrum of the real-time Doppler spectra, an interval in which the real-time Doppler spectra are significantly decaying and an interval in which the intensities of the real-time Doppler spectra have become the lowest, and thus, it is possible to set an integration interval that is more appropriate for calculating the average frequencies.

In the above-described aspect, the integration-interval setting step may include a second-order differentiating step of performing, with respect to frequency, second-order differentiation of the real-time Doppler spectra that have been subjected to the smoothing processing in the smoothing step.

By doing so, it is possible to more accurately identify, on the basis of the intensities of a second-order differential spectrum of the real-time Doppler spectra, a boundary between the interval in which the real-time Doppler spectra are significantly decaying and the interval in which the intensities of the real-time Doppler spectra have become the lowest, and thus, it is possible to set an integration interval that is more appropriate for calculating the average frequencies.

In the above-described aspect, the integration-interval setting step may include a fitting step of calculating a fitting function of the real-time Doppler spectra.

By doing so, it is possible to more appropriately set the integration interval by using the fitting function.

In the above-described aspect, in the average-frequency correcting step, the average frequencies may be corrected on the basis of a shape parameter that indicates whether overall shapes of the real-time Doppler spectra are upward convex or downward convex.

Because the real-time Doppler spectra of small blood vessels are upward-convex decay curves as a whole, there is a tendency to calculate the average frequencies that take greater values. On the other hand, because the real-time Doppler spectra of large blood vessels are downward-convex decay curves as a whole, there is a tendency to calculate the average frequencies that take lower values. Therefore, by taking the shape parameter into consideration in addition to the peak intensities, it is possible to also correct the variation in the average frequencies caused by differences in the blood-vessel diameters.

A second aspect of the present invention is a blood-vessel recognizing device including: a laser-light radiating portion that radiates laser light onto biological tissue; a spectrum acquisition portion that acquires time waveforms of intensities of scattered light generated in the biological tissue due to the irradiation with the laser light, and that acquires real-time Doppler spectra by applying Fourier transformation to the acquired time waveforms; an average-frequency calculating portion that calculates average frequencies of the real-time Doppler spectra acquired by the spectrum acquisition portion; and an average-frequency correcting portion that corrects the average frequencies calculated by the average-frequency calculating portion on the basis of peak intensities of the real-time Doppler spectra.

In the above-described second aspect may include a blood-vessel determining portion that determines the sizes of blood vessels on the basis of the average frequencies corrected by the average-frequency correcting portion.

REFERENCE SIGNS LIST

8laser light source

9visible light source

S1integration-interval setting step

S2average-frequency calculating step

T tissue