Infrared imaging of biological material

This application claims priority from Application PCT/GB2016/051210, filed Apr. 28, 2016, which is deemed incorporated by reference in its entirety in this application.

Not applicable.

The present invention relates to methods and apparatus for characterising biological material, such as tissue, using infrared absorption techniques.

In the field of medical analysis, it is frequently desirable to determine the structure and composition of a tissue sample from a patient. This can assist in the identification of tissue abnormalities and may provide an indication of tissue samples where further clinical investigation may be desirable.

One established method for identifying the chemistry of biological material such as tissue is Fourier Transform infrared spectroscopic imaging. As described in US 2012/0052063, the absorption spectrum in the mid-infrared region is used as a chemical fingerprint that can identify molecular species and their local environment and is potentially attractive for cancer pathology. The technique described in US '063 segments an infrared spectroscopic image of a tissue sample into epithelium pixels and stroma pixels and then segments the epithelium pixels into cancerous or benign categories by reference to a spatial analysis of epithelium pixels and their neighbourhoods to a probability distribution function for reference cancerous and benign samples. This therefore, provides a degree of automation to tissue examination compared to prior histological methods using stains or dyes to highlight certain cell structures under microscopic examination.

It would be advantageous to provide a numerical histological index (e.g. a measure) derivable from infrared absorption measurements which can repeatably provide a measure of one or more tissue characteristics which is useful in clinical diagnosis and/or clinical prognosis.

The invention is directed to the provision of such a numerical histological index and to apparatus for generating such an index from a sample under analysis.

According to one aspect, the invention provides a method of mapping a tissue characteristic in a tissue sample comprising:

The selected wavelengths may lie in the ranges 6.0±0.5 microns, 6.47±0.50 microns, 8.13±0.44 microns, 9.3±0.7 microns. The histological index may comprise a numeric value obtained by dividing the first measure by the second measure. The histological index, PA, may be derived according to the expression PA=[|M(λ3)−M(λ4)|]/[|M(λ1)−M(λ2)|] where: M(λn) is a measure of the absorbed energy or power at λn; λ1 is a wavelength corresponding to a peak absorption value attributable to an amide moiety; λ2 is a wavelength corresponding to a baseline absorption value attributable to an amide moiety; λ3 is a wavelength corresponding to a peak absorption value attributable to a phosphate moiety; λ4 is a wavelength corresponding to a baseline absorption value attributable to a phosphate moiety. The histological index, PA, may be derived according to the expression PA=[XM(λ3)−M(λ4)]/[XM(λ1)−M(λ2)] where: M(λn) is a measure of the absorbed energy or power at λn; λ1 is a wavelength corresponding to a peak absorption value attributable to an amide moiety; λ2 is a wavelength corresponding to a baseline absorption value attributable to an amide moiety; λ3 is a wavelength corresponding to a peak absorption value attributable to a phosphate moiety; λ4 is a wavelength corresponding to a baseline absorption value attributable to a phosphate moiety; and X is numerical factor≥1 which is set to a value sufficient to ensure that the measure M for a peak absorption values λ3 and λ1 is always greater than the measure M for the corresponding baseline absorption values λ4 and λ2 for all measurements. The value X may be set according to the signal-to-noise ratio of the measurement system. The value of X may lie in the range 1.2 to 1.5. The following values may be applied: λ1=6.0±0.1 microns; λ2=6.5±0.1 microns; λ3=8.13±0.1 microns or 9.26±0.1 microns; λ4=8.57±0.1 microns or 10.0±0.1 microns. The first measure may comprise an area under an amide absorption peak and the second measure may comprise an area under a phosphate absorption peak. The first measure may be derived from a measure of absorption at λ1=6.0 microns and at λ2=6.5 microns, and the second measure may be derived from a measure of absorption at λ3=8.13 microns or 9.26 microns and at λ4=8.57 microns or 10.0 microns. Each of the first and second measures may be obtained from: sample data S; environment data Es; background data B; and background environment data Eb, the method further comprising compensating the sample data S using Es, B, Eb. Each of the first and second measures M may be obtained from one or more measurements M according to the expression M=[S−Es]/B−Eb]. The method may include obtaining values for each of said first and second measures for each of a plurality of pixels in a two dimensional array.

The method may include performing a bad pixel replacement procedure comprising:

The method may include performing the bad pixel replacement procedure for each selected wavelength.

The method may include plotting spatial variations in the histological index in at least two dimensions. The method may include classifying each pixel as indicative of first, second or third tissue type based on the histological index for each pixel. The method may include classifying each pixel as indicative of a non-cancerous tissue candidate type or a cancerous tissue candidate type. The method may be applied to breast tissue. The method may include using the histological index to derive a cancer grading. The cancer grading may be for breast cancer.

According to another aspect, the invention provides apparatus for mapping a tissue characteristic in a tissue sample comprising:

Embodiments of the present invention will now be described by way of example and with reference to the accompanying drawings in which:

Most biological molecules have vibrational modes with wavelengths which lie in the mid-infrared spectral range between 3 μm and about 16 μm. The positions, width and strength of the vibrational modes vary with composition and structure of the molecule. Identification of vibrational modes of major biological molecules, such as proteins, lipids and nucleic acids, can be determined by Fourier transform infrared spectroscopy. Infrared radiation directed at a biological sample, e.g. a tissue sample, is variously absorbed or transmitted depending on the biological material present, i.e. compounds and functional groups present in the sample, as well as the concentration and distribution of the material in the sample. The sample's infrared spectrum exhibits characterising spectral features such as absorption bands of characteristic shape and size at characteristic frequencies. These characterising spectral features act as “fingerprints” by which to identify uniquely the presence of a particular functional group; moreover the presence of a certain functional group is indicative of a certain biological molecule.

shows a suitable apparatusfor acquiring infrared absorption data from a sample. An infrared sourceprovides an output of infrared radiationwhich can pass through a shutter(when open) and a filterto reach a samplemounted on a sample stage. The sampleeither absorbs the infrared radiation or transmits the infrared radiation according to the local structure and/or composition of the sample and the frequency/wavelength of the radiation. The transmitted infrared radiationwhich passes through the sampleis focussed by a focussing elementonto a suitable detector array.

The infrared sourcecan be of any suitable type, and preferably one capable of efficiently producing infrared outputwith wavelengths in the range approximately 5 to 9 microns. The shuttermay comprise any suitable arrangement capable of interrupting the infrared outputto prevent it reaching the sample. The filtermay be any suitable device for enabling only selected wavebands of infrared outputto reach the sample. The filtermay be a filter wheel comprising multiple separate filter elements which can be moved into the beam line or could be a tuneable filter. More generally, the filtermay be a controllable filter for enabling selection of a wavelength or narrow range of wavelengths of infrared radiation to reach the sample. In this way, separate infrared absorption measurements may be made on the sample at specific wavelengths or in specific wavebands at different times.

Infrared radiationnot absorbed by the samplemay be focussed onto the detector arrayusing any suitable focussing element, which may be a lens or multiple lenses. The detector arraymay be any suitable device such as a bolometric camera or any detector sensitive to infrared radiation.

In an alternative arrangement, a broadband infrared source, shutterand filter wheelmight be replaced with a switchable and/or tuneable infrared source capable of generating infrared output beamsat different wavelengths, such as an optical parameter generator or optical parametric oscillator, or one or more quantum cascade lasers.

Preferably, the infrared source and detector arrangement are configured to irradiate the entire sample simultaneously, or substantial parts thereof, and to capture transmitted infrared energy for a large spatial area of the sample in a single exposure. Alternatively, the apparatusmay sample only small parts of the sample at a time and use a position controllable sample stageto sequentially measure absorption in different parts of the sample, e.g. in multiple exposures.

The outputfrom the detector arrayis passed through a suitable processing deviceto perform analysis functions described hereinafter. The processor may be coupled to a suitable display devicefor functions to be described hereinafter. The display devicemay also serve as a user input device.

Infrared absorption measurements may be taken by comparing detector measurements taken with a sample in position within the infrared beam and those taken with the sample removed from the infrared beam.

The absorption measurements at each of four wavelengths λ1, λ2, λ3, λ4, may be taken in any suitable manner according to the infrared absorption analysis apparatus used. In the example of, each absorption measurement Mis preferably taken using four measurements:

(i) a sample image S taken with the sample loaded in the machine on the sample stage and the shutter open/removed;

(ii) an environment signal Es taken with the sample loaded in the machine and the shutter in/closed;

(ii) a background image B taken with the sample out of the apparatus and the shutter open/removed; and

(iv) a background environment Eb measurement taken with the sample out and the shutter in/closed.

The absorption measurements M, preferably each corresponding to an absorbed power or energy, are derived according to: M=(S−Es)/(B−Eb). Preferably, each absorption measurement Mis taken a number of times (e.g. N times) and a mean value of Mis calculated. The value of N may be selected according to the acquisition time set for the apparatus.

shows schematically an exemplary infrared absorption spectrumobtained from a sample. The infrared absorption spectrummay be derived as a function of an infrared transmittance spectrum received by the detector arrayusing techniques known in the art. The peaks,,each correspond to spectral features that are associated with a particular functional group. These groups may typically include functional groups such as those listed in the table below.

The inventors have established that determining a first measure Mof an amount of power or energy absorbed which is attributable to an amide functional group/amide moiety and a second measure Mof an amount of power or energy absorbed which is attributable to a phosphate functional group/phosphate moiety, and computing a ratio of the first measure and the second measure provides a quantitative measure which can serve as a useful histological index which is a reliable prognostic marker, at least for invasive breast cancer.

More generally, the ratio M/Mthat defines this histological index is believed to be a useful indicator of the tissue structure or structures present in the sample that are highly relevant to further clinical assessment. The ratio may therefore be extremely useful in automation of a first stage of a screening program for individuals at risk of certain types of cancer, and may be reliably indicative of a clinical prognosis. The tissue type may be breast tissue or other types of tissue and the types of cancer may include breast cancer or other types of cancer.

In a preferred aspect, the first measure Mis a measure of infrared absorption (preferably absorbed power) attributable to an amide functional group taken in the range 5.5 to 6.5 microns wavelength and the second measure Mis a measure of infrared absorption attributable to a phosphate functional group taken in the range 7.6 to 8.6 microns or 8.6 to 10.0 microns. The measurements MA and MP may therefore comprise, correspond to, or approximate, respectively, an area under the amide absorption peak and an area under the phosphate absorption peak. The first measure Mmay comprise a computed area under the absorption peak found at 6.0 microns wavelength and the second measure Mmay comprise a computed area under the absorption peak found at 8.13 microns wavelength or a computed area under the absorption peak found at 9.3 microns wavelength. The first measure Mmay comprise a measure of infrared absorption attributable to an amide functional group taken in the range 6.0±0.5 microns wavelength or 6.47±0.5 microns and the second measure Mmay comprise a measure of infrared absorption attributable to a phosphate functional group taken in the range 8.13±0.44 microns or 9.3±0.7 microns. The ranges given above preferably represent the full width at half maximum of the peak. The infrared absorption by the phosphate functional group/phosphodiester concentration is preferably measured at 8.13 microns because, although the peak at 9.3 microns gives better contrast with respect to a baseline in some cases, the signal-to-noise ratio at this wavelength may be limited due to low emissivity of some infra-red sources. However, improvements in signal-to-noise ratio may shift this preference.

The first measure Mmay comprise a difference between an absorption measurement taken at an absorption peak for an amide functional group and an absorption measurement taken at a baseline of the absorption peak for the amide functional group. The second measure Mmay comprise a difference between an absorption measurement taken at an absorption peak for a phosphate functional group and an absorption measurement taken at a baseline of the absorption peak for the phosphate functional group.

The difference between an absorption measurement taken at an absorption peak for an amide functional group and an absorption measurement taken at a baseline of the absorption peak for the amide functional group may be established by: a) measuring an absorption peak at λ1=6.0 microns, and b) measuring an absorption baseline at λ2=6.47 microns.

The difference between an absorption measurement taken at an absorption peak for a phosphate functional group and an absorption measurement taken at a baseline of the absorption peak for the phosphate functional group may be established by: a) measuring an absorption peak at λ3=8.13 microns, and b) measuring an absorption baseline at λ4=8.57 microns. The difference between an absorption measurement taken at an absorption peak for a phosphate functional group and an absorption measurement taken at a baseline of the absorption peak for the phosphate functional group may be established by: a) measuring an absorption peak at λ3=9.3 microns, and b) measuring an absorption baseline at λ4=10.0 microns.

The difference measurements may be established by using a scaling factor as will be described below.

The measurements are preferably made for a plurality of pixels in a field of view of the sampleon the sample stage, and all pixel measurements are preferably captured simultaneously by the detector array. A pixel data processing routine is described below with reference to.

shows a flow chart of a data processing technique suitable for generating the histological index for each pixel in an image of the sample. The data processing may be performed within the processorof.

First, the absorption is measured at each of λ1, λ2, λ3, λ4, for each pixel (step) to create an image map for each wavelength λ1, λ2, λ3, λ4. This is repeated a number, N, times (step) to create N image maps for each wavelength. N may be varied according system parameters including acquisition time, to optimise performance. An average value (which may be the mean) is computed (step) for each pixel in each map to create an image map or dataset Mcomprising an average value of absorption for each pixel at each wavelength.

A bad pixel replacement algorithm is then implemented to eliminate, correct, or otherwise mitigate any pixel values which are clearly outliers. An exemplary bad pixel replacement routine is as shown in stepsto. This may be useful because in a ratiometric process which follows, there could be a possibility of dividing by pixels of value zero resulting in anomalies in the image data. A histogram of pixel values occurring in each image map MA is constructed (step). Pixels having values corresponding to the lowest frequency of occurrence are identified and labelled (step). The labelled pixels are then replaced with an average (preferably a median) value of the first immediate neighbouring pixels to a labelled pixel. This is performed for each labelled pixel (step).

The procedure of stepstois repeated a number of times (step) to ensure that bad pixel clusters have been replaced. The decision when to stop repeating the procedure (step) may be determined by a simple count, i.e. repetition by a fixed number of times (e.g. 20), or may be determined by another method such as analysis of the distribution of the histogram created in step.

We now have four datasets representing four images, M, M, M, Meach comprising an array of absorption values corresponding to the pixels of the image (step). Mmaps the absorption profile across the sample due to the amide moiety, at the wavelength centred on the amide absorption peak. Mmaps the absorption profile across the sample at the wavelength immediately adjacent to the amide absorption peak and thus represents the baseline of the amide absorption peak. Mmaps the absorption profile across the sample due to the phosphate moiety, at the wavelength centred on the phosphate absorption peak. Mmaps the absorption profile across the sample at the wavelength immediately adjacent to the phosphate absorption peak and thus represents the baseline of the phosphate absorption peak.

A dataset corresponding to the histological index, PA, is then generated from the four datasets M, M, M, M(step), according to the expression:PA=[XM−M]/[XM−Mλ2]

More generally, Mis a measure of the absorbed energy or power at λn; λ1 is a wavelength corresponding to a peak absorption value attributable to an amide moiety; λ2 is a wavelength corresponding to a baseline absorption value attributable to an amide moiety; λ3 is a wavelength corresponding to a peak absorption value attributable to a phosphate moiety; λ4 is a wavelength corresponding to a baseline absorption value attributable to a phosphate moiety; and X is numerical factor≥1 which is set to a value sufficient to ensure that the measure M for peak absorption values λ3 and λ1 is always greater than the measure M for the corresponding baseline absorption values λ4 and λ2 for all measurements. The value of X may therefore be set according to the signal-to-noise ratio of the measurement system. The value of X may preferably lie in the range 1.2 to 1.5.

In the example given above, PA and Mare each datasets comprising a two dimensional array corresponding to pixel values of a spatial map of the sample.

Stepmay be specified to include further bad pixel replacement routines similar to that described in connection with stepsto. This is shown inwhich details steps that may be taken to execute step.

With reference to, the numerator expression [XM−M], which corresponds to a scaled phosphate peak height, is evaluated for each pixel (step). This provides a dataset corresponding to a pixel map of scaled phosphate peak heights, P. A histogram of pixel values occurring in the pixel map is constructed (step). Pixels having values corresponding to the lowest frequency of occurrence are identified and labelled (step). The labelled pixels are then replaced with an average (preferably a median) value of the first immediate neighbouring pixels to a labelled pixel. This is performed for each labelled pixel (step).

The procedure of stepstomay be repeated a number of times (step) to ensure that bad pixel clusters have been replaced. The decision when to stop repeating the procedure (step) may be determined by a simple count, i.e. repetition by a fixed number of times (e.g. 20), or may be determined by another method such as analysis of the distribution of the histogram created in step.

The denominator expression [XM−M], which corresponds to a scaled amide peak height, is evaluated for each pixel (step). This provides a dataset corresponding to a pixel map of scaled amide peak heights, A. A histogram of pixel values occurring in the pixel map is constructed (step). Pixels having values corresponding to the lowest frequency of occurrence are identified and labelled (step). The labelled pixels are then replaced with an average (preferably a median) value of the first immediate neighbouring pixels to a labelled pixel. This is performed for each labelled pixel (step).