WO2024067948A1

WO2024067948A1 - Method and apparatus

Info

Publication number: WO2024067948A1
Application number: PCT/EP2022/076712
Authority: WO
Inventors: Ronan CAHILL; Jeffrey DALLI
Original assignee: University College Dublin
Priority date: 2022-09-26
Filing date: 2022-09-26
Publication date: 2024-04-04

Abstract

A method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof, the method implemented by a computer comprising a processor and a memory, the method comprising: obtaining a first time series of images, including a first image and a second image, of the organ, having a set of spatial regions including a first spatial region, having the first tissue status, and a second spatial region, during a first time period after a first perfusion of the organ with a first contrast agent and before controlling perfusion of the organ; generating a first set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region, using respective intensities of the set of spatial regions of the first time series of images; obtaining a second time series of images, including a first image and a second image, of the organ, during a second time period after a second perfusion of the organ with a second contrast agent and after controlling perfusion of the organ; generating a second set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region and a second intensity-time profile of the second spatial region, using respective intensities of the set of spatial regions of the second series of images; comparing the first set of intensity-time profiles and the second set of intensity-time profiles; and discriminating between the first spatial region and the second spatial region based on a result of comparing the first set of intensity-time profiles and the second set of intensity-time profiles.

Description

METHOD AND APPARATUS

Field

The present invention relates to a method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof.

Background to the invention

Typically, surgical intervention is performed with surgeon judgement of tissue quality for reconstruction after disease excision. However, surgeon judgement may be subjective and/or may be based on general principles rather than personalised to an individual patient.

Hence, there is a need to improve judgement of tissue quality, for example for reconstruction after disease excision.

Summary of the Invention

It is one aim of the present invention, amongst others, to provide a provides a method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof which at least partially obviates or mitigates at least some of the disadvantages of the prior art, whether identified herein or elsewhere. For instance, it is an aim of embodiments of the invention to provide a method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof that improves judgement of tissue quality, for example for reconstruction after disease excision.

A first aspect provides a method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof, the method implemented by a computer comprising a processor and a memory, the method comprising: obtaining a first time series of images, including a first image and a second image, of the organ, having a set of spatial regions including a first spatial region, having the first tissue status, and a second spatial region, during a first time period after a first perfusion of the organ with a first contrast agent and before controlling perfusion of the organ; generating a first set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region, using respective intensities of the set of spatial regions of the first time series of images; obtaining a second time series of images, including a first image and a second image, of the organ, during a second time period after a second perfusion of the organ with a second contrast agent and after controlling perfusion of the organ; generating a second set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region and a second intensity-time profile of the second spatial region, using respective intensities of the set of spatial regions of the second series of images; comparing the first set of intensity-time profiles and the second set of intensity-time profiles; and discriminating between the first spatial region and the second spatial region based on a result of comparing the first set of intensity-time profiles and the second set of intensity-time profiles.

A second aspect provides an ex vivo method for treatment of an organ by surgery or therapy or an ex vivo therapy or diagnostic method practised on an organ, comprising the method according to the first aspect.

A third aspect provides a method for treatment of the human or animal body by surgery or a therapy or diagnostic method practised on the human or animal body, comprising the method according to the first aspect.

A fourth aspect provides a computer comprising a processor and a memory configured to implement a method according to the first aspect, a computer program comprising instructions which, when executed by a computer comprising a processor and a memory, cause the computer to perform a method according to the first aspect or a non-transient computer- readable storage medium comprising instructions which, when executed by a computer comprising a processor and a memory, cause the computer to perform a method according to the first aspect.

Detailed Description of the Invention

According to the present invention there is provided a method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof, as set forth in the appended claims. Also provided are an ex vivo method for treatment of an organ by surgery or therapy or an ex vivo therapy or diagnostic method practised on an organ, a method for treatment of the human or animal body by surgery or a therapy or diagnostic method practised on the human or animal body, a computer, a computer program and a non-transient computer- readable storage medium. Other features of the invention will be apparent from the dependent claims, and the description that follows.

Method The first aspect provides a method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof, the method implemented by a computer comprising a processor and a memory, the method comprising: obtaining a first time series of images, including a first image and a second image, of the organ, having a set of spatial regions including a first spatial region, having the first tissue status, and a second spatial region, during a first time period after a first perfusion of the organ with a first contrast agent and before controlling perfusion of the organ; could introduce a donor section, could be poorly perfused that you improve perfusion of, give a medication to increase blood generating a first set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region, using respective intensities of the set of spatial regions of the first time series of images; obtaining a second time series of images, including a first image and a second image, of the organ, during a second time period after a second perfusion of the organ with a second contrast agent and after controlling perfusion of the organ; generating a second set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region and a second intensity-time profile of the second spatial region, using respective intensities of the set of spatial regions of the second series of images; comparing the first set of intensity-time profiles and the second set of intensity-time profiles; and discriminating between the first spatial region and the second spatial region based on a result of comparing the first set of intensity-time profiles and the second set of intensity-time profiles.

In this way, the discriminating between the first spatial region and the second spatial region is objective and individualised (for example, personalised) for the individual organ or part thereof since discriminating is based on a result of comparing the first set of intensity-time profiles (also known as a Reference Profile r(t)) and the second set of intensity-time profiles (also known as a Acquisition Profile l(t))> generated from the respective obtained first and second time series of images and hence from the perfusion of the organ. In this way, judgement of tissue quality is improved, for example for reconstruction after disease excision, thereby better guiding a surgeon to transect the organ at an optimized transection point, since the optimized transection point of the organ has the best perfusion, thereby improving healing after transection, for example. In other words, the method provides determination of tissue resection extent, for example ex vivo or in vivo during surgery, by visual observation of perfusion over time during a procedure so that the optimum transection point can be made based on the specific perfusion profile of an individual intraoperatively. The inventors posed a research question: can computational techniques automatically, via mathematical algorithms, recommend optimal bowel transection levels during colorectal surgery based on normalised fluorescence intensity curves generated by near-infrared perfusion assessment using indocyanine green?

The inventors found that fluorescence signalling along intestinal segments prepared for transection can be mathematically fitted to the signal curves observed earlier in the same operation prior to any operative dissection. Consistent algorithmic recommendation of both large and small bowel transection levels in right- and left-sided colorectal surgical videos was thereby enabled.

The inventors concluded that via adapted clinical practice to enable quantitative fluorescence angiogram calibration and algorithmic data processing, personalised ideal colonic transection recommendation can be made. Particularly, early benchmarking of physiological perfusion in an individual better allows supported choice of transection extent with mathematical confirmation of dynamic sufficiency and optimised selection of incision level improving outcomes and making better use of resource available in healthcare.

Generally, tissues require sufficient perfusion to heal after surgery. Conventionally, operations build in such consideration by surgeon judgement regarding cut (transection) lines related to tissue resection. The inventors have developed a mathematical method and clinical process that enables identification of the most appropriate site for subsequent incision based on performing early intraoperative assessment of relevant tissues with repeated assessment after surgical preparation rather than the conventional method of assessment only when at the point of resection. The method involves the use of contrast agents (also known as dyes) and electromagnetic radiation (EMR) to excite the contrast agents and amplify the inherent perfusion patterns within tissues relevant to the disease site and at sites potentially harbouring disease and thereafter mathematical methods to compare the imagery to indicate perfusion.

The inventors have previously found that the intensity of light emitted from a target bodily tissue is lower than the intensity of light emitted from the background bodily tissue during an initial time period shortly after administration of a suitable contrast agent to a subject (i.e. an initial uptake phase), if the target bodily tissue is malignant. The inventors have also found that the intensity of light emitted from the target bodily tissue is higher than the intensity of light emitted from the background bodily tissue in a later time period after administration of the contrast agent to the subject (i.e. a washout phase), if the target bodily tissue is malignant. These differences in light emitted from the target bodily tissue and the background bodily tissue may be due to the different amounts of contrast agent present in the different tissues at different times after administration or may be due to a localised increase in brightness of the contrast agent in the target bodily tissue due to some other mechanism.

These effects are believed to be due to the differing pharmacokinetic properties of malignant tissue structures to similar benign tissue structures or adjacent healthy tissue. Therefore, these tissue types have been found to absorb and excrete compounds such as contrast agents at different rates, which provide different intensities of light emission at different time points after dosing of the contrast agent.

More broadly, the method according to the first aspect comprises and/or is a method of discriminating between a first tissue status and a second tissue status of an organ, the method implemented by a computer comprising a processor and a memory, the method comprising: providing a first set of concentration-time profiles of a respective set of spatial regions of the organ during a first time period after a first perfusion of the organ with a first contrast agent and before controlling perfusion of the organ, wherein the set of spatial regions includes a first spatial region, having a first tissue status, and a second spatial region and wherein the first set of concentration-time profiles includes a first concentration-time profile of concentrations of the first contrast agent in the first spatial region; providing a second set of concentration-time profiles of the respective set of spatial regions of the organ during a second time period after a second perfusion of the organ with a second contrast agent and after controlling perfusion of the organ, wherein the second set of concentration-time profiles includes a first concentration-time profile of concentrations of the second contrast agent in the first spatial region; comparing the first set of concentration-time profiles and the second set of concentration-time profiles; and discriminating between the first spatial region and the second spatial region based on a result of comparing the first set of concentration-time profiles and the second set of concentrationtime profiles.

In one example, the first set of concentration-time profiles are generated from signals acquired using an imaging device, for example a CCD or a CMOS device, or a finger probe.

The method is of discriminating (i.e. distinguishing, differentiating) between the first tissue status and the second tissue status of the organ or a part thereof. In one example, the first tissue status comprises and/or is healthy tissue, for example tissue having normal or relatively better perfusion such as benign tissue. In one example, the second tissue status comprises and/or is malperfused tissue, for example having subnormal or relatively poorer perfusion (malperfusion) such as diseased or malignant tissue. In this way, healthy and malperfused tissue may be discriminated. In one example, the organ comprises and/or is the digestive tract, for example the colorectal and/or internal gastrointestinal tract, or a urinary organ. In one example, the organ is in vivo i.e. in a patient. In one example, the organ is ex vivo, for example a transplant organ received from a donor before transplanting into a patient. In one example, the organ comprises and/or is a human or an animal organ i.e. originating from a human or an animal. In one example, the organ comprises and/or is an engineered organ, for example a lab-grown organ. For example, a donor section of an organ, such as from a human or an animal donor or from an engineered organ, may be introduced into a patient, for example to replace a diseased section of the patient’s organ. In one example, the organ comprises and/or is a model organ, for example for surgery training purposes.

In one example, the second spatial region has a second tissue status. In one example, the first tissue status comprises and/or is healthy tissue, for example benign tissue, for example having normal or relatively better perfusion. In one example, the second tissue status comprises and/or is malperfused tissue, for example having subnormal or relatively poorer perfusion (malperfusion) such as diseased or malignant tissue. In this way, healthy and malperfused tissue may be discriminated.

The method is implemented by the computer comprising the processor and the memory. That is, the method is a computer implemented method. It should be understood that while the method uses data (i.e. the first time series of images and the first second series of images) obtained after perfusion of the organ, the computer implemented method is not practised on the human or animal body.

The method comprises obtaining, for example from a storage, the first time series of images (i.e. successive images acquired using an imaging device for example a camera or a video camera such as a CCD or a CMOS device, for example photographs acquired periodically and/or acquired frames from a video), including the first image and the second image, of the organ, having the set of spatial regions including the first spatial region, having the first tissue status, and the second spatial region, during the first time period after, for example immediately after or after a first time duration, the first perfusion of the organ with the first contrast agent and before, for example immediately before or before a first time interval, controlling (for example changing and/or modifying) perfusion of the organ.

In one example, the method comprises acquiring the first time series of images using an imaging device for example a camera or a video camera such as a CCD or a CMOS device, for example photographs acquired periodically and/or acquired frames from a video. In one example, the method comprises acquiring the first time series of images using a fluorescence imaging device for example a camera or a video camera such as a CCD or a CMOS device, for example photographs acquired periodically and/or acquired frames from a video, wherein the first contrast agent and/or the second contrast agent comprises and/or is a fluorescent dye. In one example, the method comprises acquiring the first time series of images using fluorescence angiography. Acquiring the second time series of images may be as described with respect to acquiring the first time series of images.

In one example, the first image is an RGB image or a greyscale image. In one example, the first time series of images includes M images, wherein M is a natural number greater than or equal to 2, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000 or more. The second image may be as described with respect to the first image. The M images may be as described with respect to the first image. In one example, a first field of view of the first time series of images of the organ is fixed or constant (i.e. the first time series of images are of the same view of the organ).

In one example, the first time period (i.e. during which the first time series of images is acquired) is in a range from 1 minute to 60 minutes, preferably in a range from 2 minutes to 30 minutes, more preferably in a range from 5 minutes to 15 minutes.

In one example, the first time period is immediately after (i.e. the first time duration is zero) the first perfusion of the organ with the first contrast agent and/or immediately before controlling perfusion of the organ. In one example, the first time period is a first time duration after the first perfusion of the organ with the first contrast agent. In one example, the first time duration (i.e. during which the first time series of images is acquired) is in a range from 1 minute to 60 minutes, preferably in a range from 2 minutes to 30 minutes, more preferably in a range from 5 minutes to 15 minutes.

In one example, the first contrast agent comprises and/or is a fluorescent dye, for example indocyanine green (ICG) or methylene blue (MB), and wherein obtaining the first time series of images, including the first image and the second image, of the organ comprises fluorescence angiography.

In one example, the second contrast agent comprises and/or is a fluorescent dye, for example indocyanine green or methylene blue, and obtaining the second time series of images, including the first image and the second image, of the organ comprises fluorescence angiography.

In one example, the first contrast agent and the second contrast agent are the same contrast agent, for example both the first contrast agent and the second contrast agent are ICG or MB. ICG is a fluorescent dye, which emits fluorescence on excitation by a NIR light source at a wavelength of approximately 785 nm. The emitted fluorescence (approximate wavelength band of 800-850 nm) can be captured (imaged) and processed. Indocyanine green (ICG) is a sterile, water-soluble but relatively hydrophobic tricarbocyanine molecule. Following intravenous injection, ICG is rapidly bound to plasma proteins with minimal leakage into the interstitium and is excreted by the liver in bile about 8 min after injection. This emission intensity signal can then be used to accurately classify cancerous tissue, through the use of biophysical modelling and image analysis techniques. Similarly, MB can be excited from 550- 700 nm, with an emission centered around 690 nm.

Other fluorescent dyes are known. Fluorophore molecules may be either utilized alone, or serve as a fluorescent motif of a functional system. Based on molecular complexity and synthetic methods, fluorophore molecules may be generally classified into four categories: proteins and peptides, small organic compounds, synthetic oligomers and polymers, and multicomponent systems. See, for example, https://en.wikipedia.org/wiki/Fluorophore.

In one example, the first time period is immediately before (i.e. the first interval is zero) or before a first interval controlling perfusion of the organ. In one example, the first time interval is in a range from 1 minute to 60 minutes, preferably in a range from 2 minutes to 30 minutes, more preferably in a range from 5 minutes to 15 minutes.

Generally, a spatial region (also known as a region of interest) is a surface or near-surface region of the organ, having a surface area, that is affected by perfusion and that may be imaged. In one example, the first spatial region and the second spatial region comprise and/or are the same region. In one example, the first spatial region and the second spatial region comprise and/or are mutually overlapping regions. In one example, the first spatial region and the second spatial region comprise and/or are mutually adjacent regions. In one example, the first spatial region and the second spatial region comprise and/or are contiguous regions. In one example, the first spatial region and the second spatial region comprise and/or are mutually non-overlapping regions. In one example, the set of spatial regions includes R spatial regions, wherein R is a natural number greater than or equal to 2, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 200, 500, 1000 or more. In one example, the first spatial region has a size of a x b pixels (e.g. of a CCD or CMS imaging device for acquiring the images), wherein a and b are each natural numbers greater than or equal to 1 , for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 20, 32, 50, 64, 100, 128, 200, 256, 500, 512, 1000, 1024 or more. In one example, a = b. In one example, a = b = 1. In this way, discrimination is performed on a per pixel basis.

Techniques for perfusion of organs with contrast agents are known, as described below in more detail. Techniques for controlling perfusion of organs are known, for example by restricting blood supply thereto such as using clamps, by dosing of a medicament to increase, for example transiently, blood supply and/or by transection of blood vessels thereto and/or therefrom. For example, controlling perfusion of organs may include vascular comprise and/or operative dissection such as mesentery preparation (cut) and/or colorectal-mesocolic preparation for proximal colorectal transection. Other techniques for controlling perfusion of organs are known.

The method comprises generating, for example computationally, the first set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using respective intensities of the set of spatial regions of the first time series of images. It should be understood that the first intensity-time profile of the first spatial region comprises and/or is a series of intensity-time pairs (i.e. intensity as a function of time, which may be represented in a table or on a graph) of the first spatial region. In one example, respective intensities of the first spatial region of the first intensity-time profile comprise or are average (mean, median or modal, preferably mean) intensities or maximum intensities of the first spatial region, for example of pixels corresponding to the first spatial region of the respective images of the first time series of images.

In one example, generating the first set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using the respective intensities of the set of spatial regions of the first time series of images comprises calculating an averaged intensity-time profile by averaging the first set of intensity-time profiles of the set of spatial regions, for example by calculating the mean or the moving-average mean of the first set of intensity-time profiles of the set of spatial regions; and wherein comparing the first set of intensity-time profiles and the second set of intensity-time profiles comprises comparing the averaged intensity-time profile and the second set of intensity-time profiles. In this way, a reference profile (i.e. the averaged intensity-time profile) (also known as a baseline profile) is calculated for the organ before the perfusion thereof is controlled, thereby smoothing (i.e. attenuating) outlier data points and/or spatial regions, and the second set of intensity-time profiles is compared therewith. In this way, reproducibility and/or robustness are improved.

In one example, generating the first set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using the respective intensities of the set of spatial regions of the first time series of images comprises normalizing the respective intensities of the set of spatial regions of the first time series of images, for example with respect to the maximum intensity of the set of spatial regions of the first time series of images, and generating the first set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using the respective normalized intensities of the set of spatial regions of the first time series of images. In this way, different illumination (for example excitation) intensities (i.e. brightness) and/or changes in the illumination intensity are compensated. More generally, in this way, optical phenomena, for example arising from the imaging technique, are compensated. In this way, reproducibility and/or robustness are improved.

In one example, generating the second set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region and the second intensity-time profile of the second spatial region, using the respective intensities of the set of spatial regions of the second time series of images comprises normalizing the respective intensities of the set of spatial regions of the second time series of images, for example with respect to the maximum intensity of the set of spatial regions of the second time series of images or with respect to the maximum intensity of the set of spatial regions of the first time series of images, and generating the second set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region and the second intensity-time profile of the second spatial region, using the respective normalized intensities of the set of spatial regions of the second time series of images. In this way, different illumination (for example excitation) intensities (i.e. brightness) and/or changes in the illumination intensity are compensated. More generally, in this way, optical phenomena, for example arising from the imaging technique, are compensated. In this way, reproducibility and/or robustness are improved.

The method comprises obtaining the second time series of images, including the first image and the second image, of the organ, during the second time period after a second perfusion of the organ with the second contrast agent and after controlling perfusion of the organ, for example as described with respect to the first time series of images mutatis mutandis. That is, the organ or part thereof is imaged again after perfusion is controlled, such that a response to the organ to the controlled perfusion may be observed.

The method comprises generating the second set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region and the second intensity-time profile of the second spatial region, using respective intensities of the set of spatial regions of the second series of images, for example as described with respect to the set of intensity-time profiles mutatis mutandis.

The method comprises comparing the first set of intensity-time profiles and the second set of intensity-time profiles. In this way, a response to the organ to the controlled perfusion may be observed. In one example, comparing the first set of intensity-time profiles and the second set of intensity-time profiles comprises matching the first set of intensity-time profiles and the second set of intensity-time profiles. In this way, agreement Agreement A between the first set of intensity-time profiles and the second set of intensity-time profiles is calculated. For example, if the first set of intensity-time profiles and the second set of intensity-time profiles match exactly or closely, agreement therebetween is exact or close, indicating that perfusion of the organ before and after controlling thereof is unaffected thereby, such as for healthy tissue. For example, if the first set of intensity-time profiles and the second set of intensity-time profiles match poorly, agreement therebetween is poor, indicating that perfusion of the organ before and after controlling thereof is adversely affected thereby, such as for diseased tissue.

In one example, matching the first set of intensity-time profiles and the second set of intensitytime profiles comprises aligning, for example with respect to time or only with respect to time, the first set of intensity-time profiles and the second set of intensity-time profiles. In this way, differences in start times of the first perfusion of the organ and of the second perfusion of the organ are reduced, minimized or eliminated. In this way, reproducibility and/or robustness are improved.

In one example, aligning, for example with respect to time or only with respect to time, the first set of intensity-time profiles and the second set of intensity-time profiles comprises minimising respective differences therebetween. In this way, differences in start times of the first perfusion of the organ and of the second perfusion of the organ are minimized. In this way, reproducibility and/or robustness are improved.

In one example, aligning the first set of intensity-time profiles and the second set of intensitytime profiles comprises shifting the second set of intensity-time profiles with respect to time by a respective set of time shifts d, including a first time shift for the first intensity-time profile of the first spatial region and a second time shift for the second intensity-time profile of the second spatial region. In this way, differences in start times of the first perfusion of the organ and of the second perfusion of the organ are reduced, minimized or eliminated. In this way, reproducibility and/or robustness are improved.

In one example, aligning the first set of intensity-time profiles and the second set of intensitytime profiles comprises scaling the second set of intensity-time profiles with respect to time by a respective set of scaling factors s, including a first scaling factor for the first intensity-time profile of the first spatial region and a second scaling factor for the second intensity-time profile of the second spatial region. In this way, differences in perfusion rate of the first perfusion of the organ and of the second perfusion of the organ are reduced, minimized or eliminated. In this way, reproducibility and/or robustness are improved.

The method comprises discriminating between the first spatial region and the second spatial region based on the result of comparing the first set of intensity-time profiles and the second set of intensity-time profiles.

In one example, discriminating between the first spatial region and the second spatial region based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles comprises contrasting between the first spatial region and the second spatial region based on the set of scaling factors. The inventors have observed that decrescendo scaling factors denote perishing perfusion and are reflected in a diminishing scaling s factor distally from arterial inflow.

In one example, contrasting between the first spatial region and the second spatial region based on the set of scaling factors comprises contrasting between the first spatial region and the second spatial region based on a respective set of ratios, including a first ratio for the first scaling factor and a second ratio for the second scaling factor, of the respective scaling factors included in the set of scaling factors to a reference scaling factor. In this way, the first spatial region and the second spatial region are contrasted based on the respective ratios. In this way, differences in techniques of obtaining the first time series of images and the second time series of images for different organs, for example, are eliminated, thereby enabling direct comparison between different organs.

In one example, contrasting between the first spatial region and the second spatial region based on the respective set of ratios, including the first ratio for the first scaling factor and the second ratio for the second scaling factor, of the respective scaling factors included in the set of scaling factors to the maximum scaling factor included in the set of scaling factors comprises contrasting between the first spatial region and the second spatial region based on the respective set of ratios, including the first ratio for the first scaling factor and the second ratio for the second scaling factor, of the respective scaling factors included in the set of scaling factors to the maximum scaling factor included in the set of scaling factors and a predetermined ratio threshold for the respective ratios included in the set of ratios. In this way, an optimal transection point (for example, incision line or plane) may be determined. In one example, the predetermined ratio threshold is in a range from 50% to 99.9%, preferably in a range from 75% to 99.5%, more preferably in a range from 90% to 99%, for example 95%.

It should be understood that alternatively and/or additionally, the shifting and/or the scaling may be applied to the first set of intensity-time profiles mutatis mutandis. Distance related light intensity depreciation was overcome via peak brightness normalisation to a value of 1 for all ROI and a Reference Profile r(t) time series was synthesised from the first two minutes of the Control time-fluorescence plot. Utilising Acquisition Profile l(t) time series for each ROI in the Acquisition video, scaling s and time shift d,

Equation 1: Agreement between acquisition profile and reference profile

In one example, the method comprises calculating an agreement A(s,d) between the Acquisition Profile l(t) and the scaled, shifted Reference Profile r s(t - d)) is given by Equation 1 . In one example, the agreement A s,d) between the Acquisition Profile l(t) and the scaled, shifted Reference Profile r(s(t - d)) is maximised, wherein the scaled, shifted Reference Profile r s(t - d)) is calculated by shifting the Reference Profile r(t) by d seconds and stretching (if s < 1) or squeezing (if s > 1) in time t, but not in y-direction (i.e. intensity or normalized intensity), wherein the agreement A s, d) is the square sum of the distance between the scaled, shifted Reference Profile r s(t - d)) and the Acquisition Profile l(t)) is given by Equation 1 :

Equation 1

Decrescendo scaling factors denoted perishing perfusion at ROI and reflected in a diminishing scaling s factor distally from arterial inflow.

In one example, optimal perfusion is transcribed (i.e. the ideal transection point) at the most distal spatial region (also known as region of interest or ROI), after optimising, for example maximising, the agreement, for example by shifting and/or scaling, wherein the second set of intensity-time profiles, for example an acquisition profile or acquisition curve, has a scaling s factor within the predetermined ratio threshold, for example 95%, of the largest scaling s factor in the Acquisition Profile lit)).

Equations 2, 3 and 4: Difference between acquisition profile and reference profile

In one example, the method comprises calculating a difference between the first set of intensity-time profiles, for example a reference profile or reference curve, and the second set of intensity-time profiles, for example an acquisition profile or acquisition curve, wherein the difference is given by Equation 2, Equation 3, Equation 4 and/or a combination thereof, as described below. In one example, the method comprises optimising, for example minimising, the difference, for example by shifting and/or scaling, as described above. In one example, optimal perfusion is transcribed (i.e. the ideal transection point) at the most distal spatial region (also known as region of interest or ROI), after optimising, for example minimising, the difference, for example by shifting and/or scaling, wherein the second set of intensity-time profiles, for example an acquisition profile or acquisition curve, has an intensity within a predetermined threshold of the first set of intensity-time profiles, for example a reference profile or reference curve. In one example, the predetermined threshold is in a range from 50% to 99.9%, preferably in a range from 60% to 95%, more preferably in a range from 70% to 90%, for example 80%.

Definitions

Fmax: peak (maximum) intensity, for example fluorescence intensity, i.e the maximum brightness achieved on a time(seconds) vs intensity, for example fluorescence intensity, (e.g. in grayscale units) curve for a particular region of interest (i.e. a particular spatial region)

T max: time (seconds) to achieve Fmax from the start time (0 seconds) of the curve

Latency: time period prior to detection of the fluorescence signal i.e., recording time period to contrast agent, for example fluorescence dye, injection, i.e from start time (0 seconds) to T5 (defined later)

Fo: intensity, for example fluorescence intensity, at the beginning of the recording period (and the beginning of the latency period i.e at time 0)

T5: Time (in seconds) to achieve a 5% increase in intensity, for example fluorescence intensity, from Fo marking the end of the latency period.

Time to rise (TTR) = Tmax - Ts

Time to achieve 50% fluorescence (T1/2) = Time to achieve 50% of Fmax from start time (0 seconds)

Time Ratio (TR) = (T1/2) divided by TTR

A (TTR) = Difference in TTR

Difference between Mean TTR in the reference curves (r) and the TTR in the acquisition curve region of interest (I) is given by Equation 2: Equation 2

where j is the index (region of interest) of the reference and / is the index (region of interest) of the acquisition curve.

A T1/2 = Difference in T1/2

Difference between Mean T1/2 in the reference curves (r) and the T1/2 in the acquisition curve region of interest (/) is given by Equation 3: Equation 3

TR = Difference in TR

Difference between Mean TR in the reference curves (r) and the TR in the acquisition curve region of interest (/) is given by Equation 4: Equation 4

Visualization

In one example, the method comprises generating a visualization (for example, an image) of the organ based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles. In this way, the organ and the result may be visualized, for example by displaying the generated visualization on a display.

In one example, generating the visualization of the organ based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles comprises distinguishing respective spatial regions included in the set of spatial regions. In this way, the set of spatial regions may be mutually distinguished visually, for example based on respective relative perfusions thereof. In this way, a surgeon may be guided, for example continuously, to transect the organ at an optimized transection point, for example. In one example, distinguishing the respective spatial regions included in the set of spatial regions comprises visually distinguishing the respective spatial regions included in the set of spatial regions, for example using colour, contour lines, reference signs, such as alphanumeric characters, and/or markers, such as graphics. In this way, a surgeon may be further guided, for example continuously, to transect the organ at an optimized transection point such as guided by the visual distinguishing, for example.

In one example, generating the visualization of the organ based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles comprises indicating respective boundaries between the respective spatial regions included in the set of spatial regions. In this way, a surgeon may be guided, for example continuously, to transect the organ at an optimized transection point such as a boundary, for example.

In one example, the method comprises displaying the generated visualization of the organ during a third time period after the second time period, for example during surgery.

In one example, displaying the generated visualization on a display comprises displaying the generated visualization on an augmented reality display such as displaying the generated visualization overlaying a real time image of the organ. In this way, a surgeon may be guided, for example continuously, to transect the organ at an optimized transection point such as a boundary, for example. In one example, displaying the generated visualization on a display comprises displaying the generated visualization on a virtual reality display such as displaying the generated visualization overlaying a computer generated or stored image of the organ. In this way, a surgeon during training may be guided, for example continuously, to transect the organ at an optimized transection point such as a boundary, for example.

Ex vivo method for treatment, therapy or diagnostic method

The second aspect provides an ex vivo method for treatment of an organ by surgery or therapy or an ex vivo therapy or diagnostic method practised on an organ, comprising the method according to the first aspect.

In vivo method for treatment, therapy or diagnostic method

The third aspect provides a method for treatment of the human or animal body by surgery or a therapy or diagnostic method practised on the human or animal body, comprising the method according to the first aspect. In one example, the method comprises transecting the organ based on a result of discriminating between the first spatial region and the second spatial region.

In one example, the organ comprises and/or is the digestive tract, for example the colorectal and/or internal gastrointestinal tract, or a urinary organ.

Computer, computer program, non-transient computer-readable storage medium

The fourth aspect provides a computer comprising a processor and a memory configured to implement a method according to the first aspect, a computer program comprising instructions which, when executed by a computer comprising a processor and a memory, cause the computer to perform a method according to the first aspect or a non-transient computer- readable storage medium comprising instructions which, when executed by a computer comprising a processor and a memory, cause the computer to perform a method according to the first aspect.

Definitions

Throughout this specification, the term “comprising” or “comprises” means including the component(s) specified but not to the exclusion of the presence of other components. The term “consisting essentially of’ or “consists essentially of’ means including the components specified but excluding other components except for materials present as impurities, unavoidable materials present as a result of processes used to provide the components, and components added for a purpose other than achieving the technical effect of the invention, such as colourants, and the like.

The term “consisting of’ or “consists of’ means including the components specified but excluding other components.

Whenever appropriate, depending upon the context, the use of the term “comprises” or “comprising” may also be taken to include the meaning “consists essentially of’ or “consisting essentially of’, and also may also be taken to include the meaning “consists of’ or “consisting of’.

The optional features set out herein may be used either individually or in combination with each other where appropriate and particularly in the combinations as set out in the accompanying claims. The optional features for each aspect or exemplary embodiment of the invention, as set out herein are also applicable to all other aspects or exemplary embodiments of the invention, where appropriate. In other words, the skilled person reading this specification should consider the optional features for each aspect or exemplary embodiment of the invention as interchangeable and combinable between different aspects and exemplary embodiments.

Brief description of the drawings

For a better understanding of the invention, and to show how exemplary embodiments of the same may be brought into effect, reference will be made, by way of example only, to the accompanying diagrammatic Figures, in which:

Figure 1 shows graphs showing time fluorescence curves (scale unit vs seconds) being scaled and shifted (middle and left) in comparison the control curve (right);

Figure 2 Workflow: upper image shows the calibration Control ICGFA with a Reference Profile being generated. The lower image shows the post-resection Acquisition ICGFA charted into curves and then scaled on the horizontal axis compared to the Reference Profile. The bar chart shows the ROI 3 is selected as the most distal transection point within 95% scaling of the Reference Profile.

Figure 3 shows 13 regions of interest (ROI) overlaid on the bowel of Figure 2, imaged in white light; and

Figure 4 shows a generated image of the bowel of Figure 3, in which the respective spatial regions included in the set of spatial regions are mutually visually distinguished using colour on a per pixel basis (area bounded by dashed box overlaying an image of the bowel), for example as determined based on a difference using Equations 2, 3, 4 and/or a combination thereof, thereby visually guiding a surgeon to perform transection at an optimized site; and

Figure 5 schematically depicts a method according to an exemplary embodiment.

Detailed Description of the Drawings

Introduction

Near-infrared (NIR) surgical camera systems coupled with intravenous dyes e.g. Indocyanine Green (ICG) have permeated the operative environment and allowed beyond visual spectrum circulatory mapping. Such ICG Fluorescence angiography (ICGFA) is utilised during colorectal operations as, following cancer resection, it can reveal anastomotic perfusion for the surgeon to interpret with a view to diminishing anastomotic leakage (AL) rates. Despite clinical momentum, capitalisation of this technology has remained elusive and contemporaneous trials inconsistently demonstrate AL improvement rates. In practice, there are variations in signal excitation methods, sensing, display and interpretation while intraoperative ICG behaviour shows considerable interpatient variation (including serum protein, body weight and age, anaesthetic ventilation, circulatory output and flow.) These rich polychronous fluorescent representations have been mathematicised to temporal-fluorescence curves in the emergent field of quantitative(Q-)ICGFA. Optimal plots have been correlated to superior metabolic, and healing profiles in animal models with subsequent translation to superior clinical outcomes. However, attempts to surpass oculo-cognitive interpretation via machine learning have fallen victim to signal brightness contours¹³ rather than appreciating perfusion gradients.

We present here a novel practice of early intraoperative ICGFA (i.e. prior to tissue and oncological circulatory resection) enabling synthesis of a patient specific calibrated Q-ICGFA profile from which an optimally perfused ideal transection point is selected once the bowel is prepared for joining.

Material & methods

In a clinical trial (ethics approval 1/378/2092), consenting patients undergoing elective right and left-sided colorectal resection had ICGFA both early following abdominal access (Control ICGFA) and after colorectal-mesocolic preparation for proximal colorectal transection (Acquisition ICGFA) using a commercially available laparoscopic system (PINPOINT, Stryker) and 0.1 mg/kg intravenously injected ICG. ICGFA video was recorded at bowel in the vicinity of the planned resection at both timeframes over 5 minutes and relevant patient demographics and postoperative events were recorded. The Control ICGFA video (0.1 mg/kg ICG) was recorded early in colonic resections (prior to vascular compromise) so as to computationally synthesise a patient-specific reference colonic angiogram. This was subsequently superimposed on the post resection and pre-anastomotic ICGFA signature to algorithmically identify the optimally perfused most distal colonic transection point. Operative videos were processed post-hoc using bespoke software (IBM Research) that automatically tracks selected regions of interest (ROI) in the bowel in white light and plots fluorescence intensity over time in these same regions in the simultaneous NIR view.

Distance related light intensity depreciation was overcome via peak brightness normalisation to a value of 1 for all ROI and a Reference Profile r(t) time series was synthesised from the first two minutes of the Control time-fluorescence plot. Utilising Acquisition Profile l(t) time series for each ROI in the Acquisition video, scaling s and time shift d, agreement A(s,d) between Acquisition Profile l(t) and r(s(t - d)) was maximised by shifting Reference Profile r(t) by d seconds and stretching (if s < 1) or squeezing (if s > 1) in time t, but not in y- direction. Agreement A(s, d) was transcribed as the square sum of the distance between the two curves (i.e. between the Reference Profile r(t) and the Acquisition Profile

A(s,d) = £ ) - r(s t - d))}²

Decrescendo scaling factors denoted perishing perfusion at ROI and reflected in a diminishing scaling s factor distally from arterial inflow. Optimal perfusion was transcribed (i.e. the ideal transection point) at the most distal ROI with a scaling s factor within 95% of the largest scaling s factor in the Acquisition video.

Results

Videos from eight patients were studied resulting in 11 analyses, synthesising 213712 data points over 108 tracked minutes with small bowel peristalsis and camera movement challenging motion defeating algorithms. ICG administration and video recording (Control & Acquisition) were easily accommodated with the operative workflow. For all instances (small and large bowel) an ideal transection point at the ROI with a scale factor within 95% was identified with distal ROIs indicating malperfusion.

Post hoc computational analysis (n=8) from videos of left and right sided hemi colectomies displayed a consistent ability to select a region of interest with a digitally identified optimal perfusion from the video ICGFA on both small and large bowel. No anastomotic leakages were recorded in the cohort.

Discussion and Conclusion

Surgeons use ICGFA to inform regarding perfusion at their preselected site for transection. Here we show Q-ICGFA can indicate the site of optimal fluorescence. The adapted operative and computationally enhanced workflow has demonstrated a feasible and consistent modus operandi for patient specific, perfusion based intestinal transection recommendation. The adapted clinical practice fits operative workflows and the imagery processing enables a reproducible personalised, algorithmic optimal transection recommendation level which compensates for signal physio-optical phenomena as well as physiological variations.

It should be understood that while the method is described herein in relation to colorectal transection, the method may be applied to more generally to the colorectal and gastrointestinal (Gl) tract, the oesophagus, the stomach, the bile duct and/or the pancreas, for example. Figure 5 schematically depicts a method according to an exemplary embodiment.

The method is of discriminating between a first tissue status and a second tissue status of an organ or a part thereof, the method implemented by a computer comprising a processor and a memory, the method comprising: obtaining a first time series of images, including a first image and a second image, of the organ, having a set of spatial regions including a first spatial region, having the first tissue status, and a second spatial region, during a first time period after a first perfusion of the organ with a first contrast agent and before controlling perfusion of the organ (S501); generating a first set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region, using respective intensities of the set of spatial regions of the first time series of images (S502); obtaining a second time series of images, including a first image and a second image, of the organ, during a second time period after a second perfusion of the organ with a second contrast agent and after controlling perfusion of the organ (S503); generating a second set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region and a second intensity-time profile of the second spatial region, using respective intensities of the set of spatial regions of the second series of images (S504); comparing the first set of intensity-time profiles and the second set of intensity-time profiles (S505); and discriminating between the first spatial region and the second spatial region based on a result of comparing the first set of intensity-time profiles and the second set of intensity-time profiles (S506).

The method may include any of the steps described herein.

In summary, the invention provides method for personalised, algorithmic colonic transection recommendation.

Although a preferred embodiment has been shown and described, it will be appreciated by those skilled in the art that various changes and modifications might be made without departing from the scope of the invention, as defined in the appended claims and as described above.

Definitions At least some of the example embodiments described herein may be constructed, partially or wholly, using dedicated special-purpose hardware. Terms such as ‘component’, ‘module’ or ‘unit’ used herein may include, but are not limited to, a hardware device, such as circuitry in the form of discrete or integrated components, a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks or provides the associated functionality. In some embodiments, the described elements may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors. These functional elements may in some embodiments include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Although the example embodiments have been described with reference to the components, modules and units discussed herein, such functional elements may be combined into fewer elements or separated into additional elements. Various combinations of optional features have been described herein, and it will be appreciated that described features may be combined in any suitable combination. In particular, the features of any one example embodiment may be combined with features of any other embodiment, as appropriate, except where such combinations are mutually exclusive. Throughout this specification, the term “comprising” or “comprises” means including the components) specified but not to the exclusion of the presence of others.

Attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.

All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive.

Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

The invention is not restricted to the details of the foregoing embodiment(s). The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

Claims

1 . A method of discriminating between a first tissue status and a second tissue status of an organ or a part thereof, the method implemented by a computer comprising a processor and a memory, the method comprising: obtaining a first time series of images, including a first image and a second image, of the organ, having a set of spatial regions including a first spatial region, having the first tissue status, and a second spatial region, during a first time period after a first perfusion of the organ with a first contrast agent and before controlling perfusion of the organ; generating a first set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region, using respective intensities of the set of spatial regions of the first time series of images; obtaining a second time series of images, including a first image and a second image, of the organ, during a second time period after a second perfusion of the organ with a second contrast agent and after controlling perfusion of the organ; generating a second set of intensity-time profiles of the set of spatial regions, including a first intensity-time profile of the first spatial region and a second intensity-time profile of the second spatial region, using respective intensities of the set of spatial regions of the second series of images; comparing the first set of intensity-time profiles and the second set of intensity-time profiles; and discriminating between the first spatial region and the second spatial region based on a result of comparing the first set of intensity-time profiles and the second set of intensity-time profiles.

2. The method according to claim 1 , wherein the second spatial region has a second tissue status.

3. The method according to any previous claim, wherein the first contrast agent comprises and/or is a fluorescent dye, for example indocyanine green or methylene blue, and wherein obtaining the first time series of images, including a first image and a second image, of the organ comprises fluorescence angiography.

4. The method according to any previous claim, wherein generating the first set of intensitytime profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using the respective intensities of the set of spatial regions of the first time series of images comprises calculating an averaged intensity-time profile by averaging the first set of intensity-time profiles of the set of spatial regions; and wherein comparing the first set of intensity-time profiles and the second set of intensity-time profiles comprises comparing the averaged intensity-time profile and the second set of intensity-time profiles.

5. The method according to any previous claim, wherein generating the first set of intensitytime profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using the respective intensities of the set of spatial regions of the first time series of images comprises normalizing the respective intensities of the set of spatial regions of the first time series of images and generating the first set of intensity-time profiles of the set of spatial regions, including the first intensity-time profile of the first spatial region, using the respective normalized intensities of the set of spatial regions of the first time series of images.

6. The method according to any previous claim, wherein comparing the first set of intensitytime profiles and the second set of intensity-time profiles comprises matching the first set of intensity-time profiles and the second set of intensity-time profiles.

7. The method according to claim 6, wherein matching the first set of intensity-time profiles and the second set of intensity-time profiles comprises aligning the first set of intensity-time profiles and the second set of intensity-time profiles.

8. The method according to claim 7, wherein aligning the first set of intensity-time profiles and the second set of intensity-time profiles comprises minimising respective differences therebetween.

9. The method according to any of claims 7 to 8, wherein aligning the first set of intensity-time profiles and the second set of intensity-time profiles comprises shifting the second set of intensity-time profiles with respect to time by a respective set of time shifts, including a first time shift for the first intensity-time profile of the first spatial region and a second time shift for the second intensity-time profile of the second spatial region.

10. The method according to any of claims 7 to 9, wherein aligning the first set of intensity-time profiles and the second set of intensity-time profiles comprises scaling the second set of intensity-time profiles with respect to time by a respective set of scaling factors, including a first scaling factor for the first intensity-time profile of the first spatial region and a second scaling factor forthe second intensity-time profile of the second spatial region.

11. The method according to claim 10, wherein discriminating between the first spatial region and the second spatial region based on the result of comparing the first set of concentrationtime profiles and the second set of concentration-time profiles comprises contrasting between the first spatial region and the second spatial region based on the set of scaling factors.

12. The method according to claim 11 , wherein contrasting between the first spatial region and the second spatial region based on the set of scaling factors comprises contrasting between the first spatial region and the second spatial region based on a respective set of ratios, including a first ratio for the first scaling factor and a second ratio for the second scaling factor, of the respective scaling factors included in the set of scaling factors to a reference scaling factor.

13. The method according to claim 12, wherein contrasting between the first spatial region and the second spatial region based on the respective set of ratios, including the first ratio for the first scaling factor and the second ratio for the second scaling factor, of the respective scaling factors included in the set of scaling factors to the maximum scaling factor included in the set of scaling factors comprises contrasting between the first spatial region and the second spatial region based on the respective set of ratios, including the first ratio for the first scaling factor and the second ratio for the second scaling factor, of the respective scaling factors included in the set of scaling factors to the maximum scaling factor included in the set of scaling factors and a predetermined ratio threshold for the respective ratios included in the set of ratios.

14. The method according to any previous claim, comprising generating a visualization of the organ based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles.

15. The method according to claim 14, wherein generating the visualization of the organ based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles comprises distinguishing respective spatial regions included in the set of spatial regions.

16. The method according to claim 15, wherein distinguishing the respective spatial regions included in the set of spatial regions comprises visually distinguishing the respective spatial regions included in the set of spatial regions, for example using colour, contour lines, reference signs, such as alphanumeric characters, and/or markers, such as graphics.

17. The method according to any of claims 14 to 16, wherein generating the visualization of the organ based on the result of comparing the first set of concentration-time profiles and the second set of concentration-time profiles comprises indicating respective boundaries between the respective spatial regions included in the set of spatial regions.

18. The method according to any of claims 14 to 17, comprising displaying the generated visualization of the organ during a third time period after the second time period.

19. The method according to claim 18, wherein displaying the generated visualization comprises displaying the generated visualization on an augmented reality display and/or on a virtual reality display.

20. The method according to any previous claim, comprising calculating a difference between the first set of intensity-time profiles and the second set of intensity-time profiles, wherein the difference is given by Equation 2, Equation 3, Equation 4 and/or a combination thereof.

21 . An ex vivo method for treatment of an organ by surgery or therapy or an ex vivo therapy or diagnostic method practised on an organ, comprising the method according to any of claims 1 to 20.

22. A method for treatment of the human or animal body by surgery or a therapy or diagnostic method practised on the human or animal body, comprising the method according to any of claims 1 to 20.

23. The method according to claim 22, comprising transecting the organ based on a result of discriminating between the first spatial region and the second spatial region.

24. The method according to any of claims 22 to 24, wherein the organ comprises and/or is the digestive tract, for example the colon thereof.

25. A computer comprising a processor and a memory configured to implement a method according to any of claims 1 to 20, a computer program comprising instructions which, when executed by a computer comprising a processor and a memory, cause the computer to perform a method according to any of claims 1 to 20 or a non-transient computer-readable storage medium comprising instructions which, when executed by a computer comprising a processor and a memory, cause the computer to perform a method according to any of claims 1 to 20.