Patent Description:
Atherosclerosis is a disease of the coronary arteries wherein atheromatous plaque ("plaque") accumulates abnormally in the inner layer of an arterial wall. Significant accumulation of plaque can cause a narrowing of an artery, referred to as arterial stenosis, and consequently a reduction of blood flow. Significant arterial stenosis in the context of coronary arteries can result in heart attack and death.

A known system for identifying coronary artery disease involves determining a predicted radius from an artery centreline to an artery inner wall, and using the determination to identify disease. However, this technique has limitations in that limited information is provided in relation to disease.

<CIT> describes a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. The method involves receiving a radiological dataset including computed tomography (CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), MRI, ultrasound (US), positron emission tomography (PET), or single-photon emission tomography (SPECT) diagnostic images for a patient. The dataset is enriched by performing analyte measurement and classification of anatomic structure, shape or geometry or tissue characteristic, type or character with objective validation for a set of analytes relevant to a pathology. A machine learned classification approach is used based on known ground truths to process the enriched dataset and determine a phenotype for the pathology. The analyte measurement and classification of anatomic structure, shape or geometry and tissue characteristic, type or character includes semantic segmentation to identify and classify regions of interest in the radiological dataset.

In accordance with a first aspect of the present invention, there is provided a method of identifying coronary artery disease comprising:.

In an embodiment, the step of identifying a plurality of seed points comprises analysing the contrast cardiac CT data using machine learning to identify a plurality of predicted seed points expected to correspond to locations on a coronary artery, and applying a radiodensity test to the predicted seed points to produce a plurality of candidate seed points. The radiodensity test may comprise filtering the contrast cardiac CT data so as to pass predicted seed points that have an associated radiodensity value within a defined parameter range. In an embodiment, the radiodensity test is a Hounsfield Unit test and the defined parameter range is a Hounsfield Unit value between <NUM> and <NUM>.

In an embodiment, the method comprises determining seed points predicted to correspond to locations on a coronary artery by predicting from an instant candidate seed point a probable direction to a further seed point of the coronary artery in order to identify a line representative of the coronary artery. The step of determining seed points predicted to correspond to locations on a coronary artery may comprise selecting a candidate seed point from the plurality of candidate seed points using the predicted probable direction to a further seed point of the coronary artery. The step of predicting a probable direction to a further seed point from an instant seed point in order to identify a line representative of the coronary artery may be carried out using machine learning.

In an embodiment, the line representative of the coronary artery is a line representative of a centreline of the coronary artery.

In an embodiment, the step of predicting a probable direction to a further seed point from an instant seed point comprises starting with a seed point at or adjacent a predicted end of a coronary artery remote from the aorta and successively predicting seed points from the remote end to the aorta.

In an embodiment, the method comprises using machine learning to detect intersection locations between coronary arteries and the aorta, for example by carrying out an aorta segmentation process on the contrast cardiac CT data using machine learning to predict the location of the ascending aorta in the contrast cardiac CT data, and using the predicted ascending aorta location and the identified lines representative of the coronary arteries. The method may comprise determining whether a coronary artery connects to the aorta based on whether the coronary artery extends to a position within a defined distance, such as <NUM>, from the ascending aorta.

In an embodiment, the method comprises using a branch detection algorithm to detect branches on the primary coronary arteries that were not initially identified as viable centrelines.

In an embodiment, the method comprises a representative artery line labeller that associates labels with identified arteries, the labels identified using machine learning that may comprise at least one classifier. The at least one classifier may include an adaptive boosting (AdaBoost) algorithm to boost performance of the classifier.

In an embodiment, the at least one classifier may be arranged to classify a coronary artery based on a plurality of key coronary artery features. The key coronary artery features may include a location of an end of the coronary artery remote from the aorta, and direction vectors indicative of a plurality of different locations along the coronary artery. The plurality of different locations may be disposed at a proximal location adjacent the aorta, at a location substantially mid vessel, and at a location at or adjacent an end of the coronary artery remote from the aorta.

In an embodiment, the method comprises modifying the parameter range used by the radiodensity test if a determination is made that the identified coronary arteries are incorrect or incomplete. If the determination is that insufficient coronary arteries have been identified, the method may comprise widening the parameter range such that the number of candidate seed points increases.

In an embodiment, the transverse image slice is a slice taken perpendicular to the coronary artery.

In an embodiment, the step of analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data comprises training the machine learning component using ground truth data indicative of example transverse image slices that include inner and outer artery walls and imaging artefacts indicative of coronary artery disease.

In an embodiment, the ground truth data includes multiple cross sectional image data slices for each of a set of multiple defined points along a ground truth coronary artery, the image slices including at least one image data slice before the defined point and at least one image data slice after the defined point.

In an embodiment, the method comprises using the determined inner wall data to determine a cross-sectional lumen area, and to use the determined cross-sectional lumen area to identify stenosis.

In an embodiment, the method comprises determining a reference cross sectional area after each artery bifurcation, the reference area calculated by fitting a linear regression line to an artery portion after the artery bifurcation, the linear regression line indicative of a linear progressively reducing reference cross sectional area, and identifying stenosis based on a comparison of a determined cross-sectional area with a reference cross sectional area according to the linear regression line.

In an embodiment, the method comprises identifying stenosis if the comparison of the determined cross-sectional area with the reference cross sectional area is indicative of a defined proportional difference.

In an embodiment, the step of identifying presence of coronary artery disease comprises determining a gap region between the determined inner and outer wall data and analysing characteristics of the gap region in order to characterise the coronary artery disease.

The characteristics of the gap region may include the radiodensity of voxels associated with the gap region.

In an embodiment, the step of identifying presence of coronary artery disease comprises identifying high risk plaque that may include low attenuation plaque and/or spotty calcification.

The method may comprise identifying a spotty calcification by applying a radiodensity test to identify candidate voxels in the gap that are predicted to be associated with calcified plaques, and applying a connected component analyser to associate related voxels together as calcified volumes. The step of identifying a spotty calcification may further include applying a size test to each identified calcified volume such that a calcified volume is identified as a spotty calcification if the calcified volume has a diameter less than a defined amount, such as <NUM>.

In an embodiment, the step of identifying presence of coronary artery disease comprises identifying positive remodelling based on whether a radial dimension and/or cross-sectional area and/or volume of the gap is greater than a defined amount, or proportion compared to a normal coronary artery, such as <NUM>% greater than a normal vessel gap.

In an embodiment, the method comprises storing different resolution versions of received contrast cardiac CT data and/or facilitating conversion of received contrast cardiac CT data to lower resolutions so that coronary artery disease analysis can be carried out on a selected resolution version, the contrast cardiac CT data resolution selected based on desired accuracy and analysis speed.

In an embodiment, the method comprises enabling a user to edit the inner artery wall data.

In an embodiment, the method comprises enabling a user to edit the inner artery wall data by facilitating manual modification of a displayed inner artery wall using an interface screen, for example by displaying a plurality of control points representative of the artery inner wall, and enabling a user to move one or more of the control points.

In an embodiment, the method comprises enabling a user to edit the inner artery wall data by facilitating selection of vessel stenosis, and modifying the inner artery wall data based on the selected stenosis. A plurality of selectable stenosis ranges may be provided.

In an embodiment, the method comprises enabling a user to edit the inner artery wall data by facilitating selection of a stenosis level for a vessel segment and/or specific location within the vessel.

In an embodiment, the method comprises enabling a user to edit the inner artery wall data by facilitating selection of a stenosis level for an individual lesion.

In an embodiment, the modifying the inner artery wall data based on the selected stenosis by carrying out an iterative process including the steps of modifying the lumen area of a selected slice and subsequently recalculating vessel stenosis.

In an embodiment, if the stenosis level is proposed to be reduced, the step of modifying the lumen area of a selected slice comprises reducing the stenosis level of each slice that exceeds a maximum level associated with the selected stenosis level by increasing the lumen area of the slice.

In an embodiment, if the stenosis level is proposed to be increased, the step of modifying the lumen area of a selected slice comprises identifying a slice with maximum stenosis and increasing the stenosis level of the slice to be above the minimum level associated with the selected stenosis level by reducing the lumen area of the slice.

In an embodiment, the iterative process comprises <NUM> iterations.

In an embodiment, if the stenosis level of a vessel segment is determined to be <NUM>%, the system may be arranged to apply a disease machine learning component to the vessel segment in order to identify potentially stenotic lesions, the disease machine learning component trained to recognise disease in coronary vessels.

In accordance with a second aspect of the present invention, there is provided a system for identifying coronary artery disease comprising:.

The present disclosure relates to a system for and method of identifying coronary artery disease and in the described examples the system and method also quantifies and characterises identified coronary artery disease (CAD). The disclosed system and method use coronary computed tomography angiography (CCTA) data, and in the described examples the system and method automatically identify, quantify and characterise coronary artery disease by detecting and tracking coronary artery centrelines, estimating the location of inner and outer walls of coronary arteries, and determining the extent and characteristics of any identified disease using the estimated inner and outer walls together with an analysis of the composition and spatial characteristics of identified gaps between the inner and outer walls.

The described system and method are able to detect the presence and severity of arterial narrowing (referred to as stenosis), and identify early stages of coronary artery disease by identifying high risk plaques including spotty calcification, low attenuation plaques and positive remodelling of the vessel walls. The output from the system may be in the form of a report highlighting key findings and priority risks.

Referring to the drawings, <FIG> shows a schematic block diagram of a system <NUM> for identifying and characterising coronary artery disease.

In this example, the system <NUM> is arranged to interact with multiple providers of cardiac computed tomography angiography (CCTA) data, represented in <FIG> by CCTA scanning devices 12a, 12b and associated Picture Archiving and Communication Systems (PACS) 14a, 14b. Each PACS system 14a, 14b is arranged to manage capture and storage of medical image data produced by a CCTA scanning device 12a, 12b, and communication of the medical image data to a medical image data server <NUM>, in this example disposed remotely of the CCTA service providers, and accessible through a wide area network such as the Internet <NUM>. In this example, the medical image data server <NUM> is a Digital Imaging and Communications in Medicine (DICOM) server, although it will be understood that any suitable device for receiving and managing storage of received CCTA image data is envisaged.

The DICOM server <NUM> is arranged to store received CCTA image data in a data storage device <NUM> that may include one or more databases. In this example, the system <NUM> also includes a personal health information (PHI) anonymiser <NUM> that may be a separate component or a component incorporated into the DICOM server <NUM>. The PHI anonymiser <NUM> is arranged to encrypt patient specific meta data (typically including name, date of birth and a unique ID number) in the received CCTA image data before the CCTA image data is stored in the data storage device <NUM>. In this way, the patient specific meta data is still associated with the CCTA image data, but is only accessible by authorised people, for example using login and password data.

The system <NUM> is arranged to enable multiple authorised users to interact with the system <NUM>, for example by providing each authorised user with an interface device <NUM>. Each interface device <NUM> may include any suitable computing device, such as a personal computer, laptop computer, tablet computer or mobile computing device.

The system <NUM> also includes a coronary artery disease (CAD) analysis device <NUM> in communication with the data storage device <NUM> and arranged to analyse CCTA image data stored in the data storage device <NUM> to identify, quantify and characterise coronary artery disease in the CCTA image data, and produce reports indicative of the analysis.

The system <NUM> may be arranged to store different resolution versions of received CCTA data and/or facilitate conversion of received CCTA data to lower resolutions so that CAD analysis can be carried out on a selected resolution version. In this way, a user is able to modify CAD analysis depending on desired accuracy and analysis speed.

The system <NUM> may be arranged to facilitate access using the interface device <NUM> in any suitable way. For example, the system <NUM> may be configured such that the CAD analysis device <NUM> is accessible through a web browser on the interface device <NUM>, wherein all or most processing activity occurs remotely of the interface device <NUM>, or the system <NUM> may be configured such that at least some processing activity occurs at the interface device <NUM>, for example by providing the interface device <NUM> with at least one software application that implements at least some processing activity on the CCTA data stored at the data storage device <NUM>.

In an alternative example, instead of providing a distributed system wherein CCTA data received from patients is stored remotely at a network accessible location, one or more components of the system <NUM> may be disposed at the same location as the interface device <NUM> and/or the CT device 12a, 12b such that most or all processing activity and/or storage of the CCTA data occurs at the same location.

As indicated in <FIG>, in this example the data stored at the data storage device <NUM> may also be accessible by the interface device <NUM> directly, for example so that a user at the interface device <NUM> can view raw CCTA data.

Using the interface device <NUM>, a user is able to instigate analysis and/or view the results of analysis of CCTA data stored at the data storage device <NUM>. During analysis, the CAD analysis device <NUM> extracts relevant CCTA data from the data storage device <NUM> and carries out analysis processes on the CCTA data in order to identify, quantify and characterise coronary artery disease present in the CCTA image data, and produce relevant reports.

The CAD analysis device <NUM> is shown in more detail in <FIG>, which is a schematic block diagram illustrating functional components of the CAD analysis device <NUM>.

The CAD analysis device <NUM> relies primarily on segmentation of inner and outer walls of the coronary arteries and the information produced by this is used to detect and assess the disease burden in the scan. In order to accurately segment the vessel walls, it is first necessary to identify representative lines, in this example centrelines, of the vessels, and this is achieved by identifying a plurality of seed points for each centreline corresponding to voxels within the CT volume that are likely to be located on a centreline of a coronary artery. To facilitate this process, a contrast agent is injected into the blood stream to increase contrast and in this example increase a Hounsfield Unit (HU) value of the coronary arteries compared to the surrounding tissue.

The CAD analysis device <NUM> identifies vessel seed points using a vessel seed detector <NUM> that in this example uses multiscale filtering and supervised machine learning to detect seed points from training data. Functional components of the vessel seed detector <NUM> are shown in <FIG>.

The vessel seed detector <NUM> includes a vessel seed machine learning component <NUM>, in this example a volumetric convolutional neural network (CNN) that is trained using ground truth data indicative of a sufficient number of example coronary artery centrelines.

It should be understood that many points in the CT volume have a non-zero probability of being suitable as a vessel seed point. With this in mind, the vessel seed detector <NUM> includes a candidate seed point determiner <NUM> that identifies a set of predicted seed points present in a sample of CCTA data using the vessel seed machine learning component <NUM>, then selects candidate seed points from the set of predicted seed points that are to form the basis of centreline tracking and thereby prediction of the centrelines of the coronary arteries. The candidate vessel seed points are determined from the set of seed points based on one or more defined constraints, such as seed points that have a radiodensity value, such as a Hounsfield Unit (HU) value, above a defined amount, or a defined number of seed points above a defined HU threshold, such as a defined number of seed points that have the highest HU values. In one example, the candidate vessel seed points that have a HU value between <NUM> and <NUM> are selected as candidate seed points.

The vessel seed machine learning component <NUM> is trained using reference vessel seed points representing points that are considered by an expert to lie on coronary artery centrelines.

The CAD analysis device <NUM> tracks coronary artery centrelines using a centreline tracker <NUM>. Functional components of the centreline tracker <NUM> are shown in <FIG>.

In this example, the centreline tracker <NUM> includes a centreline tracking machine learning component <NUM>. The centreline tracker <NUM> considers the determined candidate seed points and a centreline direction predictor <NUM> uses the centreline tracking machine learning component <NUM> to predict from an instant seed point the most probable direction to the next seed point on the coronary artery in three dimensional space. In this way, vessel centreline seed points are identified that are likely to lie on the currently considered coronary artery. In this example, the centreline tracking process starts at a predicted seed point located at an endmost location on an artery centreline.

In this example, the centreline tracking machine learning component <NUM> is arranged to use data cubes and the candidate seed points to predict the most probable direction to the next centreline seed point, and based on this to select a centreline seed point from the candidate seed points. The seed points identified in this way as located on a coronary artery centreline are connected together using a seed point connector <NUM> so as to define a complete coronary artery.

An important part of centreline tracking is to detect the intersection locations between the coronary arteries and the aorta, referred to as the coronary ostia. The present system and method is arranged to detect the aorta intersection locations using an aorta intersection determiner <NUM> that uses information indicative of the location of the ascending aorta with tracking information defined by the determined centrelines, and determines whether a coronary artery connects to the aorta based on whether the coronary artery extends to a position within a defined distance, such as <NUM>, from the wall of the aorta. If so, the path of the coronary artery is projected to extend to an intersection point with the aorta and is anatomically connected. In the present example, the location of the ascending aorta is determined using a cardiac segmenter <NUM> by carrying out an aorta segmentation process on the CCTA data, the aorta segmentation process for example using a convolutional neural network, that may be a Unet or Vnet neural network, for example trained using ascending aorta ground truth data.

The centreline tracker <NUM> is arranged to detect the four main coronary arteries first - the Left Main (LM), Left Anterior Descending (LAD), Left Circumflex (LCX) and the Right Coronary Artery (RCA) - then after the main coronary arteries have been detected, an artery branch detector <NUM> detects branches on the primary coronary arteries that were not initially identified as viable centrelines. The main coronary arteries are easier to track than the branches as they carry more blood and therefore more contrast agent. Tracking smaller branches becomes increasingly difficult as the branches reduce in diameter and therefore carry less blood and less contrast agent. The branch detector <NUM> uses a branch detection algorithm.

The branch detector <NUM> examines the HU values perpendicular to the centreline direction of a vessel, and estimates the approximate radius of the vessel by finding the boundary of the coronary artery based on the HU value, since the HU value decreases significantly outside of the vessel wall. Once the boundary has been located on each side of the centreline, the vessel's diameter can be measured.

Branches are detected based on the rate of change in measured diameter of the vessel along the length of a centreline. For example, if the measured diameter of the vessel increases by more than <NUM>% along the centreline, then decreases back to its original size it is marked as a detected branch, noting that coronary vessels naturally decrease in size from a proximal to a distal location. At the coronary ostia, vessels may have a diameter of about <NUM>, whilst at a distal location the vessel diameter typically reduces to less than <NUM>. The branch detector <NUM> therefore examines the rate of change of the estimated diameter to detect points along the centreline from which another coronary artery is branching.

As shown in <FIG>, the CAD analysis device <NUM> also includes a centreline labeller <NUM> arranged to attach semantically meaningful labels to the tracked artery centrelines so that clinicians can identify the vessels. The identification process uses a centreline labelling machine learning component <NUM>, that in this example comprises a supervised classifier, to label the coronary artery centrelines on the identified structured coronary tree. Ground truth centrelines annotated with appropriate labels are used to train the classifier. A number of different classification processes may be used and in this example the classifier includes an adaptive boosting (AdaBoost) algorithm to boost performance of the classifier. In this example, the key features used by the classification process to label each coronary artery are the end location of the artery centreline remote from the aorta, and the mean direction vectors of the centreline at <NUM> different centreline locations - at a proximal location (adjacent the aorta), at a location at mid vessel, and at a distal location of the artery (a location at or adjacent an end of the artery remote from the aorta).

As shown in <FIG>, the CAD analysis device <NUM> also includes an anomaly detector <NUM> arranged to improve the reliability of the centreline tracking process by reconfiguring the vessel seed detector <NUM> if the analysis carried out by the centreline tracker <NUM> is incorrect or incomplete, for example because the vessel seed detector <NUM> has generated too many or insufficient seed points. After the detected coronary arteries have been labelled by the centreline labeller <NUM>, a seed parameter modifier <NUM> of the anomaly detector <NUM> communicates with the vessel seed detector <NUM> to reconfigure the parameters of the vessel seed detector <NUM> if a determination is made that the identified vessels are incorrect or incomplete, for example if the initial vessel seed detector configuration failed to detect a major coronary artery, such as the RCA. In this example, the seed parameter modifier <NUM> may communicate with the vessel seed detector <NUM> to lower the constraint applied by the vessel seed detector <NUM> so that more candidate vessel seed points are produced, thereby increasing the probability of detecting the vessel in a subsequent iteration. In an example, this may be achieved by widening an applied Hounsfield Unit (HU) test from <NUM> - <NUM> to <NUM> - <NUM>. A similar approach may be used to reduce the number of candidate vessel seed points if too many seed points are produced.

It will be understood that the anomaly detector <NUM> functions as a feedback loop to modify the parameters of the vessel seed detector <NUM> until an appropriate set of candidate vessel seed points are produced to detect all coronary arteries or at least a defined subset of the coronary arteries such as all of the main coronary arteries (LM, LAD, LCX, RCA).

Failure to detect a coronary artery may occur due to various factors. For example, the concentration of contrast dye provided to a patient may have reduced to the extent that the number of seed points passing the applied HU test has become too low.

In the above example, the feedback look operates automatically. However, it will be understood that manual modification of vessel tracking parameters, such as a location threshold parameter that indicates what is considered to be a valid location for a coronary artery, or the parameters of the vessel seed detector <NUM>, may be carried out in response to observed results. For example, if the results do not successfully identify all of the main coronary arteries, a user may modify a valid location parameter and/or a tracking parameter in order to increase the likelihood of more successfully identifying the main coronary arteries. In this way, the vessel tracking process is more robust because the parameters of the process are changed based on observed results, and the system is more likely to be able to deal with CCTA data that has different dynamic ranges.

After all desired coronary arteries have been satisfactorily tracked and labelled, a vessel wall segmenter <NUM> shown in <FIG> uses the tracked centrelines to analyse the CCTA data associated with the coronary arteries, in particular to carry out an inner and outer vessel wall segmentation process.

The functional components of the vessel wall segmenter <NUM> are shown in <FIG>. The vessel wall segmenter <NUM> uses a wall segmenter machine learning component <NUM> to produce inner and outer wall lumen masks that can then be used to identify coronary artery disease associated with the presence of calcified and non-calcified plaques and taking into account imaging artifacts.

In this example, the wall segmenter machine learning component <NUM> is a supervised volumetric convolutional neural network (CNN) that is trained using ground truth training data indicative of a sufficient number of example transverse coronary artery image slices, in this example image slices that are perpendicular to and intersecting with the artery centrelines. The training data in this example includes inner and outer artery walls and relevant imaging artefacts that have been annotated by medical experts, and covers a wide range of examples of different coronary vessels with varying degrees of disease and including various typical imaging artifacts indicative of abnormalities, such as vessel bulging.

In this example, in order to provide a richer data set, the wall segmenter machine learning component <NUM> is trained for points along a vessel centreline by providing multiple cross sectional image data slices, including at least one image data slice before each defined centreline point and at least one image data slice after each defined centreline point. Since disease manifests itself over multiple cross sectional slices, providing the CNN with multiple slices in this way allows the CNN to incorporate a spatial context to more accurately reflect the characteristics of the disease.

However, it will be understood that other implementations are envisaged, such as one image data slice for each defined centreline point. Long-axis image data may also be used for training purposes to provide the CNN with additional context for the semantic segmentation process. Additionally, it will be understood that data augmentation can be used to ensure that the inner and outer wall segmentation process generalises effectively from the training data to unseen cases.

During analysis of received CCTA data, the vessel wall segmenter <NUM> uses a slice analyser <NUM> to sample slices of the CCTA data, in this example perpendicular to the determined centrelines. The vessel wall segmenter <NUM> uses the wall segmenter machine learning component <NUM> to predict whether each voxel in each obtained image slice is likely to be part of an inner wall or an outer wall of a coronary artery. The voxels identified as being part of the inner and outer artery walls of a coronary artery in the sample slices are then connected together using a connected component analyser <NUM> that uses a technique to identify neighbouring voxels that belong to the inner wall or the outer wall of the vessel and thereby produce inner and outer artery wall segmentations.

Example representations of portions of a cardiac artery imaged using a CT scan after wall segmentation are shown in <FIG> shows a 'long-axis' view <NUM> of a portion of an artery that includes coronary artery disease <NUM>, in this example the view <NUM> reprojected so as to appear linear for ease of reference. <FIG> show transverse cross-sectional views <NUM>, <NUM> - 'short-axis' views - of the cardiac artery portion shown in <FIG> at different locations along the coronary artery. <FIG> represents a cross sectional view taken along a first transverse plane <NUM> shown in <FIG> represents a cross sectional view taken along a second transverse plane <NUM> shown in <FIG>.

In the sample slice shown in <FIG>, an inner wall <NUM> and an outer wall <NUM> have been identified by the vessel wall segmenter <NUM>, and it can be seen that at the location indicated by the first transverse plane <NUM>, the artery shown does not appear to be affected by disease and the vessel lumen <NUM> appears to be unobscured.

In the sample slice shown in <FIG>, an inner wall <NUM> and an outer wall <NUM> have been identified by the vessel wall segmenter <NUM>, and it can be seen that at the location indicated by the second transverse plane <NUM>, the artery shown is affected by disease <NUM> and the vessel lumen <NUM> partially obscured.

It will be understood that after completion of coronary artery wall segmentation, the system has sufficient data to define the inner and outer vessel wall configurations of the detected coronary arteries. Using this data, it is possible to determine the presence of disease by analysing voxels associated with gap regions between the inner and outer vessel walls.

For this purpose, as shown in <FIG>, the CAD analysis device <NUM> includes a disease assessment unit <NUM>. Functional components of the disease assessment unit <NUM> are shown in <FIG> and <FIG>.

In this example, the CAD analysis device <NUM> is arranged to assess different types of disease including arterial constriction, referred to as 'stenosis', and presence of high-risk plaques, including calcified, mixed or non-calcified plaques, using heuristics based on the spatial characteristics and Hounsfield Unit values of gaps in the vessel walls.

<FIG> illustrates functional components <NUM> of a stenosis detection and analysis component of the disease assessment unit <NUM>, and <FIG> illustrates functional components <NUM> of high risk plaque detection and analysis component of the disease assessment unit <NUM>.

The stenosis functional components <NUM> include a lumen area determiner <NUM> arranged to use the inner and outer wall segmentation data to determine the cross-sectional area defined by the inner wall. The vessel lumen cross-sectional area is used to determine and characterise a stenosis condition with reference to a healthy state condition.

It is understood that after bifurcation of an artery, it is common for a relatively narrow portion of the artery to exist that could produce a stenosis false positive. In order to avoid this, the stenosis functional components <NUM> include a post branch reference area determiner <NUM> that removes arterial regions immediately after each bifurcation and instead uses a calculated reference area for the region following the bifurcation. The reference area is calculated using a linear regression analyser <NUM> by fitting a linear regression line to the area along the length of the lumen section after the bifurcation. This process results in a piecewise linear structure with discontinuities at detected bifurcation points. Since coronary vessels naturally reduce in diameter as they wrap around the myocardium (heart), the piecewise linear structure essentially sets the expected or mean progressively reducing area of each healthy (non-stenotic) vessel. A defined proportional reduction compared to the expected reference area indicates that the vessel has contracted from a normal healthy state, and therefore stenosis is present. It will be understood that the stenosis assessment component may also provide determinations in relation to arteries that have expanded from a normal healthy state.

In this example, the stenosis functional components <NUM> also include a post processor <NUM> arranged to carry out additional processing on the inner and outer wall segmentation data, for example to carry out a smoothing operation on the data to reduce noise reduction due to localised variations in the lumen area not associated with disease.

In clinical reporting, stenosis is typically graded in quantitative percentage grades such as <NUM>-<NUM>%. A stenosis category determiner <NUM> is arranged to use the stenosis analysis to produce a percentage stenosis grade and a categorical value for reporting.

The high risk plaque functional components <NUM> are shown in <FIG>. High risk plaques (HRP) are also referred to as vulnerable plaques and are an early indication of coronary artery disease for a patient. The disease assessment unit <NUM> detects several forms of HRP using heuristic, rule based analysis of the artery wall segmentation, in this example low attenuation plaque, spotty calcification and positive remodelling. For this purpose, the high risk plaque functional components <NUM> include a low attenuation plaque determiner <NUM>, a spotty calcification determiner <NUM> and a positive remodelling determiner <NUM>.

Low attenuation plaques are characterised by Hounsfield Unit (HU) values in the range -<NUM> to <NUM> Hounsfield units, and therefore may be directly detected through analysis and thresholding of Hounsfield units.

A spotty calcification is defined as a relatively small calcification surrounded by non-calcified or mixed plaque. To detect spotty calcification, the spotty calcification determiner <NUM> initially determines voxels that are predicted to be associated with calcified plaques in the determined disease region between the inner and outer artery wall, for example by filtering using a defined radiodensity measure, such as a Hounsfield Unit (HU) value greater than <NUM>. A connected component analyser <NUM> is then used to associate related voxels together as calcified volumes. Spotty calcifications are characterised as being smaller than <NUM> in diameter, and accordingly a size thresholder <NUM> is used to detect these. A non-calcified/mixed plaque determiner <NUM> is also used to provide additional heuristics to determine whether the voxels surrounding the identified spotty calcifications have HU values consistent with non-calcified or mixed plaques.

Positive remodelling is characterised by an expansion of the outer vessel wall to compensate for the disease build up between the inner and outer wall. The positive remodelling determiner <NUM> is arranged to detect this using an inner/outer wall gap determiner that determines whether the gap between the inner and outer artery wall has increased beyond a defined amount, for example <NUM>% beyond a normal vessel gap. The positive remodelling determiner <NUM> also includes a gap radiodensity analyser <NUM>, in this example arranged to determine whether the voxels in the gap are consistent with non-calcified plaque, for example by determining the HU values of the voxels in the gap.

The determinations made by the disease assessment unit <NUM> form the basis of a report produced by a report generator <NUM>.

Referring to <FIG>, a flow diagram <NUM> is shown that illustrates an example method of identifying, and in this example characterising, coronary artery disease using the system shown in <FIG>.

The method comprises extracting CCTA data from the data store <NUM>, using the vessel seed point machine learning component <NUM> to identify predicted vessel seed points, and applying a radiodensity filter to the predicted vessel seed points to identify candidate vessel seed points, as indicated at steps <NUM>, <NUM> and <NUM>. The centreline tracking machine learning component <NUM> is then used to identify candidate vessel seed points predicted to be located on coronary artery centrelines by starting at a location remote from the aorta and predicting the direction to the next seed point, as indicated at step <NUM>.

As indicated at steps <NUM> and <NUM>, a cardiac region in the CCTA data is then segmented to determine the predicted location of the ascending aorta, and the intersection locations between the main coronary arteries and the aorta are predicted using the aorta segmentation. A branch detection algorithm is then applied to detect coronary artery branches, as indicated at step <NUM>.

The detected coronary arteries are labelled using a centreline labelling machine learning component, as indicated at step <NUM>.

As indicated at steps <NUM> and <NUM>, if the coronary arteries have not been sufficiently detected, the radiodensity filter parameters used by the vessel seed detector <NUM> are modified as required, and the vessel seed detection and centreline tracking processes indicated by steps <NUM> to <NUM> are reimplemented. This process repeats until the coronary arteries have been sufficiently detected.

As indicated at steps <NUM> and <NUM>, the CCTA data is sampled to produce image slice data, each image slice intersecting with a detected centreline and extending transversely across a coronary artery, and the wall segmenter machine learning component <NUM> is used to produce inner wall and outer wall lumen data based on the sampled image slices.

The inner wall data is used to determine the cross-sectional area defined by the inner wall and the determined area used to identify and characterise stenosis, and determined gaps between the inner and outer walls is used to determine presence of and characterise high-risk plaques or positive remodelling, as indicated at steps <NUM> and <NUM>.

As indicated at steps <NUM> and <NUM>, representations of coronary artery cross-sections are displayed using the inner and outer wall data and the analysis of the gaps between the inner and outer walls, and a report indicative of presence of stenosis and/or high-risk plaques in the CCTA data may be generated.

The system <NUM> may be arranged to enable a user to edit the displayed inner and optionally outer vessel walls and associated inner and outer wall data, for example because a clinician believes that the stenosis assessment provided by the system <NUM> is incorrect. In one arrangement, this may be achieved by manually modifying the relevant wall(s) using a suitable interface screen.

For example, as shown in <FIG>, a vessel wall editing screen <NUM> may be used. The screen <NUM> includes a long axis cross sectional view <NUM> of a coronary artery, and a transverse cross sectional view <NUM> of the coronary artery that shows inner <NUM> and outer <NUM> walls of the vessel. Using wall selection buttons <NUM>, a user is able to select the inner <NUM> or outer <NUM> wall, which causes control points <NUM> to be displayed that are representative of the shape of the selected wall. The user is able to change the shape of the selected wall by selecting and moving one or more of the control points <NUM>, for example using a mouse.

While the inner and optionally outer walls <NUM>, <NUM> of a vessel may be modified using the vessel wall editing screen <NUM>, the process is relatively cumbersome since the user must manually move each relevant control point <NUM> until the desired stenosis level is obtained for the displayed vessel.

An alternative vessel wall editing screen <NUM> is shown in <FIG>. With this arrangement, instead of manually amending the vessel wall by individually moving control points, the alternative vessel wall editing screen <NUM> includes a stenosis level drop down box <NUM> that enables a user to select the appropriate stenosis range <NUM> for the displayed vessel or for an individual selected lesion. In response to selection of a new stenosis range <NUM>, the system <NUM> modifies the displayed vessel inner wall <NUM> to match the selected stenosis range <NUM>.

As shown in <FIG>, the displayed vessel has a determined stenosis level of <NUM>%, but a user has selected a new stenosis level <NUM>% - <NUM>% because the user believes that <NUM>% - <NUM>% is a more appropriate stenosis level for the vessel. In response, as shown in <FIG>, a revised inner wall <NUM> is displayed that increases a gap between the new inner wall <NUM> and the outer wall <NUM>, thereby indicating an increased level of stenosis.

In this way, in order to modify a displayed vessel, a user need only select the appropriate stenosis range <NUM> for the displayed vessel or selected lesion and in response the system will modify the inner wall of the displayed vessel or lesion to a configuration that satisfies the selected stenosis range.

It will be understood that the configuration of the inner wall <NUM> of the displayed vessel that satisfies the selected stenosis level may be determined in any suitable way, for example using ground truth training data associated with diseased vessels of varying degrees.

The vessel wall editing screen <NUM> may be used to modify the stenosis level assigned to a vessel segment or to modify the stenosis level assigned to individual lesions.

In an example wherein the stenosis level assigned to a vessel segment is desired to be modified, the system <NUM> may apply the methodology illustrated in iterative process diagram <NUM> shown in <FIG> and flow diagram <NUM> shown in <FIG> that illustrates steps <NUM> to <NUM> of a method of amending a vessel wall.

In an example, a user desires to modify the determined stenosis level of a coronary artery vessel segment from a current level <NUM>% - <NUM>% to a new level <NUM>% - <NUM>% because the user believes that this is the appropriate stenosis level for the vessel segment. As indicated at step <NUM>, the user selects the new stenosis range <NUM> using the stenosis level drop down box <NUM>.

If the stenosis level is proposed to be reduced, the method involves reducing the stenosis level of each slice that exceeds the maximum level of the new stenosis category by applying a small dilation to the slice, as indicated at step <NUM>.

If stenosis level is proposed to be increased, the method involves identifying the slice with the maximum stenosis and increasing the stenosis level of the slice to be above the minimum level of the new stenosis category by applying a small erosion to the slice, as indicated at step <NUM>.

In the present example, since the stenosis level is proposed to be reduced, the stenosis level of each slice that exceeds the maximum level of the new stenosis range is reduced by applying a small dilation to the vessel inner wall associated with the slice.

Vessel slices are shown diagrammatically in <FIG> wherein <NUM> example slices <NUM> are shown at initial and <NUM> subsequent rounds of the iterative process. For each slice, the size of the vessel inner wall is represented by the size of a circle <NUM> and the level of stenosis at the slice is represented by the percentage value <NUM> in the circle. As shown, before modification of the stenosis level, the <NUM>th and <NUM>th slices are within the proposed new stenosis range, but the <NUM>th slice is at <NUM>% stenosis, which is above the proposed range. Consequently, as indicated at step <NUM>, in order to reduce the level of stenosis at the <NUM>th slice, the lumen area is increased slightly. The stenosis of the vessel segment is then recalculated taking into account modified slice lumen areas, as indicated at step <NUM>.

The lumen area may be increased or decreased in any suitable way. For example, the lumen area may be increased by dilating the inner wall using morphological operators.

Alternatively, the vessel wall segmenter <NUM> may include an alternate machine wall segmenter machine learning component <NUM> that is trained to produce lumen masks indicative of an increased level of stenosis, and a further alternate machine wall segmenter machine learning component <NUM> that is trained to produce lumen masks indicative of a reduced level of stenosis. With this arrangement, a default model may be used and the alternate or further alternate machine learning component selected depending on whether stenosis is desired to be increased or decreased.

In a further alternate arrangement, image processing techniques may be used to modify the lumen area, such as region growing and/or other segmentation techniques based on the HU value in the vessel.

As shown in <FIG>, at round <NUM> of the iterative process, after recalculation of stenosis, the stenosis level of the <NUM>th slice has reduced to <NUM>% because of the increase in lumen area. However, increasing the lumen area at the <NUM>th slice has caused the stenosis recalculation step <NUM> to increase the stenosis level at the adjacent <NUM>th slice to <NUM>% because the lumen area of slice <NUM> is now proportionally smaller relative to the <NUM>th slice. At a subsequent iteration (round <NUM>), since the stenosis of the <NUM>th slice is now above the proposed range, a small dilation is applied to the lumen area of the <NUM>th slice to reduce the stenosis level, and the stenosis of the vessel segment is again recalculated taking into account the modified slice lumen areas, as indicated at step <NUM>. This causes the stenosis level of the <NUM>th slice to reduce to <NUM>%, but increasing the lumen area of the <NUM>th slice has caused the stenosis recalculation step <NUM> to increase to <NUM>% at adjacent slice <NUM> because the lumen area of slice <NUM> is now proportionally smaller relative to the <NUM>th slice. Similarly, the stenosis level of the <NUM>th slice also increases because the lumen area of the <NUM>th slice is now proportionally smaller relative to the <NUM>th slice.

It is expected that after <NUM> iterations, the stenosis level of all slices will be within the selected proposed stenosis range <NUM>.

In some situations, it is not possible to use vessel or vessel segment-based modification of stenosis range using the above methodology. For example, in a situation wherein one or more lesion of the vessel or segment is within the range <NUM>% - <NUM>%, it is not possible to increase the stenosis level at other lesions of the vessel or segment by increasing the vessel/segment stenosis range because the stenosis range for the vessel/segment is already at the maximum level available. In this situation, the user may modify the inner wall of one or more lesions individually.

In a further situation, a clinician may wish to increase the stenosis level for a vessel or vessel segment but since the vessel/segment is determined by the system to be at <NUM>% stenosis it is difficult to determine which slices of the vessel/segment should be increased.

In this circumstance, a disease machine learning component may be used that is specifically trained to recognise disease in the vessels, the disease machine learning component being applied to the vessel/segment determined to be at <NUM>% in order to identify potentially stenotic lesions. After identifying potentially stenotic lesions, the above methodology may be used to increase the stenosis range applicable to the vessel, vessel segment or individual lesion(s). The disease machine learning component may be any suitable machine learning component, such as a U-Net trained neural network.

In an alternative arrangement for increasing the level of stenosis when the vessel/segment has been determined to be at <NUM>% stenosis, the above wall segmentation process is discarded and a fresh wall segmentation process is implemented using a machine learning component, such as a U-Net, that has been trained with ground truth vessel slice images and expert annotated stenosis values. After identifying potentially stenotic lesions, the above methodology may be used to increase the stenosis range applicable to the vessel, vessel segment or individual lesion(s).

Claim 1:
A method of identifying coronary artery disease comprising:
receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient;
analysing the contrast cardiac CT data using machine learning to identify a plurality of centreline seed points in the contrast cardiac CT data predicted to correspond to locations on centrelines of the cardiac arteries of the patient by:
analysing the contrast cardiac CT data using machine learning to identify a plurality of predicted centreline seed points; and
determining a plurality of centreline seed points corresponding to predicted locations on centrelines of the coronary arteries using machine learning by predicting from an instant determined centreline seed point a probable direction to a further centreline seed point of the coronary artery, and selecting a predicted centreline seed point from the plurality of predicted centreline seed points using the predicted probable direction to a further centreline seed point of the coronary artery;
producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified centreline seed points;
analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient; and
identifying presence of coronary artery disease using the predicted inner and/or outer walls of the coronary arteries of the patient.