Contrast agent concentrations

Abstract

A mechanism for determining a normalized or calibrated value for a concentration level of a contrast agent, such as iodine, in a pathological finding of a subject. A concentration level derived from a target region of a contrast agent map is normalized or calibrated using a calibration level derived from a reference region. The target region represents the pathological finding. The reference region represents a part of one or more arteries of the subject.

Description

[0001] CONTRAST AGENT CONCENTRATIONS

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to the field of CT imaging and, in particular, to determining a contrast agent concentration from CT image data.

[0004] BACKGROUND OF THE INVENTION

[0005] There is an increasing interest in and demand for medical imaging procedures and information derivable from medical imaging procedures. This data is particularly useful for assessment and / or diagnosis of a medical subject.

[0006] One popular form of medical imaging is computed tomography (CT) imaging, which produces CT image data. Recent developments in CT imaging, including spectral CT imaging (as known as dual energy CT), facilitate the generation of a wide variety of spectral results or maps. These spectral maps offer enhanced contrast for pathological findings and / or anatomical results.

[0007] One known type of spectral map is a contrast agent map, which defines a concentration level of contrast agent in different positions within the subject. A wide variety of contrast agents suitable for use in CT imaging procedures are known in the art, including iodine-based and barium sulfate-based contrast agents. Information contained in a contrast agent map is invaluable for a clinician. By assessing the concentration level of the contrast agent at points of interest (e.g., pathological findings and / or anatomical structures), it is possible to perform diagnostic and / or medical assessment of the imaged subject.

[0008] A typical contrast agent map comprises a plurality of pixels or voxels, each representing a different part of the subject. A value of the pixel / voxel may define a measure of concentration of the contrast agent at the corresponding part of the subject.

[0009] There is an ongoing desire to improve the accuracy and interpretability of concentration levels within a contrast agent map.

[0010] SUMMARY OF THE INVENTION

[0011] The invention is defined by the claims.

[0012] According to examples in accordance with an aspect of the invention, there is provided a computer-implemented method for generating a value of contrast agent concentration for a pathological finding in a subject. The computer-implemented method comprises: receiving CT image data of the subject, the CT image data comprising a contrast agent map, which defines a concentration level of contrast agent in different positions within the subject; identifying, in the contrast agent map, a target region containing a representation of the pathological finding in the subject, wherein the target region is smaller than the contrast agent map; identifying, in the contrast agent map, a reference region containing a part of a representation of one or more arteries of the subject, wherein the reference region is smaller than the contrast agent map; and computing a normalized or calibrated value of a contrast agent concentration for the pathological finding using the concentration level in the target region and the concentration level in the reference region.

[0013] The present disclosure proposes to calibrate or normalize a contrast agent concentration level for a pathological finding of a subject using a determined contrast agent concentration for a part of one or more arteries of the subject, e.g., a part of the aorta of the subject. This approach provides a relative measure of contrast agent concentration, which is less sensitive to non-pathological parameters or properties of the subject and / or imaging apparatus. This facilitates improved interpretability of the contrast agent concentration of the pathological finding, and improved comparability of the measure of contrast agent concentration between different pathological findings of the same subject, different instances of assessing the same pathological findings and / or pathological findings of different subjects.

[0014] More particularly, the proposed approach provides a mechanism for producing an indicator of concentration level of a contrast agent in a pathological finding that is less sensitive to any variations or change outside of the pathological finding itself. This effectively reduces a noise in the indicator of contrast agent level concentration.

[0015] The present disclosure further recognizes that there may be significant variation in a non-calibrated / non-normalized value of a concentration value due to differences in amount of contrast agent injected as well as a length of time that has elapsed since injection. Calibration and / or normalization of the concentration values allows these variations to be readily taken into account.

[0016] In some examples, a position of the reference region in the contrast agent map is responsive to a position of the target region in the contrast agent map. This approach facilitates identification of a most relevant part of the one or more arteries to the pathological finding, e.g., a closest part of the one or more arteries to the pathological finding. This approach results in the reference region having the most similar non-pathological characteristics to the target region. Calibration or normalization based on such a reference region therefore improves the noise reduction in the production of the calibrated or normalized value.

[0017] The contrast agent map may comprise a vertical dimension in which the representation of the one or more arteries of the subject generally extends; and the position of the reference region, in the vertical dimension of the contrast agent map, may algin with the position of the target region in the vertical dimension of the contrast agent map.

[0018] In some examples, the target region extends between a first position and a second position in the vertical dimension; and an entirety of the reference region lies between the first position and the second position in the vertical dimension.

[0019] In some examples, the identifying, in the contrast agent map, the reference region comprises: obtaining a voxel map of the one or more arteries; and identifying the reference region using the voxel map and the position of target region in the contrast agent map.

[0020] In some examples, the identifying, in the contrast agent map, a target region comprises using a segmentation algorithm to perform segmentation to identify the target region.

[0021] The computer-implemented method may further comprise, before computing the normalized or calibrated value, reducing the size of the reference region. This approach recognizes that there is a non-zero probability that a reference region containing a representation of part of the one or more arteries of the subject contains a representation of non-aortic parts of the subject, e.g., elements outside the bounds of the one or more arteries such as calcifications, artificial structures (such as stents) or parts of tissue bounding the one or more arteries. By reducing the size of the reference region, the risk of non-aortic parts being represented and / or amount of non-aortic parts represented in the reference region is significantly reduced. This improves the accuracy of any concentration level for the aortic derived from the reference region, i.e., to more closely reflect the true concentration level within the aortic.

[0022] Reducing the size of the reference region may comprise maintaining the center of the reference region at a same position within the contrast agent map reducing the size of the reference region. Positions closer to the center of the reference region are more likely to represent a part of the aortic than outer parts of the reference region. By maintaining the center of the reference region, there is an increased probability that parts of the reference region that represent parts of the aortic of the subject will be retained when reducing the size of the reference region. In some examples, the contrast agent map comprises one or more horizontal dimensions, wherein the representation of the one or more arteries of the subject generally extends in a direction perpendicular to each horizontal dimension; and reducing the size of the reference region comprises reducing the size of the reference region in only the one or more horizontal dimensions.

[0023] This approach recognizes that reducing the size of the reference region in a nonhorizontal dimension, i.e., a vertical dimension, will not reduce a risk of non-aortic parts being represented by the reference region. This is because the representation of the aortic extends in the vertical direction, effectively meaning that reducing the size of the reference region in the vertical direction would simply reduce the available data points for defining the concentration level in the one or more arteries, thereby reducing an accuracy of this defined concentration level.

[0024] Reducing the size of the reference region may comprise processing the reference region using a morphological erosion technique in the axial plane. This approach helps maintain a shape of the reference region, which is expected to track or mirror the shape of the part of the one or more arteries. More particularly, using a morphological erosion technique aims to only remove the outermost parts of the reference region to effectively reduce a risk of including representations of non-aortic elements of the subject in the reference region.

[0025] The step of computing a normalized or calibrated value of a contrast agent concentration for the pathological finding may comprise dividing the concentration level in the target region by the concentration level in the reference region. This provides a normalized value for the concentration of contrast agent in the pathological finding. In particular, the concentration level in the one or more arteries is expected to be a maximum possible concentration of the contrast agent in the subject for any downstream vasculature, such that this approach provides a relative measure of contrast agent concentration in the pathological finding.

[0026] In some examples, the contrast map comprises a plurality of voxels, each having a respective concentration value; the concentration level in the target region is defined using an average of the concentration values of the voxels in the target region; and the concentration level in the reference region is defined using an average of the concentration values of the voxels in the reference region.

[0027] The contrast agent map may be an iodine map, which defines a concentration level of iodine in different positions within the subject. Proposed approaches are particularly suited for use with iodine maps. The one or more arteries of the subject may be an aorta of the subject. Information from the aorta provides a reliable and repeatable reference point for calibration and / or normalization. In particular, the concentration level of the contrast agent can be expected to be at a maximum (compared to any downstream vasculature) in the aorta.

[0028] There is also proposed a computer program product comprising computer program code means which, when executed by processing circuitry, cause the by processing circuitry to perform all of the steps of any herein proposed method.

[0029] There is also provided a device for generating a value of contrast agent concentration for a pathological finding in a subject.

[0030] The device comprises: processing circuitry; and a memory containing instructions that, when executed by the processing circuitry, configure the processing circuitry to: receive CT image data of the subject, the CT image data comprising a contrast agent map, which defines a concentration level of contrast agent in different positions within the subject; identify, in the contrast agent map, a target region containing a representation of the pathological finding in the subject, wherein the target region is smaller than the contrast agent map; identify, in the contrast agent map, a reference region containing a part of a representation of the one or more arteries of the subject, wherein the reference region is smaller than the contrast agent map; and compute a normalized or calibrated value of a contrast agent concentration for the pathological finding using the concentration level in the target region and the concentration level in the reference region.

[0031] In some embodiments, the instructions may, when executed by the processing circuitry, configure the processing circuitry to perform all the steps of any herein disclosed method.

[0032] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

[0033] BRIEF DESCRIPTION OF THE DRAWINGS

[0034] For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0035] Figure 1 illustrates a system in which embodiments may be employed;

[0036] Figure 2 is a flowchart illustrating a proposed method;

[0037] Figure 3 is a flowchart illustrating an example step for use in a proposed method; Figure 4 illustrates a technique for determining a reference region; and Figure 5 is a flowchart illustrating a variant method.

[0038] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The invention will be described with reference to the Figures.

[0040] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

[0041] The invention provides a mechanism for determining a normalized or calibrated value for a concentration level of a contrast agent, such as iodine, in a pathological finding of a subject. A concentration level derived from a target region of a contrast agent map is normalized or calibrated using a calibration level derived from a reference region. The target region represents the pathological finding. The reference region represents a part of one or more arteries of the subject.

[0042] Embodiments are at least partially based on the realization that the concentration level of a contrast agent in a pathological finding is influenced by a number of factors independent of any particular characteristic of the pathological finding, such as the type of contrast agent, the amount of contrast agent used, the time at which data used to derive the concentration level is captured (e.g., a time elapsed since application of the contrast agent) and / or the location of the pathological finding within the subject. Thus, existing measures of concentration levels are difficult to compare and interpret between different findings and subjects.

[0043] The present disclosure proposes to calibrate or normalize the concentration level in a pathological finding using a concentration level of a part of one or more arteries (e.g., a part of the aorta) of the subject. This results in a relative contrast agent concentration level that is less influenced by data capture parameters and the non-pathological condition of the subject. This relative contrast agent concentration level will be less dependent upon the contrast bolus timing (i.e., the time since the contrast agent was administered) and the absolute amount of contrast agent administered. In the context of the present disclosure, the term position is considered to be semantically identical to the term location.

[0044] Examples of pathological findings are well known in the art and may include any tumors, growths, cancers, nodules, calcifications, blockage, necrotic element, and so on. A pathological finding is therefore considered to be an anatomical finding that represents or indicates a cause or potential cause of a disease or condition in the subject. In the context of the present disclosure, each pathological finding is a pathological finding that is visible (i.e., distinguishable) in CT image data, particularly in a contrast agent map of CT image data.

[0045] Figure 1 illustrates a system in which embodiments may be employed, for improved contextual understanding. The system comprises a CT scanning system 110 and a device 120.

[0046] The CT scanning system 110 is configured to capture and / or generate CT image data 150 of a subject 190. The CT image data 150 comprises at least a contrast agent map, which defines a concentration level of contrast agent in different positions within the subject.

[0047] For instance, the contrast agent map may be a 2D or 3D image having pixels or voxels, wherein a value for each pixel / voxel is a concentration value. The concentration values of the pixels / voxels may define a predicted concentration level of contrast agent at different positions within the subject. More particularly, different pixels or voxels represent different regions or points within the subject, and a value of each pixel provides, in the form of a concentration value, a quantitative measure of contrast agent concentration at that region or point.

[0048] In the hereafter described embodiments, a concentration value and a concentration level are considered to be numeric values that represent a quantitative measure of contrast agent concentration at a particular region or point. A concentration value is associated with a single pixel / voxel of the contrast agent map. A concentration level is associated with a region of the contrast agent map, where the region includes one or more pixels.

[0049] In a contrast agent map, each concentration value may change (e.g., increase and / or decrease) responsive to a concentration of the contrast agent within the position or area represented by the corresponding pixel / voxel. Thus, as a concentration, measurable in mg / ml, at a particular position changes, so the concentration value representing that particular position will also change. In preferred examples, the concentration value changes linearly or proportionally within the concentration of the contrast agent at the position or area represented by the corresponding pixel / voxel, although the concentration value(s) may be clipped to a maximum and / or minimum value. There is a wide variety of techniques for producing a contrast agent map, which is sometimes alternatively labelled a contrast agent image or contrast agent quantitative image. In general, a contrast agent map may be considered to be a material density image, in which the material is a known contrast agent.

[0050] Generally, techniques for producing a contrast agent map make use of spectral image data produced using a multi-energy (e.g., dual-energy) imaging technique upon a subject who has taken (e.g., ingested or injected) a contrast agent. Such techniques typically rely upon material decomposition of the spectral image data.

[0051] However, other approaches for generating contrast agent maps have also been suggested, such as the technique disclosed by Gao, Yuan, et al. "Iodine map synthesis from non-contrast CT using diffusion model." Medical Imaging 2024: Physics of Medical Imaging. Vol. 12925. SPIE, 2024.

[0052] Examples of suitable contrast agents are well known in the art, including iodine-based and barium sulfate-based contrast agents. Although iodine maps are considered the most common form of contrast agent map, the skilled person will appreciate that different contrast agent maps may be employed. The suitably skilled person would be able to adapt any known technique for producing a contrast agent map for a first type of contrast agent (e.g., iodine) for another type of contrast agent (e.g., barium sulfate), e.g., through appropriate modification of parameters and the like.

[0053] Herein proposed embodiments are suited for use when the contrast agent map is an iodine map, which defines a concentration level of iodine in different positions within the subject.

[0054] The device 120 comprises processing circuitry 121 and a memory 122. The memory contains instructions that, when executed by the processing circuitry, configure the processing circuitry to perform one or more tasks or functions. The device 120 may, for instance, be replaced by any other form of processing system.

[0055] The device 120 may be communicatively coupled to the CT scanning system 110 so as to receive at least the CT image data from the CT scanning system. The communicative coupling may be wired or wireless, and such approaches are known in the art.

[0056] In other approaches, the CT scanning system 110 may store the projection data in a memory or storage unit 130, which may form part of the system 100. The device 120 may be communicatively coupled to the memory or storage unit, e.g., to receive or obtain the CT image data from the memory storage unit 130. The processing circuitry 121 may include, but is not limited to, one or more of the following: conventional microprocessors, application specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs). The memory 122 may comprise any volatile and / or non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The instructions contained in the memory may effectively define one or more programs that, when executed on the processing circuitry, cause the processing circuitry to perform encoded functions.

[0057] The system 100 may further comprise a user interface 140 configured to provide a user-perceptible output (e.g., a visual representation or display). The user-perceptible output may, for instance, represent at least some of the CT image data. An operator may be able to interact with the user interface 140 to control the data or information provided by the user- perceptible output, e.g., where part of the CT image data and / or information derived therefrom is displayed. Such approaches are well known to the skilled person.

[0058] The present disclosure recognizes that there is a strong desire to improve an accuracy of determining or measuring a contrast agent concentration at a pathological finding of the subject. In particular, the value of a contrast agent concentration provides valuable information for understanding the effect of the pathological finding, e.g., on blood flow, blood blocking and / or blood dispersion.

[0059] To allow for at least improved comparability and interpretation of the contrast agent concentration, it is herein proposed to normalize or calibrate the contrast agent concentration (at least for the pathological finding), based on a reference region containing a representation of part of one or more arteries such as the aorta. This normalized or calibrated concentration results in a relative contrast agent concentration that is less influenced by imaging parameters and patient conditions. Moreover, this relative contrast agent concentration is expected to be less dependent on the contrast bolus timing and the absolute amount of contrast agent administered.

[0060] Figure 2 is a flowchart illustrating a proposed computer-implemented method 200.

[0061] The method 200 may be carried out by the processing circuitry of the previously described device. In particular, the memory may store instructions which, when carried out by the processing circuitry, cause the processing circuitry to perform the method 200.

[0062] The method 200 comprises receiving 210 CT image data of the subject. The CT image data comprises at least a contrast agent map. As previously explained, the contrast agent map defines a concentration level of contrast agent in different positions within the subject. When executed by the processing circuitry 121 previously described, the step 210 of receiving may comprise receiving the CT image data from the CT scanning system 110 and / or the memory 130. In some examples, step 210 comprises actively retrieving the CT image data from the CT scanning system and / or the memory, e.g., using a read function or the like.

[0063] The method 200 further comprises a step 220 of identifying, in the contrast agent map, a target region containing a representation of the pathological finding in the subject, wherein the target region is smaller than the contrast agent map. Preferably, the bounds of the representation of the pathological finding in the contrast agent map define the bounds of the target region. More particularly, it will be appreciated that the target region represents only part (e.g., not all) of the contrast agent map. For instance, the target region may be a bounding box or bounding volume in the contrast agent map.

[0064] In some examples, step 220 may comprise performing a segmentation or object detection algorithm on the contrast agent map received in step 210 to identify the target region.

[0065] In other examples, the CT image data comprises further image data, such as nonspectral image data (e.g., a single-energy image) that represents a same spatial region of the subject as the contrast agent map. In such examples, the further image data, optionally together with the contrast agent map, may be processed using a segmentation algorithm to identify the target region.

[0066] In this way, it is possible for the segmentation algorithm to process multi-channel image data, e.g., of spectral results including the contrast agent concentration map, to identify the target region.

[0067] In any example in which a segmentation algorithm is used, then the segmentation algorithm may be suitably designed or trained to identify a desired or particular pathological finding. A wide variety of segmentation algorithms are known in the art, such as those mentioned by Kumar, Subbiahpillai Neelakantapillai, Alfred Lenin Fred, and Paul Sebastin Varghese. "An overview of segmentation algorithms for the analysis of anomalies on medical images." Journal of Intelligent Systems 29.1 (2019): 612-625 and / or Ma, Zhen, Joao Manuel RS Tavares, and RM Natal Jorge. "A review on the current segmentation algorithms for medical images." International conference on imaging theory and applications. Vol. 1. SciTePress, 2009.

[0068] One specific example of a suitable segmentation or object detection algorithm is an appropriately trained machine-learning algorithm, such as an artificial neural network. Suitable examples include the algorithms proposed in Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016 or Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. "Deep neural networks for object detection." Advances in neural information processing systems 26 (2013). Any such suitable algorithm may be appropriately adapted for performing detection of a pathological finding.

[0069] In some examples, step 220 comprises receiving a user input that identifies the target region, e.g., in the form of one or more annotations of the pathological finding. In this way, a user or clinician is able to identify or indicate the pathological finding that defines the target region. The user input may be received directly from a user input interface or from a memory or storage unit that provides a stored user input.

[0070] In some examples, step 220 comprises receiving indicative data that indicates a position and size of the target region. This indicative data may, for instance, be stored alongside the CT image data, and can represent a pre-computed or predetermined position and size of the target region, e.g., as previously determined using a segmentation algorithm or identified by a user.

[0071] A wide variety of other techniques for determining or identifying the position and / or size of a target region containing a pathological finding will be readily apparent to the skilled person. For instance, another example makes use of super-pixel -based segmentation of the target region, e.g., as set out by Xiyu, Song, et al. "Review on superpixel methods in image segmentation." Journal of Image & Graphics 20.5 (2015).

[0072] From the foregoing, it will be apparent that identifying the target region may comprise receiving a user input identifying the target region, processing the CT image data using an automated system such as a detection / segmentation (e.g., an Al or machine-learning based algorithm or super-pixel-based segmentation), and / or receiving indicative data that indicates a position and size of the target region (e.g., from a memory or storage unit).

[0073] In particular examples, step 220 may comprise generating or producing a target region pixel or voxel mask that identifies, for each pixel or voxel of the contrast agent map, whether or not said pixel or voxel belongs to the target region. From the foregoing, it will be understood that different sources for the masks are possible. For instance, masks may be derived directly from a radiologist manually annotating the structures, from an automatic system such as an Al-based (i.e., machine-learning) object detection or segmentation algorithm and / or from an explicit algorithm such as a super-pixel-based segmentation of region of the target organ. The method 200 further comprises a step 230 of identifying, in the contrast agent map, a reference region containing a part of a representation of the one or more arteries of the subject. The reference region is smaller than the contrast agent map.

[0074] In some examples, the reference region contains a part of the representation of only a single artery. In some examples, the single artery is a predetermined artery, such as the aorta of the subject. This provides a reliable and repeatable reference point for calibration or normalization of the concentration value. In some examples, the single artery comprises only the closest artery to the target region.

[0075] However, in other examples, the reference region may comprise the representation of more than one artery.

[0076] In some examples, step 230 comprises identifying, for the contrast agent map, an artery region containing the representation of one or more arteries of the subject. The bounds of the artery region may, for instance, be the predicted bounds of the representation of one or more arteries in the CT image data.

[0077] The reference region may then be determined by selecting or identifying a part or all of the artery region.

[0078] In a simple technique, the reference region may simply comprise the entirety of the artery region.

[0079] In more complex examples, a part of the artery region is selected as the reference region, wherein the position of the reference region in the contrast agent map may be responsive to a position of the target region in the contrast agent map.

[0080] By way of example, the reference region may be determined by selecting a part of the artery region that represents an artery (represented in the artery region) closest to the target element represented by the target region. In this context, the term closest artery can refer to either a physically closest artery or a closest upstream - with respect to blood flow - artery. Using the physically closest artery simplifies the selection of the appropriate part of the artery region. Using the closest upstream artery improves the normalization and / or calibration.

[0081] Other and more detailed examples are provided later in this disclosure. Any previously described approaches for identifying the target region in the contrast agent map may be appropriately adapted for determining or otherwise identifying the position of the reference region and / or artery region.

[0082] Thus, the identifying the reference region and / or artery region may comprise receiving a user input identifying the reference region and / or artery region, processing the CT image data using an automated system such as a detection / segmentation (e.g., an Al or machine-learning based algorithm or super-pixel-based segmentation), and / or receiving indicative data that indicates a position and size of the reference region and / or artery region (e.g., from a memory or storage unit).

[0083] Specific example approaches for segmenting one or more arteries of a subject from CT image data, to identify the artery region, are suggested by Xie, Yiting, et al. "Automated aorta segmentation in low-dose chest CT images." International journal of computer assisted radiology and surgery 9 (2014): 211-219; Noothout, Julia MH, et al. "Automatic segmentation of thoracic aorta segments in low-dose chest CT." Medical Imaging 2018: Image Processing. Vol. 10574. SPIE, 2018 or Oda, Masahiro, et al. "Abdominal artery segmentation method from CT volumes using fully convolutional neural network." International journal of computer assisted radiology and surgery 14 (2019): 2069-2081. Other approaches are well known to the appropriately skilled person.

[0084] A more detailed example of an approach for determining a reference region is provided later in this disclosure.

[0085] Similarly to step 220, in particular examples, step 230 may comprise generating or producing a reference region pixel or voxel mask that identifies, for each pixel or voxel of the contrast agent map, whether or not said pixel or voxel belongs to the reference region.

[0086] The method 200 further comprises a step 240 of computing a normalized or calibrated value of a contrast agent concentration for the pathological finding using the concentration level in the target region and the concentration level in the reference region.

[0087] In particular examples, step 240 may comprise determining, as a target region concentration level, a concentration level in the target region and, as a reference region concentration level, a concentration level in the reference region using the contrast agent map. The target region concentration level and the reference region concentration level may then be processed together to produce the normalized or calibrated value of a contrast agent concentration for the pathological finding.

[0088] In some scenarios, the concentration level in the target region may be defined using an average of the concentration values of the pixels / voxels in the target region. Similarly, the concentration level in the reference region may be defined using an average of the concentration values of the voxels in the reference region.

[0089] However, other approaches for determining or defining a concentration level within a particular region, such as the target region or the reference region, may be employed. For instance, a largest concentration value in the region may be used as the concentration level for said region. For instance, one approach for determining or defining a concentration level within a region could be to perform a weighted average, e.g., where concentration values representing positions closer to a center of the region are weighted more heavily than those close to an edge or the bounds of a region.

[0090] In examples, one simple approach to performing step 240 may be to simply divide the concentration level CT in the target region by the concentration level in the reference region CR to produce the normalized or calibrated value CN. More specifically, this approach produces a normalized value CN for the concentration level in the target region. This approach can be mathematically expressed as:

[0091] Another approach to performing step 240 is to use the concentration level in the reference region to normalize the entire contrast agent map. In particular, each concentration value may be divided by the concentration level in the reference region to normalize the contrast agent map. The concentration of the target region in this contrast agent map may then define or represent the normalized or calibrated value of a contrast agent concentration for the pathological finding.

[0092] Another approach for performing step 240 is to rescale or calibrate the concentration level in the target region responsive to the concentration level in the reference region. For instance, a scaling factor may be determined using the concentration level of the reference region and applied (e.g., multiplied with) the concentration level of the target region to produce a calibrated value of the contrast agent concentration.

[0093] Some examples of step 240 comprise using the concentration level of the reference region to rescale or calibrate the entire contrast agent map. For instance, a scaling factor may be determined using the concentration level of the reference region. Each concentration value of the contrast agent map may be multiplied by the determined scaling factor to produce a calibrated contrast agent map. The target region of the calibrated contrast agent map (which shares a same position, size and shape as the target region of the contrast agent map) may be processed to compute the normalized or calibrated value of a contrast agent concentration level for the pathological finding - e.g., using an averaging or weighted averaging approach as previously described. In this technique, the concentration level of the target region does not, therefore, need to be determined until after the contrast agent map has been rescaled / recalibrated using the concentration level of the reference region - such that the determined concentration level of the target region may represent the normalized or calibrated value of a contrast agent concentration for the pathological finding.

[0094] Some previously described approaches make use of a scaling factor determined using the concentration level of the reference region. The scaling factor may be determined, for instance, by determining, as the scaling factor, a value by which the concentration level of the reference region must be multiplied to result in a predetermined concentration level. The predetermined concentration level may, for instance, define a specific contrast agent level that is expected for a particular contrast agent scheme or scenario.

[0095] The method 200 may further comprise a step 250 of outputting the normalized or calibrated value of a contrast agent concentration for the pathological finding. In particular, step 250 may comprise moving the normalized or calibrated value from the device to a different element or device.

[0096] In some examples, step 250 comprises controlling an output user interface to provide a user-perceptible output (e.g., a visual representation on a screen or an audible output via a speaker) of the normalized or calibrated value. This allows a clinician to be provided with useful information for performing a diagnostic or assessment task for the subject.

[0097] In particular examples, step 250 only controls the output user interface to provide the user-perceptible output responsive to a user request at an input user interface. Approaches for controlling when or how information or data is provided at an output user interface, e.g., responsive to a user input, are well known to the skilled person.

[0098] In some examples, step 250 comprises storing the normalized or calibrated value in a memory or storage unit, e.g., for future reference or retrieval.

[0099] In some examples, step 250 comprises passing the normalized or calibrated value to another processing element or device for further processing, e.g., as an input to a further algorithm for assessing the condition of the subject.

[0100] Thus, the skilled person will appreciate that there are a wide variety of tasks for which the normalized or calibrated value can be used or implemented.

[0101] Figure 3 is a flowchart that illustrates a method for performing a step 230 of identifying, in the contrast agent map, a reference region containing a part of a representation of the one or more arteries of the subject.

[0102] For the purposes of this approach, the contrast agent map comprises a vertical dimension in which the representation of one of the one or more arteries (such as the aorta) of the subject generally extends. The artery may be a predetermined artery, such as the aorta. In other examples, the artery may be a closest artery to the target region, as later detailed.

[0103] In some examples, the vertical dimension is an axis of a co-ordinate system of the contrast agent map that makes the smallest angle to a virtual line along which the representation of the artery extends. Thus, if a co-ordinate system defines N axes (e.g., 2 axes or 3 axes), then the vertical dimension is one of the N axes. In such approaches, the axis defining the vertical dimension can be known or determined in advance, e.g., as an assumption about the position of the subject, and therefore the artery, during imaging can be known in advance.

[0104] In other examples, the vertical dimension is a virtual dimension that extends along a virtual line (within the co-ordinate system of the contrast agent map) along which the representation of the artery extends. Approaches for identifying a virtual line in which a representation of an element (such as an artery) extends within an image or map are well known in the art. For instance, if an artery region is identified (as herein exemplified), then the virtual line in which an object extends may be identified as extending along the largest dimension of the artery region.

[0105] Step 230 here comprises a sub-step 310 of identifying, for the contrast agent map, an artery region containing the representation of the one or more arteries of the subject. The bounds of the artery region may, for instance, be the predicted bounds of the representation of the one or more arteries in the CT image data.

[0106] Identifying an artery region may, for instance, comprise generating or producing an artery region pixel or voxel mask that identifies, for each pixel or voxel of the contrast agent map, whether or not said pixel or voxel belongs to the artery region. This can be achieved using any previously described approach.

[0107] If the artery region contains a representation of more than one artery, then sub-step 310 may further comprise modifying the artery region to only contain a representation of the closest artery to the target element represented in the target region. In this context, the term closest artery can refer to either a physically closest artery or a closest upstream (with respect to blood flow) artery. This can be achieved, for instance, by segmenting or dividing the artery region into different portions, each portion representing a different artery. Approaches for distinguishing different arteries from one another are known in the art, e.g., as set out by Akinyemi, Akin, et al. "Automatic labelling of coronary arteries." 2009 17th European signal processing conference. IEEE, 2009. The portion representing the closest artery to the target element may then be used as the artery region for further processing. The vertical dimension can subsequently be defined as a dimension along which this closest artery generally extends, e.g., the dimension of the co-ordinate system that makes a smallest angle to the direction in which the closest artery extends or a virtual dimension along which the closest artery generally extends.

[0108] Of course, if the artery region contains a representation of only a single artery (e.g., the aorta), then this procedure does not need to be performed. In this case, the vertical dimension can be the dimension of the co-ordinate system that makes a smallest angle to the direction in which the single artery extends or a virtual dimension along which the single artery generally extends.

[0109] Step 230 also here comprises a sub-step 320 of using the artery region and the position of the target region in the contrast agent map to identify the position of the reference region. In particular, the position of the reference region, in the vertical dimension of the contrast agent map, may be defined to algin with the position of the target region in the vertical dimension of the contrast agent map.

[0110] More specifically, the target region may be considered to extend between a first position and a second position in the vertical dimension of the contrast agent map. Sub-step 320 may comprise setting the entirety of the reference region to, within this vertical dimension, lie between the first position and the second position in the vertical dimension. For instance, the entirety of the reference region may extend, in the vertical dimension, entirely from the first position to the second position.

[0111] More particularly, the reference region may be the segment of the artery region that extends from the first position to the second position in the vertical dimension.

[0112] Figure 4 schematically illustrates an approach to performing the previously described approach. In particular Figure 4 schematically illustrates an image 400 of a subject, the image containing a representation of a portion of the artery 401 and a pathological finding 402, e.g., a growth in the lungs of the subject.

[0113] The image has been processed to identify a target region 410, containing the representation of the pathological finding, and an artery region 420, containing the representation of the one or more arteries (here: comprising only the aorta). The representation of the aorta generally extends along a vertical dimension dv, which is here an axis of a co-ordinate system of the image.

[0114] The reference region 430 is identified using the previously disclosed approach to be the part of the artery region 420 that extends between - in the vertical dimension dv- first and second positions between which the target region extends. In this way, if a position along the vertical dimension can be referred to as a height, then the reference region is determined based on the maximum and minimum height of the target region.

[0115] Figure 5 is a flowchart illustrating a variant to the previously disclosed method 200, which may be carried out by the processing circuitry. This variant includes further optional steps.

[0116] In some examples, the method 200 may further comprise, before computing the normalized or calibrated value, a step 510 of reducing a size of the reference region.

[0117] In particular examples, step 510 comprises maintaining the center of the reference region at a same position within the contrast agent map reducing the size of the reference region. This approach helps to reduce a risk that the reference region will include non-aortic elements, such as calcifications, stents and / or elements outside of the one or more arteries.

[0118] In some approaches, the contrast agent map comprises one or more horizontal dimensions. The representation of the artery of the subject (represented by the reference region) generally extends in a direction perpendicular to the one or more horizonal dimensions.

[0119] Thus, if the contrast agent map is considered to comprise a vertical dimension in which the representation of the artery of the subject generally extends, then each horizontal dimension is perpendicular or orthogonal to the vertical dimension. Appropriate definitions for the vertical dimension, and approaches for identifying the vertical dimension, have been previously mentioned.

[0120] In some examples, step 510 comprises reducing the size of the reference region comprises reducing the size of the reference region in only the one or more horizontal dimensions. In this way, the size of the reference region in the vertical dimension is maintained when step 510 is performed.

[0121] As a working example, in a scenario in which the contrast agent map comprises three dimensions: a vertical dimension, a first horizontal dimension and a second horizontal dimension. A plane lying in the first and second horizontal dimensions (i.e., and orthogonal to the vertical dimension) may be labelled an axial plane. Step 510 may comprise reducing the size of the reference region in each axial plane.

[0122] In some examples, step 510 comprises the reference region using a morphological erosion technique in each horizontal dimension, e.g., in an axial plane defined by first and second horizontal dimensions. This approach maintains the shape of the reference region whilst removing the outer border pixels / voxels from the reference region to increase a likelihood that the reference region contains fewer pixels / voxel representing any elements or regions outside of the artery.

[0123] In some examples, the method 200 further comprises performing a step 520 of calibrating the contrast agent map. In particular, step 520 may comprise determining a calibration scaling factor using one or more subject parameters and / or contrast agent parameters. Each value of the contrast agent map may be multiplied by the calibration scaling factor to calibrate the contrast agent map, e.g., before the concentration levels are determined or otherwise defined.

[0124] In some examples, the method 200 further comprises performing a step 530 of further calibrating the normalized or calibrated value of a contrast agent concentration for the pathological finding. In particular, step 530 may comprise determining a calibration scaling factor using one or more subject parameters and / or contrast agent parameters. The normalized or calibrated value of a contrast agent concentration may be multiplied by the calibration scaling factor to perform further calibration. Although illustrated as a separate step, in practice, step 530 may form part of step 240.

[0125] Some previously described approaches make use of a calibration scaling factor determined using one or more subject parameters and / or contrast agent parameters. In such approaches, to generate the calibration scaling factor, the method may comprise processing one or more subject parameters and / or contrast agent parameters using a diffusion model to determine the calibration scaling factor.

[0126] As a working example, a diffusion model may process timing data, defining a difference between a time at which a contrast agent was administered and the CT image data was captured and subject-specific data to identify an expected maximum contrast uptake. This maximum contrast uptake may be used to define the calibration scaling factor, e.g., be mapped to a particular calibration scaling factor using a predefined mapping function or relationship.

[0127] Examples of suitable subject-specific data include any parameter of the subject that influences the diffusion of contrast agent in the subject - such as demographic information (e.g., weight, gender, age), vital sign information (e.g., heart rate, body temperature), patient geometry (e.g., the shape and / or structure of vasculature, organs, bones etc.), local tissue type (e.g., whether or not tissue is soft tissue) and / or other data parameters (e.g., total blood volume and / or blood circulation speed).

[0128] The skilled person would be readily capable of developing processing circuitry for carrying out any herein described method, e.g., when executing instructions contained or carried out by a memory. Thus, each step of the flow chart may represent a different action performed by processing circuitry, and may be performed by a respective module of the processing circuitry.

[0129] Embodiments may therefore make use of processing circuitry. Processing circuitry can be implemented in numerous ways, with software and / or hardware, to perform the various functions required.

[0130] A processor is one example of processing circuitry which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. Processing circuitry may however be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.

[0131] Examples of processing system components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

[0132] In various implementations, the processing circuitry may be associated with memory (i.e., one or more storage media) such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The memory may be encoded with instructions (i.e., one or more programs) that, when executed by the processing circuitry, perform the required functions. Various storage media or medium may be fixed within a device comprising the processing circuitry or may be transportable, such that the one or more programs stored thereon can be loaded into processing circuitry.

[0133] It will be understood that disclosed methods are preferably computer-implemented methods. As such, there is also proposed the concept of a computer program comprising code means for implementing any described method when said program is run on processing circuitry, such as a computer. Thus, different portions, lines or blocks of code of a computer program according to an embodiment may be executed by processing circuitry or computer to perform any herein described method.

[0134] There is also proposed a non-transitory storage medium or memory that stores or carries instructions (e.g., a computer program or computer code) that, when executed by processing circuitry, causes the processing circuitry to carry out any herein described method.

[0135] In some alternative implementations, the functions noted in the block diagram(s) or flow chart(s) may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

[0136] Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0137] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. If the term "adapted to" is used in the claims or description, it is noted the term "adapted to" is intended to be equivalent to the term "configured to". If the term "arrangement" is used in the claims or description, it is noted the term "arrangement" is intended to be equivalent to the term "system", and vice versa.

[0138] A single processor or other unit may fulfill the functions of several items recited in the claims. If a computer program is discussed above, it may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0139] Any reference signs in the claims should not be construed as limiting the scope.

Claims

CLAIMS:

1. A computer-implemented method for generating a value of contrast agent concentration for a pathological finding in a subject, the computer-implemented method comprising: receiving CT image data of the subject, the CT image data comprising a contrast agent map, which defines a concentration level of contrast agent in different positions within the subject; identifying, in the contrast agent map, a target region containing a representation of the pathological finding in the subject, wherein the target region is smaller than the contrast agent map; identifying, in the contrast agent map, a reference region containing a part of a representation of one or more arteries of the subject, wherein the reference region is smaller than the contrast agent map; and computing a normalized or calibrated value of a contrast agent concentration for the pathological finding using the concentration level in the target region and the concentration level in the reference region.

2. The computer-implemented method of claim 1, wherein a position of the reference region in the contrast agent map is responsive to a position of the target region in the contrast agent map.

3. The computer-implemented method of claim 2, wherein: the contrast agent map comprises a vertical dimension in which the representation of one of the one or more arteries of the subject generally extends; and the position of the reference region, in the vertical dimension of the contrast agent map, aligns with the position of the target region in the vertical dimension of the contrast agent map.

4. The computer-implemented method of claim 3, wherein: the target region extends between a first position and a second position in the vertical dimension; and an entirety of the reference region lies between the first position and the second position in the vertical dimension.

5. The computer-implemented method of claim 2, wherein the identifying, in the contrast agent map, the reference region comprises: obtaining a voxel map of the one or more arteries; and identifying the reference region using the voxel map and the position of target region in the contrast agent map.

6. The computer-implemented method of claim 1, wherein the identifying, in the contrast agent map, a target region comprises using a segmentation algorithm to perform segmentation to identify the target region.

7. The computer-implemented method of claim 1, further comprising reducing a size of the reference region before computing the normalized or calibrated value.

8. The computer-implemented method of claim 7, wherein reducing the size of the reference region comprises maintaining the center of the reference region at a same position within the contrast agent map.

9. The computer-implemented method of claim 7, wherein: the contrast agent map comprises one or more horizontal dimensions, wherein the representation of one of the one or more arteries of the subject generally extends in a direction perpendicular to each horizontal dimension; and reducing the size of the reference region comprises reducing the size of the reference region in only the one or more horizontal dimensions.

10. The computer-implemented method of claim 9, wherein reducing the size of the reference region comprises processing the reference region using a morphological erosion technique in the axial plane.

11. The computer-implemented method of claim 1, wherein the computing a normalized or calibrated value of a contrast agent concentration for the pathological finding comprises dividing the concentration level in the target region by the concentration level in the reference region.

12. The computer-implemented method of claim 1, wherein:the contrast map comprises a plurality of voxels, each having a respective concentration value; the concentration level in the target region is defined using an average of the concentration values of the voxels in the target region; and the concentration level in the reference region is defined using an average of the concentration values of the voxels in the reference region.

13. The computer-implemented method of claim 1, wherein the one or more arteries of the subject is an aorta of the subject.

14. A computer program product comprising computer program code means which, when executed by processing circuitry, cause the by processing circuitry to perform all of the steps of the method according to claim 1.

15. A device for generating a value of contrast agent concentration for a pathological finding in a subject, the device comprising: processing circuitry; and a memory containing instructions that, when executed by the processing circuitry, configure the processing circuitry to: receive CT image data of the subject, the CT image data comprising a contrast agent map, which defines a concentration level of contrast agent in different positions within the subject; identify, in the contrast agent map, a target region containing a representation of the pathological finding in the subject, wherein the target region is smaller than the contrast agent map; identify, in the contrast agent map, a reference region containing a part of a representation of one or more arteries of the subject, wherein the reference region is smaller than the contrast agent map; and compute a normalized or calibrated value of a contrast agent concentration for the pathological finding using the concentration level in the target region and the concentration level in the reference region.

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