Patent Description:
Fluoroscopy is used to form images of portions of patients (or subjects), to allow medical diagnosis, for example. Fluoroscopy provides a series of X-ray images on a monitor, to allow a clinician to analyse and identify a condition of a subject. Typically, the series of images is provided as a constant stream, effectively resembling a video sequence. This facility is an essential component of coronary catheter laboratory and radiology suites, providing diagnostic imaging for medical purposes and interventional medical treatment procedures. It also provides clinicians with a reliable means of tracking the passage of instruments or dye within a subject's body.

However, fluoroscopy carries risk to both subjects and clinicians, primarily in the form of exposure to ionising radiation. For medical practitioners in particular, the continued exposure to radiation during fluoroscopic scanning carried out over several years is a hazard. Multiple reports of presumed occupational radiation-related morbidity and mortality have been demonstrated.

To minimise exposure to radiation, fluoroscopy is preferably performed with the lowest acceptable exposure to ionising radiation for the shortest time necessary. Therefore there is a balance between obtaining optimal image quality (i.e. a sufficiently high frame rate and image area) and minimising extended or repeated imaging for a subject.

During a fluoroscopic imaging session, a clinician may adjust the settings of the image acquisition unit to raise or lower the frequency of images and to increase or reduce collimation (i.e. narrowing of the imaging field). Essentially, the ability to collimate optimally is based on a 'best guess' of which regions are of interest, based on a degree of the clinician's experience.

<CIT> describes a method for real-time collimation and ROI-filter positioning in X-ray imaging. <CIT> describes a method for controlling x-ray frame rate of an imaging system for a catheter procedure. <CIT> describes an angiographic image acquisition system with automatic shutter adaptation for yielding a reduced field of view for decreasing x-ray dose. <CIT> describes a system for a CT process for calculating a dose of radiation to which an object will be exposed. <CIT> describes a method for adjusting a radiation dose during imaging of an object within a subject. <CIT> describes a method of operating an x-ray system for examining an object. <CIT> describes a method for automatic control of image quality in imaging of an object using an ultrasound system.

The present invention relates to optimising fluoroscopic image acquisition for medical purposes, and aims to overcome or ameliorate one or more problems associated with the prior art.

In optimising the acquisition of images in this way, the system also provides an environmentally friendly reduction in energy consumption, by reducing the number of unnecessary images captured (and thus the energy consumed by the system as a whole).

According to a first aspect of the invention we provide an imaging system for use in a fluoroscopy procedure carried out on a subject, the imaging system being configured to receive images of a portion of the subject from an image acquisition device, the imaging system comprising.

According to a second aspect of the invention we provide an imaging installation comprising an imaging system according to the first aspect of the invention and an image acquisition device, for use in a fluoroscopy procedure carried out on a subject.

According to a third aspect of the invention we provide an optimisation module for an imaging system for use in a fluoroscopy procedure, the optimisation module being configured to.

Further features of these aspects of the invention are set out in the dependent claims of the appended claim set.

We now describe features of the present disclosure, by way of example only, with reference to the accompanying drawings of which.

With reference to the drawings, we provide an imaging system <NUM> for performing fluoroscopy. The imaging system <NUM> interacts with an image acquisition device <NUM> in that it receives images from the device. The imaging system <NUM> provides an interface module <NUM> that includes a user display <NUM>, to allow a clinician to view the output of the image acquisition device <NUM>. The interface module <NUM> provides information to a clinician.

In embodiments, the interface module <NUM> also allows the clinician to interact with the imaging system <NUM> by inputting commands or instructions to control the image acquisition device <NUM> and/or other aspects of the system <NUM>, for example. Typically, the system <NUM> provides a processor <NUM> and a memory device <NUM>. The interface module <NUM> may be provided alongside a control module <NUM>, or the two modules may be formed integrally, such that the control module <NUM> operates the image acquisition device <NUM> based either on the instructions or commands of the clinician (provided via the interface module <NUM>) or commands and operating instructions generated by the imaging system <NUM> in an automated manner, or by a combination of input commands and automated control.

The user display <NUM> may include one or more screens. In embodiments, one or more of the screens may also provide elements of a control interface via a touchscreen, for example, those interface elements forming a part of the interface module <NUM> that is capable of receiving input commands from the user.

It should be understood that the term image acquisition device <NUM> encompasses the use of at least one device - in particular, the image acquisition device <NUM> is a fluoroscope for using X-rays to obtain images of a portion of a subject. The device(s) may include a receiver (such as a fluorescent screen) and a source of X-rays. The device(s) may further include one or more cameras and/or X-ray image intensifiers, to improve the visibility of the images produced by the device. In general, the term image acquisition device <NUM> is meant to encompass one or more devices suitable for outputting images produced by X-ray of the subject, to the imaging system <NUM>.

The control module <NUM> provides functionality to control the operation of the image acquisition device <NUM>. For example, the control module <NUM> may turn on or off the X-ray source and/or camera(s) and/or any other components of the image acquisition device <NUM>. The control module <NUM> controls the capture of images including the rate of capture (also referred to as the frame rate) and the size of the area captured which is directly determined by the application of the X-rays and the collimation of those rays. The term collimation refers to the narrowing of the imaging field to align the rays in a narrower field or otherwise to cause the spatial cross-section of the beam to narrow, and encompasses the techniques well known in the art.

In embodiments, the control module <NUM> controls the position of the X-ray source (and/or receiver) which is provided on a device moveable relative to the position of the subject, to change the position and/or angle of the source relative to the subject. During user of the system, the subject is located in a lying position, stationary, on a surface <NUM> fixed in position relative to the imaging system (such as a table or bed). Therefore, the movement of the device is also relative to the fixed surface <NUM> on which the subject is lying.

The device on which the X-ray source and receiver are positioned may be a C-arm, of the type known in the art. A C-arm provides a C-shaped arm that connects the X-ray source at one end and its detector/receiver at its other end, so that the two may be moved and repositioned relative to the subject (and fixed surface on which the subject lies, in use) to obtain the required images, while ensuring that the source and detector remain aligned with one another.

The images are provided to the clinician (i.e. the 'user') in real-time via the user display <NUM> and/or may be stored or processed by the imaging system <NUM>.

The imaging system <NUM> includes an optimisation module <NUM>, to optimise control of the image acquisition device <NUM>. In general terms the optimisation module <NUM> determines, based on an image (or sequence of images) received from the image acquisition device <NUM>, one or more properties of the image(s), and outputs a control routine based on those properties.

In embodiments, the optimisation module <NUM> has access to a data store <NUM> containing training data relating to past image acquisition procedures. The training data include historical data comprising images and/or sequences of images (which may include video sequences). The historical data are supplemented by training information such as classifications of images or portions of images, or identified areas of images that are of interest or importance to a clinician. The historical data preferably contain image data from past image acquisition procedures that have been reviewed by a qualified clinician and classified or labelled accordingly. For example the clinician may classify an image or a portion of an image as being significant, if it contains an image of a part of a subject that will be of significance when carrying out a clinical assessment or procedure. For example, a portion of an image is significant if it shows signs of disease or ailment.

In this way, the training data may provide a list of positive examples of images, features, or portions of images that are significant to a clinician overseeing an image acquisition procedure. Significant portions of an image may include vessels, portions of organs, signs of disease or ailment, or other portions of a body that are generally of interest to a clinician when examining for a particular condition, for example.

In embodiments, the training data may further include negative examples - images, features or portions of images that contain data that are not significant to a clinician. Such portions may include no signs of disease or ailment, or portions of a body that are generally of less interest to the clinician when examining for a particular condition. A clinician may indicate a feature within an image, or a portion of an image containing such a feature, or an entire image, that would not provide useful information to the clinician carrying out the procedure. Such data may be classified as being insignificant. In such embodiments, the training data provide a record of which images, features, or portions of images are significant to a clinician, and those which are not.

In embodiments, the optimisation module <NUM> is trained using training data from the data store <NUM>, and then when in use, the optimisation module <NUM> may require no direct access to the data store <NUM> (where determinations are made using a trained mechanism such as an artificial neural network or other form of trained classifier using a support vector machine, for example). In other embodiments, the optimisation module <NUM> accesses the training data during use, to make determinations through comparison of the images to data in the training data set (using a high-dimensional clustering or kernel-based classifier, for example).

In some embodiments, the optimisation module <NUM> has access to the data store <NUM> to store images processed by the optimisation module <NUM> in use. The stored images may be supplemented by data recorded from the control module <NUM> indicating manual control inputs from the clinician made in response to the images, so as to augment the existing training data. In effect, the data recorded from the control module <NUM> may be used to classify the images, or portions of the images, as being significant or insignificant, based on the actions taken by the clinician.

In use, the optimisation module <NUM> processes data obtained via the image acquisition device <NUM> to determine whether they contain significant images/ features or not. The optimisation module <NUM> may receive images directly from the image acquisition device <NUM>, or may receive images that are sent to the user display <NUM>. The images may be received from a screen capture device, for example, which interacts with the user display <NUM>.

In embodiments, if the processed image (or a portion of the image) is determined to be significant, the optimisation module <NUM> determines that the acquisition framerate should be high, so that the number of images captured is sufficient, and the associated detail contained in those numerous images is of sufficient quality. In embodiments, if the processed image is not determined to be significant, the optimisation module <NUM> determines that the acquisition framerate should be low. Alternatively, the optimisation module <NUM> may determine that the framerate should be set to some intermediate value (i.e. between the high and low values) if it is unclear whether the image(s) contain features of significance, or if only a small portion of the image is on significance, for example. These mechanisms limit the exposure of the clinician and subject to potentially harmful X-rays where the images captured are not important to the clinician. It is also important to note that automated systems for performing diagnosis or treatment, or systems assisting clinicians in diagnosis or treatment, rely on receiving an appropriate qualify of image (generally a high-quality image) in order to provide accurate analysis and computer-aided decision-making.

In embodiments, if a portion of a processed image is deemed to be significant, but other portions of the same image are deemed not to be significant, the optimisation module <NUM> determines that the size of the area of acquisition of the image should be decreased (i.e. increasing collimation). In this way, the image may be focussed on only the portion of the image that is significant (or a portion containing a significant feature). In reducing the area of image capture, the area of exposure of the subject to potentially harmful X-rays is reduced.

In embodiments, the optimisation module <NUM> determines from the processed image or from a sequence of processed images that the image acquisition device should be moved to a different location or to a different angle, relative to the fixed surface <NUM> on which the subject is lying. The optimisation module <NUM> may determine that only a portion of the processed image is significant, and if that significant portion is located at or towards an edge region of the image, the optimisation module <NUM> determines that more significant features lie in that direction (i.e. towards the area deemed to be of significance).

Where the optimisation module <NUM> makes a determination that the framerate should be high, or low, or should be set to some intermediate value, the imaging system <NUM> may provide this information to a user via the user display <NUM>. The user may then control the imaging system <NUM> accordingly, via the interface module <NUM>, to cause the control module <NUM> to vary or maintain the framerate of the image acquisition device <NUM>. In embodiments, the information is provided to the user in the form of a prompt to confirm a proposal to change the framerate, prior to the image system <NUM> automatically changing the framerate via the control module <NUM>. In other embodiments, the process of causing the control module <NUM> to vary or maintain the framerate is carried out automatically by the control module <NUM> when the determination is made by the optimisation module <NUM>, without requiring input from the user. The information regarding the change (or maintenance) of the framerate may still be displayed to the user via the user display <NUM>.

Similarly, where the optimisation module <NUM> makes a determination that the area of acquisition of the image (i.e. collimation) should be increased, or decreased, the imaging system <NUM> may provide this information to a user via the user display <NUM>. The same applies where the optimisation module <NUM> determines that the image acquisition device <NUM> should be moved to a different location or to a different angle, relative to the fixed surface <NUM> on which the subject is lying. In either case, the user may then control the imaging system <NUM> accordingly, via the interface module <NUM>, to cause the control module <NUM> to operate the image acquisition device <NUM> accordingly. As before, in embodiments, the information is provided to the user in the form of a prompt to confirm a proposal to control the image acquisition device <NUM>, prior to the image system <NUM> automatically controlling the image acquisition device <NUM>. In other embodiments, the process of controlling the image acquisition device <NUM> is carried out automatically by the control module <NUM> when the determination is made by the optimisation module <NUM>, without requiring input from the user.

In this way, the imaging system <NUM> is controlled in a way that optimises the process, reducing the amount of X-ray exposure for the clinician and the subject where possible, without compromising the capture of significant images for use by the clinician.

Controlling the image acquisition device <NUM> to provide an increased or decreased area of image acquisition may involve moving collimation plates (i.e. lead plates) relative to the radiation beam so as to extend or narrow the field of the beam.

In embodiments, optimisation module <NUM> forms an integral component of an imaging system <NUM>. In other embodiments, the optimisation module <NUM> is provided as a plug-in to an existing system, in which image data from the image acquisition device <NUM> or user display <NUM> (or screen capture device associated with the user display) is received as an input, processed by the optimisation module <NUM>, from which an output provides feedback to a user via the interface module <NUM>, and/or control functionality via the control module <NUM>, as described above.

It should be understood that one or more components of the imaging system <NUM> may be provided remotely from the other components. For example, the data store <NUM> may be situated remotely. The data store <NUM> may comprise a cloud-based data store, accessible via the internet. Alternatively, the data store <NUM> may be a storage device located alongside the other components, within a self-contained unit, or may be located on local area network, or may have a wired connection with one or more other components of the system.

Similarly the interface module <NUM> may be located remote from other components of the system. The interface module <NUM> may, for example, be formed with the user display <NUM>, as a touch screen interface for example. Alternatively, the interface module <NUM> may include an operator control panel or one or more other hardware components providing control functionality to a user. In embodiments, the interface module <NUM> and control module <NUM> may combined to form an integral unit.

To illustrate the method of using the imaging system <NUM> and optimisation module <NUM> as described above, we examine a process for using the system in relation to a coronary angiogram procedure.

At the start of a coronary angiogram procedure, the user obtains a non-collimated view (i.e. with a wide area of image acquisition). This image is displayed to the user via the user display <NUM>.

The optimisation module <NUM> is provided with a sequence of images as described above, and analyses the images for features that it has been trained to classify as being significant. As an example of how this works, in the instance of a coronary angiogram, the images are effectively trained against (i.e. compared to) training data containing images in which the arteries are clearly defined (e.g. with adequate contrast). In addition, the optimisation module <NUM> determines the position of an angiography catheter in relation to the heart border in order to further refine the optimal degree of collimation.

The optimisation module <NUM> then identifies an optimal position for the catheter to be positioned, relative to the image. The identification may involve a prompt for the user to position or move the catheter relative to its current position. The optimisation module <NUM> determines a portion of the image that provides an optimal field of view, and indicates that to the user or provides control via the control module <NUM> to achieve that optimal field of view. The output of the optimisation module <NUM> may also provide an overlay to improve the visibility of vessels and arteries, and portions such as an ostium, in images displayed to the user, by increasing contrast for example, or by adding an outline to those features where identified. Outlining features in this way provides assistance to a clinician performing intubation on a subject.

The user may then control the image acquisition device <NUM> to move it to its optimal position to achieve that optimal field of view by driving the motors to move the metallic lead plates that bound the radiation beam exposure to control collimation, so as to alter the image acquisition area. Once the X-ray has been collimated (i.e. the field of view restricted) to the optimal position, the operator then acquires the fluoroscopic image. This image acquisition may be controlled by means of a foot pedal, in line with routine practice.

With reference to <FIG>, the optimisation module <NUM> displays an overlaid circle (<NUM>) indicating an optimal position (i.e. to prevent panning). The user then moves the catheter to position it correctly relative to the circle (<NUM>). The optimisation module <NUM> determines a portion of the image that is significant, and a portion of the image that is not significant (<NUM>), leading to control of the image acquisition device <NUM> to a position in which the image is collimated so that the significant portion is captured, and the insignificant portion is not (<NUM>).

In embodiments, the optimisation module <NUM> may provide a control facility to the user directly, via the interface module <NUM> where that includes an interface via the user display <NUM>. For example, the optimisation module <NUM> may indicate that the image acquisition area should be reduced, or that the framerate should be reduced, via the interface module <NUM>. The user may then interact directly with the interface module <NUM> to cause the proposed adjustment to be made.

Turning to <FIG>, we note that there are certain steps in the coronary angioplasty procedure where the highest image quality is required. This is so that small adjustments can be made by the clinician to position a stent optimally prior to its deployment. Once deployed, a stent is not retrievable. In these situations, image quality can be improved by collimating out areas from the field of view that are not directly related to the optimal deployment of the stent (e.g. distal vessels (shown as <NUM>,<NUM>), as depicted in <FIG>).

Looking at <FIG>, the left image <NUM> shows the original angiogram, with the clinician's label of the region of interest outlined as a superimposed box. The right image <NUM> shows the segmentation of the angiogram proposed by the optimisation module <NUM>, in which the shaded areas represent areas the algorithm classifies as being outside of the region of interest. The region of interest is indicated by the area in which the original angiogram is viewable. The accuracy of the optimisation module <NUM>, once trained, can be gauged by a comparison of the size and location of the region proposed as being significant against the region indicated by the clinician as being relevant in the training data. It has been shown that, training the optimisation module <NUM> using the methods described, a high accuracy of prediction is obtained.

The optimisation module <NUM> is used to detect the stage in the angioplasty procedure where the images must be of highest quality, based on the training data used to train the module. The optimisation module <NUM> may therefore automatically collimate to optimise image quality.

To provide an example of the training process for the optimisation module, in more detail, we use a deep convolutional encoder-decoder architecture for pixel-wise labelling of images to output a probability matrix describing whether each pixel is inside or outside the recommended image boundary.

The training data may include images alongside data identifying the important (i.e. significant) areas within each image. The identification may be indicated by a rectangular box within the image, identifying the area of significance within the rectangle.

As an example method, a data file is exported for each image, containing data of the image and of its identified region of significance. The image is reshaped to be 224x224 pixels (alternative dimensions may be used in other embodiments, subject to storage and processing restrictions).

A <NUM> dimensional array of one-hot encoded representations for each channel, where we have two channels, one for 'inside labelled field' (i.e. significant data) and one channel for 'outside labelled field' (insignificant data). One-hot encoding is a binary encoding denoting whether the channel value is significant, for example. For example '<NUM>' may identify a significant region, and '<NUM>' may identify an insignificant region. e.g. for a 4x4 image, where only the middle section should appear on the angiogram this would be represented by the following encoding: [[[<NUM>,<NUM>,<NUM>,<NUM>],[<NUM>,<NUM>,<NUM>,<NUM>],[<NUM>,<NUM>,<NUM>,<NUM>],[<NUM>,<NUM>,<NUM>,<NUM>]], [[<NUM>,<NUM>,<NUM>,<NUM>],[<NUM>,<NUM>,<NUM>,<NUM>],[<NUM>,<NUM>,<NUM>,<NUM>],[<NUM>,<NUM>,<NUM>,<NUM>]]].

Using this training data, the artificial neural network (ANN) may be encoded as follows. The ANN uses a deep convolutional encoder-decoder architecture. It contains <NUM> blocks (<NUM> up and <NUM> down). Each block comprises a series of <NUM>-<NUM> groups containing a Convolution layer, a Batch Normalisation, and a ReLU nonlinearity. The "down" groups finish with a MaxPooling layer to cause down-sampling (i.e. taking a max value from each pool of pixels). The "up" groups start with an UpSampling2D layer. The desired training can be achieved when configured such that the Convolutional layers have <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> filters, respectively, to their blocks. The network then finishes with a Reshape, Permute and a Softmax activation to output a 3D array of one-hot encoded representations in the same format as the training data. It should be understood that ANN of different structures and numbers of layers may be used to train the classification algorithm used by the optimisation module <NUM>.

The ANN is trained using an ADADELTA optimiser (for example) to minimise the Categorical Cross-entropy loss function (as is common in logistic regression). The network may be trained for <NUM> epochs, using a batch size of <NUM> images, for example.

In general terms, it should be understood that while a convolutional ANN has been used in this example, other forms of classifier may be employed to identify significant and/or insignificant portions of images for use in the optimisation module <NUM>.

In embodiments, by way of the supervised learning mechanisms employed for training the optimisation module <NUM>, the optimisation procedure effectively tracks the stage of the process being performed by a clinician on a subject. For example, certain imagery or portions of an image are likely to occur at the same stage of the process when performing an angioplasty procedure. The optimisation module <NUM> therefore is effectively trained to recognise crucial stages of that procedure, and to increase the quality of the images provided to the clinician accordingly (by automatically collimating and/or increasing the frame rate to achieve a suitable image quality).

In other exemplary embodiments, in addition to or instead of the optimisation module <NUM> optimising the frame rate of image acquisition and/or the area or locations of image acquisition, the optimisation module <NUM> controls the angle of image acquisition relative to the surface <NUM> fixed in position relative to the imaging system (and therefore relative to the subject). The optimisation module <NUM> determines from the images obtained by the image acquisition device <NUM> whether one or more features of importance overlie one another.

For example, when viewing a single image (also referred to as a 'projection') of a portion of the coronary system of a subject, one or more vessels may overlap one another. In that case, the clinician may not be able to see one or more of the important feature in sufficient detail. To resolve the issue, the clinician may consider moving the image acquisition device <NUM> by moving the C-arm, by tilting it, to change its angle relative to the position of the subject (i.e. to the position of the surface <NUM>). In doing so, the angle of acquisition is altered. The user makes minor repeated amendments to the angle of the projection and repeats the X-ray process to try to capture a fluoroscopic image without the vessels overlapping (see <FIG>, for example). Often this process leads to several images being captured, each exposing the subject and clinician to radiation, and consuming valuable time.

Additionally, if there is no such issue of overlapping vessels of importance to the clinician, the acquisition of multiple images from different angles may not be required as they may provide little or no significant additional information to the clinician.

The optimisation module <NUM>, trained using a training dataset containing images showing overlapping vessels (and/or other overlapping significant features) may provide guidance and/or control functionality to address this problem. For example, in embodiments, the optimisation module <NUM> is able to detect the presence of overlapping features of significance in an observed image.

In embodiments, the optimisation module <NUM> determines from the processed image (see <NUM>) or from a sequence of processed images that the image acquisition device <NUM> should be moved to a different angle relative to the fixed surface <NUM>, to achieve a different projection of the features. Following this determination, the imaging system <NUM> may provide this information to a user via the user display <NUM>. For example, the system <NUM> may suggest the next optimal projection (e.g. 'x' degrees X 'y' degrees) that is required to resolve the overlap of the significant features. The user may then control the imaging system <NUM> accordingly, via the interface module <NUM>, to cause the control module <NUM> to operate the image acquisition device <NUM> to achieve the proposed angle and placement (see <NUM> and <NUM>, in <FIG>). In embodiments, the information may be provided to the user in the form of a prompt to confirm a proposal to control the image acquisition device <NUM>, prior to the image system <NUM> automatically controlling the image acquisition device <NUM>. In other embodiments, the process of controlling the image acquisition device <NUM> is carried out automatically by the control module <NUM> when the determination is made by the optimisation module <NUM>, without requiring input from the user.

In embodiments, the optimisation module <NUM> is operable to identify whether a sufficient level of vessel opacification has been achieved before or during an image acquisition process. To perform an angiogram, contrast dye is injected into the coronary vessel during simultaneous fluoroscopic image acquisition. This process increases the opacity of the internal lumen of the vessel and is the primary method for visually identifying narrowed portions of vessels (stenoses). Using such a process, coronary angiography provides the best current test for identifying coronary artery disease.

The process of administering dye manually is reliant on the operator's skill and experience. If too little dye is administered, or the dye is administered too slowly, opacification of the vessel lumen will be suboptimal. This leads to a 'streaming' artefact as the low quantify of dye mixes with the blood in the vessel. For optimal opacification, dye should be sufficient to remove blood from the vessel lumen.

Suboptimal vessel opacification limits the diagnostic yield of the angiogram, leading to uncertainty of the severity of a narrowing in a vessel. Frequently this requires repeat dye injection and image acquisition, causing repeat radiation exposure. In certain situations, repeated suboptimal vessel opacification may render the entire angiogram non-diagnostic, necessitating a repeat of the process.

To address this problem, in embodiments, the optimisation module <NUM> determines from the images captured by the image acquisition device <NUM> whether the level of opacification of the vessels shown in the images is adequate. For example, the optimisation module <NUM>, presented with an image, provides a determination of 'adequate' or 'inadequate', subject to classification using a classification algorithm. In embodiments, the classification algorithm applies a threshold to the observed relative level of contrast, or brightness, of portions of the image identified as being vessels compared against the surrounding portions of the image. Values that are lower than a predetermined threshold are determined to be inadequate, for example.

In this way, in embodiments, the optimisation module <NUM> determines from the processed image or from a sequence of processed images whether a sufficient level of opacity is present in the vessels in the image(s) (and, by association, whether a sufficient level of dye has been injected). Following this determination, the imaging system <NUM> provides this information to a user via the user display <NUM>. For example, the system <NUM> may suggest that additional dye should be administered to the subject. The suggestion may include a suggested dosage. The clinician may then apply the dosage manually, and continue the procedure. Or else the clinician may then wait for a prompt from the imaging system <NUM> confirming that a sufficient level of opacity has been reached.

In embodiments, the user may interact with the imaging system <NUM> via the interface module <NUM>, to cause the control module <NUM> to operate a device for administering dye. In this way, the user may confirm a proposed dosage to be administered to the subject. In embodiments, the information may be provided to the user in the form of a prompt to confirm a proposal to control the image acquisition device <NUM> so as to apply the dosage, prior to the image system <NUM> automatically controlling device for administering the dosage.

In other embodiments, the process of administering the required dosage may be carried out automatically by the control module <NUM> when the determination is made by the optimisation module <NUM>, with or without requiring input from the user as desired. Such an automated system is reliant on receiving sufficiently high-quality images from the image acquisition device <NUM>, for which the ability to identify significant features (such as vessels) in the images and to collimate according to those features and at a sufficiently high frame rate, is of course important.

The learning techniques for training a suitable classifier for assessing the level of dye in observed images, or for optimising image acquisition by altering the angle of image acquisition to lessen or remove vessel overlap, may be as previously described - using a training data set provided in the data store <NUM>. As previously described, in embodiments the optimisation module <NUM> has access to the data store <NUM> during use, so as to perform comparative assessment with the labelled data in the store. In other embodiments, the optimisation module <NUM> has no access to the data store <NUM> during use, and has been trained prior to use of the system so as to provide determinations based on observed images only with no requirement to access past data.

In embodiments, the optimisation module <NUM> is provided separately from the imaging system <NUM>. In other words, an optimisation module <NUM> may be retrofitted to an existing imaging system <NUM> by communicative connection to the existing system such that the optimisation module <NUM> is configured to receive an image from an image acquisition device <NUM>, and to determine, based on the received image, one or more properties of the image. The optimisation module <NUM> then outputs a control routine that includes instructions for controlling the image acquisition device <NUM>, or provides instructions to display to a user via the interface module (i.e. a proposed control action that the user should take to control the image acquisition device <NUM>).

As before, the optimisation module <NUM> has access to a data store <NUM> which may be provided integrally with or separate from the optimisation module <NUM>.

<FIG> illustrates the imaging system <NUM> in the context of an imaging installation in a screening facility, for example. An X-Y platform provides the surface <NUM> on which the subject <NUM> is positioned. For conducting aspects of the medical procedure, in examples, a catheter may be used and controlled via a catheter guide <NUM>. The catheter and associated wires <NUM> may provide data (such as pressure data) that is input to the system via a data input port <NUM>.

The imaging system <NUM> itself forms part of a console <NUM> and control system, for controlling aspects of the procedure, the imaging equipment, and the environment. The imaging system <NUM> is connected to the user display <NUM>. Either the user display <NUM> or the console <NUM> or both may provide control functionality via inputs to the control module <NUM> for controlling the image acquisition device <NUM>. The user display <NUM>, as previously stated, may comprise one or more screens which may be mounted to the console <NUM> and control system (i.e. providing a standalone unit, as shown), or more commonly may be mounted to a wall of the screening facility, or suspended by mounting equipment from the ceiling or from a wall, for example.

In this example, the image acquisition device <NUM> comprises an image intensifier <NUM> (i.e. the receiver / detector) and an X-ray source <NUM> disposed on either side of a portion of the subject <NUM>, so as to acquire images of the subject <NUM>. The image intensifier <NUM> and X-ray generator <NUM> are supported on the C-arm <NUM>, and the components of the image acquisition device <NUM> are controlled via the C-arm base and drive, operating on inputs from an imaging control device <NUM> such as a foot pedal, as illustrated. Data captured by the image acquisition device <NUM> is output to the imaging system <NUM> (i.e. the data comprises the angiogram images). It is envisaged that the imaging system <NUM> described herein may be used in combination with automated apparatus for performing diagnostic or treatment routines on subjects, to provide images of features relevant to that procedure, of a sufficient quality, without exposing the subject to unnecessary doses of radiation.

While example embodiments are described herein, it should be understood that features of different embodiments may be combined with one another, in isolation from one another or in any combination, unless stated otherwise. The scope of the invention being defined by the appended claims.

Claim 1:
An imaging system (<NUM>) for use in a fluoroscopy procedure carried out on a subject, the imaging system (<NUM>) being configured to receive images of a portion of the subject from an image acquisition device (<NUM>), the imaging system (<NUM>) comprising:
an interface module (<NUM>) for displaying images received from the image acquisition device (<NUM>) to a user, and
an optimisation module (<NUM>) for determining, using an artificial neural network and based on an image received from the image acquisition device (<NUM>), one or more properties of the received image, the properties including the location and area of a feature deemed to be significant within the received image, and
the artificial neural network having been trained using training data, the training data including a list of positive examples of images, features, or portions of images that are significant to a clinician overseeing an image acquisition procedure,
wherein the optimisation module is configured to output a control routine based on the determined one or more properties of the received image,
wherein a determined property of the image is the presence of overlapping features of significance within the image, and wherein the control routine includes changing the angle or position of the image acquisition device (<NUM>) relative to the subject or to a surface (<NUM>) on which the subject is located.