Patent Description:
Nasal airway obstruction (NAO) is a relatively frequent problem encountered by ear-nose-throat (ENT or ORL) physicians. Increasing incidence of allergy additionally increases the number of patients with nasal obstruction. Septoplasty and turbinate surgery appear to be treatments of choice in case of anatomic abnormalities and are among the most frequently performed operations by ENT surgeons. The decision to proceed to surgery is generally based on a surgeon's assessment. This assessment is impeded by a poor correlation between existing objective tests on the one hand, such as tomographic imagery, visual inspection, rhinomanometry and/ or acoustic rhinometry, and patient's symptoms or patient's subjective feelings on the other hand. Given this impediment and the complex nasal anatomy, it is not surprising that the overall results for long-term relief of NAO and associated improvement of quality of life are very unsatisfactory.

An objective tool for a pre-operative assessment of the nasal cavity may be found in computational fluid dynamics (CFD). CFD is a technology which can allow researchers to predict airflow throughout the nasal cavity based on a three-dimensional model of the nasal cavity which may be derived from for example tomographic imaging (e.g. CT-scans). However, the conversion from tomographic data to a model of the nasal cavity has proven to be a labour-intensive task, inhibiting its clinical application.

As an example, Keustermans et al (<NUM>-<NUM>-<NUM>) disclose a technique to provide a more comprehensive nasal cavity shape model which is derived from individual models generated from CT data of a limited number of patients. However, the technique disclosed in the paper cannot be put into practice for a large number of patients for the reason mentioned above. Moreover, the technique is still based on medical imagery.

An additional problem is formed by the fact that a nasal cavity does not have a static shape over time. The shape of the nasal cavity is subject to the so-called nasal cycle, which is a natural process in which the nasal mucosa periodically congests and decongests over a period of a couple of hours. The left and right nasal channel do this in antiphase, i.e. when the left nasal channel is congested, the right nasal channel is decongested and vice versa. A single tomographic imaging is therefore only a snapshot in time of the nasal anatomy. At the same time, multiple tomographic imaging is not feasible due to the danger of the radiation involved and/or due to the high costs of for example MRI scans.

Additionally, in the pharmaceutical industry, there is a growing interest in nasal drug delivery. However, current nasal drug delivery devices appear to perform sub-optimally. In this application as well, CFD simulations may potentially improve performance of nasal drug delivery devices. But obtaining tomographic imagery of the nasal cavity may not be feasible in this field since there may not be a medical indication to perform such imagery.

It is therefore an aim of the present invention to solve or at least alleviate one or more of the above-mentioned problems. In particular, the invention aims at providing an improved model for describing a specific nasal cavity shape as well as a method for modelling a specific nasal cavity shape in a relatively fast and cost-efficient manner allowing subsequent CFD being performed thereon and possibly avoiding any radiation due to imaging.

Disclosed is a non-claimed computer-implemented model for describing a specific nasal cavity shape. The model comprises a generic nasal cavity shape model including an average nasal cavity shape and a set of nasal cavity shape eigenmodes. The model further comprises a set of specific parameters such that the specific nasal cavity shape is modelled by a combination, in particular by a sum, of the average nasal cavity shape and a linear combination of the set of specific parameters with the set of nasal cavity shape eigenmodes. Said set of specific parameters is derived from measurement data of the specific nasal cavity, in particular from measurement data on quasi static and/or dynamic nasal pressure changes. Such measurement data may for example encompass data on quasi static nasal pressure changes, such as rhinomanometry data, and/or dynamic nasal pressure changes such as acoustic rhinometry data. In the context of the present application, the term 'specific' refers to what is linked to a specific person. As explained above, nasal cavity shapes can vary widely between different persons, as well as over time for a specific person. A general nasal cavity model may therefor differ substantially from a specific nasal cavity of a specific person at a given moment in time. Since the present model can describe a specific nasal cavity shape based only on a generic nasal cavity model in combination with measurement data of the specific nasal cavity of a given person, the model can provide a solution to the above-identified problem, without the need for additional tomographic imaging, which may be a costly and time-consuming procedure.

The average nasal cavity shape and the set of nasal cavity shape eigenmodes may be 3D surface representations of nasal cavities. These 3D surface representations are preferably point clouds in which the points are distributed over a surface or boundary of the nasal cavity shape. These surface representations can allow further identifications of corresponding points located on the same anatomical position between surface representations of different nasal cavity shapes. Alternatively, these 3D surface representations may also be surface meshes including edges and faces configured to indicate a degree of connectivity between points.

Disclosed is a non-claimed computer-implemented method for modelling a specific nasal cavity shape. The method comprises the steps of obtaining measurement data of a nasal cavity and feeding said measurement data into a neural network. Said measurement data include data on quasi static and/or dynamic nasal pressure changes, such as for example rhinomanometry data or acoustic rhinometry data. The neural network is trained to output a set of specific parameters such that the specific nasal cavity shape is modelled by a combination, in particular by a sum, of an average nasal cavity shape and a linear combination of the set of specific parameters with a set of nasal cavity shape eigenmodes. As mentioned above, the present method can provide a specific nasal cavity model based only measurement data which are relatively easy to obtain from a person, such as acoustic rhinometry or rhinomanometry data, and an inventive combination of such data with a generic nasal cavity model.

The obtaining of measurement data can preferably include obtaining acoustic rhinometry measurement data. Such measurement data can provide a cross-sectional surface area in function of a depth of at least one of a right nasal channel and a left nasal channel of a nasal cavity of a person. This is a well-known measurement technique in ORL practice, for example in allergen provocation tests. The size and pattern of reflected sound waves can provide information on the structure and dimensions of the nasal cavity, with the time delay of reflections correlating with the distance from the nostril. Such measurements have been shown to correlate relatively well with measurements on CT scans. Moreover, these acoustic rhinometry measurement data can be obtained in a non-invasive way. Alternatively, rhinomanometry measurements could be used.

According to an aspect of the invention, there is provided a computer-implemented method of training a neural network to output a set of specific parameters derived from measurement data of a nasal cavity such that a specific nasal cavity shape is modelled by a combination, in particular by a sum, of an average nasal cavity shape and a linear combination of the set of specific parameters with a set of nasal cavity shape eigenmodes. The training of the neural network includes the steps of randomly generating sets of specific parameters simulating measurement data of nasal cavities. Then specific nasal cavity shape models are generated, said models including a combination of an average nasal cavity shape and a linear combination of said simulated sets of specific parameters with a set of nasal cavity shape eigenmodes. Next, a cross-sectional surface area of at least one of the nasal channels of the specific nasal cavity shape models provided by said simulated sets of specific parameters is determined. The determined cross-sectional surface area of at least one of the nasal channels is then fed into the neural network. Finally, the neural network is trained to output the sets of specific parameters, which had been randomly generated. In this way, the neural network can be trained relatively easily by simulated data which are based on randomly generated sets of specific parameters. No additional measurements or imagery on a person is needed. Alternatively, the training can be done by labelled data based on real measurement data, for example acoustic rhinometry data, from a person for whom data obtained from for example tomographic imaging is present.

The generating specific nasal cavity shape models can include obtaining a generic nasal cavity shape model. Such a generic nasal cavity shape model can be obtained by generating 3D surface representations of a plurality of nasal cavities. Said 3D surface representations may for example be clouds of points. Each 3D surface representation includes a same number of points. Next, corresponding points between said 3D surface representations of the plurality of nasal cavities are being identified such that said corresponding points are located on a same anatomic position. Then an average nasal cavity shape is generated based on average values of said corresponding points. From said 3D surface representations a set of nasal cavity shape eigenmodes is extracted. The generic nasal cavity shape model can then include the average nasal cavity shape and the set of nasal cavity shape eigenmodes. In this way, even if the generation of a generic nasal cavity shape model may have been relatively labour- or cost-intensive, the generic nasal cavity shape model can now allow a relatively easy generation of specific nasal cavity shape models with relatively little effort.

The generating of 3D surface representations may for example be based on tomographic images of a plurality of nasal cavities. The tomographic images may be images of pathological or non-pathological nasal cavities. Databases of tomographic images in ORL centres or hospitals may be used to obtain a relatively large amount of tomographic images of various nasal cavities. Any other kind of available data, for example other imaging data, may also be used to generate said 3D surface representations.

The generating of 3D surface representations can include mirroring said 3D surface representations. Since nasal cavities are asymmetric, and the left nasal cavity channel is not a mirror image of the right nasal cavity channel, each mirror image can represent an additional 3D surface representation. In this way, the number of available 3D surface representations can be easily doubled without the need for more imagery on people.

The finding of corresponding points can for example include applying a cylindrical parametrization technique for mapping tubular surfaces. It has been found that the shape of one of the nasal channels followed by the other of the nasal channel can be approximated relatively well by a tubular surface, which can simplify parametrization of the surface. An example of such a cylindrical parametrization technique has been disclosed in <CIT>, which has the advantage of being able to map a relatively complex topology, such as of a nasal cavity shape, onto a cylinder on which calculations can be speeded up. Said technique can allow to decrease stretch distortions. Other parametrization techniques may also be used.

The generating of the average nasal cavity shape and the extracting the set of nasal cavity shape eigenmodes is done by applying a principal component analysis. This is a well-known dimensionality reduction technique which can be implemented in a relatively efficient way. The set of nasal cavity shape eigenmodes is preferably an orthogonal set of eigenmodes to reduce computation time and to ensure that each nasal cavity shape model can be attributed a unique set of specific parameters, which can favour an accurate training of the neural network. Other dimensionality reduction techniques may be used instead, such as for example an independent component analysis.

Disclosed is a non-claimed computer-implemented neural network for modelling a specific nasal cavity shape. The neural network comprises a generic nasal cavity shape model including an average nasal cavity shape and a set of nasal cavity shape eigenmodes. The neural network is trained to output a set of specific parameters derived from measurement data, in particular measurement data on quasi static and/or dynamic nasal pressure changes, of the specific nasal cavity such that the specific nasal cavity shape is modelled by a combination of the average nasal cavity shape and a linear combination of the set of specific parameters with the set of nasal cavity shape eigenmodes. Such a neural network can provide an efficient tool for ORL specialists: only by feeding person-specific measurement data into the neural network, the neural network can provide a specific nasal cavity shape model in a relatively fast manner, which model can then help an ORL practitioner to decide on a treatment if needed. In industry and research, for example in pharmaceutical industry, such specific nasal cavity shape models can help in deciding on person-specific dosages of medical nasal sprays or on administration strategies. Since the specific nasal cavity shape model is a computer-implemented model, the model can further be used to perform computational fluid dynamics calculations on the model. The neural network can for example be trained as previously described, providing one or more of the described advantages.

<FIG> illustrates the step of obtaining measurement data of a nasal cavity of the method for modelling a specific nasal cavity shape. Measurement data can for example be obtained by acoustic rhinometry. A practitioner <NUM> uses a dedicated device <NUM> configured to generate and emit acoustic waves into a nasal channel of a person <NUM>. The acoustic waves are reflected within the nasal channel. The device <NUM> is further configured to detect a reflection by the nasal channel of said emitted acoustic waves. Time delays between emission and detection of reflected acoustic waves can then provide information on a cross-sectional area in function of depth per nasal channel as illustrated in <FIG> showing a schematic graph of the measurement data <NUM>. These measurement data <NUM> are then fed into a neural network <NUM>, as will be illustrated in <FIG>. The neural network <NUM> is trained to output a set of specific parameters such that the specific nasal cavity shape of the person <NUM> is modelled by a combination of an average nasal cavity shape and a linear combination of the set of specific parameters with a set of nasal cavity shape eigenmodes. The model for describing the specific nasal cavity shape <NUM> can then be visualized by the practitioner <NUM>, and/or the practitioner <NUM> can do calculations, for example CFD calculations on the specific model <NUM>.

<FIG> shows a schematic cross-sectional view of a nasal cavity <NUM> to be modelled, as well as a perspective view on a model <NUM> of said nasal cavity <NUM>. The nasal cavity is a large air-filled space behind the nose <NUM> and forms the upper part of the respiratory system. The nasal cavity is divided into two nasal channels or fossae 20a, 20b through which air can flow between the nostrils and the pharynx <NUM> and the rest of the respiratory tract. The nasal cavity <NUM> can further include an inferior meatus or passage <NUM>, a middle meatus <NUM>, a superior meatus <NUM>, and in some cases a supreme meatus. A model of the nasal cavity may also comprise the nasopharynx <NUM>, connecting the nasal cavity with the pharynx. The model may comprise the entire nasal cavity or only part of it, with or without the nasopharynx, or for example including only one nasal channel or part of it. Nasal functions can include olfaction, ventilation, heating and/or humidification of inhaled air and filtration of potentially harmful particles. These nasal functions can be severely impaired in case of nasal airway obstruction, which may for example be caused by anatomic deformities such as a septal deviation or perforation, an enlarged turbinate or others.

<FIG> shows a schematic representation of a neural network. As explained before, measurement data <NUM>, such as for example acoustic measurement data obtained by a practitioner <NUM> of a nasal cavity of a person <NUM>, are input into the neural network <NUM>. The neural network <NUM> has been trained, or in other words, parameters of at least one hidden layer <NUM> have been tweaked, such that the neural network <NUM> outputs a set of specific parameters <NUM>. The specific nasal cavity shape of the person <NUM> can then be modelled by a combination of an average nasal cavity shape and a linear combination of said set of specific parameters <NUM> output by the trained neural network <NUM> with a set of nasal cavity shape eigenmodes, as will be explained under <FIG>.

<FIG> shows a schematic representation of the model for describing a specific nasal cavity shape. The model <NUM> comprises a generic nasal cavity shape model <NUM> including an average nasal cavity shape <NUM> and a set of nasal cavity shape eigenmodes <NUM>. The model <NUM> further comprises a set of specific parameters <NUM> such that the nasal cavity shape of a specific person <NUM> is modelled by a combination, in particular by a sum, of the average nasal cavity shape <NUM> and a linear combination of the set of specific parameters <NUM> with the set of nasal cavity shape eigenmodes <NUM>. In an inventive way, the set of specific parameters <NUM> is derived from measurement data <NUM> of the nasal cavity <NUM> of a specific person <NUM>, preferably by a neural network <NUM> trained thereto. The average nasal cavity shape <NUM> and the set of nasal cavity shape eigenmodes <NUM> are preferably based 3D surface representations <NUM> of nasal cavities, as will be shown in <FIG>.

<FIG> shows a perspective view and a top view respectively on a 3D representation <NUM> of a nasal cavity. Such a 3D representation can for example be based on tomographic images of a nasal cavity. In order to build a generic nasal cavity shape model <NUM>, a relatively large number, for example between <NUM> to <NUM> or preferably many more, of tomographic images, such as for example CT scans, may be used to generate 3D surface representations of said plurality of nasal cavities. Each 3D representation can preferably include a same number of points, for example <NUM><NUM> points or more or less, which are distributed over the nasal cavity's boundaries or surface. Since nasal cavities are not symmetric, the 3D surface representations may be mirrored to increase a variety in the sample of nasal cavities. Starting from said 3D surface representations, corresponding points between representations of different nasal cavities can be identified which are located on the same anatomical positions on all 3D surface representations.

<FIG> show a preferred embodiment of the step of finding corresponding points between 3D surface representations of the method of training a neural network, in particular of the method of obtaining a generic nasal cavity shape model, according to an aspect of the invention. <FIG> show a sagittal view on a nasal cavity model of two different subjects, whereas <FIG> show an axial or top view on the respective nasal cavity models of <FIG>. The models have been derived from 3D surface representations as shown in <FIG>. A surface parametrization has then been applied to all 3D surface representations. Each nasal cavity has been mapped onto a cylindrical surface such as shown in <FIG>, as if a nasal cavity were a long cylinder folded as a U-shape from a first nasal channel over the nasopharynx to the other nasal channel, as can be seen in the top views of <FIG>. Locations of corresponding points, for example of points A, D, E, and many others, can then be expressed in a cylindrical coordinate system (u, v). To make sure that all meshes include a same number of vertices located at the same anatomical position, a minimum description length correspondence optimization has been applied, such as for example described in <CIT>, which can result in the statistical shape models as shown in <FIG>. In a next step, a principal component analysis has been applied on the vertices of the 3D surface representations.

<FIG> shows a preferred embodiment of a set of nasal cavity shape eigenmodes resulting from the principal component analysis (PCA) on the vertices of the 3D surface representations <NUM> of the plurality of nasal cavities. The PCA has generated an average nasal cavity shape <NUM> based on average values of said corresponding points, such as for example A, D, E. The PCA has also extracted a set, preferably an orthogonal set, of nasal cavity shape eigenmodes <NUM> from said 3D surface representations. Said set of nasal cavity shape eigenmodes <NUM> can represent the shape variations or deformations with respect to the average nasal cavity shape <NUM>. In <FIG>, an example of the first three shape eigenmodes is given. The colour map represents a magnitude of variation for the corresponding principal component. The specific shape parameters can be expressed as a multiple of a standard deviation of a distribution of the eigenvalues linked to a given eigenvector. Since higher principal components represent smaller shape variations, most variation can be represented with a limited number of parameters, for example based on the first <NUM> to <NUM> principal components, more preferably on the first <NUM> - <NUM> principal components.

This generic nasal cavity shape model <NUM> now allows to model any specific nasal cavity shape as a combination, in particular as a sum, of an average nasal cavity shape <NUM> and a linear combination of the set of specific parameters <NUM> with a set of nasal cavity shape eigenmodes <NUM>, as shown in <FIG>. According to an aspect of the invention, said set of specific parameters <NUM> is based on measurement data on quasi static and/or dynamic nasal pressure changes, in particular on acoustic rhinometry measurement data <NUM>, which are fed into the neural network <NUM>. In order to train the neural network <NUM> to output said set of specific parameters <NUM>, an aspect of the invention provides a method of training the neural network <NUM>, of which a preferred embodiment is shown in <FIG>. In a first step <NUM>, sets of specific parameters <NUM>' are randomly generated simulating measurement data of nasal cavities. Then in step <NUM>, specific nasal cavity shape models are generated by introducing said simulated sets of specific parameters <NUM>' into the generic model <NUM> as shown in <FIG>. The generated specific nasal cavity shape models <NUM>' thus include a combination of an average nasal cavity shape <NUM> and a linear combination of said simulated sets of specific parameters <NUM>' with a set of nasal cavity shape eigenmodes <NUM>. In a next step <NUM>, a cross-sectional surface area of at least one of the nasal channels of the specific nasal cavity shape models <NUM>' provided by said simulated sets of specific parameters <NUM>' is determined, which can be done on the computer-implemented models <NUM>'. Then in step <NUM> the determined cross-sectional surface area of at least one of the nasal channels is fed into the neural network <NUM>, as for example shown in <FIG>. Finally, the neural network <NUM> is trained, i.e. network parameters <NUM> are tweaked, to output the sets of specific parameters <NUM>' which had been randomly generated.

<FIG> shows a suitable computing system <NUM> comprising circuitry enabling the performance of steps of embodiments of the method for modelling a specific nasal cavity shape according to an aspect of the invention, or the method for training a neural network according to another aspect of the invention. Computing system <NUM> may in general be formed as a suitable general-purpose computer and comprise a bus <NUM>, a processor <NUM>, a local memory <NUM>, one or more optional input interfaces <NUM>, one or more optional output interfaces <NUM>, a communication interface <NUM>, a storage element interface <NUM>, and one or more storage elements <NUM>. Bus <NUM> may comprise one or more conductors that permit communication among the components of the computing system <NUM>. Processor <NUM> may include any type of conventional processor or microprocessor that interprets and executes programming instructions. Local memory <NUM> may include a random-access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor <NUM> and/or a read only memory (ROM) or another type of static storage device that stores static information and instructions for use by processor <NUM>. Input interface <NUM> may comprise one or more conventional mechanisms that permit an operator or user to input information to the computing device <NUM>, such as a keyboard <NUM>, a mouse <NUM>, a pen, voice recognition and/or biometric mechanisms, a camera, etc. Output interface <NUM> may comprise one or more conventional mechanisms that output information to the operator or user, such as a display <NUM>, etc. Communication interface <NUM> may comprise any transceiver-like mechanism such as for example one or more Ethernet interfaces that enables computing system <NUM> to communicate with other devices and/or systems, for example with other computing devices <NUM>, <NUM>, <NUM>. The communication interface <NUM> of computing system <NUM> may be connected to such another computing system by means of a local area network (LAN) or a wide area network (WAN) such as for example the internet. Storage element interface <NUM> may comprise a storage interface such as for example a Serial Advanced Technology Attachment (SATA) interface or a Small Computer System Interface (SCSI) for connecting bus <NUM> to one or more storage elements <NUM>, such as one or more local disks, for example SATA disk drives, and control the reading and writing of data to and/or from these storage elements <NUM>. Although the storage element(s) <NUM> above is/are described as a local disk, in general any other suitable computer-readable media such as a removable magnetic disk, optical storage media such as a CD or DVD, - ROM disk, solid state drives, flash memory cards,. could be used.

Claim 1:
A computer-implemented method of training a neural network (<NUM>) to output a set of specific parameters (<NUM>) derived from measurement data (<NUM>) of a nasal cavity such that a specific nasal cavity shape (<NUM>) is modelled by a combination of an average nasal cavity shape (<NUM>) and a linear combination of the set of specific parameters with a set of nasal cavity shape eigenmodes (<NUM>), the training including the steps of
- randomly generating (<NUM>) sets of specific parameters simulating measurement data of nasal cavities;
- generating (<NUM>) specific nasal cavity shape models, said models including a combination of an average nasal cavity shape and a linear combination of said simulated sets of specific parameters with a set of nasal cavity shape eigenmodes;
- determining (<NUM>) a cross-sectional surface area of at least one of the nasal channels of the specific nasal cavity shape models provided by said simulated sets of specific parameters;
- feeding (<NUM>) the determined cross-sectional surface area of at least one of the nasal channels into the neural network;
- training (<NUM>) the neural network to output the sets of specific parameters.