METHOD AND APPARATUS FOR PHYSIOLOGICAL FUNCTIONAL PARAMETER DETERMINATION

Abstract

The present invention relates to a determination of a physiological functional parameter of a living being. Ultrasound image data and Doppler image data of a vessel structure are provided (101) and registered (102). The vessel structure is segmented (103) to generate (104) a representation of the vessel structure. The flow velocity inside a vessel of the vessel structure is determined (105) based on the Doppler image data. A physiological functional parameter determination model defining a value of a functional physiological parameter in dependence of a representation of a vessel structure and a flow velocity inside a vessel of the vessel structure is used (106) to determine (107) the physiological functional parameter inside the vessel of the vessel structure. The representation of the vessel structure and/or the flow velocity values can be constantly updated upon receipt of further input images to provide an estimation of the functional physiological parameter in real-time.

Claims

1. An apparatus for determining a physiological functional parameter of a living being, the apparatus comprising: an image providing unit for providing ultrasound image data and Doppler image data of a vessel structure of the living being; a registration unit for registering the ultrasound image data and the Doppler image data; a segmentation unit for segmenting the vessel structure in the ultrasound image data, thereby generating a vessel structure segmentation; a representation generation unit for generating a representation of the vessel structure based on the vessel structure segmentation; a flow velocity determination unit for determining flow velocity values inside a vessel of the vessel structure based on the Doppler image data; and a physiological functional parameter determination unit for determining the physiological functional parameter of the living being, wherein the physiological functional parameter determination unit is adapted to a) provide a functional parameter determination model defining a functional physiological parameter in dependence of a representation of a vessel structure and flow velocity values inside a vessel of the vessel structure, and b) determine the physiological functional parameter by using the functional parameter determination model, the generated representation of the vessel structure and the determined flow velocity values.

2. The apparatus according to claim 1, wherein the Doppler image data are first Doppler image data of a first portion of the vessel structure and the determined flow velocity values are first flow velocity values inside a vessel of the first portion of the vessel structure; the image providing unit is configured to provide second Doppler image data of a second portion of the vessel structure; the registration unit is configured to register the second Doppler image data with the ultrasound image data; the flow velocity value determination unit is configured to determine second flow velocity values inside a vessel of the second portion of the vessel structure based on the second Doppler image data; and the physiological functional parameter determination unit is configured to determine the physiological functional parameter using the provided functional parameter determination model, the generated representation of the vessel structure and the determined first and second flow velocity values.

3. The apparatus according to claim 1, wherein the ultrasound image data are first ultrasound image data covering a first part of the vessel structure, wherein the generated vessel structure segmentation is a first vessel structure segmentation and wherein the image providing unit is further configured to provide second ultrasound image data of the vessel structure covering a second part of the vessel structure, wherein the second part differs at least partially from the first part; the registration unit is configured to register the second ultrasound image data with the first ultrasound image data; the segmentation unit is configured to segment the vessel structure in the second ultrasound image data, thereby generating a second vessel structure segmentation; the representation generation unit is configured to generate the representation of the vessel structure based on the first vessel structure segmentation and based on the second vessel structure segmentation.

4. The apparatus according to claim 1, wherein the registration unit comprises a position detection unit providing position data of an ultrasound probe used to generate the ultrasound image data and the Doppler image data, wherein the registration unit is adapted to use the position data for registering the ultrasound image data and the Doppler image data.

5. The apparatus according to claim 1, wherein the segmentation unit is configured to identify moving and static areas in the Doppler image data and to segment the vessel structure based at least in part on the identified moving and static areas.

6. The apparatus according to claim 5, wherein the segmentation unit is configured to determine local peak flow values for a vessel of the vessel structure in the moving areas of the Doppler image data, to determine cross-sectional areas of the vessel based on the local peak flow values and to segment the vessel structure in the ultrasound image data by using the cross-sectional areas.

7. The apparatus according to claim 5, wherein the segmentation unit is further configured to segment the vessel structure in the ultrasound image data by using a lumen edge detection algorithm.

8. The apparatus according to claim 1, wherein the physiological functional parameter is a vascular pressure gradient or a peripheral fractional flow reserve.

9. The apparatus according to claim 1, wherein the functional parameter determination model is a reduced order functional model.

10. A method for determining a physiological functional parameter of a living being, the method comprising: providing ultrasound image data and Doppler image data of a vessel structure of the living being; registering the ultrasound image data and the Doppler image data; segmenting the vessel structure in the ultrasound image data; generating a representation of the vessel structure based on the segmented vessel structure, thereby generating a vessel structure segmentation; determining flow velocity values inside a vessel of the vessel structure based on the Doppler image data; providing a functional parameter determination model defining a functional physiological parameter in dependence of a representation of a vessel structure and flow velocity values inside a vessel of the vessel structure; determining the physiological functional parameter of the living being by using the functional parameter determination model, the generated representation of the vessel structure and the determined flow velocity values.

11. The method according to claim 10, wherein the Doppler image data are first Doppler image data of a first portion of the vessel structure and the determined flow velocity values are first flow velocity values inside a vessel of the first portion of the vessel structure, the method further comprising: providing second Doppler image data of a second portion of the vessel structure; registering the second Doppler image data with the ultrasound image data; determining second flow velocity values inside a vessel of the second portion of the vessel structure based on the second Doppler image data; and determining the physiological functional parameter using the provided model, the generated representation of the vessel structure and the first and second flow velocity values.

12. The method according to claim 10, wherein the ultrasound image data are first ultrasound image data covering a first part of the vessel structure, wherein the generated vessel structure segmentation is a first vessel structure segmentation and wherein the method further comprises: providing second ultrasound image data of the vessel structure covering a second part of the vessel structure, wherein the second part differs at least partially from the first part; segmenting the vessel structure in the second ultrasound image data, thereby generating a second vessel structure segmentation; generating the representation of the vessel structure based on the first vessel structure segmentation and the second vessel structure segmentation.

13. A computer program for determining a physiological functional parameter of a living being executable in a processing unit of an apparatus, the computer program comprising program code means for causing the processing unit to carry out a method as defined in claim 10 when the computer program is executed in the processing unit.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In the following drawings:

[0058] FIG. 1 schematically and exemplarily shows an embodiment of an apparatus for determining a physiological functional parameter of a living being;

[0059] FIG. 2 schematically and exemplarily shows an embodiment of a method for determining a physiological functional parameter of a living being;

[0060] FIG. 3 schematically and exemplarily illustrates a vessel structure derivable with an embodiment of an apparatus or method for determining a physiological functional parameter of a living being;

[0061] FIG. 4 schematically and exemplarily shows a lumped parameter model usable to determine a physiological functional parameter a living being; and

[0062] FIG. 5 schematically and exemplarily illustrates a measured flow velocity along a vessel and a derived cross-sectional area along the vessel.

DETAILED DESCRIPTION OF EMBODIMENTS

[0063] FIG. 1 schematically and exemplarily shows an embodiment of an apparatus 10 for determining a physiological functional parameter of a living being. In this embodiment, the apparatus 10 comprises an ultrasound probe 1 for providing ultrasound image data when being traversed over the skin of a person 2 lying on support means 3. The ultrasound probe 1 can provide both common ultrasound image data as well as Doppler image data wherein the operation mode may be changed manually, e.g. upon pressing a particular button, or automatically, e.g. on a predetermined periodical basis or whenever the probe is moved by a predetermined distance. The ultrasound probe may comprise a tracking device (not shown) specifically designed for the ultrasound probe or a transducer-internal tracking device (gyrometer) (not shown). In order to determine the ultrasound probe's position other hardware may be used, e.g. a set of external tracking cameras followed by image processing, or specific distance sensors for instance integrated in the support means 3. The apparatus 10 further comprises input means 5 which allow the input of specific commands by a user, like start and stop, relevant patient data and/or position data which may optionally be attached to the image data captured with the ultrasound probe 1 for further processing. The ultrasound probe 1 is communicatively coupled to the apparatus 10, either wired or wirelessly. Thus, the ultrasound probe 1 may be within the same room as the apparatus 10 but the apparatus 10 may also be at a completely different location and the ultrasound probe 1 is merely connected to the apparatus 10 via the Internet.

[0064] The apparatus 10 has an image providing unit 11 to provide data, preferentially live from the ultrasound probe 1. Alternatively or in addition, the image providing unit 11 may provide images from memory, either stored locally at the apparatus or on a remote server. For instance, in case ultrasound images have already been taken from the person 2, already existing data may be loaded by the image providing unit 11. The image providing unit 11 may also provide partially existing data from memory and add for instance Doppler images for certain positions within the existing pure ultrasound images.

[0065] The apparatus 10 further comprises a registration unit 12 for registering the images provided by the image providing unit. With regard to the spatial alignment different sets of data are transformed into one coordinate system. If the ultrasound probe 1 is for instance moved along the skin of the person 2, and a first and second ultrasound image cover a common area of the vessel structure, then similarities in the image data can be used to overlay the images and stitch them together.

[0066] However, ultrasound images, in particular Doppler images, may differ not only when the ultrasound probe 1 is moved along the skin of the person 2 and thus the spatial information is changed. The blood flow inside a vessel also fluctuates within a cardiac cycle and an artery or vein might slightly vary in position and/or cross section over a cardiac cycle. If the image data is thus taken once at the peak of a cardiac cycle and once on the lowest point, automatic detection of similarities might require larger error margins to match respective image contours or specific features. Thus, temporal information preferably associated with the cardiac cycle might be used by the registration unit to stitch images together which cover respective first and second parts of a vessel structure having a certain overlay. Furthermore, absolute or relative position data of the ultrasound probe 1 can also be provided to the registration unit 12 to provide a consistent mosaic of ultrasound images and additional Doppler images. Usually ultrasound images are provided in grey scale while Doppler images are overlaid in a color scale, e.g. from blue to red. Usually blue color indicates a flow away from the ultrasound probe 1, and red color indicates a flow towards the ultrasound probe 1. Depending on the chosen resolution, the color might change during a cardiac cycle. When a temporal resolution is required which is sensitive to the cardiac cycle, respective flow profiles across a cardiac cycle are generated. Doppler image data may then comprise a set of Doppler images for respective times within the cardiac cycle. They may be generated from data over one or more cardiac cycles. The flow velocities determined for vessels in the Doppler images may then be provided as flow velocity profiles across a cardiac cycle. If such a temporal resolution is not necessary, static average flow velocity values are determined from the Doppler image data.

[0067] Optionally, 3-D ultrasound transducers may be used to gather additional information for the rigid image registration. For instance, in conjunction with an optoelectric sensor (as used in a computer mouse) the trajectory of the transducer can be traced. In order to simplify the image registration, restrictions on the possible trajectories may be imposed by means of a user manual. Registration can be done in the simplest instance by matching image locations using squared distance or correlation measures. Given the information from external or internal tracking devices, the latter could be used to fine-tune the registration.

[0068] The apparatus 10 further comprises a segmentation unit 13 for segmenting a vessel structure identified in ultrasound image data. If there is more than one ultrasound image provided by the image providing unit 11, the segmentation unit may also segment a vessel structure from first and second ultrasound images stitched by the registration unit 12. The segmentation unit 13 may use static image features such as the edges of the lumen to segment the vessel structure and/or use dynamic features derived from the Doppler images which for instance indicates flowing blood and thus help to determine the inside of a vessel. The surrounding tissue of a vessel is rather static. The segmentation unit 13 may also allow or request user input to improve or initiate image segmentation. Furthermore, the segmentation unit 13 may smooth the image data provided to compensate for local imaging artifacts. The segmentation unit 13 may also be provided with external input provided by a user via the input means 5 in order to guide the segmentation (a priori) or to correct the segmentation (a posteriori). In order to support the user's assessment, overlays of ultrasound images and the deduced segmentation could be displayed on a display unit 4.

[0069] The vessel structure segmented from the aligned ultrasound images is used by a representation generation unit 14 to generate a common representation 200 of the corresponding vessel structure of interest, preferably as a 3D-model. This representation 200 is continuously updated whenever new input data is provided to the segmentation unit, e.g. whenever the ultrasound probe 1 is moved along the skin of the person 2.

[0070] Along with the live-growing representation, a physiological functional parameter determination model, e.g. a lumped parameter model 210, is generated and continuously updated which allows to estimate for instance pressure gradients for vessels inside the vessel structure in real-time given a set of suitable boundary conditions. The flow velocity inside a vessel can be determined from the Doppler images and can be used as boundary condition for the lumped parameter model 210. Therefore, the apparatus 10 further comprises a flow velocity determination unit 15 for determining average flow velocities or flow velocity profiles which are provided as boundary conditions to the lumped parameter model 210. Since the Doppler data may only be provided for a portion of the vessel structure, the determination of flow velocities is restricted to these portions. They will preferably capture the most relevant arteries and/or veins in the vessel structure of interest. The apparatus 10 further comprises a physiological functional parameter determination unit 16 for determining pressure gradients or peripheral FFR values using the lumped parameter model 210. Since lumped model predictions can be computed extremely fast, the apparatus 10 may provide real-time or at least near-real-time feedback in clinical practice and thus support sonographers and/or physicians in the diagnosis and treatment of for instance arteriosclerosis. The feedback may be presented in form of the representation 210 which is preferentially color coded according to the determined pressure gradients at the display unit 4 of the apparatus 10 in FIG. 1. Alternatively or in addition, the apparatus 10 may also provide the output to a remote display connected either wired or wirelessly with the apparatus. This way, the person conducting the examination of the person 2 and the person assessing the determined data do not have to be the same. The latter person does not even have to be within the same room. The apparatus 10 may provide the data output for storage, either locally, on hard drive or removable storage, or remote, e.g. at a server or cloud.

[0071] In the following an embodiment of a method for determining a physiological functional parameter of a living being will be exemplarily described with reference to a flow chart shown in FIG. 2.

[0072] In step 101 first ultrasound image data covering a first part of the vessel structure of interest are provided, preferentially obtained from an ultrasound probe 1. Furthermore, Doppler image data are provided covering at least a portion of the vessel structure covered by the ultrasound image data. The ultrasound image data are then registered in step 102 with the Doppler image data. In step 103 a vessel structure is segmented from the image data. Step 103 may be performed parallel, subsequent or iteratively with step 102. In case only the ultrasound image data are used for the segmentation, step 103 may be performed independent of step 102. The segmentation comprises basic identification of image structures, such as contours and edges as well as high-level image segmentation specific to structures and patterns typical for the vessel structures. The segmentation step 103 might use further input extracted from the Doppler image data. In that case the previous registration in step 102 is required to merge the information from both image data. In step 104, a representation of vessel structure of interest is generated from the segmented image data. This representation is preferentially a 3D-representation of the main vessels of a vessel structure of interest.

[0073] In step 105 flow velocity values are determined from the Doppler image data for respective positions within one or more vessels of a vessel structure. Depending on the temporal resolution average flow velocity values, or flow velocity profiles across a cardiac cycle can be determined. This step may be performed simultaneously to the steps 102 and 103. The Doppler image data may optionally be used for segmentation as indicated by the dashed line between step 105 and step 103. The Doppler image data allow identification of areas with moving blood, in general inside an artery or vein, and the surrounding tissue, which is static and thus does not show the Doppler effect.

[0074] In step 107 a physiological functional parameter, e.g. the vascular pressure gradient or the (peripheral) FFR, may be determined for vessels in the representation of the vessel structure using a reduced-order functional model based on determined flow velocities, and the representation of the vessel structure provided in step 106. The determination may use a lumped parameter model wherein the average values of flow velocity, or the flow velocity profiles as well as the representation of the physical vessel structure are used as boundary conditions.

[0075] The method may comprise a further step 108 of outputting the representation, preferentially as 3D-model, together with the determined physiological functional parameter values which might be presented in a color-coded manner, wherein specific colors are assigned to predetermined thresholds values of the physiological functional parameter to ease a fast assessment and assist a physician in the diagnosis.

[0076] Whenever the ultrasound probe is moved, the method starts again at step 101. Additional image data are provided and registered using internal image information like characteristic patterns as well as external data, cardiac cycle data, position data of the ultrasound probe, etc. The method may also only be fed with further Doppler image data in a region already captured by pure ultrasound image data. In that case, the method continues with step 105 and from there either to step 103, where the Doppler image data are used for segmentation purposes and/or to step 107 to update and extend the lumped parameter model accordingly and determine the physiological functional parameter for the (extended) vessel structure based on the further flow velocity measurements derived from the further Doppler image data. Besides outputting the 3D-representation and the determined physiological functional parameters, the output may also be stored in step 109 in a local or remote storage device, such as a hard drive, optical disc, removable storage medium (USB stick), or on a remote server. Furthermore, the step 108 may comprise transmission of the data via a network to a remote display, such that the person viewing the live-growing anatomical and functional representation does not have to be in the same room as the person 2 examined with the ultrasound probe 1.

[0077] FIG. 3 schematically and exemplarily illustrates a vessel structure derivable from the ultrasound image data. The vessel structure may be determined using static image features such as the edges of the lumen or dynamic features derivable from Doppler image data which allow to distinguish moving areas, e.g. flowing blood instance areas, e.g. tissue. Additionally or in cases where the full cross section of a vessel is not visible in the ultrasound image data, the local peak flow velocity values 301 derivable from the Doppler image data can be used to derive the local cross sectional area 302 (CSA) as indicated in FIGS. 5A and 5B and thus might help to segment the vessel structure.

[0078] FIG. 5A shows the flow velocity 301 over the vessel length 300. The resulting curve 310 has the opposite distribution as curve 320, showing the cross sectional area 302 over the vessel length 300. Here a constant blood volume is assumed. Estimating or measuring outflow can further refine this calculation.

[0079] The extracted anatomical vessel structure 200 comprising branches 201, 202 and 203 may then be used together with the flow velocity measurements as boundary condition for a reduced-order functional model i.e. a lumped parameter model 210.

[0080] FIG. 4 schematically and exemplarily shows a lumped parameter model 210 with three branches 211, 212 and 213 corresponding to branches 201 to 203 of the vessel structure representation 200. The lumped parameter model comprises n=8 elements and m=3 nodes including ground. The black boxes 220, 221, 222 indicate inflow and outflow boundary conditions. The white tubes 230 representing tree segment transfer functions are composed of a series of linear and nonlinear resistance elements reflecting both the local vessel geometry and hydraulic effects. Starting from the tree representation shown in FIG. 3, a circuit with two macroscopic component types is set up: nonlinear vessel segment resistors 230 and boundary conditions 220, 221, 222. The boundary condition may be a pressure or flow source driving the network; but any (lumped) boundary condition driving a conventional finite element model can be used here. Methods how to translate the local geometry of the vessel (radius, perimeter, cross-sectional area) into parameters of the nonlinear resistor are well known and disclosed, for instance, in the article Learning patient-specific lumped models for interactive coronary blood flow simulations by Hannes Nickisch et al. cited herein above.

[0081] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

[0082] Other 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.

[0083] 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.

[0084] A computer program 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.

[0085] Any reference signs in the claims should not be construed as limiting the scope.