US-12626384-B2 - Determining interventional device shape

US12626384B2US 12626384 B2US12626384 B2US 12626384B2US-12626384-B2

Abstract

A computer-implemented method of providing a neural network for predicting a three-dimensional shape of an interventional device disposed within a vascular region, includes: training (S 140 ) a neural network ( 140 ) to predict, from received X-ray image data ( 120 ) and received volumetric image data ( 110 ), a three-dimensional shape of the interventional device constrained by the vascular region ( 150 ). The training includes constraining the adjusting of parameters of the neural network such that the three-dimensional shape of the interventional device predicted by the neural network ( 150 ) fits within the three-dimensional shape of the vascular region represented by the received volumetric image data ( 110 ).

Inventors

Ayushi Sinha
Grzegorz Andrzej TOPOREK
Molly Lara Flexman
Jochen Kruecker
Ashish Sattyavrat PANSE

Assignees

KONINKLIJKE PHILIPS N.V.

Dates

Publication Date: 20260512
Application Date: 20211116

Claims (14)

1 . A computer-implemented method of providing a neural network for predicting a three-dimensional shape of an interventional device disposed within a vascular region, the method comprising: receiving volumetric image data representing a three-dimensional shape of the vascular region; receiving X-ray image data representing one or more two-dimensional projections of the interventional device within the vascular region; receiving ground truth interventional device shape data representing a three-dimensional shape of the interventional device within the vascular region corresponding to the one or more two-dimensional projections of the interventional device; and training a neural network to predict, from the received X-ray image data and the received volumetric image data, a three-dimensional shape of the interventional device constrained by the vascular region, by: inputting the received X-ray image data and the received volumetric image data into the neural network, and adjusting parameters of the neural network based on a first loss function representing a difference between a three-dimensional shape of the interventional device predicted by the neural network, and the received ground truth interventional device shape data, and constraining the adjusting such that the three-dimensional shape of the interventional device predicted by the neural network fits within the three-dimensional shape of the vascular region represented by the received volumetric image data.
2 . The computer-implemented method according to claim 1 , wherein the adjusting parameters of the neural network is based further on a second loss function representing a difference between a two-dimensional projection of the three-dimensional shape of the interventional device predicted by the neural network, and the received X-ray image data; the two-dimensional projection of the three-dimensional shape of the interventional device, and the received X-ray image data being projected onto a common surface.
3 . The computer-implemented method according to claim 1 , further comprising computing an estimated uncertainty of the three-dimensional shape of the interventional device predicted by the neural network.
4 . The computer-implemented method according to claim 1 , wherein the volumetric image data comprises one or more of: computed tomography image data; contrast-enhanced computed tomography image data; 3D ultrasound image data; cone beam computed tomography image data; magnetic resonance image data; anatomical atlas model data; and reconstructed volumetric image data generated by reconstructing X-ray image data representing one or more two-dimensional projections of the vascular region.
5 . The computer-implemented method according to claim 1 , comprising: segmenting the received X-ray image data to provide the one or more two-dimensional projections of the interventional device, and wherein the inputting the received X-ray image data into the neural network comprises inputting the segmented received X-ray image data into the neural network.
6 . The computer-implemented method according to claim 1 , wherein the ground truth interventional device shape data comprises one or more of: computed tomography image data; contrast-enhanced computed tomography image data; cone beam computed tomography image data; fiber optical shape sensing position data generated by a plurality of fiber optic shape sensors mechanically coupled to the interventional device; electromagnetic tracking position data generated by one or more electromagnetic tracking sensors or emitters mechanically coupled to the interventional device; dielectric mapping position data generated by one or more dielectric sensors mechanically coupled to the interventional device; and ultrasound tracking position data generated by one or more ultrasound tracking sensors or emitters mechanically coupled to the interventional device.
7 . The computer-implemented method according to claim 1 , wherein the training the neural network comprises constraining the adjusting such that the three-dimensional shape of the interventional device predicted by the neural network satisfies one or more mechanical constraints of the interventional device.
8 . The computer-implemented method according to claim 1 , wherein the neural network comprises one or more of: a convolutional neural network, an encoder-decoder network, a generative adversarial network, a capsule network, a regression network, a reinforcement learning agent, a recurrent neural network, a long short-term memory network, a temporal convolutional network, and a transformer.
9 . The computer-implemented method according to claim 1 , wherein the received volumetric image data further represents a three-dimensional shape of an anatomical feature, wherein the received X-ray image data further represents a two-dimensional projection of the anatomical feature; and wherein the training the neural network further comprises training the neural network to predict, from the received X-ray image data, a position of the anatomical feature relative to the three-dimensional shape of the interventional device, and further comprising constraining the adjusting based on a difference between the predicted position of the anatomical feature relative to the three-dimensional shape of the interventional device, and the position of the anatomical feature relative to the three-dimensional shape of the vascular region in the received volumetric image data.
10 . A computer-implemented method of predicting a three-dimensional shape of an interventional device disposed within a vascular region, the method comprising: receiving volumetric image data representing a three-dimensional shape of the vascular region; receiving X-ray image data representing one or more two-dimensional projections of the interventional device within the vascular region; and predicting, from the received X-ray image data and the received volumetric image data, a three-dimensional shape of the interventional device constrained by the vascular region; wherein a neural network is trained to predict, from one and only one two-dimensional projection of the interventional device within the vascular region, and the received volumetric image data, the three-dimensional shape of the interventional device constrained by the vascular region.
11 . The computer-implemented method according to claim 10 , further comprising projecting the predicted three-dimensional shape of the interventional device, onto at least one surface, to provide a at least one predicted two-dimensional projection of the interventional device.
12 . The computer-implemented method according to claim 11 , wherein the projecting comprises projecting the predicted three-dimensional shape of the interventional device onto a plurality of intersecting surfaces.
13 . A system for predicting a three-dimensional shape of an interventional device disposed within a vascular region; the system comprising one or more processors configured to perform the method according to claim 10 .
14 . A non-transitory computer-readable storage medium having stored a computer program comprising instructions which, when executed by a processor, cause the processor to: receive volumetric image data representing a three-dimensional shape of the vascular region; receive X-ray image data representing one or more two-dimensional projections of the interventional device within the vascular region; and predict, from the received X-ray image data and the received volumetric image data, a three-dimensional shape of the interventional device constrained by the vascular region; wherein a neural network is trained to predict, from one and only one two-dimensional projection of the interventional device within the vascular region, and the received volumetric image data, the three-dimensional shape of the interventional device constrained by the vascular region.

Description

CROSS-REFERENCE TO PRIOR APPLICATIONS This application is the U.S. National Phase application under 35 U.S.C. § 371 of International Application No. PCT/EP2021/081758, filed on Nov. 16, 2021, which claims the benefit of U.S. Provisional Patent Application Ser. No. 63/116,184, filed on Nov. 20, 2020. These applications are hereby incorporated by reference herein. TECHNICAL FIELD The present disclosure relates to determining a three-dimensional shape of an interventional device disposed within a vascular region. A computer-implemented method, a processing arrangement, a system, and a computer program product, are disclosed. BACKGROUND Many interventional medical procedures are carried out under X-ray imaging. The two-dimensional images provided by X-ray imaging assist physicians to navigate interventional devices such as guidewires and catheters within the anatomy. Dense regions of the anatomy such as bone, and interventional devices, are highly visible under X-ray imaging. However, soft tissue anatomical regions such as the vasculature are often poorly visible under X-ray imaging, hampering navigation. Mentally mapping the shape of interventional devices seen in the two-dimensional X-ray images, to the three-dimensional anatomy, can also be challenging. By way of an example, in prostatic artery embolization, PAE, interventional devices such as a guidewire and a catheter are navigated through the vasculature under X-ray imaging to a treatment site where microparticles are injected in order to block small blood vessels and thereby inhibit the supply of blood to the prostate. The vasculature is often poorly visible in the X-ray images, and mentally mapping the shape of the interventional devices seen in the X-ray images, to the three-dimensional anatomy, can be challenging. In order to improve the visibility of the vasculature, two-dimensional intra-procedural digital subtraction angiography, DSA, images are often captured with the use of contrast agents. Some patients may however have adverse reactions to contrast agents, limiting their use. In order to address the challenge of mentally mapping the shape of the interventional device in the X-ray images to the three-dimensional anatomy, a pre-operative three-dimensional image of the vasculature, such as a computed tomography angiography, CTA, image, may be obtained prior to such interventions. The CTA image serves as a roadmap to guide the interventional procedure. In spite of these measures, however, a physician often needs to make multiple two-dimensional X-ray images of the vasculature from different projection angles in order to confirm the position of the interventional device. Generating multiple two-dimensional X-ray images of the vasculature from different projection angles, also suffers from drawbacks. Aside from the increased radiation dose, a desired projection angle may be unobtainable because certain configurations of the C-arm supporting the X-ray source and detector are inhibited by the position of the patient or the patient table. Consequently, there remains room to improve the way in which the shape of interventional devices disposed within vascular regions is determined from X-ray images. SUMMARY According to a first aspect of the present disclosure, a computer-implemented method of providing a neural network for predicting a three-dimensional shape of an interventional device disposed within a vascular region, is provided. The method includes: receiving volumetric image data representing a three-dimensional shape of the vascular region;receiving X-ray image data representing one or more two-dimensional projections of the interventional device within the vascular region;receiving ground truth interventional device shape data representing a three-dimensional shape of the interventional device within the vascular region corresponding to the one or more two-dimensional projections of the interventional device; andtraining a neural network to predict, from the received X-ray image data and the received volumetric image data, a three-dimensional shape of the interventional device constrained by the vascular region, by: inputting the received X-ray image data and the received volumetric image data into the neural network, and adjusting parameters of the neural network based on a first loss function representing a difference between a three-dimensional shape of the interventional device predicted by the neural network, and the received ground truth interventional device shape data, and constraining the adjusting such that the three-dimensional shape of the interventional device predicted by the neural network fits within the three-dimensional shape of the vascular region represented by the received volumetric image data. According to a second aspect of the present disclosure, a computer-implemented method of predicting a three-dimensional shape of an interventional device disposed within a vascular region, is disclosed. The method includes: receiving