Identification of respiratory phases in a medical procedure

Patent metadata

Field	Value
Publication number	US-2021251611-A1
Application number	US-202016795220-A
Country	US
Kind code	A1
Filing date	Feb 19, 2020
Priority date	Feb 19, 2020
Publication date	Aug 19, 2021
Grant date	—

Abstract

The present disclosure relates to automatically determining respiratory phases (e.g., end-inspiration/expiration respiratory phases) in real time using ultrasound beamspace data. The respiratory phases may be used subsequently in a therapy or treatment (e.g., image-guided radiation-therapy (IGRT)) for precise dose-delivery. In certain implementations, vessel bifurcation may be tracked and respiration phases determined in real time using the tracked vessel bifurcations to facilitate respiration gating of the treatment or therapy.

First claim

What is claimed is:

1 . A method to generate a patient-specific respiration model, comprising the steps of:
acquiring magnetic resonance (MR) image data and ultrasound beamspace data of a patient over time; in the ultrasound beamspace data, tracking one or more vascular vessel bifurcations over time; based on a measure of the tracked vascular vessel bifurcations, determining one or more respiration phases of the patient over time; generating a 3D MR volume from the MR image data; and for a respective respiration phase of the one or more respiration phases, determining a 3D MR respiration phase volume for the patient from the 3D MR volume and specific to the respective respiration phase.

2 . The method of claim 1 , wherein the ultrasound beamspace data comprises two-dimensional (2D) ultrasound beamspace data or three-dimensional (3D) ultrasound beamspace data.

3 . The method of claim 1 , wherein the MR image data and the ultrasound beamspace data are acquired concurrently.

4 . The method of claim 1 , wherein the measure of the tracked vascular vessel bifurcations comprises displacements of one or more centroids corresponding to the one or more vascular vessel bifurcations.

5 . The method of claim 1 , wherein tracking the one or more vascular vessel bifurcations over time comprises:
providing the ultrasound beamspace data to a neural network trained to track vessel bifurcations over time and to output displacements of one or more centroids corresponding to the one or more vascular vessel bifurcations.

6 . The method of claim 1 , wherein determining the one or more respiration phases of the patient over time comprises:
performing a cluster analysis using the measure of the tracked vascular vessel bifurcations to identify one or more clusters, wherein each cluster corresponds to a respiration phase of the patient.

7 . The method of claim 5 , wherein the cluster analysis comprises a graph-based cluster analysis.

8 . The method of claim 5 , further comprising training a supervised machine learning model using displacement of the tracked vascular vessel bifurcations and corresponding cluster labels.

9 . The method of claim 1 , further comprising:
registering the three-dimensional MR respiration phase volume to a CT volume containing a target anatomic region for a treatment.

10 . The method of claim 1 , wherein the respective respiration phase comprises an expiration phase.

11 . A method for respiration gating a patient treatment, comprising:
during a treatment procedure, acquiring ultrasound beamspace data of a patient over time; in the ultrasound beamspace data, tracking one or more vascular vessel bifurcations over time; based on a measure of the tracked vascular vessel bifurcations, predicting a series of activation respiration phases of the patient during the treatment; determining one or both of a location or orientation of a target anatomic region during the activation respiration phases of the patient using a 3D MR respiration phase volume for the patient generated prior to the treatment procedure and specific to the activation respiration phase; and applying the treatment to the target anatomic region during the series of activation respiration phases.

12 . The method of claim 11 , wherein the series of activation respiration phases of the patient during the treatment are predicted using a machine learning model trained using displacement of the tracked vascular vessel bifurcations and corresponding cluster labels.

13 . The method of claim 11 , wherein the ultrasound beamspace data comprises two-dimensional (2D) ultrasound beamspace data or three-dimensional (3D) ultrasound beamspace data.

14 . The method of claim 11 , wherein the 3D MR respiration phase volume for the patient is generated using concurrently acquired MR image data and ultrasound beamspace data of the patient over time prior to the treatment procedure.

15 . The method of claim 11 , wherein the measure of the tracked vascular vessel bifurcations comprises displacements of one or more centroids corresponding to the one or more vascular vessel bifurcations.

16 . The method of claim 11 , wherein tracking the one or more vascular vessel bifurcations over time comprises:
providing the ultrasound beamspace data to a neural network trained to track vessel bifurcations over time and to output displacements of one or more centroids corresponding to the one or more vascular vessel bifurcations.

17 . The method of claim 11 , wherein the 3D MR respiration phase volume for the patient is registered to a CT volume containing the target anatomic region for the treatment.

18 . An image guided treatment system comprising:
a memory encoding processor-executable routines; and a processing component configured to access the memory and execute the processor-executable routines, wherein the routines, when executed by the processing component, cause the processing component to perform actions comprising:
acquiring magnetic resonance (MR) image data and three-dimensional (3D) ultrasound beamspace data of a patient over time;
in the 3D ultrasound beamspace data, tracking one or more vascular vessel bifurcations over time;
based on a measure of the tracked vascular vessel bifurcations, determining one or more respiration phases of the patient over time;
generating a 3D MR volume from the MR image data;
for a respective respiration phase of the one or more respiration phases, determining a 3D MR respiration phase volume for the patient from the 3D MR volume and specific to the respective respiration phase; and
respiration gating application of a treatment to the patient using the 3D MR respiration phase volume and an indication of respiration phase of the patient determined using 3D ultrasound beamspace data acquired during the application of the treatment.

19 . The image guided treatment system of claim 18 , further comprising a radiation emitting component configured to emit radiation during the treatment.

20 . The image guided treatment system of claim 18 , wherein tracking the one or more vascular vessel bifurcations over time comprises:
providing the ultrasound beamspace data to a neural network trained to track vessel bifurcations over time and to output displacements of one or more centroids corresponding to the one or more vascular vessel bifurcations.

21 . The image guided treatment system of claim 18 , wherein determining the one or more respiration phases of the patient over time comprises:
performing a cluster analysis using the measure of the tracked vascular vessel bifurcations to identify one or more clusters, wherein each cluster corresponds to a respiration phase of the patient

Assignees

• Gen Electric
• Wisconsin Alumni Res Found

Inventors

• Mitra Jhimli
• Chatterjee Sudhanya
• Foo Thomas Kwok-Fah
• Yeo Desmond Teck Beng
• Bednarz Bryan Patrick
• Jupitz Sydney

Classifications

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• A61B5/1135

  by monitoring thoracic expansion · CPC title

• A61B5/7267

  involving training the classification device · CPC title

• A61B5/7289

  Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition · CPC title

• A61B5/0816

  Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00) · CPC title