SYSTEM AND METHOD FOR ASSISTED PATIENT POSITIONING

• US 20190318497A1
• Filed: 04/11/2018
• Published: 10/17/2019
• Est. Priority Date: 04/11/2018
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Healthcare GMBH (Siemens AG)
• Inventors: Zhao, Zhuokai, Chang, Yao-jen, Sa, Ruhan, Ma, Kai, Wang, Jianping, Singh, Vivek Kumar, Chen, Terrence, Wimmer, Andreas, Tamersoy, Birgi
• Granted Patent: US 10,783,655 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/70 Means for positioning the patient in relation to the detecting, measuring or recording means
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/0407 Supports, e.g. tables or beds, for the body or parts of the body
• CPC Class Codes: A61B 6/0487 Motor-assisted positioning
• CPC Class Codes: A61B 6/547 involving tracking of position of the device or parts of the device
• CPC Class Codes: G06T 2207/10012 Stereo images
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10108 Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 7/285 using a sequence of stereo image pairs
• CPC Class Codes: G06T 7/593 : from stereo images
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80

Abstract

A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Claims

1. A method for generating a medical image, comprising:

obtaining, via a camera, at least one surface image of a patient;

determining, from the at least one surface image, a pose of the patient using at least one spatial information module;

positioning, via a moveable bed, the patient at an imaging start position; and

obtaining, via a medical image modality, a medical image of the patient.

2. The method of claim 1, wherein the at least one spatial information module comprises at least one of a single-frame analytics network or a multi-frame analytics network.

3. The method of claim 2, wherein the single-frame analytics network comprises a fully convolutional neural network.

4. The method of claim 1, wherein the at least one spatial information module comprises a Dense Block Hourglass (DBHG) network.

5. The method of claim 1, comprising generating a composite image from two or more surface images of the patient, wherein the composite image is generated by a temporal information module in signal communication with the spatial information module.

6. The method of claim 5, wherein the temporal information module comprises a relative motion recovery module and an image synthesis module.

7. The method of claim 6, wherein the relative motion recovery module comprises a synchronized patient bed position reading to infer a relative motion between the patient and the camera.

8. The method of claim 6, wherein the relative motion recovery module comprises a structure from motion module, a simultaneous localization and mapping module, or a combination thereof.

9. The method of claim 6, wherein the image synthesis module comprises a depth fusion module.

10. The method of claim 9, wherein the depth fusion method aggregates the depth map for each frame estimated from multi-view stereo matching process.

11. The method of claim 9, wherein the image synthesis module synthesizes an extended field of view based on a coarse geometry fitting to the patient.

12. The method of claim 1, comprising preprocessing the at least one surface image of the patient prior to determining a pose of the patient.

13. The method of claim 1, wherein the temporal information module comprises a recurrent neural network.

14. A system of generating a medical image, comprising:

an imaging modality configured to obtain a medical image of a patient;

an imaging device configured to obtain at least one surface image of a patient; and

a processor configured to implement at least one of a spatial information module, a temporal information module, or a combination thereof, wherein the processor is configured to receive the at least one surface image of the patient and verify at least one of a patient pose or a body region, and wherein the processor is configured to execute a medical imaging procedure using the imaging modality when the patient pose or body region is verified.

15. The system of claim 14, wherein the spatial information module comprises a fully convolutional neural network.

16. The system of claim 14, wherein the spatial information module comprises a Dense Block Hourglass (DBHG) network.

17. The system of claim 14, wherein the temporal information module is configured to generate a composite image from two or more surface images of the patient.

18. The system of claim 17, wherein the temporal information module comprises a relative motion recovery module and an image synthesis module.

19. The system of claim 18, wherein the relative motion recovery module comprises a simultaneous localization and mapping module, a synchronized bed position module configured to infer a relative motion between the patient and the camera, or a combination thereof.

20. A non-transitory computer-readable medium encoded with computer executable instructions, the computer executable instructions, when executed by a computer in a system for reviewing neural scans, cause the system for reviewing neural scans to execute the steps of:

receiving at least one surface image of a patient;

determining a pose of the patient from the at least one surface image using at least one spatial information module;

positioning, via a moveable bed, the patient at an imaging start position; and

obtaining, via a medical image modality, a medical image of the patient.