Body structure imaging

• US 10,939,822 B2
• Filed: 01/24/2014
• Issued: 03/09/2021
• Est. Priority Date: 01/24/2013
• Status: Active Grant

• Current Assignee: Tylerton International Holdings Inc.
• Original Assignee: Tylerton International Holdings Inc.
• Inventors: Ben-Haim, Shlomo, Zilberstien, Yoel, Roth, Nathaniel
• Primary Examiner(s): Bui Pho, Pascal M
• Assistant Examiner(s): Bruce, Farouk A
• Application Number:
  US14/762,879
• Publication Number:
  US 20150351834A1
• Time in Patent Office: 2,601 Days
• Field of Search: None
• CPC Class Codes: A61B 18/1492 having a flexible, catheter-like structure, e.g. for heart ablation A61B18/1477 takes precedence
• CPC Class Codes: A61B 2018/00434 Neural system
• CPC Class Codes: A61B 2018/00577 Ablation
• CPC Class Codes: A61B 2018/00642 with feedback, i.e. closed loop control
• CPC Class Codes: A61B 5/0035 adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
• CPC Class Codes: A61B 5/004 adapted for image acquisition of a particular organ or body part A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062
• CPC Class Codes: A61B 5/0044 for the heart
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/4041 Evaluating nerves condition
• CPC Class Codes: A61B 5/4893 Nerves
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7425 Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image geometric transforms for image registration G06T3/14; enhancement using image fusion G06T5/50; algorithms for registration of images G06T7/30
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/506 for diagnosis of nerves
• CPC Class Codes: A61B 6/5235 combining images from the same or different ionising radiation imaging techniques, e.g. PET and CT
• CPC Class Codes: A61B 6/5247 combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound
• CPC Class Codes: A61B 8/085 for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules
• CPC Class Codes: G06T 11/003 : Reconstruction from projections, e.g. tomography
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2200/04 : involving 3D image data
• CPC Class Codes: G06T 2207/10076 : 4D tomography; Time-sequential 3D tomography
• CPC Class Codes: G06T 2207/10081 : Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10104 : Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10136 : 3D ultrasound image
• CPC Class Codes: G06T 2207/30024 : Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30048 : Heart; Cardiac
• CPC Class Codes: G06T 2211/40 : Computed tomography
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/174 : involving the use of two or more images

Abstract

A method of guiding a cardiac treatment using a functional imaging modality, comprising: providing functional imaging modality data from a functional imaging modality which images an intrabody volume of a patient containing a heart, the patient having been injected with an imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS) of the heart, the ANS comprising at least one GP; locating the at least one GP innervating the heart based on the functional imaging modality data; and providing the located at least one GP.

Claims

1. A method of guiding a cardiac treatment using a functional imaging modality, comprising:

providing functional imaging modality data from a nuclear medicine imaging modality which images an intrabody volume of a patient containing a heart, the patient having been injected with a radioactive imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS) of the heart, the ANS comprising at least one ganglionic plexus (GP);

receiving at least one image mask corresponding to location of a fat pad at a predefined anatomical region identified on an anatomical image acquired by an anatomical imaging modality, wherein said at least one GP is not identifiable on said anatomical image;

applying the at least one image mask to the functional imaging modality data to the location corresponding to the fat pad at the predefined anatomical region;

locating the at least one GP innervating the heart within the at least one image mask applied on the functional imaging modality data, by differentiating the at least one GP from the fat pad

based on at least one predefined rule, said rule relying on a calculated activity of a radio labeled tracer within a spot in said respective image mask being indicative of the at least one GP, said activity being larger than activity of an area surrounding the spot within the respective image mask indicative of the fat pad, by more than a predefined threshold; and

guiding an ablation cardiac treatment to the located at least one GP within the fat pad for treating cardiac disease.

2. The method of claim 1, wherein said functional imaging modality is a member of a group consisting of:

single-photon emission computed tomography (SPECT), electrocardiogram-gated SPECT (GSPECT), SPECT-CT, D-SPECT, A-SPECT and functional magnetic resonance imaging (fMRI).

3. The method of claim 1, wherein said locating is based on analyzing said functional imaging modality data at multiple resolutions and identifying as ganglia objects which appear in multiple sizes of filters.

4. The method of claim 1, wherein said locating includes enhancing a resolution of the functional imaging by focusing the functional imaging on regions of interest (ROIs).

5. The method of claim 1, wherein said locating is based on a plurality of predefined rules derived from anatomical information.

6. The method of claim 1, wherein said locating the at least one GP innervating the heart based on the functional imaging modality data is according to an analysis of synapse distribution and activity identified in the functional imaging modality data.

7. The method of claim 1, further comprising:

receiving an anatomical image of heart structures innervated by the ANS;

selecting the at least one image mask by processing the anatomical image, the at least one image mask is selected to correspond in dimensions to regions of the intrabody volume containing nerve tissues.

8. The method of claim 1, wherein the predefined threshold is based on a predefined factor times a calculated standard deviation of activity within the image mask above a calculated average activity within the image mask, and a calculated adjacent activity surrounding an active region is lower than half of the activity of the active region.

9. The method of claim 1, wherein the radio labeled tracer is metaiodobenzylguanidine (mIBG).

10. The method of claim 1, wherein the rule comprises comparing activity level of the radio labeled tracer to surrounding activity within the image mask.

11. The method of claim 1, wherein the rule comprises a size of an active spot of activity of the radio labeled tracer being larger than a threshold.

12. The method of claim 1, wherein the at least one GP innervating the heart is located within the at least one image mask applied on the functional imaging modality data according to an analysis of synapse distribution or activity identified in the functional imaging modality data corresponding to the applied at least one image mask.

13. A system for identifying ANS tissue within an image of a heart of a patient and providing cardiac treatment, the system comprising:

a non-transitory computer readable medium storing program instructions;

at least one hardware processor in communication with said non-transitory computer readable medium, that when said instructions are executed by said at least one hardware processor, the execution causes said at least one hardware processor to;

receive functional imaging modality data from a nuclear medicine imaging modality which images an intrabody volume of a patient containing a heart, the patient having been injected with a radioactive imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS) of the heart, the ANS comprising at least one ganglionic plexus (GP);

receive anatomical imaging modality data from an anatomical imaging modality which images an intrabody volume of a patient containing the heart;

receive at least one image mask corresponding to location of a fat pad at a predefined anatomical region identified on an anatomical image reconstructed from said anatomical imaging modality data, wherein said at least one GP is not identifiable on said anatomical image;

apply the at least one image mask to the functional imaging modality data to the location corresponding to the fat pad at the predefined anatomical region;

locate the at least one GP innervating the heart within the at least one image mask applied on the functional imaging modality data, by differentiating the at least one GP from the fat pad

based on at least one predefined rule, said rule relying on a calculated activity of a radio labeled tracer within a spot in said respective image mask being indicative of the at least one GP, said activity being larger than activity of an area surrounding the spot within the respective image mask indicative of the fat pad, by more than a predefined threshold;

position the located at least one GP within the fat pad on the anatomical imaging data; and

generate and output to display a combined image from said anatomical imaging modality data and said functional imaging modality data of the positioned at least one GP.

14. The system of claim 13, wherein the ANS comprises at least one GP comprising one or more of:

superior left GP (SLGP), inferior left GP (ILGP), anterior right GP (ARGP), inferior right GP (IRGP), and Marshall GP.

15. The system of claim 13, wherein the ANS comprises two, three or more GPs from two or three or more of:

superior left GP (SLGP), inferior left GP (ILGP), anterior right GP (ARGP), inferior right GP (IRGP), and Marshall GP.

16. The system of claim 13, wherein the anatomical imaging modality data is received before and/or during a treatment procedure.

17. The system of claim 13, further comprising an intra-body treatment probe for ablation of the nervous tissue within the heart.

18. The system of claim 17, wherein the intra-body treatment probe is at least one of a radiofrequency (RF) treatment probe, a cryosurgery treatment probe, and a probe that injects a toxin or medication.

19. The system of claim 13, wherein said program instructions include instructions that when executed by said at least one hardware processor cause said at least one hardware processor to compare a distribution of the imaged nervous tissue with one or more sets of expected distributions, and to detect abnormal synaptic distribution and/or activity based on the comparison.

20. The system of claim 13, wherein said program instructions include instructions that when executed by said at least one hardware processor cause said at least one hardware processor to track changes in the distribution of the imaged nervous tissue over time.

21. The system of claim 13, further comprising a memory for storing at least one of generated ANS models and diagnosis.

22. The system of claim 13, wherein said program instructions include instructions that when executed by said at least one hardware processor cause said at least one hardware processor to estimate the prediction of success of an ablation procedure based on measured uptake of the functional image.

23. The system of claim 13, further comprising a user input device for receiving manual input from a user, wherein said program instructions include instructions that when executed by said at least one hardware processor cause said at least one hardware processor to annotate the located nervous tissue based on the received manual input, said user input device is a member of a group consisting of at least a keyboard, a mouse and a touch screen.

24. The system of claim 13, wherein said program instructions are organized in a plurality of distributed software modules.

25. The system of claim 13, wherein when the instructions are executed by the at least one hardware processor, the execution causes the at least one hardware processor to locate the at least one GP innervating the heart based on the functional imaging modality data according to an analysis of synapse distribution and activity identified in the functional imaging modality data.

26. A system for identifying ANS components within an image of a heart of a patient and providing cardiac treatment, the system comprising:

a non-transitory computer readable medium storing program instructions;

at least one hardware processor in communication with said non-transitory computer readable medium, that when said instructions are executed by said at least one hardware processor, the execution causes said at least one hardware processor to;

receive functional imaging modality data from a nuclear medicine imaging modality which images an intrabody volume of a patient containing a heart, the patient having been injected with a radioactive imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS) of the heart, the ANS comprising at least one GP comprising one or more of;

superior left GP (SLGP), inferior left GP (ILGP), anterior right GP (ARGP), inferior right GP (IRGP), and Marshall GP;

receive anatomical imaging modality data from an anatomical imaging modality which images an intrabody volume of a patient containing the heart;

receive at least one image mask corresponding to location of a fat pad at a predefined anatomical region identified on an anatomical image reconstructed from said anatomical imaging modality data, wherein said at least one GP is not identifiable on said anatomical image;

apply the at least one image mask to the functional imaging modality data to the location corresponding to the fat pad at the predefined anatomical region;

locate the at least one GP in the intrabody volume of the heart within the at least one image mask applied on the functional imaging modality data and anatomical imaging modality data, by differentiating the at least one GP from the fat pad

based on at least one predefined rule relying on calculated activity of a radio labeled tracer within an activity spot above a predefined threshold in comparison to surrounding activity within the at least one image mask being indicative of the at least one GP, said activity being; and

generate and output to display a combined image from said anatomical imaging modality data and said functional imaging modality data of the positioned at least one GP within a fat pad.

27. The system of claim 26,

wherein said functional imaging modality is a member of a group consisting of:

single-photon emission computed tomography (SPECT), electrocardiogram-gated SPECT (GSPECT), SPECT-CT, D-SPECT, A-SPECT and functional magnetic resonance imaging (fMRI); and

wherein said anatomical imaging modality is a member of a group consisting of;

X-rays, ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI).

28. The system of claim 26, wherein when the instructions are executed by the at least one hardware processor, the execution causes the at least one hardware processor to locate the at least one GP in the intrabody volume of the heart based on the functional imaging modality data and anatomical imaging modality data, according to an analysis of synapse distribution and activity identified in the functional imaging modality data.