SYSTEMS, METHODS AND DEVICES FOR FORMING RESPIRATORY-GATED POINT CLOUD FOR FOUR DIMENSIONAL SOFT TISSUE NAVIGATION

• US 20130223702A1
• Filed: 02/22/2013
• Published: 08/29/2013
• Est. Priority Date: 02/22/2012
• Status: Active Grant

• Current Assignee: Veran Medical Technologies, Inc. (Olympus Corporation)
• Original Assignee: Veran Medical Technologies, Inc. (Olympus Corporation)
• Inventors: HOLSING, Troy L., HUNTER, Mark
• Granted Patent: US 9,138,165 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 10/0283 with vacuum aspiration, e.g. caused by retractable plunger or by connected syringe A61B10/025, A61B10/0266 take precedence
• CPC Class Codes: A61B 10/04 Endoscopic instruments
• CPC Class Codes: A61B 10/06 Biopsy forceps , e.g. with cup-shaped jaws
• CPC Class Codes: A61B 17/29 Forceps for use in minimally invasive surgery
• CPC Class Codes: A61B 17/32002 with continuously rotating, oscillating or reciprocating cutting instruments
• CPC Class Codes: A61B 17/32053 Punch like cutting instruments, e.g. using a cylindrical or oval knife A61B17/326 takes precedence; trepans A61B17/1695; for use on the cornea A61F9/007
• CPC Class Codes: A61B 17/3478 Endoscopic needles, e.g. for infusion biopsy needles A61B10/0233; catheters with injection needles A61M25/0067
• CPC Class Codes: A61B 18/02 by cooling, e.g. cryogenic techniques devices for cooling specific reflex points of the body within cell-life limits A61H39/06
• 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 18/1815 using microwaves
• CPC Class Codes: A61B 18/24 with a catheter A61B18/26, A61B18/28 take precedence
• CPC Class Codes: A61B 2010/0216 Sampling brushes
• CPC Class Codes: A61B 2017/003 Steerable
• CPC Class Codes: A61B 2017/00314 Separate linked members
• CPC Class Codes: A61B 2017/00323 Cables or rods
• CPC Class Codes: A61B 2017/00398 using powered actuators, e.g. stepper motors, solenoids
• CPC Class Codes: A61B 2017/00685 Archimedes screw
• CPC Class Codes: A61B 2017/00694 with means correcting for movement of or for synchronisation with the body
• CPC Class Codes: A61B 2017/00699 correcting for movement caused by respiration, e.g. by triggering
• CPC Class Codes: A61B 2017/00809 Lung operations
• CPC Class Codes: A61B 2017/2927 : the angular position of the head being adjustable with respect to the shaft
• CPC Class Codes: A61B 2017/320775 : Morcellators, impeller or propeller like means
• CPC Class Codes: A61B 2018/00541 : Lung or bronchi
• CPC Class Codes: A61B 2034/2051 : Electromagnetic tracking systems
• CPC Class Codes: A61B 2034/2061 : using shape-sensors, e.g. fiber shape sensors with Bragg gratings
• CPC Class Codes: A61B 2034/2072 : Reference field transducer attached to an instrument or patient
• CPC Class Codes: A61B 2090/061 : for measuring dimensions, e.g. length
• CPC Class Codes: A61B 2090/364 : Correlation of different images or relation of image positions in respect to the body
• CPC Class Codes: A61B 2090/376 : using X-rays, e.g. fluoroscopy
• CPC Class Codes: A61B 2090/378 : using ultrasound
• CPC Class Codes: A61B 2090/3925 : ultrasonic
• CPC Class Codes: A61B 2090/3966 : Radiopaque markers visible in an X-ray image
• CPC Class Codes: A61B 34/20 : Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
• CPC Class Codes: A61B 5/0066 : Optical coherence imaging
• CPC Class Codes: A61B 5/01 : Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/061 : Determining position of a probe within the body employing means separate from the probe, e.g. sensing internal probe position employing impedance electrodes on the surface of the body A61B1/00158 takes precedence
• CPC Class Codes: A61B 5/062 : using magnetic field
• CPC Class Codes: A61B 5/11 : Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/113 : occurring during breathing
• CPC Class Codes: A61B 5/6852 : Catheters
• CPC Class Codes: A61B 5/725 : using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7285 : for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: A61B 5/7292 : Prospective gating, i.e. predicting the occurrence of a physiological event for use as a synchronisation signal
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 6/032 : Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/12 : Arrangements for detecting or locating foreign bodies
• CPC Class Codes: A61B 6/485 : involving fluorescence X-ray imaging
• CPC Class Codes: A61B 6/487 : involving fluoroscopy
• CPC Class Codes: A61B 6/5223 : generating planar views from image data, e.g. extracting a coronal view from a 3D image
• CPC Class Codes: A61B 8/0841 : for locating instruments
• CPC Class Codes: A61B 8/483 : involving the acquisition of a 3D volume of data
• CPC Class Codes: A61B 90/37 : Surgical systems with images on a monitor during operation
• CPC Class Codes: A61M 25/0108 : using radio-opaque or ultrasound markers
• CPC Class Codes: A61N 5/1007 : Arrangements or means for the introduction of sources into the body
• CPC Class Codes: G06T 1/00 : General purpose image data processing
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 19/00 : Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 2200/28 : involving image processing hardware
• CPC Class Codes: G06T 2207/10028 : Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10121 : Fluoroscopy
• CPC Class Codes: G06T 2207/10132 : Ultrasound image
• CPC Class Codes: G06T 2207/20124 : Active shape model [ASM]
• CPC Class Codes: G06T 2207/30061 : Lung
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 2210/56 : Particle system, point based geometry or rendering
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/12 : Edge-based segmentation
• CPC Class Codes: G06T 7/149 : involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/33 : using feature-based methods

Abstract

A surgical instrument navigation system is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient'"'"'s respiratory system and for placing a localization element in an organ of a patient.

Claims

1. A method of modifying a segmented image dataset for a region of a respiratory system to match the corresponding anatomy of a patient'"'"'s respiratory system, the method comprising:

(i) forming a respiratory-gated point cloud of data that demarcates anatomical features in a region of a patient'"'"'s respiratory system at one or more discrete phases within a respiration cycle of the patient,

(ii) density filtering the respiratory-gated point cloud,

(iii) classifying the density filtered respiratory-gated point cloud according to anatomical points of reference in a segmented image dataset for the region of the patient'"'"'s respiratory system, and

(iv) modifying the segmented image dataset to correspond to the classified anatomical points of reference in the density filtered respiratory-gated point cloud.

2. The method of claim 1 wherein the discrete phases are expiration, inspiration and discrete phases there-between.

3. The method of claim 1 wherein the segmented image dataset is from a first discrete phase of the patient'"'"'s respiration cycle and the respiratory-gated point cloud is from a second and different discrete phase of the patient'"'"'s respiration cycle.

4. The method of claim 1 wherein the segmented image dataset is from a first discrete phase of the patient'"'"'s respiration cycle and the respiratory-gated point cloud is from a second and different discrete phase of the patient'"'"'s respiration cycle.

5. The method of claim 1 wherein the segmented image dataset is a skeletonized segmented image dataset.

13. A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process, the code comprising code to carry out one or more elements of the method of claim 1.

6. A method of simulating the movement of a patient'"'"'s respiratory system in the patient'"'"'s respiration cycle during respiration comprising:

(i) forming a respiratory-gated point cloud of data that demarcates anatomical features in a region of a patient'"'"'s respiratory system at one or more discrete phases within a respiration cycle of a patient,

(ii) density filtering the respiratory-gated point cloud,

(iii) classifying the density filtered respiratory-gated point cloud according to anatomical points of reference in a segmented image dataset for the region of the patient'"'"'s respiratory system,

(iv) creating a cine loop comprising a plurality of modified segmented image datasets through multiple modifications of the segmented image dataset to correspond to a plurality of classified anatomical points of reference in the respiratory-gated point cloud over the respiration cycle, and

(v) displaying the cine loop comprising the plurality of modified segmented image datasets over the patient'"'"'s respiration cycle.

7. The method of simulation of claim 6 wherein displaying the cine loop comprising the plurality of modified segmented image datasets over the patient'"'"'s respiration cycle is synchronized with the patient'"'"'s respiration cycle.

14. A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process, the code comprising code to carry out one or more elements of the method of claim 6.

8. A method of preparing a segmented image dataset to match the anatomy of a patient'"'"'s respiratory system, the method comprising:

(i) forming a respiratory-gated point cloud of data that demarcates anatomical features in a region of a patient'"'"'s respiratory system at one or more discrete phases within a respiration cycle of the patient,

(ii) density filtering the respiratory-gated point cloud,

(iii) classifying the density filtered respiratory-gated point cloud according to anatomical points of reference in a segmented image dataset for the region of the patient'"'"'s respiratory system,

(iv) registering the classified respiratory-gated point cloud to the segmented image dataset,

(v) comparing the classified respiratory-gated point cloud to a segmented image data set to determine the weighting of points comprised by the respiratory-gated point cloud,

(vi) distinguishing regions of greater weighting from regions of lesser weighting and optionally increasing the data set comprised by the respiratory-gated point cloud for regions of lesser weighting,

(vii) modifying the registered segmented image dataset to correspond to the classified anatomical points of reference in the respiratory-gated point cloud.

9. The method of claim 8 wherein registering the respiratory-gated point cloud to the segmented image data set comprises:

registering the respiratory-gated point cloud representing at least one branch of the patient'"'"'s respiratory system to corresponding anatomical points of reference in the registered segmented image data set representing the branch(es) of the patient'"'"'s respiratory system.

10. The method of claim 8 wherein the respiratory-gated point cloud is registered to a plurality of branches of the patient'"'"'s respiratory system, wherein the plurality of branches comprise the trachea, the right main bronchus, and the left main bronchus.

11. The method of claim 8 wherein the discrete phases are expiration, inspiration and discrete phases there-between.

12. The method of claim 8 wherein the segmented image dataset is from a first discrete phase of the patient'"'"'s respiration cycle and the respiratory-gated point cloud is from a second and different discrete phase of the patient'"'"'s respiration cycle.

15. A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process, the code comprising code to carry out one or more elements of the method of claim 8.