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CT-Video Registration Accuracy for Virtual Guidance of Bronchoscopy. James P. Helferty, 1,2 Eric A. Hoffman, 3 Geoffrey McLennan, 3 and William E. Higgins 1,3. 1 Penn State University, University Park, PA 16802 2 Lockheed-Martin, King of Prussia, PA 3 University of Iowa, Iowa City, IA 52242

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ct video registration accuracy for virtual guidance of bronchoscopy
CT-Video Registration Accuracy for Virtual Guidance of Bronchoscopy

James P. Helferty,1,2 Eric A. Hoffman,3Geoffrey McLennan,3andWilliam E. Higgins1,3

1Penn State University, University Park, PA 16802

2Lockheed-Martin, King of Prussia, PA

3University of Iowa, Iowa City, IA 52242

SPIE Medical Imaging 2004, San Diego, CA, 14-19 February 2004

ct guided bronchoscopy for lung cancer staging
CT-Guided Bronchoscopy for Lung Cancer Staging

ROI seen in CT but not in video

CT Scan of Chest

  • Bronchoscopic biopsy critical for staging.
  • Physicians make errors when maneuvering bronchoscope to a biopsy site.
  • Lymph nodes are hidden from endoscopic video, but visible in 3D CT analysis  exploit CT using image guidance
  • CT-guidance of bronchoscopy reduce errors, improve biopsy success rate

Videoendoscopy

Inside airways

Matching Video and CT Rendering

image guided bronchoscopy systems

No device

needed

Image-Guided Bronchoscopy Systems

Show potential, but recently proposed systems have limitations:

  • McAdams et al.(AJR 1998) and Hopper et al.(Radiology 2001)
    • Virtual bronchoscopy for lymph-node biopsy, but no live guidance.
  • Solomon et al. (Chest 2000) – E/M sensor attached to scope
    • limited planning, many potential errors, limited guidance
  • Bricault et al. (IEEE-TMI 1998) –no device needed
    • Registered videobronchoscopy to CT, but no live guidance.
  • Mori et al. (SPIE Med. Imaging 2001, 2002) – no device needed
    • Registered videobronchoscopy to CT and tracked video.
    • Efforts not interactive: >20 sec to process each video frame.
slide4

PC Enclosure

AVI File

RGB,Sync,Video

Video

Stream

Matrox

Cable

Video

Capture

Main Thread

Video Tracking

OpenGL Rendering

Worker Thread

Mutual Information

Dual CPU System

Scope Monitor

Scope Processor

Rendered

Image

Light Source

Endoscope

Polygons, Viewpoint

Image

Matrox PCI card

Video AGP card

Our Group’sImage-Guided Bronchoscopy System

Software written in Visual C++.

slide5

System Processing Flow

Data

Sources

3D CT Scan

Bronchoscope

Stage 1: 3D CT Assessment and Planning

  • Segment 3D Airway Tree
  • Calculate Centerline Paths
  • Define Target ROI biopsy sites

Stage 2: Live Bronchoscopy

  • Capture Endoscopic Video
  • Correct Video’s Barrel Distortion
  • Track/Register Video and Virtual CT
  • Map Target ROIs on Video

Image

Processing

HTML Multimedia Case Study

Site

List

Segmented Airway Tree

Centerline Paths

Screen Snapshots

Recorded Movies

Physician Notes

See: Helferty et al., SPIE Med. Imaging 2001; Swift et al., Comp. Med. Imag. Graph. 2002.

slide6

Display during Stage-2 Bronchoscopy

Case h005_512_85. Root site = (253,217,0), seger = (RegGrow, no filter), ROI #2 considered (Blue)

stage 2 ct guided bronchoscopy protocol

Key Step:

CT-Video Registration

Stage 2: CT-Guided Bronchoscopy Protocol
  • Provide Virtual-World CT rendering  ICT
  • Move bronchoscope “close” to ICT  target view IV
  • Register Virtual World to target view IV
  • Go to Step 1 unless biopsy site reached

Endoluminal 3D

CT rendering

Live video from bronchoscope

slide8

Optimal CT rendering

IV =

Target video

co

ICT

ct

IV

CT-Video Registration Problem:Viewpoints

6-parameter viewpoint

3D position

3-angle direction

Standard camera

direction matrix

slide9

CT-Video Registration Problem:Optimization Problem

h(V), h(CT) – entropies based on image histograms (PDFs)

c

ICT

ci – starting point for

Normalized Mutual Information (NMI):

NMI Optimization:

Ref: Studolme et al., Pattern Recognition, 1/99.

slide10

CT-Video Registration Problem:Optimization Algorithms Tested

  • Steepest Ascent
  • Nelder-Meade Simplex
  • Simulated Annealing
slide11

co

ICT

needle position for bronchoscope ( )

“needle” position for optimal CT view ( )

IV

CT-Video Registration Problem: Error Measures for Tests

Position error

Angle error

Needle error

where:

po

no

slide12

co

ICT

errors for acceptable registrations

Registration Protocol for Tests

IV

  • Target video frame: View to optimize:

2. Registration process:

    • Fix 5 parameters of ICT’s viewpoint to IV’s true viewpoint: -10 mm < DX, DY, DZ < 10 mm -20o < Da , Db , Dg < 20o
    • Initialize ICT’s remaining parameter away from true value
    • Run NMI optimization until convergence
    • Measure errors
slide13

IV

  • Target video frame: -- known fixed virtual CT view
  • View to optimize: -- based on SAME 3D CT image as

3. Test each optimization algorithms: stepwise, simplex, annealing

IV

co

ICT

Test #1: Performance of Optimization Algorithms(a) Eliminate video and CT source differences

(b) Measure registration error precisely

slide14

co

co

co

co

ICT

ICT

ICT

ICT

Test #1 -- Performance of Optimization Algorithms

Example Registrations

  • Test “video” view IV
  • “Good” simplex result (DX=8mm)
  • “Poor” annealing result (DY=10mm)
  • “Poor” annealing result (D yaw = 20o)
slide15

na= 5o

Threshold for acceptable angle errorna

Test #1 -- Performance of Optimization Algorithms

Example Error Plot (na)

Initial Db (yaw) varied.

Other 5 parameters

of ICT‘sviewpointc

start at “true” values

slide16

ROI 3

Test #2: Impact of Airway Morphology -- 6 Test ROIs

(a), (b) proximal and distal trachea

(c), (d) proximal and distal right main bronchus

(e), (f) proximal and distal left main bronchus

 Run Simplex

Algorithm

slide18

Test #2: Impact of Airway Morphology

Ranges of Starting Points that result in acceptable registrations

slide19

CT

V

CT

IV

IV

ICT

Iv

ROI

Grp 3

co

co

ICT

ICT

Compare final registered result to

Test #3: Registering CT to Real Video

* 6 Matching Test Pairs

Compare final registered result to

slide21

Test #3: Registering CT to Real Video

* Summary over 6 ROI Pairs

Ranges of Starting Points that result in acceptable registrations

slide22

co

TLC

ICT

ICT

Test #4: Sensitivity to Different Lung Capacities

* CT scan – done at full inspiration (TLC) * Bronchoscopy – done with chest nearly deflated (FRC)

FRC

  • Target “video” frame: = -- known fixed CT view (from FRC CT volume)
  • View to optimize: -- CT view from TLC CT volume
  • Run Simplex optimization algorithm:  Compare final result to previously matched result

IV

ICT

co

ICT

slide23

FRC

IV =

ICT

ROI

Pair 2

TLC

ICT

Test #4: Sensitivity to Different Lung Capacities

* 3 TLC/FRC Matching Pairs (Pig data; volume controlled)

TLC FRC

TLC FRC

TLC FRC

slide24

Test #4: Sensitivity to Different Lung Capacities

* ROI Pair #2 (Pig data; volume controlled)

DZ

slide25

Test #4: Sensitivity to Different Lung Capacities

* 3 TLC/FRC Matching Pairs (Pig data; volume controlled)

Ranges of Starting Points that result in acceptable registrations

discussion
Discussion
  • Method successful and runs in near real-time (5 sec per registration).
  • Good airway segmentation and video/CT “camera” calibration important.
  • Registration successful:

a. over a wide range of anatomy

b. Independent of lung volume

c. +/- 8-10 mm position deviations, +/-15-20o direction deviation

  • Head toward continuous video tracking and CT-video registration.

 Helferty et al. SPIE Med. Imaging 2003

Acknowledgements

This work was partially supported by NIH grants #CA074325 and CA091534, and by the Olympus Corporation.

slide28

Steepest Ascent Algorithm

Also tested Nelder-Meade Simplex and Simulated Annealing

slide29

IV

IV

co

ICT

Test #2: Impact of Airway Morphology

Consider 6 Varied Airway Locations (ROIs)

  • Target video frame: -- a known fixed virtual CT view
  • View to optimize: -- based on SAME 3D CT image as

3. Run Simplex optimization algorithm.

slide30

V

V

ICT

ICT

co

co

ICT

ICT

Test #3: Registering CT to Real Video

  • Target video frame: -- known fixed video frame; have matching
  • View to optimize: -- from corresponding CT image

3. Run Simplex optimization algorithm:

    • Fix 5 parameters of ICT’s viewpoint to IV’s true viewpoint
    • Run optimization
    • Compare final registered result to

4. Test on three target “video/CT” matching pairs

IV

slide31

co

TLC

ICT

ICT

Test #4: Sensitivity to Different Lung Capacities

* CT scan – done at full inspiration (TLC) * Bronchoscopy – done with chest nearly deflated (FRC)

FRC

  • Target “video” frame: = -- known fixed CT view (from FRC CT volume)
  • View to optimize: -- CT view from TLC CT volume

3. Run Simplex optimization algorithm:

    • Fix 5 parameters of ICT’s viewpoint to IV’s true viewpoint
    • Run optimization
    • Compare final result to previously matched result

4. Test on three “FRC/TLC” matching pairs

IV

ICT

co

ICT