Geometry Calibration for High Resolution Small Animal Imaging Vi-Hoa (Tim) Tran Thomas Jefferson National Accelerator Facilities.

Slides:



Advertisements
Similar presentations
Results/Conclusions: In computer graphics, AR is achieved by the alignment of the virtual camera with the actual camera and the virtual object with the.
Advertisements

Gamma Camera Quality Control
Some problems... Lens distortion  Uncalibrated structure and motion recovery assumes pinhole cameras  Real cameras have real lenses  How can we.
 Nuclear Medicine Effect of Overlapping Projections on Reconstruction Image Quality in Multipinhole SPECT Kathleen Vunckx Johan Nuyts Nuclear Medicine,
Institut für Elektrische Meßtechnik und Meßsignalverarbeitung Professor Horst Cerjak, Augmented Reality VU 2 Calibration Axel Pinz.
Geometry 2: A taste of projective geometry Introduction to Computer Vision Ronen Basri Weizmann Institute of Science.
Computed Tomography II
Seeram Chapter 13: Single Slice Spiral - Helical CT
S. M. Gibson, IWAA7 November ATLAS Group, University of Oxford, UK S. M. Gibson, P. A. Coe, A. Mitra, D. F. Howell, R. B. Nickerson Geodetic Grids.
Scanning Confocal Basics n What’s so special ? n Confocals give higher resolution than normal microscopes. n Confocals can OPTICALLY SECTION a sample.
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
O AK R IDGE N ATIONAL LABORATORY U.S. DEPARTMENT OF ENERGY Animal Tracking Status (ORNL)
Computer vision. Camera Calibration Camera Calibration ToolBox – Intrinsic parameters Focal length: The focal length in pixels is stored in the.
Camera Models A camera is a mapping between the 3D world and a 2D image The principal camera of interest is central projection.
BMME 560 & BME 590I Medical Imaging: X-ray, CT, and Nuclear Methods Tomography Part 3.
Motion Tracking. Image Processing and Computer Vision: 82 Introduction Finding how objects have moved in an image sequence Movement in space Movement.
Geometry of Images Pinhole camera, projection A taste of projective geometry Two view geometry:  Homography  Epipolar geometry, the essential matrix.
Group S3. Lab Session 5 Following on from our previous lab session decided to find the relationship between Disparity vs Camera Separation. Measured Disparity.
Stereo Algorithm Grimson’s From Images to Surfaces stereo algorithm Multi-resolution Proceed from coarse to fine level Assume 0 initial disparity — depth-dependent.
MSU CSE 803 Stockman Perspective algebra: quick- and-dirty first look Geometry of similar triangles yields algebra for computing world-image transformation.
Epipolar Geometry and the Fundamental Matrix F
Camera model Relation between pixels and rays in space ?
On-Orbit Adjustment Calculation for the Generation-X X-ray mirror Figure D. A. Schwartz, R. J. Brissenden, M. Elvis, G. Fabbiano, D. Jerius, M. Juda, P.
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Single-view geometry Odilon Redon, Cyclops, 1914.
Projected image of a cube. Classical Calibration.
CS223b, Jana Kosecka Rigid Body Motion and Image Formation.
Generation-X telescope: Measurement of On-Orbit Adjustment Data Dan Schwartz, R. J. Brissenden, M. Elvis, G. Fabbiano, T. Gaetz, D. Jerius, M. Juda, P.
Instance-level recognition I. - Camera geometry and image alignment Josef Sivic INRIA, WILLOW, ENS/INRIA/CNRS UMR 8548 Laboratoire.
T. Bajd, M. Mihelj, J. Lenarčič, A. Stanovnik, M. Munih, Robotics, Springer, 2010 ROBOT SENSORS AND ROBOT VISON T. Bajd and M. Mihelj.
Cameras Course web page: vision.cis.udel.edu/cv March 22, 2003  Lecture 16.
O AK R IDGE N ATIONAL LABORATORY U.S. DEPARTMENT OF ENERGY Acquisition and Control Benjamin L. Welch Thomas Jefferson National Accelerator Facility Newport.
Optical Alignment System for Muon Tracker 1.Hardware 2.Learned from RUN3/4 3.Upgrade 4.Plan and Summary Atsushi Taketani, RIKEN/RIKEN Brookhaven Research.
Computer Tomography By Moustafa M. Mohamed. Introduction to Medical Imaging Uses of medical imaging Obtain information about internal body organs or the.
Course 12 Calibration. 1.Introduction In theoretic discussions, we have assumed: Camera is located at the origin of coordinate system of scene.
3D SLAM for Omni-directional Camera
I. Introduction Objectives  To solve geometric problem(misalignment) in cone-beam computed tomography (CBCT) system.  To adjust X-ray focal spot using.
Vision-Based Reach-To-Grasp Movements From the Human Example to an Autonomous Robotic System Alexa Hauck.
Fundamental Limits of Positron Emission Tomography
Overview of recent ORNL progress in restraint free laboratory animal imaging Bethesda May 20, 2005.
Image Restoration Juan Navarro Sorroche Phys-6314 Physics Department The University of Texas at Dallas Fall 2010 School of Natural Sciences & Mathematics.
Metrology 1.Perspective distortion. 2.Depth is lost.
Z. El Bitar1, R. H. Huesman2, R. Buchko2, D. Brasse1, G. T. Gullberg2
CT Instrumentation and X-ray system
Vision Review: Image Formation Course web page: September 10, 2002.
Professor Brian F Hutton Institute of Nuclear Medicine University College London Emission Tomography Principles and Reconstruction.
Peripheral drift illusion. Multiple views Hartley and Zisserman Lowe stereo vision structure from motion optical flow.
Lecture 03 15/11/2011 Shai Avidan הבהרה : החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.
O AK R IDGE N ATIONAL LABORATORY U.S. DEPARTMENT OF ENERGY Image Reconstruction of Restraint-Free Small Animals with Parallel and Multipinhole Collimation:
Single-view geometry Odilon Redon, Cyclops, 1914.
Raquel A. Romano 1 Scientific Computing Seminar May 12, 2004 Projective Geometry for Computer Vision Projective Geometry for Computer Vision Raquel A.
Centre of Rotation: Is there a problem in the Y dimension? Stephen Brown - Southend Mike Avison - Bradford.
Awake Animal SPECT: Overview and Initial Results O AK R IDGE N ATIONAL LABORATORY U.S. DEPARTMENT OF ENERGY Drew Weisenberger 1, Brian Kross 1, Stan Majewski.
1 Chapter 2: Geometric Camera Models Objective: Formulate the geometrical relationships between image and scene measurements Scene: a 3-D function, g(x,y,z)
Figure 6. Parameter Calculation. Parameters R, T, f, and c are found from m ij. Patient f : camera focal vector along optical axis c : camera center offset.
Motion Correction Riku Klén
Part No...., Module No....Lesson No
Polarization Characteristic of Multi-layer Mirror for Hard X-ray Observation of Astrophysical Objects T. Mizuno 1, J. Katsuta 2, H. Yoshida 1, H. Takahashi.
Computer vision: models, learning and inference M Ahad Multiple Cameras
Part II Plane Equation and Epipolar Geometry. Duality.
Reconstruction from Two Calibrated Views Two-View Geometry
Single-view geometry Odilon Redon, Cyclops, 1914.
11/25/03 3D Model Acquisition by Tracking 2D Wireframes Presenter: Jing Han Shiau M. Brown, T. Drummond and R. Cipolla Department of Engineering University.
CS682, Jana Kosecka Rigid Body Motion and Image Formation Jana Kosecka
Single Slice Spiral - Helical CT
Image quality and Performance Characteristics
Multiple View Geometry for Robotics
                                                                                                                                                                                                                                                               
9-3 Rotations on the Coordinate Plane
9-3 Rotations on the Coordinate Plane
Presentation transcript:

Geometry Calibration for High Resolution Small Animal Imaging Vi-Hoa (Tim) Tran Thomas Jefferson National Accelerator Facilities

heavy duty rotation disk slider pinhole detector AOR parallel detector Gamma Cameras optical tracking CCD cameras Rotating SPECT Gantry

Geometry Calibration To determine the spatial relationship between the coordinate systems of the rotating detectors and the objects

AOR 360 degree acquisition pinhole detector parallel hole detector Calibration Phantom with 3 Point Sources Co-57

Pinhole Projections Over 360 Degrees axial motion (v) Transaxial motion (u) measured predicted Transaxial direction (u) Axial direction (V)

Bequé’s Calibration Model (2003) v u z y x z y x v u   The detector and object coordinate systems are not aligned

heavy duty rotation disk slider pinhole detector AOR ideal case: rigid body real case: flexing/rotation radial movement axial movement AOR z0z0 parallel hole detector A B C Normal ray A possible cause

A Model of Detector Motion Normal Ray z d f v z pinhole

Extension To Bequé’s Model g =  z.sin(  +ξ) sinusoidal axial motion

measured predicted w/ correction predicted w/o correction axial motion before and after correction

Improved tracking for both pinhole & parallel hole collimators

Summary  Prediction errors due to flexing gantry  Modeling of detector movement  Extension to Bequé’s calibration model  Prediction errors minimized