1. To move or not to move: A Medical Imaging Perspective Debasis Mitra & Daniel Eiland Thomas Welsh Antall Fernanades Mahmoud Abdalah dmitra@cs.fit.edu Department of Computer Science Florida Institute of Technology Melbourne

2. October 15, 2010 Computer Science Seminar 2 Who are we? Computer Sciences Florida Tech Radiotracer and Imaging Department Life Sciences Division Lawrence Berkeley National Lab California Nuclear Medicine, MIMA Melbourne, Florida

3. October 15, 2010 Computer Science Seminar 3 Why Tomography? Tomography is imaging by sections or sectioning (Wikipedia) Image from different angles & combine them for 3D view of target Based on the mathematical procedure called Based on the mathematical procedure called tomographic reconstruction Non-invasive visualization for diagnosis and prognosis

4. October 15, 2010 Computer Science Seminar 4 Two Steps of Tomography As in most experiment / observation - - - Data collection: Data collection: Engineering & Physics Data Processing: Data Processing: Math & Computation

5. October 15, 2010 Computer Science Seminar 5 The World of Tomography Medicine Medicine Archaeology Archaeology Biology Biology Geophysics Geophysics Oceanography Oceanography Materials science Materials science Astrophysics Astrophysics Network tomography! Network tomography! Cargo inspection Cargo inspection

6. October 15, 2010 Computer Science Seminar 6 Medical TmographyTools Positron: Positron Emission Tomography (PET) Positron: Positron Emission Tomography (PET) X-ray: Computed Tomography X-ray: Computed Tomography Ultrasound Ultrasound Infrared Infrared Gamma-ray: Single Photon Emission CT (SPECT) Gamma-ray: Single Photon Emission CT (SPECT) Magnetic Resonance Imaging (MRI) Magnetic Resonance Imaging (MRI)

7. 10/13/10 Tomography Tomography is the creation of images by sections (or slices) through the use of any kind of penetrating wave This collected image captured about a central point is known as a sinogram Circular Tomography System The reconstructed image is known as a tomogram

8. 10/13/10 Motion Artifacts Tomography works well for stationary objects. However if the object being imaged moves, there will be motion artifacts in the final tomogram Cardiac tomogram showing motion-induced artifacts (at arrows) – induced by cardiac contraction Cardiac tomogram without motion artifacts (area is now visible) – corrected via substitution Image ©2006 by Radiological Society of North America

9. 10/13/10 System after patient motion Motion and Tomography There are two major classes of motion that can occur during the image capture process Rigid body motion – including patient motion and internal shifting of organ(s) Non-Rigid body motion – including cardiac contraction and respiratory motion Patient motion occurs when the patient’s body shifts as the image capture device (detector) rotates around the patient. System before patient motion

10. 10/13/10 Detecting Patient Motion Using a 180˚/360˚ Tomography system where the angle between adjacent images is small. We have assumed the motion caused by detector rotation is insignificant. From here we have presumed that the difference between adjacent images will be small and that the centroid of adjacent images will be similiar. When adjacent images do not have the same centroid, we can conclude that motion has occurred. Images from previous system From the centroids, we can see that a motion vector of V={x-a, y-b} has occurred between images 2 and 3.

11. 10/13/10 Motion Correction Motion correction of the initial image where motion has been detected is a fairly simple task – every pixel in the image (with motion) is simply shifted by the motion vector. However every subsequent frames taken after the initial movement-frame must also be corrected as they also suffer from the effects of motion. The correction of these images is not as simple because the angle between them and the initial image grows too large to be ignored.

12. 10/13/10 Motion Correcting the Entire System Using the following system, let us assume that we detect a motion vector V for an image a created by Detector b.

13. 10/13/10 The Motion Vector V is made up of two values - {c, d}. Based on the system diagram and detector layout, it can be seen that the d-value always defines motion along the z-axis. While the c- value defines motion along the combination of the x-axis and y-axis which is based on the angle of the detector. Before we can apply V to a given projection x, we must determine how much of the c- value applies to it. This is based on angle between projection x and projection a.

14. 10/13/10 Deriving the Motion Vector By examing at the system diagram again, we can see that for frame c (formed by Detector position c), the angle is 90. Because Detector-position c can only see movement of objects along the x and z axis and Detector-position a can only image objects along the y and z axis – the c value must be 0 since any motion detected will only be along the y-axis. For frame e, the angle is 180. Detector-position e can image objects along the y and z axis, but because it is flipped (compared to Detector-position a), the c-value must be reversed (-c). Based on these values it can be derived that the c-value for any given image is c * cos(|Current θ – Initial θ|).

15. 10/13/10 ImageJ Open Source tool used for image rendering/correction Open Source tool used for image rendering/correction Supports a variety of formats including RAW and DICOM Images Supports a variety of formats including RAW and DICOM Images DICOM is the format used by many Sinograms DICOM is the format used by many Sinograms

16. 10/13/10 Integration into ImageJ User opens sinogram using ImageJ User opens sinogram using ImageJ User selects Frame where original motion has occurred User selects Frame where original motion has occurred User selects menu option for motion correction and fills in parameters User selects menu option for motion correction and fills in parameters ImageJ runs motion correction algorithm from this frame onward. ImageJ runs motion correction algorithm from this frame onward.

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18. Integration into ImageJ Behind the scenes Behind the scenes ImageJ takes user input parameters such as angle camera in the image, distance from subject, etc. ImageJ takes user input parameters such as angle camera in the image, distance from subject, etc. Passes image and user input to a C- Process for motion-correction Passes image and user input to a C- Process for motion-correction C-Process does image correction algorithm and saves to a new file C-Process does image correction algorithm and saves to a new file New filename is passed back to ImageJ which then opens the new file to show the user. New filename is passed back to ImageJ which then opens the new file to show the user.

19. October 15, 2010 Computer Science Seminar 19 Future with motion in SPECT/PET To breath or not to breath? To breath or not to breath? Respiratory movement needs correction Respiratory movement needs correction for better quality image Intra-frame motions Intra-frame motions Ultimate game: Guess what? Heart beats – fast! Ultimate game: Guess what? Heart beats – fast! Not just motion-correction, Not just motion-correction, but has diagnostic implication! Non-rigid motion!!!

20. October 15, 2010 Computer Science Seminar 20 Thanks! Debasis Mitra Room 325 Harris dmitra@cs.fit.edu