Medical Image Registration Yi-Yu Chou Nov 14, 2003 Overview 1. Introduction to image registration - The goal of image registration - Motivation for medical image registration - Classification of image registration - Registration approaches 2. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images ( D. Rueckert,* L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach and D.J. Hawke, IEEE Trans. Med. Imaging, Vol. 18, Vo. 8, August 1999) The goal of image registration is to determine a common coordinate system in which images can be compared or fused on a pixel-by-pixel basis. The Goal of Image Registration Motivation for Medical Image Registration 1. To fuse information from multiple imaging devices to correlate different measures of structures and function PET image with MRI Head and neck MRI-CT image Motivation for Medical Image Registration (cont.) 2. To measure dynamic patterns of structure change during brain development, tumor growth, degenerative disease processes or pre- and post intervention images. Normal brain image / Alzheimer’s brain image Pre- and post-surgery of brain MRI Motivation for Medical Image Registration (cont.) 3. Passing segmentation or labeling information from the atlas to subject image Brain atlas and MRI
Transcript
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Medical Image Registration
Yi-Yu ChouNov 14, 2003
Overview1. Introduction to image registration
- The goal of image registration- Motivation for medical image registration- Classification of image registration- Registration approaches
2. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images ( D. Rueckert,* L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach and D.J. Hawke, IEEE Trans. Med. Imaging, Vol. 18, Vo. 8, August 1999)
The goal of image registration is to determine a common coordinate system in which images can be compared or fused on a pixel-by-pixel basis.
The Goal of Image Registration Motivation for Medical Image Registration
1. To fuse information from multiple imaging devices to correlate different measures of structures and function
PET image with MRI Head and neck MRI-CT image
Motivation for Medical Image Registration (cont.)
2. To measure dynamic patterns of structure change during brain development, tumor growth, degenerative disease processes or pre- and post intervention images.
Normal brain image / Alzheimer’s brain image Pre- and post-surgery of brain MRI
Motivation for Medical Image Registration (cont.)
3. Passing segmentation or labeling information from the atlas to subject image
Brain atlas and MRI
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1. Rigid
2. Affine
3. Projective
4. Non-rigid
Classification of Image Registration
rigid affine projective Non-rigid
Original grid
1. Spline warps
2. Basis functions
2. Physical models
3. Optical flow-based methods
or
1. Feature based (points, edges, surfaces)
2. Intensity based (work directly with image intensity value)
Registration Approaches
Application of Rigid (Affine) Registration
1. To reduce device induced geometric distortion.2. To reduce the error from patient motion.3. To overcome the global deformation4. To register skull or spinal cord.
Rigid registration of CT bone image to MR T1 weighted image. The outline of the thresholded CT image has been overlayed on both images
Limitation of Rigid (Affine) Registration
Tissue deformation is nonrigid so that the rigid or affine transformations are not sufficient for the correction of the images
Original image Target image
Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images
Carcinoma of the breast is the most common malignant disease in women in the western world. 9.5% of women will develop the disease in the United Kingdom.
The detection and diagnosis of the breast cancer:- X-ray mammography
- MRI: require the injection
of a contrast agent
X-ray mammography Pre- and post-injection breast MRI
Any motion of the patient between scan, or even normal respiratory and cardiac motion, complicates the detection.
Problem Summary
(a) Before motion (b) After motion (c) After subtracting (b) from (a) without registration
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Registration Algorithm
The global motion of the breast is modeled by an affine transformation, while the local breast motion is described by a free-form deformation (FFD) based on B-splines.
),,(),,(),,( zyxTzyxTzyxT localglobal +=
Global Motion Model
3D affine transformation :
+
=
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),,(θθθ
θθθθθθθθθ
zyx
zyxTglobal
Local Motion Model
Cubic B-spline
Control point spacing : 10mm
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1,1,1
)()()(),,(
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Φ=∑∑∑= = =
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Optimization
To find the optimal transformation, they minimize a cost function associated with the global transformation parameters θ, as well as the local transformation parameters Φ.
(a) before motion (b) after motion (c) rigid (d) affine (e) nonrigid. The corresponding difference images are shown in (f)-(i)
Results:Volunteer data without contrast enhancement
(a) (b) (c) (d) (e)
(f) (g) (h) (i)
(a) before motion (b) after motion (c) rigid (d) affine (e) nonrigid. The corresponding difference images are shown in (f)-(i)
Results:Contrast-enhanced patient study
(a) (b) (c) (d) (e)
(f) (g) (h) (i)
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(a) Without registration (b) with rigid (c) with affine (d) with nonrigidregistration
Results:Difference images of the patient study
(a) (b)
(d)(c)
Conclusion
The nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithm
