Interpolation Artifacts in Multimodality Image Registration

IEEE Transaction on Medical Imaging, 2003

Jeffrey Tsao (editor)

Keumsil Lee Feb. 9, 2004

Introduction

1. Multimodality image registration? - Images are acquired in different poses- Complementary information: anatomy and physiology

2. Wood’s method VS MI- Voxel intensities at corresponding positions conform to a many-to-one

mapping- No intensity mapping relationship between the images

3. Prefers match metric with fewer assumptions4. Four steps of image registration

- Tentative pose relative to one another- Joint histogram of voxel intensities- Determine match metric- Iterative optimizing of match metric

Mutual Information

v applied to measure the statistic dependence between voxels intensities of the images

v assumed to be maximal if the images are geometrically aligned

Goal of Study

1. Patterns of interpolation artifacts2. Strategies of overcome these artifacts.3. Effects of eight interpolation schemes

- Nearest neighbor (NN)- Linear- Cubic Catmull-Rom- Hamming-windowed sinc- Partial volume- NN with jittered sampling (JIT)- NN with histogram blurring (BLUR)- NN with JIT and BLUR

4. Impacts of using different numbers of intensity bins5. Artifact-reducing effects of image rotation and sampling

Methods

1. Human brain images

2. 4 pairs of images

3. Image degradation

128X128 MRI 128X128 SPECT

Ł

Methods - continue

4. Registration curves- Translational misregistration : 10 vixels

- Rotational misregistration: 180

5. Translation & Rotation applied to SPECT image- Less signal energy

- High spatial frequencies

- Less susceptible to degradation in image quality from interpolation

6. MI was the maximum when the images were registered

Evaluation interpolation artifacts: MI

1. Steps - Images were overlaid

- Joint histogram

- Quantized voxel intensity with fixed number of discrete bins

- Different numbers of intensity bins

2. Determination of MI:

Evaluation interpolation artifacts: Smoothness of Registration Curve

1. Steps- Normalize the Y axis of each registration curve

- Subtract the curve from a smoothed version

- The difference of them represents the interpolation artifacts

2. Filters- Median filtering & Convolution with a Hamming filter

- 1.5 voxels in translation & 5 in rotation along the X axis

3. Fewer interpolation artifacts à smoother à higher smoothness value

Translational Misregistration: 10 Voxels

Rotational Misregistration: 180

Smoothness

Ł # of bins

Discussion

v Number of Intensity Bins

v Classification of Interpolator (Intensity interpolator VS non-)

v Intensity interpolator

v Match metric and Generalization

Summary

v Interpolation artifacts severely affect registration

v Artifacts can be reduced at misaligned voxel gridsà NN interpolation & BLUR can be considered to save time

v Well aligned voxel grids need special attention

v Suggesting strategies1) Avoid extremely large or small number of intensity bins2) Resampling: rotated orientation & unequal voxel sizes3) JIT4) BLUR

By initial transformation estimate

v

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Example

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Overview of SPM Analysis

Methods

1. Human head images- 128 x 128 axial MR (Magnetic Resonance) image

- 128 x 128 SPECT (single photon emission computed tomography) image

2. 4 pairs of images1. aligned : preregistered using Wood’s Method

2. rotated : SPECT image was 30 counter-clockwise rotated

3. 128/129 : SPECT image was rescaled 129x129

4. /3 : SPECT image was rescaled by increasing the voxel size by /3

3. Image degradation