Difference between revisions of "2011 Winter Project Week:RegistrationAnisotropy"
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| − | + | <gallery widths="400px" perrow="2"> | |
| − | + | Image:RegEval_JointHist_AGif.gif|Fig.1: Example joint histogram blurring effects from anisotropic voxel size : 1x to 20x anisotropy | |
| + | Image:RegEval_JHBlur_RotX_AGif.gif|Fig.2: for comparison: example joint histogram blurring effects from rotation around x (LR) axis 1-15 degrees | ||
| + | Image:RegEval_JHBlur_RotZ_AGif.gif|Fig.3: for comparison: example joint histogram blurring effects from rotation around z (IS) axis 1-15 degrees | ||
| + | Image:MMI_plot.png|Fig.4: MMI optimization landscape (1-DOF) blurred (red) by anisotropy. We measure the width of the blurred peak or relate the drop in intensity to an equivalent shift away from the optimum. | ||
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| + | = Voxel Anisotropy in Registration = | ||
==Key Investigators== | ==Key Investigators== | ||
| − | * BWH: Dominik Meier, | + | * BWH: Dominik Meier, Andriy Fedorov, William Wells |
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Aside from designing experiments for metrics that are specific to this effect, we also seek interaction with other projects that have highly anisotropic image data. | Aside from designing experiments for metrics that are specific to this effect, we also seek interaction with other projects that have highly anisotropic image data. | ||
| + | Example Experiment: produce directly joint histograms and difference(ratio) images w/o the need to run the actual registration, i.e. we compare the effects of voxel anisotropy on the theoretical optimum: Move, filter and subsample identical image pair, then resample back to original position and build joint histograms and subtraction images. Because of the increasing PV effects we expect to see a degenerating joint histogram and a subtraction image with increasing edge artifacts. We can then try to interpret how the optimizer will behave in this environment. The benefit of this metric is that we circumvent the stochastic nature of the registration output. | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | *see | + | *see images above |
| + | *we identified '''prostate MRI''' as a promising dataset for testing. Since hi-res isotropic data is never avail., we will move from the current anisotropy (x7) toward isotropic. Also we have 2 orientations (coronal vs. sagittal) and can compare the effects of having the anisotropy run in a different direction. | ||
| + | *progress on exposing the '''ITK joint histogram''' functions and MI metric for quantifying the above shown effects. | ||
| + | *we anticipate the optimum in the MI landscape to be blurred and potentially shifted by anisotropy. Using translation as a 1-DOF experiment, we calculate the new landscape and measure the blurring (and ev. shift) of the peak as a metric for reduction in accuracy and robustness | ||
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<div style="width: 97%; float: left;"> | <div style="width: 97%; float: left;"> | ||
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== Progress == | == Progress == | ||
| − | + | *see above | |
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Latest revision as of 17:29, 14 January 2011
Home < 2011 Winter Project Week:RegistrationAnisotropy
Fig.1: Example joint histogram blurring effects from anisotropic voxel size : 1x to 20x anisotropy
Fig.2: for comparison: example joint histogram blurring effects from rotation around x (LR) axis 1-15 degrees
Fig.3: for comparison: example joint histogram blurring effects from rotation around z (IS) axis 1-15 degrees
Fig.4: MMI optimization landscape (1-DOF) blurred (red) by anisotropy. We measure the width of the blurred peak or relate the drop in intensity to an equivalent shift away from the optimum.
Voxel Anisotropy in Registration
Key Investigators
- BWH: Dominik Meier, Andriy Fedorov, William Wells
Objective
For this work we seek insight into the effects of voxel anisotropy and image inhomogeneity on registration accuracy and robustness.
Approach, Plan
Aside from designing experiments for metrics that are specific to this effect, we also seek interaction with other projects that have highly anisotropic image data.
Example Experiment: produce directly joint histograms and difference(ratio) images w/o the need to run the actual registration, i.e. we compare the effects of voxel anisotropy on the theoretical optimum: Move, filter and subsample identical image pair, then resample back to original position and build joint histograms and subtraction images. Because of the increasing PV effects we expect to see a degenerating joint histogram and a subtraction image with increasing edge artifacts. We can then try to interpret how the optimizer will behave in this environment. The benefit of this metric is that we circumvent the stochastic nature of the registration output.
Progress
- see images above
- we identified prostate MRI as a promising dataset for testing. Since hi-res isotropic data is never avail., we will move from the current anisotropy (x7) toward isotropic. Also we have 2 orientations (coronal vs. sagittal) and can compare the effects of having the anisotropy run in a different direction.
- progress on exposing the ITK joint histogram functions and MI metric for quantifying the above shown effects.
- we anticipate the optimum in the MI landscape to be blurred and potentially shifted by anisotropy. Using translation as a 1-DOF experiment, we calculate the new landscape and measure the blurring (and ev. shift) of the peak as a metric for reduction in accuracy and robustness
Progress
*see above
