Difference between revisions of "Projects:RegistrationDocumentation:ParameterTesting"

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**range of fiducial misalignment & distributions
 
**range of fiducial misalignment & distributions
 
**Plot error vs. initial misalignment (where does registration begin to fail), plot error vs. parameter settings (which setting works best for the toughest case)
 
**Plot error vs. initial misalignment (where does registration begin to fail), plot error vs. parameter settings (which setting works best for the toughest case)
[[Image:SRegTest_FLAIR-T1_LRRot15.png|left|500px|360 Level Self Validation Test]]
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[[Image:SRegTest_FLAIR-T1_LRRot15.png|left|500px|360 Level Self Validation Test: FLAIR to T1, left-right rotation]]
[[Image:RegistrationISMRM10_Disturb_12_hist.png|left|300px|RMS Histogram]]
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[[Image:RegistrationISMRM10_Disturb_12_hist.png|left|300px|RMS Histogram comparison for 12 degrees misalignment]]
[[Image:RegistrationISMRM10_RMS.png|left|300px|360 Level Sensitivity]]
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[[Image:RegistrationISMRM10_RMS.png|left|300px|Sensitivity (RMS range) vs. rotational misalignment]]

Revision as of 22:06, 27 October 2009

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ISMRM abstract 2010

  • Title: MR-protocol Tailored Medical Image Registration
  • Objective: Determine optimized sets of parameters for successful automated registration of MR-MR images within the 3DSlicer software. DOF, cost function, initialization and optimization strategy will differ because of the differences in image contrast and/or content. This work will present approaches and solutions for successful registration for a large set of combinations of MRI pairings. This is part of a concerted effort to build a Registration Case Library available to the medical imaging research community.
  • Method:
    • 1- choose 3-4 subjects/exams with 3-4 different contrast pairings: T1, T2, PD, FLAIR: ~12-16 images. We choose sets for which we have a good registration solution
    • 2- disturb each pair by a known transform of varying rotational & translational misalignment
    • 3- run registration for a set of parameter settings and save the result Xform, e.g. metric: NormCorr vs. MI , 2% vs 5% sampling, 50 vs. 100 iteration max
    • 4- evaluate registration error as point distance and RMS.
    • 5- run sensitivity analysis and report the best performing parameter set for each MR-MR combination
    • 6-extension 1: add different voxel sizes, i.e. emulate 1,3,5mm slice thickness
  • This self-validation scheme avoids recruiting an expert reader to determine ~ 3-5 anatomical landmarks on each unregistered image pair (time constraint). Also we can cover a wider range of misalignment and sensitivity by controlling the input Xform. It also facilitates batch processing.
  • Results:
    • range of fiducial misalignment & distributions
    • Plot error vs. initial misalignment (where does registration begin to fail), plot error vs. parameter settings (which setting works best for the toughest case)
360 Level Self Validation Test: FLAIR to T1, left-right rotation
RMS Histogram comparison for 12 degrees misalignment
Sensitivity (RMS range) vs. rotational misalignment