Projects:RegistrationDocumentation:ParameterTesting

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ISMRM abstract 2010

• Title: Protocol-Tailored Automated MR Image Registration
• Significance: Fast and accurate automated image registration algorithms are commonly available. But systematic knowledge about choosing registration strategies and parameters for them is lacking. As image contrast and content and initial misalignment vary, so do the appropriate choices of DOF, cost function, initialization and optimization strategy. But finding a successful strategy to co-registration of different MR datasets is empirical and largely based on trial and error. The landscape for optimizing the tradeoff between accuracy and robustness is largely unknown.
• Objective: We will determine optimized sets of parameters for successful automated registration of MR-MR images within the 3DSlicer software 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)
  • Examples below are cost function comparisons: MI vs NCorr