Projects:RegistrationDocumentation:RegEval Anisotropy

Effects of Voxel Anisotropy and Intensity-Inhomogeneity on Image-based 3D Registration

Dominik Meier, William Wells III, Andryi Fedorov, C.F. Westin, Ron Kikinis

Summary / Questions

This is a planned experiment & manuscript for results/models on how voxel-anisotropy and MR bias fields affect automated, intensity-based image registration, mostly in terms of precision and robustness. Given the complexity of interaction between cost function, optimizer & input data, much of the experiments will likely be empirical evidence, but other (theoretical/analytical) exploits would be welcome. Chief questions are:

• Anisotropy
  • at what point does voxel anisotropy seriously affect registration performance?
  • are individual DOF affected in different ways, i.e. how much more sensitive is rotation to this effect than other DOF?
  • what are the remedies & recommendations, e.g. images with voxel anisotropy ratios above X should be resampled or traverse DOF space in different ways (e.g. does isolate/constrain the most sensitive DOF help)?
  • strategies for non-rigid registration
• Intensity-Inhomogeneity
  • does bias field inhomogeneity in MRI images affect the quality of automated registration?
  • What is the relative sensitivity of different cost functions (MI vs. NormCorr)?
  • Is there a level where the bias field begins to compete with image content?
  • Should a bias-field correction be applied beforehand or after? Is bias correction affected by prior registration?
  • what is the effect of "differential bias"? for the purpose of subtraction and/or ratio images, is differential bias correction (Lewis et al. NeuroImage 2004) preferable to correcting each bias individually? Determined as residual in validation images of zero or predefined diff. -> moving out of scope a bit, because it is no longer pure registration but includes change detection via subtraction. On the other hand that is the common application and hence clarity on bias sources is needed. We can argue that all intra-subject intra-modality registration is done for the purpose of change detection.

Methods

The basic experiment proposed is to take reference images with high-resolution and isotropic voxel size; move them by a known amount, then filter & subsample to simulate anisotropy; and finally register & evaluate residual error. The self-validation format is there because having a ground truth is the easiest way to isolate the effects under study, but roughly any image pair with an acceptable gold-standard alignment is eligible. More detailed analyses of cost function behavior, capture range etc. are also possible.

• Test Data
  • must have 1mm isotropic resolution
  • kidney CT, breast MRI,
  • DTI
  • Brainweb
  • modalities where anisotropy is common

• Anisotropy Experiment

1. take 1mm iso ref volume and move a known amount
2. filter (1-D avg) & subsample both image grids
3. register & evaluate residual error (evaluate RMS distance: distance of ICC points sent through R1*inv(R2)

• BiasField Experiment:

1. take 1mm iso ref volume and move a known amount
2. apply bias field to both image grids
3. register & evaluate residual error

• Variational Parameters
  • voxel size factors: x 1 , 1.2 , 1.5 , 3 , 5, 10
  • bias field: derive from actual case, then amplify x 1 , 1.2 , 1.5
  • reference motion
  • reg. sampling rate
• to obtain Relevant Reference XForm
  • take 2 real-life scans of different protocols, e.g. FLAIR and T1, and perform BSpline registration, use that as reference + add additional translation & rotation
  • take average of real-life bias fields then amplify x 1 , 1.2 , 1.5

• Evaluation
  • registration error as RMS residual
  • cost function
  • ROC?

Timeframe

• body of results by registration retreat in February 2011
• manuscript submitted around April 2011