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Registration motivation

Registration motivation. Mads Nielsen. Registration in Medical Imaging. Find correspondences – intrapatient:. inhale phase to exhale phase. 1. 1. Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T. 2009 “A framework for

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Registration motivation

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  1. Registration motivation Mads Nielsen

  2. Registration in Medical Imaging • Find correspondences – intrapatient: inhale phase to exhale phase 1 1 Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T. 2009 “A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets” Phys Med Biol 54 1849-1870.

  3. Monitoring subtle changesLocal is better! • Problem: which pixel goes where? Follow-up Baseline

  4. Image registration Moving Image Fixed Image Optimization Transform Incorporate model of density change Transform Coefficients Cost Function

  5. Disease monitoring usingimage registration • Possible to indicate which locations change • Consistent in time • Local changes predict decline better

  6. Registration in Medical Imaging • Find correspondences – intrapatient: disease progression 2 2 Marcus, DS, Fotenos, AF, Csernansky, JG, Morris, JC, Buckner, RL, 2009. Open Access Series of Imaging Studies (OASIS): Longitudinal MRI Data in Nondemented and Demented Older Adults. Journal of Cognitive Neuroscience, in press.

  7. Readings: Atrophy Assessment

  8. Readings: Atrophy Accuracy Test 101 Subjects From ADNI. Changes from BLM12

  9. Registration at Large and Small Scale disease progression • Inter-patient variation at large scaleAtrophy at smaller scale Data from: Marcus, DS, Fotenos, AF, Csernansky, JG, Morris, JC, Buckner, RL, 2009. Open Access Series of Imaging Studies (OASIS): “Longitudinal MRI Data in Nondemented and Demented Older Adults.“ Journal of Cognitive Neuroscience, in press. 2

  10. LDDMM/LDDKBM registration • Large Deformation Diffeomorphic Metric Mapping • Domain , deformations • Find minimizing • regularization/smoothness term • matching term Images

  11. Manifold/Lie Group Formulation • in LDDMM, regularization is the length of minimal paths • If Sobolev-norm <Lv,v> • on v, then diffeomorphism • L is the momentum • Operator: Lv = a • Vt= ∫K(:,x)at(x)dx

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