fast intra- and intermodal deformable registration based on local subvolume matching matthias söhn...
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Fast Intra- and Intermodal Fast Intra- and Intermodal Deformable Registration Based Deformable Registration Based on Local Subvolume Matchingon Local Subvolume Matching
Matthias Söhn1, Verena Scheel2, Markus Alber1
(1) Radiooncological Clinic, Section for Biomedical Physics, University of Tübingen, Germany
(2) Laboratory for Preclinical Imaging and Imaging Technology, Department of Radiology, University of Tübingen, Germany
Forschungszentrum für Hochpräzisionsbetrahlung
2ESTRO 2007 Barcelona – Söhn et al. UKTübingen
Deformable Registration for RadiotherapyDeformable Registration for Radiotherapy
Requirements & Challenges:• accuracy• fast• no or little user interaction• versatility
4D-CT
CT-ConeBeamCT
CT-MRI
Featurelet-baseddeformable registration
our approach…
3ESTRO 2007 Barcelona – Söhn et al. UKTübingen
Algorithmic ImplementationAlgorithmic Implementation
1Cover region of interest in reference image with regular 3D-grid of featurelets
typical size:1.5x1.5x1.5 cm
4ESTRO 2007 Barcelona – Söhn et al. UKTübingen
reference image(exhale)
target image(inhale)
2
for each featurelet
Individual rigid registration of each featurelet
maximization of local normalized mutual information (NMI)
allowing 3D-shifts within local search region
fast & parallelizable!
5ESTRO 2007 Barcelona – Söhn et al. UKTübingen
reference image(exhale)
target image(inhale)
2
regions withmismatched featurelets
Individual rigid registration of each featurelet
for each featurelet
maximization of local normalized mutual information (NMI)
allowing 3D-shifts within local search region
fast & parallelizable!
6ESTRO 2007 Barcelona – Söhn et al. UKTübingen
Automatic assessment of local registration quality3reference featurelet
registeredtarget featurelet
local similaritymeasure field (NMI)
accept position=>
shift to positionwith minimal local deformation energy
=>
…shift to position with minimal local deformation energy within NMI-optimum
=>
7ESTRO 2007 Barcelona – Söhn et al. UKTübingen
Automatic assessment of local registration quality -- Result3
8ESTRO 2007 Barcelona – Söhn et al. UKTübingen
Relaxation Step: Iterative Minimization of Deformation Energy for mismatched Featurelets
4
9ESTRO 2007 Barcelona – Söhn et al. UKTübingen
B-Spline Interpolation of Featurelet shift vectors5target image, final featurelet positions
interpolationof shift vectors
=> continuous deformation field!
10ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults RCCT Inhale-Exhale deformable registration: Visual evaluation
before… …after registration
11ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults CT-ConeBeamCT deformable registration: Visual evaluation
Elekta XVI ConeBeam-CT data,courtesy D. Yan, Y. Chi (Beaumont)
before… …after registration
12ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults CT-MRI deformable registration: Visual evaluation
before… …after registration
CT
MRI MRI (backtransformed)
13ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults Quantitative evaluation: Anatomical landmarks
N
3D-residuals [mm]…
before registration
after registration
pat. 1 15 8.2±4.6 1.3±0.9
pat. 2 11 4.2±1.5 1.5±1.0
pat. 3 14 10.4±5.5 1.8±0.7
pat. 4 15 7.8±5.7 1.8±1.3
avg.7.8±5.1
(max. 21.3)1.6±1.0
(max. 4.6)
N=55 landmarks altogether
marked in inhale and exhale CTs of 4 patients
[Siemens Somatom Sensation Open RCCT datasets @ 1x1x3mm voxelsize]
14ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults Quantitative evaluation: Virtual phantom
courtesy D. Yan, Y. Chi (Beaumont Hospital)
Virtual thorax phantom:
known deformation field used to to deform real lung CT dataset
[based on ~740.000 voxels]
before: 2.9±2.8mmafter: 1.1±1.2mm
Residuals of featurelet algorithm based on thorax phantom:
15ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults Computational performance
test case registered region [voxels]
calculation time
(dual-core Xeon PC, 2x2.66GHz)
Thorax (CT-CT) 360x270x120 2min 12sec
H&N (CT-CBCT) 225x225x115 49sec
H&N (CT-MRI) 378x210x70 1min 23sec
calculation time mainly depends on… size of registered region size of local search region featurelet size
16ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ResultsResults Computational performance
test case registered region [voxels]
calculation time
(dual-quadcore Xeon PC, 8x2.66GHz)
Thorax (CT-CT) 360x270x120 38sec
H&N (CT-CBCT) 225x225x115 14sec
H&N (CT-MRI) 378x210x70 19sec
“online” deformable registration!
17ESTRO 2007 Barcelona – Söhn et al. UKTübingen
ConclusionsConclusions
Featurelet-based deformable registration:
fast, parallizable
model-independent, fully automatic
enables multi-modality registration due to use of mutual information
sub-voxel registration accuracyas shown by landmark-based evaluation and virtual thorax phantom
‘online’ multi-modality deformable registration within reach!