fast intra- and intermodal deformable registration based on local subvolume matching

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!