OWD2010 - 5 - Onderwijstechnologie voor dummies/docenten - Janneke van der Loo
MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and...
-
date post
15-Jan-2016 -
Category
Documents
-
view
212 -
download
0
Transcript of MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and...
![Page 1: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/1.jpg)
MSc project Janneke Ansems
21-08-2007
Intensity and Feature Based 3D Rigid Registration
of Pre- and Intra-Operative MR Brain Scans
Committee:Prof. dr. ir. B.M. ter Haar RomenyProf. dr. ir. F.N. van de VosseDr. ir. B. PlatelDr. ir. G.J. Strijkers
Two 3-D point sets are given: fpi g and fp0i g; i = 1, 2, : : :, N. The equation
that needs to be solved is then:
p0i = Rpi + T + Ni (1)
where R is a 3£3 rotation matrix, T a translation vector (3£1 column matrix)and Ni a noisevector. Therotation R and translation T haveto befound suchthat the following equation is minimized:
§ 2 =NX
i=1
k p0i ¡ (Rpi + T) k2: (2)
First the point sets are translated such that both point sets have the samecentroid. Then the rotation matrix R is calculated using the singular valuedecomposition (SVD). Finally the translation vector T is computed. Theregis-tration is completed by transforming the entire dataset using the just found Rand T.
RMS =1N
kp0i ¡ (Rpi + T)k: (3)
![Page 2: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/2.jpg)
2
Outline Introduction Registration Materials and Methods Results Discussion and Conclusion Recommendations
![Page 3: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/3.jpg)
3
IntroductionMedical Background
Brain tumors Cancer is 2nd major cause of death in the Netherlands at
present time Each year 1000 people in the Netherlands are diagnosed with a
brain tumor
Treatment Radiotherapy Resection surgery
Figure 1: benign (left) and malignant tumor
![Page 4: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/4.jpg)
4
IntroductionImage-Guided Surgery
Image-Guided Surgery The use of images to guide a surgeon during the procedure
Medtronic Stealth Station Surgeon is able to verify the location
of a tumor directly with the imagesusing an image guided probe
But: pre-operative images do not alwaysresemble the real-time situation duringsurgery!
Figure 2: Medtronic Stealth Station
![Page 5: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/5.jpg)
5
IntroductionIntra-Operative Imaging
Brain shift Intra-operative imaging during surgery gives a more
accurate view on the real-time situation
Figure 3: Axial slices during a craniotomy showing brain shift.
![Page 6: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/6.jpg)
6
IntroductionIntra-Operative MRI
The Polestar N20 open intra-operative MR scanner (Medtronic Inc.) Field strength: 0.15 Tesla Resolution: 128x128x64 Field of view: 20x20x19 cm
Chosen for its: Relative low cost Open access to the patient Mobility Local shielding Compatibility with Medtronic
Stealth Station Compromise: image qualityFigure 4: The Polestar N20 system in the operating
room.
![Page 7: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/7.jpg)
7
IntroductionImage quality Polestar
Due to low field the Polestar scanner is susceptible to noise and artifacts Intensity Gradient Distortions
Figure 5: Images of Phantoms scanned by the polestar N20 showing distortion (left) and intensity gradient.
![Page 8: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/8.jpg)
8
IntroductionAim
Register 3D pre-operative high resolution MR data and the intra-operative MR data from the Polestar N20 with maximum accuracy.
In this way high resolution accurate information is available for navigational purposes during neurosurgery, focus on datasets with skull intact.
![Page 9: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/9.jpg)
9
Outline Introduction
Registration Materials and Methods Results Discussion and Conclusion Recommendations
![Page 10: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/10.jpg)
10
Registration Definition:
Given a reference image R and a template image T, find a suitable transformation y such that the transformed image T[y] looks similar to the reference image R
ReferenceTemplate
![Page 11: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/11.jpg)
11
RegistrationTransformation Model
Definition: A mapping of locations of one image to new locations in another
image
![Page 12: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/12.jpg)
12
RegistrationSimilarity Measure Definition:
Equation that measures how much two images are alike
Intensity-based Methods Sum of Squared Differences:
Gradient-based Methods Normalized Gradient Field:
x
2SSD ))( - )(( T] [R,S xRxT
22
2NGF
),(
))(())(( T] [R,S
T
TxTn
xTnxRn
Reference Template
![Page 13: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/13.jpg)
13
RegistrationSimilarity Measure
![Page 14: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/14.jpg)
14
RegistrationOptimizing Scheme
Definition: An optimizing scheme calculates the transformation
parameters to achieve maximum similarity
Steepest Descent Gauss-Newton Levenberg-Marquardt
Figure 6: A comparison of steepest descent (green) and Gauss-Newton's method (red) for minimizing a function
![Page 15: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/15.jpg)
15
OptimizationGauss-Newton
The objective function f (w), w are transformation parameters:
f (w) =12
k T(y(w)) ¡ R k22 : (1)
Updatew by s, thesecond order Taylor approximation is:
f (w+ s) = f (w) + sr f (w) +12sT r 2f (w)s: (2)
The derivativeof s is taken and set to zero:
s =¡ r f (w)r 2f (w)
; (3)
in which r f (w) and r 2f (w) are:
r f (w) = Tyyw(T(y(w)) ¡ R) and r 2f (w) = (Tyyw)T (Tyyw): (4)
By updating w by s the new transformation parameters w are calculated.
![Page 16: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/16.jpg)
16
RegistrationMultilevel approach
Definition: Register from coarse to fine to optimize for speed and
robustness
![Page 17: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/17.jpg)
17
RegistrationSummary Intensity Based
![Page 18: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/18.jpg)
18
RegistrationFeatures Definition:
Features are a finite number of pixels or groups of pixels that are unique and exist in both images.
Given features r1, … , rn in reference image and t1, … , tn in template image, find a transformation y such that:ntyr ii ,...,1ifor )(
![Page 19: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/19.jpg)
19
Automatic feature detection Scale Invariant Feature Transform (SIFT) by Lowe 2D version gave promising results
RegistrationFeatures
Figure 6: Matches found by SIFT algorithm in Polestar data (left) and high resolution data
![Page 20: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/20.jpg)
20
Manual feature selection Transformation using Arun’s algorithm
Demo Brainmark
RegistrationFeatures
test
p0i = Rpi + T + Ni (1)
where R is a 3£3 rotation matrix, T a translation vector (3£1 column matrix)and Ni a noisevector. The rotation R and translation T haveto be found suchthat the following equation is minimized:
§ 2 =NX
i=1
k p0i ¡ (Rpi + T) k2: (2)
test
Two 3D point sets are given, the equation that needs to be solved is then:
ri = Rti + T (1)
Root mean square error (RMS):
RMS =1N
kri ¡ (Rti + T)k: (2)
![Page 21: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/21.jpg)
21
![Page 22: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/22.jpg)
22
Outline Introduction Registration
Materials and Methods Results Discussion and Conclusion Recommendations
![Page 23: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/23.jpg)
23
Materials and MethodsDatasets
Four datasets: Pre-operative 1.5 Tesla and intra-operative 0.15 Tesla MR data Two datasets of healthy volunteers Two partial datasets of patients
Figure 7: Mid-sagittal slices of the high and low resolution MR data of a healthy volunteer (left) and patient.
![Page 24: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/24.jpg)
24
Materials and MethodsPreprocessing
Initial Alignment Important for optimization scheme Gravity point of nonzero voxels in sagittal direction
Gradient removal Skull removal
![Page 25: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/25.jpg)
25
Materials and MethodsPreprocessing
Global Intensity Gradient Removal Subtract peaks from Gaussian blurred image
![Page 26: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/26.jpg)
26
Materials and MethodsPreprocessing
Gradient Removal Subtract peaks from Gaussian blurred image
![Page 27: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/27.jpg)
27
Materials and MethodsPreprocessing
‘Skull’ stripping Dilation and erosion of a binary mask
![Page 28: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/28.jpg)
28
Materials and MethodsPreprocessing
‘Skull’ stripping Dilation and erosion of a binary mask
![Page 29: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/29.jpg)
29
Materials and MethodsRegistration programs
Intensity-based registration programs: Rigid transformation model Sum of Squared Differences and Normalized Gradient Field Gauss-Newton optimization scheme Multilevel approach
Feature-based registration program: Manual selection of 10-15 features Arun’s algorithm for transformation
![Page 30: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/30.jpg)
30
Materials and MethodsExperiments
Intensity-based registrations Four datasets were registered using the SSD and NGF
programs Multilevel approach: 1, 2 and 3 levels (resolution steps) were
used
Feature-based registration Four datasets were registered The results will be used as initial parameter guess for the
optimizing scheme of the NGF program to register the patient datasets
![Page 31: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/31.jpg)
31
Materials and MethodsVisualization
To inspect registration results, a Graphical User Interface (GUI) was built
Figure 8: A screenshot of Regview to inspect registration results visually. The green lines indicate the cross-section with the other two views.
![Page 32: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/32.jpg)
32
Materials and MethodsVisualization
Three different settings to inspect registration results
Figure 9: Checkerboard (left), fusion (middle) and transition visualization.
![Page 33: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/33.jpg)
33
Outline Introduction Registration Materials and Methods
Results Discussion and Conclusion Recommendations
![Page 34: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/34.jpg)
34
ResultsTable 1.1: Results from the SSD program.
Dataset Nr of Nr of F inal SSD Computing Quality Commentsresolution iterations value time [s] A ssessment
steps V isualInspection
VolunteerNeuro001 3 9+11+3=23 1539509 65 Good2 24+3=27 1539294 66 Good1 35 1540095 196 Average
VolunteerNeuro002 3 16+13+10=39 1427983 82 Bad No skull2 44+9=53 1378021 91 Average removal1 35 1378671 378 Bad
PatientNeuro003 3 54+47+6=107 759877 98 Good Manual2 125+7=132 983955 370 Bad initial1 154 1059637 730 Bad alignment
PatientNeuro004 3 - - - Unable2 - - - Unable1 - - - Unable
![Page 35: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/35.jpg)
35
Results Influence of resolution steps, VolunteerNeuro002
One resolution step
Two resolution steps
![Page 36: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/36.jpg)
36
ResultsTable 1.2: Results from the NGF program.
Dataset Nr of Nr F inal NGF Computing Quality A ssessmentresolution steps iterations value time [s] V isual Inspection
VolunteerNeuro001 3 18+11+10=39 20,13 117 Bad2 13+5=18 8,97 117 Excellent1 31 8,97 205 Excellent
VolunteerNeuro002 3 15+12+7=34 8,59 168 Good2 14+7=21 8,59 138 Excellent1 22 17,64 172 Bad
PatientNeuro003 3 40+20+10=70 40,96 195 Bad2 14+30=44 98,84 233 Bad1 7 121,0 88 Bad
PatientNeuro004 3 - - - Unable2 - - - Unable1 - - - Unable
![Page 37: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/37.jpg)
37
ResultsRegistration of VolunteerNeuro001
using NGF, two resolution steps:
![Page 38: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/38.jpg)
38
ResultsTable 1.3: Results from the feature-based program.Dataset Nr of selected R M S error [cm] Quality A ssessment Comments
features V isual InspectionVolunteerNeuro001 5 0.157 Poor
10 0.166 Average15 0.128 Average
VolunteerNeuro002 5 0.203 Poor10 0.130 Average15 0.093 Average
PatientNeuro003 4 0.119 Poor5 0.120 Poor6 0.121 Poor7 0.162 Poor8 0.139 Average8 - Good After NGF
PatientNeuro004 4 0.201 Poor5 0.161 Poor6 0.146 Poor7 0.136 Poor8 0.130 Poor9 0.124 Average10 0.150 Average10 - Excellent After NGF
![Page 39: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/39.jpg)
39
Results
After manual selection of 10 features
Using feature based initialization for NGF registration program
![Page 40: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/40.jpg)
40
Results The intensity-based registration programs managed to
register the datasets of the healthy volunteers
However both intensity-based programs were not able to register the partial datasets of the patients without manual initial parameter guess
Best results were obtained by using a feature-based registration as initial parameter guess for the intensity-based programs.
![Page 41: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/41.jpg)
41
Outline Introduction Registration Materials and Methods Results
Discussion and Conclusion Recommendations
![Page 42: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/42.jpg)
42
Conclusion and Discussion All datasets are registered, results were inspected using
visual inspection
Preprocessing important for intensity-based programs
Accuracy of the voxelsize is feasible in the center of the field of view
However this accuracy is not attainable at the edge of the field of view due to distortions and artifacts resulting from the low field of the Polestar N20
![Page 43: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/43.jpg)
43
Outline Introduction Registration Materials and Methods Results Discussion and Conclusion
Recommendations
![Page 44: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/44.jpg)
44
Recommendations Image quality Polestar
Currently a phantom is developed to measure and correct the distortion
Next step: registration after skull opening but before tumor resection Non rigid transformation model Computation time Validation
![Page 45: MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.](https://reader035.fdocuments.us/reader035/viewer/2022070412/56649d625503460f94a45284/html5/thumbnails/45.jpg)
45
Thank you for your attention!Questions?