Multimodality & 4D Imaging: Registration and Fusion for Treatment Planning and Delivery Multimodality & 4D Imaging: Registration and Fusion for Treatment Planning and Delivery Marc L Kessler, PhD The University of Michigan Marc L Kessler, PhD The University of Michigan ASTRO 2007 - 49 th Annual Meeting Wednesday, October 31, 2007 1:30 – 2:45 PM ASTRO 2007 - 49 th Annual Meeting Wednesday, October 31, 2007 1:30 – 2:45 PM Laura A Dawson, MD Princess Margaret Hospital Laura A Dawson, MD Princess Margaret Hospital
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Multimodality & 4D Imaging:Registration and Fusion for
Treatment Planning and Delivery
Multimodality & 4D Imaging:Registration and Fusion for
Treatment Planning and Delivery
Marc L Kessler, PhD The University of MichiganMarc L Kessler, PhD The University of Michigan
ASTRO 2007 - 49th Annual MeetingWednesday, October 31, 2007
1:30 – 2:45 PM
ASTRO 2007 - 49th Annual MeetingWednesday, October 31, 2007
1:30 – 2:45 PM
Laura A Dawson, MD Princess Margaret HospitalLaura A Dawson, MD Princess Margaret Hospital
DisclosuresDisclosures
• Research Grant- Varian Medical Systems
ObjectivesObjectives
Understand the basic mechanics of multimodality and 4D image registration techniques
Understand the different techniques used to combine, display and interact with multimodality and 4D image and dose data
Understand the clinical use and limitations of these techniques for Tx planning, Txdelivery and plan adaptation
Understand the basic mechanics of multimodality and 4D image registration techniques
Understand the different techniques used to combine, display and interact with multimodality and 4D image and dose data
Understand the clinical use and limitations of these techniques for Tx planning, Txdelivery and plan adaptation
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OutlineOutline
Motivation
Mechanics!
Clinical Use
Motivation
Mechanics!
Clinical Use
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MotivationMotivation
Precision radiation therapy requires accurate delineation of the tumor and normal tissues in the planning phase and accurate localization of these structures during the delivery phase
Precision radiation therapy requires accurate delineation of the tumor and normal tissues in the planning phase and accurate localization of these structures during the delivery phase
…with the aid of imaging…with the aid of imaging
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MotivationMotivation
DeliveryDelivery
ImagingImaging
PlanningPlanningImagingImaging
entireentireOptimization of the radiotherapy process requires that we anticipate, measure & adapt to changes in the patient
Optimization of the radiotherapy process requires that we anticipate, measure & adapt to changes in the patient
on-lineon-line
off-lineoff-line
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We now have many cameras available… which provide complementary data! We now have many cameras available… which provide complementary data!
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X-ray CTX-ray CT MRIMRI Nuc MedNuc Med
??
??
Normal TissuesNormal Tissues Target VolumesTarget VolumesMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
Repeat ImagingRepeat Imaging
Balter / UMBalter / UM
4-D Imaging4-D Imaging
… assess motion… assess motion
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The Big PictureThe Big Picture
MRMR
Tx Plan3D DoseTx Plan3D Dose
NMNM
CTCT
CBCT1…n
CBCT1…n
PortalImagesPortal
Images
4D CBCT4D
CBCT
USUS
3D DoseDay n
3D DoseDay nUSUS
“Adapting”PatientModel
“Adapting”PatientModel
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The GoalThe Goal
Ideally, we would like to have a timedependent vector of information for every “point” in an anatomic object
Ideally, we would like to have a timedependent vector of information for every “point” in an anatomic object
image information (MR, CT, NM, … )
physiologic information ( )
anatomic label information
dose information … with time stamp !
image information (MR, CT, NM, … )
physiologic information ( )
anatomic label information
dose information … with time stamp !
ττ
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4-D Vectors4-D VectorsMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
MechanicsMechanics… determine the geometric transformation that maps corresponding points from one image series to another
… determine the geometric transformation that maps corresponding points from one image series to another
Form of the transformation TForm of the transformation T
Number of degrees of freedoms βNumber of degrees of freedoms β
… from rigid to fully freeform… from rigid to fully freeform
… from 3 to 3 x N*… from 3 to 3 x N*
*N = number of voxels*N = number of voxelsMarc L Kessler, PhD - ASTRO 2007 Refresher Course Marc L Kessler, PhD - ASTRO 2007 Refresher Course
TransformationTransformation… determine the geometric transformation that maps corresponding points from one image series to another
… determine the geometric transformation that maps corresponding points from one image series to another
XB = T ( XA , { ß })XB = T ( XA , { ß })(x,y,z) coordinates of a point in Series B(x,y,z) coordinates of a point in Series B (x,y,z) coordinates
of a point in Series A(x,y,z) coordinates of a point in Series A
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Degrees of FreedomDegrees of Freedom
FewFew ManyManyNone ?None ?
PET/CTPET/CT MR - CTMR - CT 4D CT4D CT
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What is T ?What is T ?
Rigid / Affine
Full 3D / 4D Deformation
Parametric models
Free-form models
Rigid / Affine
Full 3D / 4D Deformation
Parametric models
Free-form models
Global, regional, or piecewiseGlobal, regional, or piecewise
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What is T ?What is T ?
Rigid / Affine Rigid / Affine
Global, regional, or piecewiseGlobal, regional, or piecewise
xB = A xA + b (up to 12 DOF)xB = A xA + b (up to 12 DOF)
y = m x + b … in 3Dy = m x + b … in 3D
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Parallel lines stay parallel !Parallel lines stay parallel !3 or 4 DOF3 or 4 DOF
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Transformation parameters to apply to a particular point depends on the location of the point !
Transformation parameters to apply to a particular point depends on the location of the point !
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XB = T ( XA , { ß(XA, φ )})XB = T ( XA , { ß(XA, φ )})Marc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
Full 3D / 4D DeformationFull 3D / 4D Deformation
ParametricParametric FreeformFreeform
… up to 3 x N… up to 3 x N
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Full 3D / 4D DeformationFull 3D / 4D Deformation
Various splines ( TPS , B-splines )
Other basis functions
Various splines ( TPS , B-splines )
Other basis functions
ParametricParametric
Finite element models
Flow models ( optical, viscous )
Finite element models
Flow models ( optical, viscous )
FreeformFreeform
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Full 3D / 4D DeformationFull 3D / 4D Deformation
B-Splines … local
Thin-Plate splines … global
Finite element … bio-mechanical
Intensity flow … image forces
B-Splines … local
Thin-Plate splines … global
Finite element … bio-mechanical
Intensity flow … image forces
Each have some distinct propertiesEach have some distinct properties
( mono-modality )( mono-modality )
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Full 3D / 4D DeformationFull 3D / 4D Deformation
ParametricParametric FreeformFreeform
Warp Space /… Drag Objects
Warp Space /… Drag Objects
Warp Objects /… Drag Space
Warp Objects /… Drag Space
Brock / PMH
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How Do We Compute { } ?How Do We Compute { } ?
Construct a metric that measures the mismatch (or similarity) between a pair of datasets
Construct a metric that measures the mismatch (or similarity) between a pair of datasets
Apply an optimization algorithm to determine the parameters (DOF) that minimize (maximize) this metric
Apply an optimization algorithm to determine the parameters (DOF) that minimize (maximize) this metric
22
11
ββ
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How Do We Compute { } ?How Do We Compute { } ?ββ
{ β }Marc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
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Geometry-Based MetricsGeometry-Based Metrics
Point MatchingLeast Squares
Point MatchingLeast Squares
( XB - XA ) 2( XB - XA ) 2ΣΣ
min distance 2min distance 2ΣΣSurface MatchingChamfer Matching
Surface MatchingChamfer Matching
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… depends on the image characteristics!… depends on the image characteristics!Marc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
How About An Example?How About An Example?
CTCT
PETPETTransformation
Rotate - Translate
Registration Metric
Mutual Information
Optimizer
Simplex Algorithm
Transformation
Rotate - Translate
Registration Metric
Mutual Information
Optimizer
Simplex Algorithm
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How About An Example?How About An Example?
CTCT
PETPET
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How About An Example?How About An Example?
CTCT
PETPET
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How About Deformations ?How About Deformations ?Balter / UMBalter / UM
Multiphasic CT DataMultiphasic CT Data
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Exhale StateExhale State Inhale StateInhale State
Transformation
B-Splines ( multi-resolution )
Registration Metric
Sum Squared Difference
Optimizer
Gradient decent
Transformation
B-Splines ( multi-resolution )
Registration Metric
Sum Squared Difference
Optimizer
Gradient decent
How About Deformations ?How About Deformations ?
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Only small additional computation cost when increasing the number of knots. Only small additional computation cost when increasing the number of knots.
Successively increase the resolution of the knot spacingSuccessively increase the resolution of the knot spacing
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Multiresolution DeformationsMultiresolution DeformationsSuccessively increase the resolution of the image dataSuccessively increase the resolution of the image data
CoarseCoarse
¼ Resolution¼ Resolution Full ResolutionFull Resolution
FineFineMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
Multiresolution DeformationsMultiresolution DeformationsSuccessively increase the resolution of the image dataSuccessively increase the resolution of the image data
60 x 60 x 48 mm60 x 60 x 48 mm 4 x 4 x 3 mm4 x 4 x 3 mm
CoarseCoarse FineFineMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
Registration Metric vs. IterationRegistration Metric vs. Iteration
Change inknot spacing Change inknot spacing
Low ResLow Res
High ResHigh Res
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We Are Not Really Splines !We Are Not Really Splines !Ruan / UMRuan / UM
No “stiffness”information
No “stiffness”information
ExtractedRibcageExtractedRibcage
ExhaleExhaleDeform InhaleDeform Inhale
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“Stiffness” Weighting“Stiffness” Weighting
wc(x)wc(x)
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using “stiffness”information
using “stiffness”information
Using “Prior” InformationUsing “Prior” InformationRuan / UMRuan / UM
ExtractedRibcageExtractedRibcage
ExhaleExhaleDeform InhaleDeform Inhale
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Tissue SlidingTissue SlidingBalter / UMBalter / UM
Deal with different organs individually?Deal with different organs individually?
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Tissue SlidingTissue SlidingBalter / UMBalter / UM
Deal with different organs individually?Deal with different organs individually?
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Ribs driven by large lung deformations
Ribs driven by large lung deformations
Ribs not affected by lung registrationRibs not affected
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Finite Element ModelingFinite Element ModelingBrock / UMBrock / UM
ExhaleExhale
InhaleInhale
Take into account physical tissue properties (directly)Take into account physical tissue properties (directly)
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Finite Element ModelingFinite Element ModelingBrock / PMHBrock / PMH
… thorough segmentation is necessary… thorough segmentation is necessaryMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
The Future ?The Future ?
Family of Generalized, Customizable,Patient Models
Family of Generalized, Customizable,Patient Models
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Is The Future Here Already?Is The Future Here Already?
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…from Atlas to Individual…from Atlas to Individualwww.mimvista.comwww.mimvista.com
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…from Individuals to Atlas…from Individuals to AtlasThompson / UCLAThompson / UCLA
Brain Mapping: The Disorders, Academic Press, 1999Brain Mapping: The Disorders, Academic Press, 1999
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Meyer/UMMeyer/UM
Segment /register / averageSegment /register / average
withoutwithout withwith
Segment using atlasSegment using atlasMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
In The Meantime …In The Meantime …
ImageImage DoseDoseAnatomyAnatomyMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
Anatomy MappingAnatomy Mapping
Boolean ORBoolean OR
… map to CT… map to CT
Use superior MR contrast for targetingUse superior MR contrast for targetingMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
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“Delivery” CT“Delivery” CTSegmentation done w/ the aid of a registration!Segmentation done w/ the aid of a registration!Marc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
More Than DeformationsMore Than Deformations
… not just deformation!… not just deformation!
deformationdeformation
resectionresection
weight lossweight loss
Δ vascularΔ vascular
shrinkageshrinkage
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Dose MappingDose MappingDealing with volume elements that may:Dealing with volume elements that may:
change shape / appear / disappear
… need proper spatial re-sampling
don’t necessarily add in a linear fashion
… need some sort of radiobiology
exist in homogenous intensity regions
… hard to evaluate registration
change shape / appear / disappear
… need proper spatial re-sampling
don’t necessarily add in a linear fashion
… need some sort of radiobiology
exist in homogenous intensity regions
… hard to evaluate registration
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ValidationValidation
How do we know how well these registration methods perform?
build phantoms and test them
we can know the truth!
provide tools to examine results
we don’t know the truth!
How do we know how well these registration methods perform?
build phantoms and test them
we can know the truth!
provide tools to examine results
we don’t know the truth!
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Validation PhantomsValidation PhantomsCTCT
MRMR
19861986
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Validation PhantomsValidation PhantomsKashani / UMKashani / UM
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AAPM Task Group 132AAPM Task Group 132
Methods to assess the accuracy of image registration and fusion
Issues related to acceptance testing and quality assurance for image registration and fusion
Methods to assess the accuracy of image registration and fusion
Issues related to acceptance testing and quality assurance for image registration and fusion
Use of Image Registration and Data Fusion Algorithms and Techniques in Radiotherapy
Use of Image Registration and Data Fusion Algorithms and Techniques in Radiotherapy
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More than just mechanics!More than just mechanics!What Now ?What Now ?
MR volumes mapped to CT studyMR volumes mapped to CT studyMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistributeMarc L Kessler, PhD - ASTRO 2007 Refresher Course - Please do not redistribute
SummarySummary
GeometryGeometry IntensityIntensity
InteractiveInteractive AutomatedAutomated
AffineAffine non-Affinenon-Affine
Taxonomy of Registration ProcessTaxonomy of Registration Process
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SummarySummaryTools are now available to register and integrate image, anatomy & dose for both Tx planning and Tx delivery
These tools can be used to help build better models of the patient and to help customize and adapt therapy
Work towards more standard and robust tools and validations methods (for non-rigid) situations continues
Tools are now available to register and integrate image, anatomy & dose for both Tx planning and Tx delivery
These tools can be used to help build better models of the patient and to help customize and adapt therapy
Work towards more standard and robust tools and validations methods (for non-rigid) situations continues
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Multimodality and 4D Imaging: Multimodality and 4D Imaging: Registration and Fusion for Registration and Fusion for
Treatment Planning and Treatment Planning and Delivery: The clinical Delivery: The clinical
perspectiveperspective……
Laura Dawson, TorontoLaura Dawson, TorontoMarc Kessler, Ann ArborMarc Kessler, Ann Arbor
Other Future Uses of Image Other Future Uses of Image RegistrationRegistration
Planning: PETCTPlanning: PETCT--MRMR
• 56 yo man with clinical T3N0 SCC of oropharynx• PET-CT and MR obtained in treatment position,
on hard table top with mask, on study• Rigid image registration in region of interest
including vertebral bodies
• Benefits:• MR helped define primary tumor superiorly in
region of CT with dental artifact• PET helped confirm suspicious node on CT as
high risk
Courtesy of John Waldron and Stephen Breen, PMH
CT
Courtesy of John Waldron and Stephen Breen, PMH
MR
Courtesy of John Waldron and Stephen Breen, PMH
PET
? physiologic
uptake
GTV
?tumor
Courtesy of John Waldron and Stephen Breen, PMH
PET-CT
physiologic uptake:• muscle • tonsil• vesselGTV
muscle
The fused images are most useful when all information can be
evaluated together
Good alignment in region of tumorAlignment in base of skull not perfect
Good alignment in region of tumor
Courtesy of John Waldron and Stephen Breen, PMH
Planning: CTPETPlanning: CTPET--MRMR
• Looking at CT-PET fusion far more helpful than PET alone
• CT-PET registration not perfect in entire field of view, due to residual rotations, deformation
• Many normal variances of PET
Clinical interpretation of fused images important!
Planning: CTPlanning: CT--MR ProstateMR Prostate
• Advantages of MR for prostate cancer RT– Improve inter-observer variability– Provide more anatomy for organ /nerve sparing
approaches etc.
• New opportunities with MRS, diffusion MR, …– Better knowledge of gross disease– ‘Functional imaging’– Dose painting– Monitoring of change during RT and adaptation
Planning: CTPlanning: CT--MR ProstateMR Prostate
• MR can improve contouring in patients with bilateral hip replacements
Charnley et al, British J Radiology, 2005
Excellent localization of ‘sensitive’ structuresExcellent localization of ‘sensitive’ structures
Allows delineation not possible or difficult on CT aloneAllows delineation not possible or difficult on CT alone
McLaughlin / UMMcLaughlin / UM
… potency sparing?… potency sparing?
Planning: CTPlanning: CT--MR ProstateMR Prostate
MR with endorectal coilMR with endorectal coil
Courtesy of Cynthia Menard and Kristy Brock, PMH
1. MRI, no endorectal coil2. Planning CT3. MRI, endorectal coil
Deformable registration to planning CT
4. Regions of tumor burden, functional data can then be visualized in planning CT space
Liver cancer: MRI can show different volumes, more foci of tumor, especially for HCC
Tumors often easier to see
Necessary for GTV definition if CT contrast allergy
Planning: CTPlanning: CT--MR LiverMR Liver
Auto-fuse whole field of view
Planning: CTPlanning: CT--MR for liver cancerMR for liver cancer
CT MR CT MR
Vertebral body match
Planning: CTPlanning: CT--MR for liver cancerMR for liver cancer
CT MR CT MR
Liver match
Planning: CTPlanning: CT--MR for liver cancerMR for liver cancer
CT MR
• Once CT and MR liver are registered, the GTV on both can be compared
• Different phases of CT and MR can be complimentary
CT-arterial CT-venous MR-venous
Voroney, et al, IJROBP, 2006
Planning: CTPlanning: CT--MR for liver cancerMR for liver cancer
Before AfterLiver Deformable Registration
coronal
sagittal
Prior to Deformable Registration
GTV VolumeCT = 13.9 ccMR = 6.7 ccΔVol = 7.2 cc
(52%)
Planning: CTPlanning: CT--MR for liver cancerMR for liver cancerLiver deformation ?
• 26 patients with liver cancer investigated• GTV defined on CT and MR• CT Liver-MR Liver deformable registration• Med % surface of GTVs differed > 5 mm = 26%• Largest differences for HCC and
cholangiocarcinoma
Voroney, et al, IJROBP, 2006
DeformationDeformation
• Even though deformation is ‘scary’ and challenging to validate, measure,describe, we need to be aware that deformation and other volumetric change exists.
• Volume change and deformation is another source of error and there are strategies to deal with it
• Deformable image registration tools not available commercially.
• Clinical input and QA important despite technological advances and automation in IR.
AcknowledgementsPMHCynthia MenardAndrea BezjakJohn WaldronCharles CattonDavid Jaffray Mike SharpeDoug MoseleyJeff SiewerdsenTom PurdieJean Pierre BissonnetteKristy BrockCynthia EcclesJane HigginsRobert Case Regina TseMaria HawkinsMark Lee