Post on 14-Jan-2016
Focal Analysis of Knee Articular Cartilage
Quantity and QualityDr. Tomos G. Williams
Imaging Science and Biomedical EngineeringUniversity of Manchester
Presentation for:
AstraZeneca Joint Imaging GroupAlderley Park
Thursday Sept. 16th 2004
Contents
Background Thickness mapping methodology
– Illustrated on CP77 Thickness mapping of CP78
– Preliminary Results for 9 patients Project Work Plan
– Disease Progression Hypotheses – Statistical Analyses
Deliverables
Image Acquisition and SegmentationCP77 CP78
CartilageT1 Fat-Suppressed
TOSCARegionGrowingat AZ
EndPointLiveWireat AZ
BoneT2
EndPointManualat ISBE
EndPointLiveWireat AZ(incomplete& poorquality)
50 60 70 80 90 100 110
70
80
90
100
110
120
130
140
150
X
AB121247_a2BFem
Y
Primary Analysis — Volume Measurement
No significant patterns during disease progression (OA16 Study)– Conflicting Literature on OA Cartilage
compartmental volume change Conclusions:
– Disease process more subtle• Involves swelling followed by
thinning– Need to detect focal changes
• Difficulties in defining cartilage edge in parallel planar segmentations
– Still need population trials• Changes too small to detect
in individuals Requirements:
– Aggregate, detailed thickness maps• Detect focal changes in a
population– Statistical analysis
Published Approaches Cohen, Z. A. et al.
– “Templates of the cartilage layers of the patellofemoral joint and their use in the assessment of osteoarthritic cartilage damage”. Osteo. and Cart., 11, 2003.
Kauffmann et al. – “Computer aided methods for
quantification of cartilage thickness and volume changes”. IEEE Trans. Biomed. Eng., 50(8), 2003.
Surface Alignment– Does not guarantee
anatomical equivalence– Problematic in
corresponding cartilages with lesions, especially on edges
Method Overview — CP77 as Illustration
Optimal Shape Optimal Shape ModelModel
CorrespondencCorrespondenceses
RegistratioRegistrationn
Cartilage Cartilage Thickness MapsThickness Maps
Aggregate Aggregate Thickness MapsThickness Maps
ImageImage
SegmentationsSegmentations
3D Surface3D Surface
BoneBoness
CartilageCartilagess
Identifying Correspondences
Use bone as a frame of reference– More stable than cartilage in longitudinal
studies– Consistent across population
Statistical Shape Model (SSM)– Set of corresponding points on each
example– A description of how these point vary
over the set of examples Optimal SSMs
– Correspondences that lie on anatomically equivalent points lead to simpler models
– Minimum Description Length approach to building optimal shape models
– Manipulate correspondences on each example to simplify the model
anatomically equivalent correspondences
Initial Model MDL: 4.297
Optimised MDL: 4.018
CP77 Bone Statistical Shape Models
N=19, One from each patient
Separate model for each bone compartment
Bone and Cartilage Surface Registration
Correct for:– Chemical shift artefact– Patient movement
• 10 minute scans Method:
– Register all 3 bone surfaces simultaneously
– Achieve consistent cortical bone thickness
SurfacesSurfacesCortical Bone Cortical Bone
ThicknessThickness
Unre
giste
red
Unre
giste
red
Registe
red
Registe
red
Measuring Cartilage Thickness
Define 3D normal to the bone surface
Individual cartilage thickness map
Inner Cartilage
Cartilage Thickness
Bone
Outer Cartilage
3D Normal
CP77 cartilage coverage
CP77 Cartilage ThicknessMean
Standard Deviation
80% Coverage Threshold
Thicker cartilage on load bearing regions
Consistent and Low Standard Deviation
CP77 Minimum/Maximum Thickness
Complete valid readings coverage
Statistical AnalysisLeft (reflected)
Right
Left - Right
P<0.002 that the difference observed was a chance effect
CP78 bone segmentation Lacking segmentations
for CP78 bones Augment CP77 Statistical
Shape Models with Image Intensity information – Active Appearance
Models Active Search of CP78
bone images– Automatic Segmentation
of bone structures– Automatic identification
of CP77 correspondence points
Fully implemented within EndPoint
CP78 Preliminary Results (n=9)
Mean Thickness MapsBaseline
6 months
Coverage
CP78 Mean Difference Map (n=9)
Disease Progression Hypotheses
1. Anatomical– Simultaneous analysis of thickness change at each
and every correspondence point.
2. Abnormality– Assess thickness change in regions where cartilage
is thin or thick at baseline (in comparison with CP77).
3. Lesion proximity– Assess thickness changes in regions surrounding a
lesion in the cartilage at baseline.
4. Opposition– Asses thickness changes at locations which
articulate with regions that are abnormal at baseline.
Work Plan – Statistical Analyses
Test What? Large ROIe.g. Femoral Cartilage
Small Anatomical ROI e.g. Central Medial Tibial Plateau
Statistical Approach
VolumeAnalysis 1
Analysis 2Univariate tests“Has the cartilage volume changed?”
Overall Thickness Map
Analysis 4 Analysis 3Multivariate Tests“Has the pattern of cartilage thickness changed?
Thickness at each Location
Analysis 5 Analysis 6Multiple Comparison Tests“Where has the thickness changed?”
Deliverables Results
– CP77 Normal Thickness Range
– CP78 Disease Progression Maps
– Statistical Analysis Publications
1. Bone corresponded cartilage thickness mapping methodology (CP77 for illustration)
2. Changes in Cartilage Morphology in Longitudinal Population Studies (CP78)
3. Normal Range Cartilage Thickness Maps (CP77 results)
Software Tools– Identification of
corresponding points through SSM optimisation
– Bone AAM for automatic segmentation
– Visualisation of thickness data on average bone surfaces
– Propagation of correspondence points to allow comparison of cartilage thickness with normal range.
– Implemented within EndPoint package