1. Focal Analysis of Knee Articular Cartilage Quantity and Quality Dr. Tomos G. Williams Imaging Science and Biomedical Engineering University of Manchester Presentation for: AstraZeneca Joint Imaging Group Alderley Park Thursday Sept. 16 th 2004

2. 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

3. Image Acquisition and Segmentation CP77CP78 Cartilage T1 Fat- Suppressed TOSCA Region Growing at AZ EndPoint LiveWire at AZ Bone T2 EndPoint Manual at ISBE EndPoint LiveWire at AZ (incomplete & poor quality)

4. 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

5. 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

6. Method Overview — CP77 as Illustration Optimal Shape Model Correspondences Registration Cartilage Thickness Maps Aggregate Thickness Maps Image Segmentations 3D Surface BonesCartilages

7. 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

8. CP77 Bone Statistical Shape Models N=19, One from each patient Separate model for each bone compartment

9. 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 Surfaces Cortical Bone Thickness Unregistered Registered

10. Measuring Cartilage Thickness Define 3D normal to the bone surface Individual cartilage thickness map Inner Cartilage Cartilage Thickness Bone Outer Cartilage 3D Normal

11. CP77 cartilage coverage

12. CP77 Cartilage Thickness Mean Standard Deviation 80% Coverage Threshold Thicker cartilage on load bearing regions Consistent and Low Standard Deviation

13. CP77 Minimum/Maximum Thickness Complete valid readings coverage

14. Statistical Analysis Left (reflected) Right Left - Right P<0.002 that the difference observed was a chance effect

15. 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

16. CP78 Preliminary Results (n=9) Mean Thickness Maps Baseline 6 months Coverage

17. CP78 Mean Difference Map (n=9)

18. 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.

19. Work Plan – Statistical Analyses Test What?Large ROI e.g. Femoral Cartilage Small Anatomical ROI e.g. Central Medial Tibial Plateau Statistical Approach Volume Analysis 1 Analysis 2 Univariate tests “Has the cartilage volume changed?” Overall Thickness Map Analysis 4Analysis 3 Multivariate Tests “Has the pattern of cartilage thickness changed? Thickness at each Location Analysis 5Analysis 6 Multiple Comparison Tests “Where has the thickness changed?”

20. 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