Acknowledgements. Atlas-based automatic measurements of the morphology of the tibiofemoral joint

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1 Atlas-based automatic measurements of the morphology of the tibiofemoral joint M Brehler 1, G Thawait 2, W Shyr 1, J Ramsay 3, JH Siewerdsen 1,2, W Zbijewski 1 1 Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 2 Russel H. Morgan Dept. of Radiology, Johns Hopkins University, Baltimore, MD USA 3 Natick Soldier Research, Development and Engineering Center (NSRDEC), Natick, MA USA Acknowledgements The I-STAR Laboratory Imaging for Surgery, Therapy, and Radiology Collaborators S Demehri (JHU Radiology) J Keplan (NSRDEC) M Coyne (NSRDEC) T Brown (Boise State University) Funding Support US Army NSRDEC W911QY-14-C-0014 Carestream Health NIH 1R01-EB

2 Motivation AP radiograph projection Coronal CBCT slice Morphology measurements Used in diagnosis, treatment and implant planning Anatomical landmarks are manually set Difficult to identify landmarks Planar to volumetric imaging Variability in the orientation of the extremity Projection plane / principal viewing planes [1] [1] Measurements and Classifications in Musculoskeletal Radiology (2014), Thieme Motivation Morphology measurements Used in diagnosis, treatment and implant planning Anatomical landmarks are manually set Difficult to identify landmarks Planar to volumetric imaging Variability in the orientation of the extremity Projection plane / principal viewing planes Dedicated Extremities CBCT Weight-bearing imaging Sitting and standing configuration Compact, low dose High spatial resolution (~200 m) Soft-tissue contrast resolution Source: 90 kv, 10 mgy / scan Scan time: ~30s Detector: mm pixel FDK: ~20 cm 3 with 0.3 mm voxels 2

3 Metrics (2D) a) Static Alignment (SA) b) Medial Tibal Slope (MTS) c) Lateral Tibial Slope (LTS) d) Coronal Tibial Slope (CTS) [1] [2] [2] [1] Measurements and Classifications in Musculoskeletal Radiology (2014), Thieme [2] Atlas of the Patellofemoral Joint (2013), Springer Metrics (2D) a) Static Alignment (SA) b) Medial Tibal Slope (MTS) c) Lateral Tibial Slope (LTS) d) Coronal Tibial Slope (CTS) femoral axis α (a) (b) β (c) γ (d) δ tibial axis projected tibial axis projected tibial axis tibial axis Coronal Sagittal Sagittal Coronal 3

4 Metrics (3D) a) Static Alignment (SA) b) Medial Tibal Slope (MTS) c) Lateral Tibial Slope (LTS) d) Coronal Tibial Slope (CTS) Based on 10 anatomical landmarks Lateral plateau Femur Patella Medial plateau Tibia JMAT: Joint Morphology Analysis Tool DICOM-compatible multi-planar viewer Implemented in C++ (Qt, ITK and VTK) Semi-automated analysis of anatomical metrics Guides the user through selection of landmarks Computes metrics from the landmarks Reuses landmarks across metrics Load DICOM volume (CT or MR) Description Anatomical metrics: Medial, Lateral Tibial Slopes (MTS, LTS) Coronal Tibial Slope (CTS) Medial, Lateral Tibial Depth (MTD, LTD) ICD:TPW Ratio (ICD-TPW) Static Alignment (SA) Coronal Femoral Slope (CFS) Notch Width Index (NWI) Tibial Tubercle-Trochlear Groove (TT-TG) Bisect Offset (BO) Patellar Tilt (PT) Insall-Salvatti Ratio (ISR) Build and Save report 4

5 Intra- and inter-reader variability Intra-reader variability: SA ~0.6 o, MTS 0.5 o, LTS 0.8 o, CTS 0.6 o (6 repeats x 4 subjects) Intra-reader variability: ~0.8 o reported in MRI [1] Inter-reader variability correlation: 0.67 and more SA MTS LTS CTS Pearson correlation coefficient: r ICC(AGREEMENT): ρ r = 0.80 ρ = 0.71 r = 0.9 ρ = 0.9 r = 0.67 ρ = 0.64 r = 0.94 ρ = 0.94 [1] Hashemi J et al, J Bone Joint Surg Am, , 2008 Automatic measurements Disagreement between two observers Difficult to calculate landmarks in 3D Basic idea Use annotated images as atlas Transform landmarks to new images No segmentation of new image needed No large data base needed 5

6 Workflow Scan Atlas Atlas Atlas Atlas Rigid USE THIS AREA TO SHOW THE CURRENT STEP whole image Tibia Femur Initial transform Workflow Scan Atlas Atlas Atlas Atlas whole image Rigid Fixed image Initial USE THIS AREA Single TO bone SHOW THE CURRENT s STEP Tibia Femur Initial transform Single bone Single Single bone Masked bone single Similarity Similarity Similarity bone Similarity pick best atlas 6

7 Workflow Scan Atlas Atlas Atlas Atlas whole image Rigid Fixed image USE THIS AREA TO SHOW THE CURRENT STEP Tibia Femur Initial transform Single bone Single Single bone Masked bone single Similarity Similarity Similarity bone Similarity pick best atlas Surface deformation Transform landmarks Workflow Scan Atlas Atlas Atlas Atlas whole image Rigid Fixed image USE THIS AREA TO SHOW THE CURRENT STEP Tibia Femur Initial transform Single bone Single Single bone Masked bone single Similarity Similarity Similarity bone Similarity pick best atlas Surface deformation Transform landmarks Calculate metrics 7

8 Surface deformation Atlas registered contour Find the best fitting atlas GC I 0, I 1 = 1 3 NCC d dx I d dx I 1 + NCC d dy I d dy I 1 + NCC d dz I d dz I 1 Surface deformation Atlas Find the best fitting atlas registered contour I GC I 0, I 1 = 1 3 NCC d dx I d dx I 1 + NCC d dy I d dy I 1 + NCC d dz I d dz I 1 8

9 Gradient maginitude Gradient maginitude Surface deformation Atlas Find the best fitting atlas 0.15 v i registered contour I v i : Surface normals GC I 0, I 1 = 1 3 NCC d dx I d dx I 1 + NCC d dy I d dy I 1 + NCC d dz I d dz I 1 Surface deformation Atlas 0.15 v i registered contour deformed surface Find the best fitting atlas I Surface deformation v i : Surface normals GC I 0, I 1 = 1 3 NCC d dx I d dx I 1 + NCC d dy I d dy I 1 + NCC d dz I d dz I 1 9

10 Gradient maginitude Gradient maginitude Surface deformation Atlas 0.15 v i registered contour deformed surface Find the best fitting atlas I Surface deformation v i : Surface normals Transform landmarks GC I 0, I 1 = 1 3 NCC d dx I d dx I 1 + NCC d dy I d dy I 1 + NCC d dz I d dz I 1 Surface deformation Atlas 0.15 v i registered contour deformed surface Find the best fitting atlas I Surface deformation v i : Surface normals Transform landmarks GC I 0, I 1 = 1 3 NCC d dx I d dx I 1 + NCC d dy I d dy I 1 + NCC d dz I d dz I 1 10

11 Evaluation 24 healthy subjects (natural standing stance) Manual measurements using JMAT Evaluation methodology Leave-one-out cross-validation as a function of atlas size A: set of all images B: subset A\{p1} from which all possible k-subsets of Atlas images are drawn k: atlas size A p2 B p3 p4 p1 Evaluation 24 healthy subjects (natural standing stance) Manual measurements using JMAT Evaluation methodology Leave-one-out cross-validation as a function of atlas size A: set of all images B: subset A\{p1} from which all possible k-subsets of Atlas images are drawn k: atlas size A p2 B p3 p4 p1 k = 2 Atlas images Measure p1 using: (p2,p3) (p2,p4) (p3,p4) 11

12 MTS Evaluation 24 healthy subjects (natural standing stance) Manual measurements using JMAT Evaluation methodology Leave-one-out cross-validation as a function of atlas size A: set of all images B: subset A\{p1} from which all possible k-subsets of Atlas images are drawn k: atlas size A p2 B p3 p4 p1 k = 2 Atlas images Measure p1 using: (p2,p3) (p2,p4) (p3,p4) Measure p2 using: (p1,p3) (p1,p4) (p3,p4) Atlas size 1 volume / atlas 15 volumes / atlas 20 volumes / atlas image number image number image number 1 Atlas image 15 Atlas images 20 Atlas images 23 data points per box data points per box 1771 data points per box 12

13 Manual (Expert 1) [ ] Manual (Expert 2) [ ] Atlas size vs interquartile range and RMSE Reproducibility of observer measurements Leave-one-out (atlas set of 23) Expert 1 Set atlas landmarks Perfect prediction for a new sample r = 0.99 ρ = 0.98 r = 1.0 ρ = 1.0 r = 0.99 ρ = 0.99 r = 0.99 ρ = 0.99 r = 0.82 ρ = 0.76 r = 0.9 ρ = 0.9 r = 0.65 ρ = 0.63 r = 0.94 ρ = 0.94 Expert 2 different expertise different institution Automatic method within expert-toexpert variability r=0.8, ρ=0.71 r=0.9, ρ=0.9 r=0.67, ρ=0.64 r=0.94, ρ=

14 Joint Space Morphology Tibial plateau joint space maps Joint space width Load-bearing vs non-load-bearing Articular surfaces modeled as conductor Field lines connect corresponding points [1] Characterization of 3D joint space morphology using an electrostatic model (with application to osteoarthritis), Q. Cao et al. PMB 2015 Conclusions Automated anatomical measurements Transfer of 2D metrics to 3D Analysis of reproducibility with respect to atlas size Correlation with manual readers Reflects agreement between readers Excellent (MTS, CTS ρ >0.9) Ongoing work Include Patellar metrics (Patellar Tilt, TTTG) Foot and ankle Correlate with clinical outcomes Application development Joint Space Maps Load-bearing vs non-load-bearing 14