SPATIO-TEMPORAL ANALYSIS OF THE SIGNIFICANT CHANGES IN CARTILAGE MORPHOLOGY: DATA FROM THE OSTEOARTHRITIS INITIATIVE Jose Tamez-Pena 1, Patricia Gonzalez.

Slides:



Advertisements
Similar presentations
TWO STEP EQUATIONS 1. SOLVE FOR X 2. DO THE ADDITION STEP FIRST
Advertisements

Quantitative Methods Topic 5 Probability Distributions
Course Solving Equations with Variables on Both Sides
Are You Smarter Than an 8 th Grade Math Student?
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Arithmetic and Geometric Means
The student will be able to:
Do Now Solve. 1. x – 17 = y + 11 = = x = x – 9 = 20 x = 49 y = 30 w 5 w = 90 x = 9 x = 29 Hwk: p29 odd, Test this Friday.
2 pt 3 pt 4 pt 5 pt 1 pt 2 pt 3 pt 4 pt 5 pt 1 pt 2 pt 3 pt 4 pt 5 pt 1 pt 2 pt 3 pt 4 pt 5 pt 1 pt 2 pt 3 pt 4 pt 5 pt 1 pt Time Money AdditionSubtraction.
Year 6 mental test 10 second questions
Lecture 2 ANALYSIS OF VARIANCE: AN INTRODUCTION
School Shop. Welcome to my shop. You have 10p How much change will you get? 7p 3p change.
Around the World AdditionSubtraction MultiplicationDivision AdditionSubtraction MultiplicationDivision.
Excel Functions. Part 1. Introduction 2 An Excel function is a formula or a procedure that is performed in the Visual Basic environment, outside the.
Solve Multi-step Equations
Objective - To simplify expressions using the order of operations. Simplify each expression below. 1) 6 + 5(8 - 2) 2) 3) 4)
ORDER OF OPERATIONS LESSON 2 DAY 2. BEDMAS B – Brackets E – Exponents D – Division from left to right M – Multiply from left to right A – Add from left.
Biostatistics Unit 5 Samples Needs to be completed. 12/24/13.
Splash Screen. Then/Now I CAN use theorems to determine the relationships between specific pairs of angles and use algebra to find angle measures. Learning.
Factoring Quadratics — ax² + bx + c Topic
Cost Control and the Menu—Determining Selling Prices and Product Mix
Cost Control and the Menu—Determining Selling Prices and Product Mix
(This presentation may be used for instructional purposes)
Mental Math Math Team Skills Test 20-Question Sample.
3-5 Warm Up Problem of the Day Lesson Presentation
1 Slides revised The overwhelming majority of samples of n from a population of N can stand-in for the population.
Columbus State Community College
Oil & Gas Final Sample Analysis April 27, Background Information TXU ED provided a list of ESI IDs with SIC codes indicating Oil & Gas (8,583)
Some problems produce equations that have variables on both sides of the equal sign.
Risk and Return Learning Module.
Factor P 16 8(8-5ab) 4(d² + 4) 3rs(2r – s) 15cd(1 + 2cd) 8(4a² + 3b²)
SYSTEMS OF EQUATIONS.
Hours Listening To Music In A Week! David Burgueño, Nestor Garcia, Rodrigo Martinez.
A longitudinal study of bone density in reassigned transsexuals R. A. Jones 1, C. G. Schultz 2, B. E. Chatterton 2 1. The Adelaide Private Menopause Clinic,
Do Now 1/10/11 Copy HW in your planner. Copy HW in your planner. Text p. 430, #4-20 evens, evens Text p. 430, #4-20 evens, evens Text p. 439,
National Alliance for Medical Image Computing Slide 1 NAMIC at UNC DTI, Shape and Longitudinal registration Closely linked with Utah.
Addition 1’s to 20.
25 seconds left…...
Factoring Grouping (Bust-the-b) Ex. 3x2 + 14x Ex. 6x2 + 7x + 2.
Solving Equations by Adding or Subtracting Warm Up Lesson Presentation
Preview Warm Up California Standards Lesson Presentation.
Statistics for the Social Sciences
We will resume in: 25 Minutes.
Bottoms Up Factoring. Start with the X-box 3-9 Product Sum
Chapter 11: The t Test for Two Related Samples
Experimental Design and Analysis of Variance
Migration of the Elderly in Maryland Data from the 2000 Census Presented to the State Data Center Affiliate Meeting, January 26, 2005.
Connectivity of aMRI and fMRI data Keith Worsley Arnaud Charil Jason Lerch Francesco Tomaiuolo Department of Mathematics and Statistics, McConnell Brain.
Combining the strengths of UMIST and The Victoria University of Manchester Tom Williams, Chris Taylor Andrew Holmes, John Waterton, Rose Maciewicz Graham.
Author :Monica Barbu-McInnis, Jose G. Tamez-Pena, Sara Totterman Source : IEEE International Symposium on Biomedical Imaging April 2004 Page(s): 840 -
Focal Analysis of Knee Articular Cartilage Quantity and Quality Dr. Tomos G. Williams Imaging Science and Biomedical Engineering University of Manchester.
NBIA Login and Search 29 July Replace Images: 6 possible images on next 3 pages.
Automatic pipeline for quantitative brain tissue segmentation and parcellation: Experience with a large longitudinal schizophrenia MRI study 1,2 G Gerig,
Author :J. Carballido-Gamio J.S. Bauer Keh-YangLeeJ. Carballido-GamioJ.S. BauerKeh-YangLee S. Krause S. MajumdarS. KrauseS. Majumdar Source : 27th Annual.
NA-MIC National Alliance for Medical Image Computing Statistical Models of Anatomy and Pathology Polina Golland.
Functional cartilage MRI T2 mapping: evaluating the effect of age and training on knee cartilage response to running  T.J. Mosher, Y. Liu, C.M. Torok 
C. D. Jordan, E. J. McWalter, U. D. Monu, R. D. Watkins, W. Chen, N. K
The effect of anterior cruciate ligament injury on bone curvature: exploratory analysis in the KANON trial  D.J. Hunter, L.S. Lohmander, J. Makovey, J.
C.P. Neu  Osteoarthritis and Cartilage 
Automatic morphometric cartilage quantification in the medial tibial plateau from MRI for osteoarthritis grading  E.B. Dam, Ph.D., J. Folkesson, M.Sc.,
Subjects with higher physical activity levels have more severe focal knee lesions diagnosed with 3T MRI: analysis of a non-symptomatic cohort of the osteoarthritis.
MRI morphological and quantitative evaluation of knee allograft repair at 3, 6 and 9 months post-op: early surveillance demonstrates nascent physiological.
T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative  K.L. Urish, M.G. Keffalas, J.R. Durkin,
Quantitative MRI (QMRI) features predict symptomatic knee pain during the next year: data from the OAI  J.G. Tamez-Pena, J. Farber, J.I. Galvan-Tejada,
Can bone shape predict who will have their knee replaced
X. Li, Ph. D. , C. Benjamin Ma, M. D. , T. M. Link, M. D. , D. -D
Region of interest analysis: by selecting regions with denuded areas can we detect greater amounts of change?  D.J. Hunter, L. Li, Y.Q. Zhang, S. Totterman,
Quantitative 3D MRI reveals limited intra-lesional bony overgrowth at 1 year after microfracture-based cartilage repair  M.S. Shive, A. Restrepo, S. Totterman,
Longitudinal analysis of cartilage T2 relaxation times and joint degeneration in African American and Caucasian American women over an observation period.
Presentation transcript:

SPATIO-TEMPORAL ANALYSIS OF THE SIGNIFICANT CHANGES IN CARTILAGE MORPHOLOGY: DATA FROM THE OSTEOARTHRITIS INITIATIVE Jose Tamez-Pena 1, Patricia Gonzalez 2, Edward Schreyer 2, Saara Totterman 2, 1 Biomedicine, Tec de Monterrey, Monterrey, Nuevo Leon, Mexico; 2 Qmetrics Technologies, Rochester, NY, USA

Objective Visualize, Follow and Quantitate the Areas of Cartilage Loss in an OA population

Introduction Problem: Cartilage Thickness Changes are focal, spatially heterogeneous and bi-directional: Thinning and thickening Cohort studies look at population averages The average of this heterogeneous data has a very small responsiveness Solution: For a subject: Localize and isolate the changes For a cohort: Count map of the significant changes in cartilage thickness

Material & Methods Osteoarthritis Initiative (OAI) 3D DESS data sets: –Releases 0.C.2, 1.C.2 and 3.C.1 from Progression cohort. Three time points: Baseline, 12 month and 24 month. 138 subjects with 3 time points –Nonexposed Data Release 0.E.1, 1.E.1 and 3.E.1 (n=108) Three time points: Baseline, 12 month and 24 month. –OAI Pilot Scan-Rescan Longitudinal Data for the estimation of scan-rescan variability (n=24)

Multi-Atlas-Based Segmentation Generate Atlas Register and Segment Each MRI to the Atlas (ITK registration modules) Postprocess the segmentation to match underlying MRI information. Visually score the quality of the segmentation. Use the registration data to map each segmentation to the atlas space Subtract each mapped segmentation to compute change in cartilage thickness Compute Significant Changes Compute Cohort Averages

Quantitation

Standardized Analysis: Changes in Cartilage Thickness Medial Lateral

Change Measurement: Significant Change Maps Minus Change Map Scan-Rescan SDD Map Significant Change Map (Activation Map) Baseline24 Month = The Scan-Rescan Standard Deviation of the Differences (SDD) is used to mark changes in thickness values that higher than the scan-rescan paired errors ( Delta < -1.96*SDD )

Population Maps Average Referenced Thickness 12 Month Change Map 24 Month Change Maps Average Change Map Significant Changes Prevalence Map =

24 Month Results NonexposedNo DenudedLow DenudedHigh Denuded n=103n=51n= % 9.3%8.1%11.6% 17.3% 14.7% 3.4% 6.8% Baseline 24 Month Change 24 Month Heat Map

SRM=0.73 P<0.001 P=0.007P=0.054 SRM=0.36 SRM=0.39 P<0.001 SRM=0.31

P=0.009P=0.001 P=0.038 Fisher’s Exact Test

Limitations Small OA population Multi-atlas based segmentation is biased towards atlas models –Less accurate at advanced OA cases –Higher noise at advanced OA cases

Conclusion The automated analysis methodology enabled the localization and mapping of the significant changes in cartilage Thickness. The significant changes are heterogeneous –Non Exposed OAI cohort did not change –OA subjects with no denuded areas had 2.4% of new areas of cartilage loss every year. SRM=0.73 The methodology indicated that not all subjects are affected by loss, and that the prevalence of loss is greater at more advanced OA groups.

Acknowledgements The OAI for all the imaging and clinical data

Baseline24 MonthChange Map Change Measurement: Significant Change Maps