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  • Kelly H. Zou, PhD; zou@bwh.harvard.edu

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  • An EM-Algorithm for Analyzing Multi-Center Repeated fMRI Data Kelly H. Zou, PhD Assistant Professor of Radiology Department of Radiology, Brigham and Women’s Hospital Department of Health Care Policy, Harvard Medical School Joint Statistical Meetings August 8, 2004 Quality Assessment BWH, HMS
  • fBIRN Functional Imaging Research for Schizophrenia Testbed, Biomedical Informatics Research Network Quality Assessment BWH, HMS
  • Co-Authors Steven D. Pieper, PhD Meng Wang, MSE Simon K. Warfield, PhD William M. Wells, PhD Ron Kikinis, MD FIRST BIRN Brigham and Women‘s Hospital Harvard Medical School NCRRP41RR13218 Quality Assessment BWH, HMS
  • Background Multi-Site BIRN Study: 11 Sites (MN, UCI, UNC, UCLA…, BWH, MGH) 5 Healthy males as “Human Phantoms” 2 Visits on separate days per site per subject 2 extra visits at one site for 3 of the 5 subjects 4 Sensory motor (SM), 2 cognitive (Cog), 2 breath-hold (BH) runs per visit BWH, HMS Quality Assessment
  • BWH, HMS Quality Assessment
  • Clinical Objectives It is meaningful to pool the data to yield a larger sample size in the next-phase clinical study ( Schizophrenic vs. normal controls)? How to assess the effects of various factors? BWH, HMS Quality Assessment
  • Statistical Objectives Ultimate problem (Pooling): How to combine multi-site data and to validate the pooling mechanism? Current problem (Calibration): How to compare and combine data in the calibration step? https://share.spl.harvard.edu/users/zou BWH, HMS Quality Assessment
  • Materials and Methods Focus on Reproducibility of the SM Task Subjects perform bilateral finger tapping on button boxes (1 dummy button box and 1 actual) in time with 3Hz audio cue and flashing checkerboard square They press buttons 1 - 4 in consecutive order and then back again using both hands at simultaneously and in sync BWH, HMS Quality Assessment
  • BWH, HMS Quality Assessment 3D SPGR 3D Spoiled Grass, axial 256x192 matrix 124-128 slices, 1.2mm Scan time=9:02 min EPI or Spiral GRE Echo-planar imaging or spiral gradient echo imaging, oblique axial 64x64 matrix, 1 shot 35 slices, 4mm F T2SE Spin echo T2-weighted, oblique axial 256x192 matrix 35 slices, 4mm inter Scan time=2:24 min A Pulse Sequences
  • Materials and Methods Task : Sensory Motor Site : 5 Sites with 1.5T, 4 with 3T, 1 with 4T Subject : #101; 103; 104; 105; 106 Run : 4 and registered Day : #101; 103; 106 tested on 4 days at Stanford and other subjects tested on 2 Days/Site Threshold : Activation data:  = – log 10 (p-value)sign(F-statistic)=10 -9 BWH, HMS Quality Assessment
  • Materials and Methods Image registration over the repeated runs across sites using FreeSurfer Voxel-to-voxel registration of the anatomical with the functional volume to convert the subject's anatomical volume to the corresponding functional space using a transformation matrix BWH, HMS Quality Assessment
  • Materials and Methods TkRegister defines the registration matrix T= -d c 0 0 (N c /2)d c 0 0 d s -(N s /2)d s 0 -dr 0 (N r /2)d r 0 0 0 1 d c , d r , and d s are the resolutions, N c , N r , and N s are the number of columns, rows, and slices BWH, HMS Quality Assessment
  • Materials and Methods BWH, HMS Quality Assessment Raster, Spiral, Dual-Echo Raster K-Space 7 Siemens, GE, Picker Maker 6 1.5T, 3T, 4T Strength 5 1 - 4/visit Run 4 1, 2 (all); 1 - 4 (1site 3 subjects) Visit 3 1 - 10 Site 2 1 - 5 Subject 1 # Values Name Variable
  • Materials and Methods Selection of Threshold: The threshold was selected on the scale of the activation data The 3D activation map was further standardized using the absolute value for each voxel prior to statistical inferences BWH, HMS Quality Assessment
  • Materials and Methods Selection of Threshold: The threshold was selected on the scale of the activation data The 3D activation map was further standardized using the absolute value for each voxel prior to statistical inferences BWH, HMS Quality Assessment
    • Complete data density:
    • Binary ground truth T i for voxel i
    • Expert j segmentation D ij
    • Expert performance characterized by
    • sensitivity p and specificity
    • We observe expert decisions D
    • To construct maximum likelihood estimates
    • for each expert’s sensitivity and specificity
    Materials and Methods Quality Assessment BWH, HMS
    • Solve the incomplete-data log likelihood
    • maximization problem by Expectation
    • Maximization (EM)
    Quality Assessment BWH, HMS
  • Materials and Methods BWH, HMS Quality Assessment
  • Materials and Methods Statistical methods Activation percentage Sensitivity and Specificity Receiver operating characteristic curve Linear model Analysis of variance   BWH, HMS Quality Assessment
  • Results Subject 104 Visit 1 Slice #18
  • Results Statistical significant factors impacting on sensitivity : subject (p=0.01) on specificity : subject (p=0.04) run (p=0.04)   BWH, HMS Quality Assessment
  • Results Activation Percentage BWH, HMS Quality Assessment
  • Results Sensitivity BWH, HMS Quality Assessment
  • Results Specificity BWH, HMS Quality Assessment
  • Conclusion BWH, HMS Quality Assessment Site vs. Subject : Variability across subjects >variability across sites Field Strength : 3T and 4T were better than 1.5T yielding more activation and less variability in sensitivity and specificity Runs : There was a non-constant effect after resting and task periods
  • Remark BWH, HMS Quality Assessment Our findings may help develop a calibration plan to minimize the variability introduced by the sites Enabling us to pool independent functional data of normal and schizophrenic subjects across different institutions
  • Future Research
    • Standardization across subjects
    • Degree of smoothing
    • schizophrenic vs. healthy controls
    • Longitudinal changes overtime
    BWH, HMS Quality Assessment
  • References
    • Genovese CR, Noll, DC and Eddy, WF. Estimating test-retest reliability in fMRI I. statistical methodology. Magnetic Resonance in Medicine 1997; 38: 497-507.
    • Le TH and Hu X. Methods for assessing accuracy and reliability in functional MRI. NMR in Biomedicine 1997; 10: 160-164.
    • Machielsen WCM, Rombouts SARB, Barkhof F, Scheltens P, and Witter MP. fMRI of visual encoding: reproducibility of activation. Human Brain Mapping 2000; 9: 156-164.
    • Maitra R, Roys SR, and Gullapalli RP. Test-retest reliability estimation of functional MRI Data. Magnetic Resonance in Medicine 2002; 48: 62-70.
    Quality Assessment BWH, HMS
    • Warfield SK, Zou KH, Wells WM III. Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation. IEEE Transactions on Medical Imaging 2004; 23: 903-921.
    • Warfield SK, Zou KH, Wells WM III. Validation of image segmentation and expert quality with an expectation-maximization algorithm. MICCAI 2002, LNCS 2002; 2488: 290-297.
    • Wei XC, Yoo S-S, Dickey CC, Zou KH, Guttmann CRG,
    • Panych LP. Functional MRI of auditory verbal working
    • memory: long-term reproducibility analysis. NeuroImage 2004; 21: 1000-1008.
    References on fMRI and EM Quality Assessment BWH, HMS
    • Zou KH, Wells MW III, Kikinis R, Warfield. Three validation metrics for automated probabilistic image segmentation of brain tumors. Statistics in Medicine 2004; 23: 1259-1282.
    • Zou KH, Warfield SK, Fielding JR, Tempany CM, Wells MW III, K aus MR, Jolesz FA, Kikinis R. Statistical validation based on parametric receiver operating characteristic analysis of continuous classification data. Academic Radiology 2003; 10: 1359-1368.
    • Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus M, Haker S, Wells WM III, Jolesz FA, Kikinis R. Statistical validation of imagage segmentation quality based on a spatial overlap index. Academic Radiology 2004; 11: 178-189.
    References on Validation Metrics Quality Assessment BWH, HMS