Neuroimage Analysis Center An NCRR National Resource Center Time Series MRI Core Analysis, Modeling - toward Dynamic Surrogates of Disease Dominik S. Meier, Ph.D. Center for Neurological Imaging BWH Radiology & Neurology

Neuroimage Analysis Center An NCRR National Resource Center TSA Paradigm: Capture Processes Research and technology development for longitudinal studies of neurodegenerative disease involving MRI morphometry as outcome measure. Core work will explore the ability of serial in vivo MRI to illuminate the timing and sequence of the individual pathological processes underlying neurodegenerative disease. Segmentation of Change vs. Change of Segmentation Current/Common paradigm:Segmentation -> Trend Analysis TSA paradigm: Trend Analysis -> Segmentation

Neuroimage Analysis Center An NCRR National Resource Center Aims Aim 1: Time Series Fusion Develop integrated methods for serial image data fusion concatenates multiple 3-D MRI datasets into a single coherent 4-D space. spatial and intensity normalization voxel-based "chronobiopsy" Aim 2: Time Series Change Detection Develop a new hierarchical framework for change detection and delineation. 3-level hierarchy of (1) detection, (2) delineation, and (3) segmentation. specificity in detection and precision in segmentation Detection requires high levels of expert knowledge enhanced precision for delineation requires automation Aim 3: Time Series Modeling Develop a framework for change characterization and visualization parametric models of MRI intensity change on each voxel time-series profile within the areas of change investigate the serial MRI data from the viewpoint of a specific biological or clinical hypothesis "temporal differentiation before spatial integration" Aim 4: Time Series Validation Investigate ways to obtain error estimates and sensitivity to change. scan-rescan data, automated calculation of residual from the fused 4D set confidence intervals on the model parameters areas of reference with no pathologic change sensitivity analyses:sensitivity to change in both the spatial and the intensity domain

Neuroimage Analysis Center An NCRR National Resource Center Prelim. Results Application: In preparation

Neuroimage Analysis Center An NCRR National Resource Center Spectrum of Serial Morphometry Differentiation -> Classification: “new/enlarging” (red), “stable” (green) “resolving” (blue) V(t1) V(t2) V(t3) Serial VolumetryDifferential MorphometrySegmentation of Change Time Series Modeling - Spatially nonspecific- Sensitive to Registration Error -Greater Expert Input -+segmentation of change + Controlled Sensitivity -Model Required -+ Controlled Sensitivity +Segmentation implied 1.Classifier/Segmentation 2. Differentiation 2. TS Modeling 2. Integration 4. Integration 3.Classifier +Integration 3. Differentiation 3.Classifier / Segmentation 1.Registration1.Normalization 0 R 2 =0.670 R 2 =0.943 weeks T2 intensity Classifier/Segmentation 3. Differentiation

Neuroimage Analysis Center An NCRR National Resource Center Technological Biological / Clinical can dynamic metrics derived from serial MRI provide surrogates with stronger pathological specificity (inflammatory, degenerative, reparatory processes ) ? Different pathol. processes have different time signatures, even if their morphological footprints remain the same.. E.g. Inflammation creates mass effect and occurs rapidly. Inflammation Blood Brain Barrier breakdown Edema Cellular Infiltration Degeneration Demyelination Axonal Damage Repair Macrophage activity Astrocytosis Remyelination Axonal Repair? ~ weeks ~ months - years ~ months The cross-sectional concept revisited Avoid data reduction compare first – reduce later The longitudinal concept revisited Avoid data reduction differentiate first – integrate later Segmentation of Change vs. Change of Segmentation

Neuroimage Analysis Center An NCRR National Resource Center Data Fusion Pipeline Effective spatial resolution loss in serial imaging for tissue-specific normalization Registration Segmentation Bias Field Correction Partial Volume Filter Intensity Normalization Baseline Normalization t1 baseline t2 follow-up t3 follow-up t4 follow-up coil sensitivity bias variable head positioning variable gain, scanner drift, upgrades etc. Differential: detection of change

Neuroimage Analysis Center An NCRR National Resource Center Two-Process Time Series Model weeks Y1: Inflammation / Degeneration Y2: Resorbtion / Repair Y1 + Y2 MRI intensity Example: New MS lesion formation We model MRI intensity change as the superposition of two opposing processes, one causing T2 prolongation, another T2 shortening.

Neuroimage Analysis Center An NCRR National Resource Center Time Series Modeling Example: MS Lesion Formation 0 R 2 =0.670 R 2 =0.943 weeks T2 intensity F1 = Level of hyperintensity F2 = Level or recovery F3 = Duration weeks complete recovery partial recovery no recovery F1 F2 F2=0 F3 MRI intensity

Neuroimage Analysis Center An NCRR National Resource Center Example: Feature Maps of Change F1: Hyperintensity, F2: residual damage, F3: duration [weeks]

Neuroimage Analysis Center An NCRR National Resource Center sensitivity to change precision of trend assessment estimated error in measuring new lesion change Differentiation before Segmentation

Neuroimage Analysis Center An NCRR National Resource Center Error Accumulation / Sensitivity Analysis / Pipeline Design 1 dimension of variation: add and show all results N=191N=59N=43N=39 p=0.25p=0.003*p=0.33 Data Preprocessing Volumetry Modeling How one parameter at last step of pipeline affects results is easily tested. The effect of a parameter early in the pipeline is much more difficult to assess.

Neuroimage Analysis Center An NCRR National Resource Center Conclusions: Repair does occur in MS, varying in extent by location & subject MRI intensity dynamics provide reliable metrics of activity Short-term T2 lesion recovery shows links to progression in both atrophy and disability SPMSS shows trends to different lesion patterns than RRMS Dissociation between new lesion size and residual damage “big lesion small damage”, NO equivalence in total lesion burden Spatial patterns that match histopathological observations