Review of Longitudinal MRI Analysis for Brain Tumors. Elsa Angelini 17 Nov. 2006

Transcription

1 Review of Longitudinal MRI Analysis for Brain Tumors Elsa Angelini 17 Nov. 2006

2 MRI Difference maps «Longitudinal study of brain morphometrics using quantitative MRI and difference analysis», Liu,Lemieux, Bell, Sisodiya, Shorvon, Sander, Duncan, NeuroImage, Serial volumetry and voxel-based differences Quantify and characterize longitudinal changes. Hippocampus, cerebellum, neocortex. 90 subjects (14-77 years old) divided into 3 epochs (<35, 35-54, >54). Paired MRI scans 3.5 years apart.

3 MRI Difference maps MRI Processing: Inhomogeneity correction, Brain extraction (Exbrain) Registration (MReg) Segmentation of brain+csf (Exbrain) Difference Maps MReg estimation of noise level across the data set. Structured Difference Image (SDI) = (Direct_MRI_Difference) > (3 Noise_level) & (Spatial_extent > 7 voxels).

4 MRI Difference maps Difference Maps (cont.) Structured Noise Map (SNM) = Anatomical map of artifacts built from 40 control SDI, coregistered to Talairach space (SPM99). Normalized SDI (n-sdi): T1 to SPM T1 template: Transfo matrix R. SDI R[SDI] + NN interpolation Significance of n-sdi: Threshold of significance = 1:40 n-sdi(x) if (P SNM (x) <0.025)

5 MRI Difference maps

6 MRI Difference maps Fig. 1. Coronal and sagittal views of the structured noise map. The slices have been selected for their concentration of structural differences, which are maximal in the temporal lobes, inferior frontal lobe, occipital regions, and around the ventricles. The concentration of structured noise in the temporal lobes reflects susceptibility and pulsation artifact (associated with the middle cerebral artery).

7 MRI Difference maps Fig. 3. Distribution of age-related changes in each age band, obtained by summing the SDIs followed by filtering with the SNM. The greater the intensity of a given voxel, the greater the likelihood of that voxel showing a change in signal intensity over 3 years. The figures represent: (a) individuals under 35 years, (b) individuals between 35 and 54 years, and (c) individuals over 54 years of age. The absence of visible hippocampal changes may reflect structural heterogeneity, as well as limitations in the precision of the normalization process.

8 MRI Difference maps

9 MRI Difference maps

10 MRI Difference maps Limitations: Assume that normal noise maps are applicable to patients (wrt standard artifacts). Large lesions can affect normalization algorithms. Reslicing of the data involved.

11 MRI Longitudinal Segmentation + Deformations 4D segmentation of longitudinal MRI data with iterative: 1. Segmentation via 4-D image-adaptive clustering based on current estimate of longitudinal deformations in the image series. 2. Refinment of the longitudinal deformation using 4D elastic warping.

12 MRI Longitudinal Segmentation + Pre-processing: Deformations Inhomogeneity correction, intensity normalization and rigid registration.

13 MRI Longitudinal Segmentation + Deformations Segmentation Functional: Spatially varying centroids 4D sums Use spatial and temporal smoothing Local spatial average of fuzzy weights Local temporal average of fuzzy weights Spatially adaptive smoothing parameters

14 MRI Longitudinal Segmentation + Implementation Deformations - Evaluated on small longitudinal evolutions (atrophy) + BLSA for aging. - Requires «smooth» longitudinal evolution.

15 MRI Histrogram Normalization Training Global linear mapping of intensity range value to a standard range of values. Averaging of intensity values of percentile landmark levels. Normalization Series of linear mappings between landmark levels.

16 MRI Histrogram Normalization

17 MRI Histrogram Normalization

18 MRI Histrogram Normalization

19 MRI Histrogram Normalization Limitations No meaning of the landmark points wrt to anatomical tissues: might strech differently two parts of a single tissue (WM). Training with a priori maximum intensity range known.

20 Tumor-induced Assymetry Assess mass-effect and infiltration effects from brain tumors Large and small structural variations + intensity variations

21 Tumor-induced Assymetry Variational Framework Symmetry plane Tumor effect Tumor Effect

22 Tumor-induced Assymetry

23 Tumor-induced Assymetry

24 Tumor growth Labeling

25 Tumor growth Labeling

26 Tumor growth Labeling

27 Tumor Growth Index «Continuous growth of mean tumor diameter in a subset of grade II gliomas», Mandonnet E, Delattre JY, Tanguy ML, Swanson KR, Carpentier AF, Duffau H, Cornu P, Van Effenterre R, Alvord EC Jr, Capelle L. Ann Neurol Apr;53(4): Abstract: Serial magnetic resonance images of 27 patients with untreated World Health Organization grade II oligodendrogliomas or mixed gliomas were reviewed retrospectively to study the kinetics of tumor growth before anaplastic transformation. Analysis of the mean tumor diameters over time showed constant growth. Linear regression, using a mixed model, found an average slope of 4.1mm per year (95% confidence interval, mm/year). Untreated lowgrade oligodendrogliomas or mixed gliomas grow continuously during their premalignant phase, and their pattern of growth can be predicted within a relatively narrow range. These findings could be of interest to optimize patients management and follow-up.

28 Tumor Growth Manual Variability

29 Tumor Longitudinal Volume Measurement

30 Tumor Longitudinal Volume Measurement Tumor knowledge

31 Tumor Longitudinal Volume Measurement Longitudinal