PVE for MRI Brain Tissue Classification Zeng Dong SLST, UESTC 6-9

PVE Partial Volume Effect

Contents Overview Method Nei. PVE Model MAP Result Discussion Conclusion

Overview 1-role Roles of Segmentation in qualitative in visualization

Overview 2 – difficulty difficulty inhomogeneous PVE hard soft segmentation Statistic MAP MRF

Overview 3 - PVE IEE95 NOISE MODELS based Sampling noise Material-dependent Noise PVE direct indirect

Direct Determine PVC directly IEEE91 multichannel

continuous IEE03 Only tow types

continuous 02 Fuzzy Markov discrete PVC

Indirect PV class Determine PVC based on PV voxels

continuous More Tow Not Multi-channal Discrete PVC Boundary voxels More accurate, more efficient

Method-Nei. PVE N: numbers of pixels Assume: mask

continuous K: numbers of pure g(.,.): Gaussian function

continue Observed is mixed with its nei.s meanly during sampling Nei. Size M

continue

continue L kinds of mixed types: Mixed set Assume A mixed type

continue

PVE Segmentation MAP Y: observed images X: segmentation images

Likelihood term Assume that the intensity at voxel i does not depend on the tissue content of the other voxels.

Prior Assume X is MRF on nei. System C, and x is a realization RF X Z: Partition function U(x): Potential energy function

continue

continue

continue

ICM Iterative Constrained Mode local combination

continue

continue

continue 1. Init X (beta = 0, M=1) 2. Update mixel mean and variance 3. ICM 4. goto 2

Result Brain tissue classification K=3: CSF, GM, WM N=1,3,7,27

continue Generate Mixed types: for CSF=M:0 for GM=(M-CSF):0 { WM=M-CSF-GM … }

continue Example(K=3,M=7) Reduce: Not tow maximization( 3) CSF !=0 && GM != 0 (18) CSF GM WM

continue Ori seg Ori Seg with b Seg without b

continue compare 数据 100_23 1_24 11_3 110_3 111_2 112_2 12_3 13_3 15_3* 17_3* CSF GM WM 数据 17_3 191_3 202_3 205_3 CSF GM WM Mean: csf 20% GM 86% WM :83%

Discussion 4-1 Mixel of CSF and WM 0: : : : : : : : : : : mix mean and var 0: : : : : : : : : : : : : : : : : : : : : : : :

continous Mixel of CSF and WM

Discussion 4-2 Measure Parameter estimation csf mean : csf var : gm mean : gm var : wm mean : wm var : csf mean : csf var : gm mean : gm var : wm mean : wm var : Result para:Ori para Csf mean: Csf var: gm mean: gm var: wm mean: wm var: Init para(ML)

Discussion 4-3 Prior Parameter estimation

Discussion 4-4 Intensity inhomogeneous

Conclusion More accurate, more efficient Unify framework generalization

Thanks