reconstruction of fetal brain mri with intensity matching...

Motivation IM & BC SR Results Contributions Limitations

Reconstruction of fetal brain MRI with

intensity matching and

complete outlier removal

M Kuklisova-Murgasova, G Quaghebeur, MA Rutherford,JV Hajnal, JA Schnabel

Med. Image Anal., vol. 16, no. 8, pp. 1550-64, 2012

presented by Michael Ebner

June 10, 2015

Reconstruction of fetal brain MRI with intensity matching . . . Kuklisova-Murgasova et al. (2012) 1


Agenda

1 Motivation

2 Intensity matching and bias correction

3 Super-resolution reconstruction

4 Results

5 Contributions

6 Limitations



Motivation



Motivation

1. Inhomogeneity of magnetic field

Relative position to scannerchanges due to motion

Intensity bias

Intensity matchingand

bias correction

2. Motion corrupted slices

Identification

Possible exclusion

Robust statistics



Intensity matchingand

bias correction



Intensity matching and bias correction

Voxels of observed k-th slice yk = {yk,j}Nkj=1

Voxels of unkown volume x = {xi}Ni=1

Bias field model

Bias fields bk = {bk,j}Nkj=1

Scaling factor sk

y ∗k,j := sk e

−bk,jyk,j

Slice acquisition model

Scaled and bias corrected slices y∗kSpatially aligned discretized PSF Mk

y∗k ≈Mkx


Motivation IM & BC SR Results Contributions Limitations Optimization problem Robust statistics Algorithm

Super-resolution reconstruction



Super-resolution reconstruction

Point spread function (Matrix Mk):

In-plane: Gaussian withFWHM = 1.2×in-plane resolution

Through-plane: Gaussian withFWHM = slice thickness

Optimization problem:

ek := y∗k −Mkx→ min

Regularized cost function:∑k,j

e2k,j + λR(x)→ min



Robust statistics

1 Classification of voxels in inliers and outliers

2 Posterior probability of voxel being classified as inlier pk,j3 Weighted cost function:∑

k,j

pk,je2k,j + λR(x)→ min

4 Gradient descent step:

x(n+1)i = x

(n)i + α

∑k,j

mkj ,i

(n)p(n)k,j e

(n)k,j + αλ

∂

∂xiR(x(n))

5 Classification of slices as inliers and outliers ( pslicek )



Advantage of EM robust statistics approach

ek = y∗k −Mkx



Overview of algorithm

y∗k,j = sk e

−bk,j yk,j , y∗k ≈ Mkx, ek = y∗k −Mkx


Motivation IM & BC SR Results Contributions Limitations Simulation Fetal data Leave-one-out

Results



Evaluation of method

1 Simulated experiments

Preterm neonate with GA = 27 weeksT2-weighted fast-spin echo, 3 TTR = 8620 ms, TE = 169 ms, voxel sizes = (1 mm)3

Adding of artificial motion corruption

2 Reconstruction of clinical fetal data

10 clinical fetal brain MRI: GA between 20 and 37 weeksT2-weighted single-shot turbo spin echo sequence, 1.5 TTR = 32 805 ms, TE = 100 ms, flip angle = 90◦

Voxel sizes = 0.75 mm× 0.75 mm× 3 (or 4) mm

3 Leave-one-out analysis of clinical data reconstruction



Simulation



Reconstruction of clinical fetal data



Leave-one-out strategy



Contributions



Contributions

Comprehensive reconstruction algorithm of fetal brainMRI

Novel intensity matching and bias field correction ofacquired 2D slices

Novel robust statistics to exclude identified misregisteredor corrupted voxels and slices

Multi-resolution approach

Estimation that five stacks of thick-slice data (3 mm to4 mm) with a gap (0.5 mm) sufficient for goodreconstruction of fetal brain

New comparison method: Leave-one-out analysis



Limitations



Limitations

More rigorous mathematical framework missing

Complexity of algorithms

Precomputed low resolution representation of PSF pervoxel and subsequent linear interpolation to keepcomputational time reasonable

Iteration between volume reconstruction andslice-to-volume computationally expensive

Depends on external slice-to-volume registration algorithm

Target stack chosen and segmented manually



Thanks!

Questions?



Appendix



Simulated stacks



Bias field


reconstruction of fetal brain mri with intensity matching...

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