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Reconstruction of fetal brain MRI with intensity matching and complete outlier removal.

Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA - Med Image Anal (2012)

Bottom Line: The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices.The best results are obtained when thin-slice data and the correct approximation of the point spread function is used.This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientific literature.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK. maria.murgasova@eng.ox.ac.uk

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Comparison of slice weights using (a) EM and (b) Huber robust statistics, plotted against TRE calculated for each slice. Corrupted slices are shown as red circles and the slices which have been deliberately assigned large displacements during simulation are denoted by green crosses. Black asterisks denote slices with small region of interest with little information to guide registration towards correct alignment. The corrupted and misplaced slices are completely removed using EM robust statistics (zero weights), while their weight is only reduced when Huber robust statistics are used. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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f0025: Comparison of slice weights using (a) EM and (b) Huber robust statistics, plotted against TRE calculated for each slice. Corrupted slices are shown as red circles and the slices which have been deliberately assigned large displacements during simulation are denoted by green crosses. Black asterisks denote slices with small region of interest with little information to guide registration towards correct alignment. The corrupted and misplaced slices are completely removed using EM robust statistics (zero weights), while their weight is only reduced when Huber robust statistics are used. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Mentions: Fig. 5 compares slice weights assigned by EM and Huber robust statistics for reconstruction from six simulated stacks. It can be seen that EM robust statistics assign zero weights to all corrupted and obviously misregistered slices, while Huber statistics only reduce their weights. Several slices have rather small TRE, but were assigned relatively small weights (below 0.5) by EM robust statistics. Visual inspection revealed that these are slices with high information content, where a small displacement can result in a relatively large intensity error. Conversely, some slices with small regions of interest and low information content, which are especially prone to misregistrations, can exhibit rather small intensity errors despite larger TRE and are thus not excluded.


Reconstruction of fetal brain MRI with intensity matching and complete outlier removal.

Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA - Med Image Anal (2012)

Comparison of slice weights using (a) EM and (b) Huber robust statistics, plotted against TRE calculated for each slice. Corrupted slices are shown as red circles and the slices which have been deliberately assigned large displacements during simulation are denoted by green crosses. Black asterisks denote slices with small region of interest with little information to guide registration towards correct alignment. The corrupted and misplaced slices are completely removed using EM robust statistics (zero weights), while their weight is only reduced when Huber robust statistics are used. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4067058&req=5

f0025: Comparison of slice weights using (a) EM and (b) Huber robust statistics, plotted against TRE calculated for each slice. Corrupted slices are shown as red circles and the slices which have been deliberately assigned large displacements during simulation are denoted by green crosses. Black asterisks denote slices with small region of interest with little information to guide registration towards correct alignment. The corrupted and misplaced slices are completely removed using EM robust statistics (zero weights), while their weight is only reduced when Huber robust statistics are used. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Mentions: Fig. 5 compares slice weights assigned by EM and Huber robust statistics for reconstruction from six simulated stacks. It can be seen that EM robust statistics assign zero weights to all corrupted and obviously misregistered slices, while Huber statistics only reduce their weights. Several slices have rather small TRE, but were assigned relatively small weights (below 0.5) by EM robust statistics. Visual inspection revealed that these are slices with high information content, where a small displacement can result in a relatively large intensity error. Conversely, some slices with small regions of interest and low information content, which are especially prone to misregistrations, can exhibit rather small intensity errors despite larger TRE and are thus not excluded.

Bottom Line: The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices.The best results are obtained when thin-slice data and the correct approximation of the point spread function is used.This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientific literature.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK. maria.murgasova@eng.ox.ac.uk

Show MeSH