Elsevier

Medical Image Analysis

Volume 16, Issue 8, December 2012, Pages 1550-1564
Medical Image Analysis

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

https://doi.org/10.1016/j.media.2012.07.004Get rights and content
Under a Creative Commons license
open access

Abstract

We propose a method for the reconstruction of volumetric fetal MRI from 2D slices, comprising super-resolution reconstruction of the volume interleaved with slice-to-volume registration to correct for the motion. The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices. The reconstruction method is applied to motion-corrupted data simulated from MRI of a preterm neonate, as well as 10 clinically acquired thick-slice fetal MRI scans and three scan-sequence optimized thin-slice fetal datasets. The proposed method produced high quality reconstruction results from all the datasets to which it was applied. Quantitative analysis performed on simulated and clinical data shows that both intensity matching and robust statistics result in statistically significant improvement of super-resolution reconstruction. The proposed novel EM-based robust statistics also improves the reconstruction when compared to previously proposed Huber robust statistics. 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.

Highlights

► A new method for reconstruction of 3D fetal brain MRI from 2D slices is proposed. ► The super-resolution reconstruction is interleaved with motion correction. ► Corrupted or misaligned slices are automatically excluded. ► Novel intensity matching is shown to be essential for quality of reconstruction. ► Excellent results for clinical and optimized data.

Keywords

Fetal MRI
3D reconstruction
Super-resolution
Bias field
Intensity matching

Cited by (0)

1

This work was supported by the Wellcome/EPSRC Centre of Excellence in Medical Engineering – Personalised Healthcare, WT 088877/Z/09/Z.