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

Kuklisova‐Murgasova, Maria; Quaghebeur, Gerardine; Rutherford, Mary A.; Hajnal, Joseph V.; Schnabel, Julia A.

doi:10.1016/j.media.2012.07.004

Cited by 370 publications

(495 citation statements)

References 28 publications

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“…Several of these image stacks are usually acquired, parallel and perpendicular to the fetus' main axis. This allows to account for movement artifacts during later post-processing steps [4]. In this work, we use one randomly selected image stack from each subject to show our method's ability to cope with through-plane movement artifacts.…”

Section: Region Refinementmentioning

confidence: 99%

“…This detection process produces a reliable classification of brain voxels. It can simplify the application of advanced motion correction methods [4], which produce a high resolution volume from consecutive single-shot fast spin echo (SSFSE) T2-weighted scans. Alternatively, it can serve as a starting point for automatic or interactive segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

Kainz

Keraudren

Kyriakopoulou

et al. 2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90.

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Section: Region Refinementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

Kainz

Keraudren

Kyriakopoulou

et al. 2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

Self Cite

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“…They form the data input for a bespoke 5 step post-processing framework developed in-house using IRTK building on [10,7], as illustrated in Fig. 3.…”

Section: Post-processing Algorithmmentioning

confidence: 99%

“…Motion correction In step 4, all the low-b volumes as well as the two or three additionally acquired orthogonal low-b volumes are combined as input to a slice-to-volume (SVR) alignment and reconstruction process [7] to create a geometrically self consistent 3D fetal brain volume. The process intersperses a registration step to progressively refine the position estimate of each slice in anatomical space, with a super-resolution reconstruction step that uses all newly aligned data to generate a 3D volume that can then be used as a registration target for the next iteration.…”

Section: Dynamic Distortion Correctionmentioning

confidence: 99%

Dynamic Field Mapping and Motion Correction Using Interleaved Double Spin-Echo Diffusion MRI

Hutter

Christiaens

Deprez

et al. 2017

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

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Abstract. Diffusion MRI (dMRI) analysis requires the combination of data from many images and this generally requires corrections for image distortion, particularly in regions of varying susceptibility, and for subject motion during what may be a prolonged acquisition. The two can interact, and particularly in non-brain applications changes in pose such as through respiration can case the distortions to be time varying, so that correction with a static field map does not provide full correction. Also highly diffusion weighted (high b-value) images have low signal-to-noise ratio (SNR), which can make motion estimation using image registration problematic. In this work we develop an approach that breaks the traditional "one-volume -one-weighting" paradigm by interleaving low-and high-b slices in every volume and we combine this with a phase-encoding reversed double-spin echo sequence. Interspersing low and high b-value slices within each acquired volume, ensures that there is always higher SNR, low b, data in close spatial and temporal proximity to support field map determination from the double spin echo and motion estimation based on image registration. This information can be propagated to low SNR, high b, slices by local interpolation across space and time. The method is tested in the challenging environment of fetal dMRI and it is demonstrated using data from 8 pregnant volunteers that combining dynamic distortion correction with slice-by-slice motion correction increases data consistency to produce data suitable for advanced analyses where conventional methods fail.

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“…Several orthogonal stacks of slices can be acquired and combined to one high resolution volume using approaches based on slice to volume registration (SVR). This way we obtain an artifact free, high resolution volume of a fetal target region [11,15]. So far, this process has been applied only to large, rigid and well structured fetal brain regions.…”

Section: Introductionmentioning

confidence: 99%

Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI

Kainz

Malamateniou

Murgasova

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature.

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

Cited by 370 publications

References 28 publications

Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

Dynamic Field Mapping and Motion Correction Using Interleaved Double Spin-Echo Diffusion MRI

Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI

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