Fetal motion manifests as signal degradation and image artifact in the acquired time series of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) studies. We present a robust preprocessing pipeline to specifically address fetal and placental motion-induced artifacts in stimulus-based fMRI with slowly cycled block design in the living fetus. In the proposed pipeline, motion correction is optimized to the experimental paradigm, and it is performed separately in each phase as well as in each region of interest (ROI), recognizing that each phase and organ experiences different types of motion. To obtain the averaged BOLD signals for each ROI, both misaligned volumes and noisy voxels are automatically detected and excluded, and the missing data are then imputed by statistical estimation based on local polynomial smoothing. Our experimental results demonstrate that the proposed pipeline was effective in mitigating the motion-induced artifacts in stimulus-based fMRI data of the fetal brain and placenta.
Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed from ROI-averaged signals. The results demonstrate that the proposed method is effective in enhancing motion correction in fetal fMRI without large data loss, compared to traditional motion correction methods.
Spinal cord (SC) tissue loss is known to occur in some patients with multiple sclerosis (MS), resulting in SC atrophy.
Currently, no measurement tools exist to determine the magnitude of SC atrophy from Magnetic Resonance Images
(MRI). We have developed and implemented a novel semi-automatic method for quantifying the cervical SC volume
(CSCV) from Magnetic Resonance Images (MRI) based on level sets. The image dataset consisted of SC MRI exams
obtained at 1.5 Tesla from 12 MS patients (10 relapsing-remitting and 2 secondary progressive) and 12 age- and gender-matched
healthy volunteers (HVs). 3D high resolution image data were acquired using an IR-FSPGR sequence acquired
in the sagittal plane. The mid-sagittal slice (MSS) was automatically located based on the entropy calculation for each of
the consecutive sagittal slices. The image data were then pre-processed by 3D anisotropic diffusion filtering for noise
reduction and edge enhancement before segmentation with a level set formulation which did not require re-initialization.
The developed method was tested against manual segmentation (considered ground truth) and intra-observer and inter-observer
variability were evaluated.
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