| Motion Correction|
Traditional PosterAcquisition, Reconstruction & Analysis
Monday, 24 April 2017
|Exhibition Hall 1272-1296 ||08:15 - 10:15|
Pei Huang, David Hayes, Marta Correia
Prospective motion correction for MRI has been shown to greatly improve image quality for structural scans but its impact on fMRI data is still unclear. In this work, we studied the effectiveness of prospective motion correction by analysing the accuracy of the tracking system and looking at the effects of motion correction on resting-state fMRI with no instructed subject motion.
Weidao Chen, Bo Peng, Yi Sun, Gang Chen, Anna Roe, Yakang Dai, Xiaotong Zhang
Accurate subject-specific 3D modeling of macaque brain with anatomical subdivisions is important for neuroscience, neurophysiology and engineering researches. In this study, we have proposed a feasible approach for automatically creating 3D models of macaque brain based on in vivo MR images. A 3D template of macaque brain, consisting of scalp, skull, grey matter, white matter, and cerebrospinal fluid, was firstly constructed from 7T T1w images over an anesthetized macaque; then, by implementing symmetric feature-based pairwise registration method, this template was used to register another in vivo 7T dataset of macaque brain, which enables automatic and subject-specific 3D macaque brain modeling.
Tao Zhang, Ty Cashen, Kang Wang, André Fischer, Ersin Bayram
Stack of stars trajectory with golden angle ordering provides better motion robustness than Cartesian imaging for abdominal MRI. However, image reconstruction for non-Cartesian datasets is usually time-consuming, especially for datasets with high-density coil arrays. While additional motion correction methods can improve image quality for stack of stars, they often further increase the reconstruction time. In this work, we aim to reduce the reconstruction time for stack of stars using coil compression and improve motion robustness with a similar reconstruction time using soft gating.
Li Feng, Hersh Chandarana, Tiejun Zhao, Mary Bruno, Daniel Sodickson, Ricardo Otazo
This work compares golden-angle stack-of-stars sampling and golden-angle Cartesian sampling for free-breathing liver MRI with eXtra-Dimensional (XD) compressed sensing reconstruction. For Cartesian sampling, the phase-encoding steps in the ky-kz plane are segmented into multiple interleaves that rotate at a golden angle. Each interleave starts from the center (ky=kz=0) of k-space and follows a pseudo-radial pattern on a Cartesian grid. Results from this initial study suggest that golden-angle Cartesian sampling achieves higher effective spatial resolution than radial sampling, but it still suffers from residual ghosting artifacts due to respiratory motion for free-breathing liver imaging.
Pei Huang, Nikolaus Kriegeskorte, Richard Henson, Arjen Alink, Marta Correia
Prospective Motion Correction (PMC) using an optical tracking system has been shown to improve data quality. We conducted a study on 18 subjects using robust visual stimuli to quantify the effectiveness of PMC on task-based fMRI. Our results show that PMC improves voxel-to-voxel registration across time and leads to better contrast-to-noise ratio. This is particularly evident in analyses which are more sensitive to inaccurate voxel registration and motion-induced noise.
Wanyong Shin, Erik Beall, Mark Lowe
Participants in block design finger tapping fMRI have a tendency to have paradigm coherent head motion. This finding appears to be very strong in HCP data, which utilizes spatially and temporally accelerated SMS sequence. We have introduced slice-oriented motion correction method (SLOMOCO1), and found SLOMOCO removed the head motion efficiently in HCP data, especially in the case that head motion pattern is synchronized to task paradigm. In this study, we compared the various motion correction methods in finger tapping fMRI HCP data.
Gaojie Zhu, Xiang Zhou, Hai Luo, Bin Wang, Xia Liu, Ziyue Wu, Leping Zha, Qing-San Xiang
Patient motion produces artifacts in MRI due to k-space data corruption. Ghosted images can be considered as a combination of ghost-free images and ghost masks. If two ghosted images contain the same ghost-free image component and different ghost components, the images and the ghost components can be separated. For images fully sampled with array coils, multiple images can be produced with parallel reconstruction with differently selected raw data subsets. In this work, we propose a new motion artifacts reduction algorithm, which regenerates a new k-space dataset based on data consistency, and then decomposes images into mostly ghost-free images and ghost masks.
Bian Li, Huajun She, Shu Zhang, Jochen Keupp, Ivan Dimitrov, Albert Montillo, Ananth Madhuranthakam, Robert Lenkinski, Elena
VinogradovIn image registration, mutual information (MI) has proved to be an effective similarity measure and is widely used for medical image registration. However, the MI algorithm does not consider spatial dependencies of voxels and introduces significant errors when registering images with large intensity changes, like in Z-spectral images of CEST-MRI. This abstract shows that by the incorporation of structural information the SMI algorithm demonstrates robust performance registering Z-spectral images with large and complex intensity variations.
Zhongbiao Xu, Mengye Lyu, Edward Hui, Yingjie Mei, Zhifeng Chen, Wufan Chen, Ed X. Wu, Yanqiu Feng
The recently proposed magnetic resonance fingerprinting (MRF) technique demonstrates to be motion insensitive, but the early motion during the acquisition can still lead to severe errors in parameter quantification. In this study, we present a novel motion correct method for MRF based on sliding-window reconstruction and image registration.
Junshen Xu, Yibo Zhao, Kui Ying
Joint Reconstruction (JR) is an important approach to utilize the similarity of PET and MRI in simultaneous PET/MR imaging. For now, almost all the JR models ignore the effect of motion during scan, leading to blurring in images. We propose a motion correction method under the framework of JR, assuming that PET images and MRI images share exactly the same motion field and using a B-spline free deformation model to describe the motion. Both simulation and patient study show that the proposed method can reduce the blurring caused by motion in PET and MR images.