1,897 research outputs found
Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction
Purpose: We implemented the blip-up, blip-down circular echo planar imaging
(BUDA-cEPI) sequence with readout and phase partial Fourier to reduced
off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based
low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be
effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is
computationally intensive. Thus, we developed an ML-based reconstruction
technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction.
Methods: BUDA-cEPI RUN-UP - a model-based framework that incorporates
off-resonance and eddy current effects was unrolled through an artificial
neural network with only six gradient updates. The unrolled network alternates
between data consistency (i.e., forward BUDA-cEPI and its adjoint) and
regularization steps where U-Net plays a role as the regularizer. To handle the
partial Fourier effect, the virtual coil concept was also incorporated into the
reconstruction to effectively take advantage of the smooth phase prior, and
trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.
Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of
off-resonance, partial Fourier, and residual aliasing artifacts. However, the
reconstruction time is approximately 225 seconds per slice, which may not be
practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP
yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5%
normalized root mean square error detected, while the reconstruction time is
approximately 3 seconds.
Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by
~88x when compared to the state-of-the-art technique, while preserving imaging
details as demonstrated through DTI application.Comment: Number: Figures: 8 Tables: 3 References: 7
Quantitative diffusion MRI with application to multiple sclerosis
Diffusion MRI (dMRI) is a uniquely non-invasive probe of biological tissue properties, increasingly able to provide access to ever more intricate structural and microstructural tissue information. Imaging biomarkers that reveal pathological alterations can help advance our knowledge of complex neurological disorders such as multiple sclerosis (MS), but depend on both high quality image data and robust post-processing pipelines. The overarching aim of this thesis was to develop methods to improve the characterisation of brain tissue structure and microstructure using dMRI. Two distinct avenues were explored. In the first approach, network science and graph theory were used to identify core human brain networks with improved sensitivity to subtle pathological damage. A novel consensus subnetwork was derived using graph partitioning techniques to select nodes based on independent measures of centrality, and was better able to explain cognitive impairment in relapsing-remitting MS patients than either full brain or default mode networks. The influence of edge weighting scheme on graph characteristics was explored in a separate study, which contributes to the connectomics field by demonstrating how study outcomes can be affected by an aspect of network design often overlooked. The second avenue investigated the influence of image artefacts and noise on the accuracy and precision of microstructural tissue parameters. Correction methods for the echo planar imaging (EPI) Nyquist ghost artefact were systematically evaluated for the first time in high b-value dMRI, and the outcomes were used to develop a new 2D phase-corrected reconstruction framework with simultaneous channel-wise noise reduction appropriate for dMRI. The technique was demonstrated to alleviate biases associated with Nyquist ghosting and image noise in dMRI biomarkers, but has broader applications in other imaging protocols that utilise the EPI readout. I truly hope the research in this thesis will influence and inspire future work in the wider MR community
Modeling high resolution MRI: Statistical issues with low SNR
Noise is a common issue for all Magnetic Resonance Imaging (MRI) techniques and obviously leads to variability of the estimates in any model describing the data. A number of special MR sequences as well as increasing spatial resolution in MR experiments further diminish the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from its theoretical value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating process and a characterization of the resulting distribution of the imaging data. We provide an adequate quasi-likelihood approach that employs these characteristics. We elaborate on the effects of typical data preprocessing and analyze the bias effects related to low SNR for the example of the diffusion tensor model in diffusion MRI. We then demonstrate that the problem is relevant even for data from the Human Connectome Project, one of the highest quality diffusion MRI data available so far
Modeling high resolution MRI: Statistical issues with low SNR
Noise is a common issue for all Magnetic Resonance Imaging (MRI)
techniques and obviously leads to variability of the estimates in any model
describing the data. A number of special MR sequences as well as increasing
spatial resolution in MR experiments further diminish the signal-to-noise
ratio (SNR). However, with low SNR the expected signal deviates from its
theoretical value. Common modeling approaches therefore lead to a bias in
estimated model parameters. Adjustments require an analysis of the data
generating process and a characterization of the resulting distribution of
the imaging data. We provide an adequate quasi-likelihood approach that
employs these characteristics. We elaborate on the effects of typical data
preprocessing and analyze the bias effects related to low SNR for the example
of the diffusion tensor model in diffusion MRI. We then demonstrate that the
problem is relevant even for data from the Human Connectome Project, one of
the highest quality diffusion MRI data available so far
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Employing Symmetry Features for Automatic Misalignment Correction in Neuroimages
A novel method to automatically compute the symmetry plane and to correct the 3D orientation of neuro-images is presented. In acquisition of neuroimaging scans, the lack of perfect alignment of a patient's head makes it challenging to evaluate brain images. By deploying a shape-based criterion, the symmetry plane is defined as a plane that best matches external surface points on one side of the head, with their counterparts on the other side. In our method, the head volume is represented as a re-parameterized surface point cloud, where each location is parameterized by its elevation (latitude), azimuth (longitude), and radius. The search for the best matching surfaces is implemented in a multi-resolution paradigm, and the computation time is significantly decreased. The algorithm was quantitatively evaluated using in both simulated data and in real T1, T2, Flair magnetic resonance patient images. This algorithm is found to be fast (< 10s per MR volume), robust and accurate (< .6 degree of Mean Angular Error), invariant to the acquisition noise, slice thickness, bias field, and pathological asymmetries
Computational aberration correction of VIS-NIR multispectral imaging microscopy based on Fourier ptychography
Due to the chromatic dispersion properties inherent in all optical materials, even the best-designed multispectral objective will exhibit residual chromatic aberration. Here, we demonstrate a multispectral microscope with a computational scheme based on the Fourier ptychographic microscopy (FPM) to correct these effects in order to render undistorted, in-focus images. The microscope consists of 4 spectral channels ranging from 405 nm to 1552 nm. After the computational aberration correction, it can achieve isotropic resolution enhancement as verified with the Siemens star sample. We image a flip-chip to show the promise of our system to conduct fault detection on silicon chips. This computational approach provides a cost-efficient strategy for high quality multispectral imaging over a broad spectral range
What's new and what's next in diffusion MRI preprocessing
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing
Altered corticomotor-cerebellar integrity in young ataxia telangiectasia patients
Magnetic resonance imaging (MRI) research in identifying altered brain structure and function in ataxia-telangiectasia, an autosomal recessive neurodegenerative disorder, is limited. Diffusion-weighted MRI were obtained from 11 ataxia telangiectasia patients (age range, 7-22 years; mean, 12 years) and 11 typically developing age-matched participants (age range, 8-23 years; mean, 13 years). Gray matter volume alterations in patients were compared with those of healthy controls using voxel-based morphometry, whereas tract-based spatial statistics was employed to elucidate white matter microstructure differences between groups. White matter microstructure was probed using quantitative fractional anisotropy and mean diffusivity measures. Reduced gray matter volume in both cerebellar hemispheres and in the precentral-postcentral gyrus in the left cerebral hemisphere was observed in ataxia telangiectasia patients compared with controls (P < 0.05, corrected for multiple comparisons). A significant reduction in fractional anisotropy in the cerebellar hemispheres, anterior/posterior horns of the medulla, cerebral peduncles, and internal capsule white matter, particularly in the left posterior limb of the internal capsule and corona radiata in the left cerebral hemisphere, was observed in patients compared with controls (P< 0.05). Mean diffusivity differences were observed within the left cerebellar hemisphere and the white matter of the superior lobule of the right cerebellar hemisphere (P< 0.05). Cerebellum-localized gray matter changes are seen in young ataxia telangiectasia patients along with white matter tract degeneration projecting from the cerebellum into corticomotor regions. The lack of cortical involvement may reflect early-stage white matter motor pathway degeneration within young patients
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