76,704 research outputs found
Incorporating Relaxivities to More Accurately Reconstruct MR Images
Purpose
To develop a mathematical model that incorporates the magnetic resonance relaxivities into the image reconstruction process in a single step.
Materials and methods
In magnetic resonance imaging, the complex-valued measurements of the acquired signal at each point in frequency space are expressed as a Fourier transformation of the proton spin density weighted by Fourier encoding anomalies: T2â, T1, and a phase determined by magnetic field inhomogeneity (âB) according to the MR signal equation. Such anomalies alter the expected symmetry and the signal strength of the k-space observations, resulting in images distorted by image warping, blurring, and loss in image intensity. Although T1 on tissue relaxation time provides valuable quantitative information on tissue characteristics, the T1 recovery term is typically neglected by assuming a long repetition time. In this study, the linear framework presented in the work of Rowe et al., 2007, and of Nencka et al., 2009 is extended to develop a Fourier reconstruction operation in terms of a real-valued isomorphism that incorporates the effects of T2â, âB, and T1. This framework provides a way to precisely quantify the statistical properties of the corrected image-space data by offering a linear relationship between the observed frequency space measurements and reconstructed corrected image-space measurements. The model is illustrated both on theoretical data generated by considering T2â, T1, and/or âB effects, and on experimentally acquired fMRI data by focusing on the incorporation of T1. A comparison is also made between the activation statistics computed from the reconstructed data with and without the incorporation of T1 effects.
Result
Accounting for T1 effects in image reconstruction is shown to recover image contrast that exists prior to T1 equilibrium. The incorporation of T1 is also shown to induce negligible correlation in reconstructed images and preserve functional activations.
Conclusion
With the use of the proposed method, the effects of T2â and âB can be corrected, and T1 can be incorporated into the time series image-space data during image reconstruction in a single step. Incorporation of T1 provides improved tissue segmentation over the course of time series and therefore can improve the precision of motion correction and image registration
Incremental Optimization Transfer Algorithms: Application to Transmission Tomography
No convergent ordered subsets (OS) type image
reconstruction algorithms for transmission tomography have been
proposed to date. In contrast, in emission tomography, there
are two known families of convergent OS algorithms: methods
that use relaxation parameters (Ahn and Fessler, 2003), and
methods based on the incremental expectation maximization (EM)
approach (Hsiao et al., 2002). This paper generalizes the incremental
EM approach by introducing a general framework that
we call âincremental optimization transfer.â Like incremental EM
methods, the proposed algorithms accelerate convergence speeds
and ensure global convergence (to a stationary point) under mild
regularity conditions without requiring inconvenient relaxation
parameters. The general optimization transfer framework enables
the use of a very broad family of non-EM surrogate functions.
In particular, this paper provides the first convergent OS-type
algorithm for transmission tomography. The general approach is
applicable to both monoenergetic and polyenergetic transmission
scans as well as to other image reconstruction problems. We
propose a particular incremental optimization transfer method
for (nonconcave) penalized-likelihood (PL) transmission image
reconstruction by using separable paraboloidal surrogates (SPS).
Results show that the new âtransmission incremental optimization
transfer (TRIOT)â algorithm is faster than nonincremental
ordinary SPS and even OS-SPS yet is convergent.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85800/1/Fessler200.pd
Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction
Diffusion MRI is commonly performed using echo-planar imaging (EPI) due to
its rapid acquisition time. However, the resolution of diffusion-weighted
images is often limited by magnetic field inhomogeneity-related artifacts and
blurring induced by T2- and T2*-relaxation effects. To address these
limitations, multi-shot EPI (msEPI) combined with parallel imaging techniques
is frequently employed. Nevertheless, reconstructing msEPI can be challenging
due to phase variation between multiple shots. In this study, we introduce a
novel msEPI reconstruction approach called zero-MIRID (zero-shot
self-supervised learning of Multi-shot Image Reconstruction for Improved
Diffusion MRI). This method jointly reconstructs msEPI data by incorporating
deep learning-based image regularization techniques. The network incorporates
CNN denoisers in both k- and image-spaces, while leveraging virtual coils to
enhance image reconstruction conditioning. By employing a self-supervised
learning technique and dividing sampled data into three groups, the proposed
approach achieves superior results compared to the state-of-the-art parallel
imaging method, as demonstrated in an in-vivo experiment.Comment: 10 pages, 4 figure
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Velocity Profiles in a Cylindrical Liquid Jet by Reconstructed Velocimetry
An experimental setup and a simple reconstruction method are presented to measure velocity fields inside slightly tapering cylindrical liquid jets traveling through still air. Particle image velocimetry algorithms are used to calculate velocity fields from high speed images of jets of transparent liquid containing seed particles. An inner central plane is illuminated by a laser sheet pointed at the center of the jet and visualized through the jet by a high speed camera. Optical distortions produced by the shape of the jet and the difference between the refractive index of the fluid and the surrounding air are corrected by using a ray tracing method. The effect of the jet speed on the velocity fields is investigated at four jet speeds. The relaxation rate for the velocity profile downstream of the nozzle exit is reasonably consistent with theoretical expectations for the low Reynolds numbers and the fluid used, although the velocity profiles are considerably flatter than expected.This work was sponsored by EPSRC grant number RG5560
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