178 research outputs found
Aggregated motion estimation for real-time MRI reconstruction
Real-time magnetic resonance imaging (MRI) methods generally shorten the
measuring time by acquiring less data than needed according to the sampling
theorem. In order to obtain a proper image from such undersampled data, the
reconstruction is commonly defined as the solution of an inverse problem, which
is regularized by a priori assumptions about the object. While practical
realizations have hitherto been surprisingly successful, strong assumptions
about the continuity of image features may affect the temporal fidelity of the
estimated images. Here we propose a novel approach for the reconstruction of
serial real-time MRI data which integrates the deformations between nearby
frames into the data consistency term. The method is not required to be affine
or rigid and does not need additional measurements. Moreover, it handles
multi-channel MRI data by simultaneously determining the image and its coil
sensitivity profiles in a nonlinear formulation which also adapts to
non-Cartesian (e.g., radial) sampling schemes. Experimental results of a motion
phantom with controlled speed and in vivo measurements of rapid tongue
movements demonstrate image improvements in preserving temporal fidelity and
removing residual artifacts.Comment: This is a preliminary technical report. A polished version is
published by Magnetic Resonance in Medicine. Magnetic Resonance in Medicine
201
GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging
Tomography has made a radical impact on diverse fields ranging from the study
of 3D atomic arrangements in matter to the study of human health in medicine.
Despite its very diverse applications, the core of tomography remains the same,
that is, a mathematical method must be implemented to reconstruct the 3D
structure of an object from a number of 2D projections. In many scientific
applications, however, the number of projections that can be measured is
limited due to geometric constraints, tolerable radiation dose and/or
acquisition speed. Thus it becomes an important problem to obtain the
best-possible reconstruction from a limited number of projections. Here, we
present the mathematical implementation of a tomographic algorithm, termed
GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between
real and reciprocal space, GENFIRE searches for a global solution that is
concurrently consistent with the measured data and general physical
constraints. The algorithm requires minimal human intervention and also
incorporates angular refinement to reduce the tilt angle error. We demonstrate
that GENFIRE can produce superior results relative to several other popular
tomographic reconstruction techniques by numerical simulations, and by
experimentally by reconstructing the 3D structure of a porous material and a
frozen-hydrated marine cyanobacterium. Equipped with a graphical user
interface, GENFIRE is freely available from our website and is expected to find
broad applications across different disciplines.Comment: 18 pages, 6 figure
Rapid 3D Phase Contrast Magnetic Resonance Angiography through High-Moment Velocity Encoding and 3D Parallel Imaging
abstract: Phase contrast magnetic resonance angiography (PCMRA) is a non-invasive imaging modality that is capable of producing quantitative vascular flow velocity information. The encoding of velocity information can significantly increase the imaging acquisition and reconstruction durations associated with this technique. The purpose of this work is to provide mechanisms for reducing the scan time of a 3D phase contrast exam, so that hemodynamic velocity data may be acquired robustly and with a high sensitivity. The methods developed in this work focus on the reduction of scan duration and reconstruction computation of a neurovascular PCMRA exam. The reductions in scan duration are made through a combination of advances in imaging and velocity encoding methods. The imaging improvements are explored using rapid 3D imaging techniques such as spiral projection imaging (SPI), Fermat looped orthogonally encoded trajectories (FLORET), stack of spirals and stack of cones trajectories. Scan durations are also shortened through the use and development of a novel parallel imaging technique called Pretty Easy Parallel Imaging (PEPI). Improvements in the computational efficiency of PEPI and in general MRI reconstruction are made in the area of sample density estimation and correction of 3D trajectories. A new method of velocity encoding is demonstrated to provide more efficient signal to noise ratio (SNR) gains than current state of the art methods. The proposed velocity encoding achieves improved SNR through the use of high gradient moments and by resolving phase aliasing through the use measurement geometry and non-linear constraints.Dissertation/ThesisPh.D. Bioengineering 201
A fast and exact -stacking and -projection hybrid algorithm for wide-field interferometric imaging
The standard wide-field imaging technique, the -projection, allows
correction for wide-fields of view for non-coplanar radio interferometric
arrays. However, calculating exact corrections for each measurement has not
been possible due to the amount of computation required at high resolution and
with the large number of visibilities from current interferometers. The
required accuracy and computational cost of these corrections is one of the
largest unsolved challenges facing next generation radio interferometers such
as the Square Kilometre Array. We show that the same calculation can be
performed with a radially symmetric -projection kernel, where we use one
dimensional adaptive quadrature to calculate the resulting Hankel transform,
decreasing the computation required for kernel generation by several orders of
magnitude, whilst preserving the accuracy. We confirm that the radial
-projection kernel is accurate to approximately 1% by imaging the
zero-spacing with an added -term. We demonstrate the potential of our
radially symmetric -projection kernel via sparse image reconstruction, using
the software package PURIFY. We develop a distributed -stacking and
-projection hybrid algorithm. We apply this algorithm to individually
correct for non-coplanar effects in 17.5 million visibilities over a by
degree field of view MWA observation for image reconstruction. Such a
level of accuracy and scalability is not possible with standard -projection
kernel generation methods. This demonstrates that we can scale to a large
number of measurements with large image sizes whilst still maintaining both
speed and accuracy.Comment: 9 Figures, 19 Pages. Accepted to Ap
Adaption in Dynamic Contrast-Enhanced MRI
In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps
Sodium ((23)Na) ultra-short echo time imaging in the human brain using a 3D-Cones trajectory
Object: Sodium magnetic resonance imaging ((23)Na-MRI) of the brain has shown changes in (23)Na signal as a hallmark of various neurological diseases such as stroke, Alzheimer's disease, Multiple Sclerosis and Huntington's disease. To improve scan times and image quality, we have implemented the 3D-Cones (CN) sequence for in vivo (23)Na brain MRI.
Materials and Methods: Using signal-to-noise (SNR) as a
measurement of sequence performance, CN is compared
against more established 3D-radial k-space sampling
schemes featuring cylindrical stack-of-stars (SOS) and
3D-spokes kooshball (KB) trajectories, on five healthy
volunteers in a clinical setting. Resolution was evaluated by simulating the point-spread-functions (PSFs) and experimental measures on a phantom.
Results: All sequences were shown to have a similar SNR
arbitrary units (AU) of 6–6.5 in brain white matter, 7–9 in
gray matter and 17–18 AU in cerebrospinal fluid. SNR
between white and gray matter were significantly different
for KB and CN (p = 0.046 and\0.001 respectively), but
not for SOS (p = 0.1). Group mean standard deviations
were significantly smaller for CN (p = 0.016). Theoretical
full-width at half-maximum linewidth of the PSF for CN is
broadened by only 0.1, compared to 0.3 and 0.8 pixels for
SOS and KB respectively. Actual image resolution is
estimated as 8, 9 and 6.3 mm for SOS, KB and CN
respectively.
Conclusion: The CN sequence provides stronger tissue
contrast than both SOS and KB, with more reproducible
SNR measurements compared to KB. For CN, a higher true
resolution in the same amount of time with no significant
trade-off in SNR is achieved. CN is therefore more suitable
for 23Na-MRI in the brain
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