Very fast MR imaging by field echoes and small angle excitation

Meulen, P. van der; Groen, J.P.; Cuppen, J.J.M.

doi:10.1016/0730-725x(85)90362-5

Cited by 116 publications

(24 citation statements)

References 2 publications

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“…(1)(2) Jin Jin (1) Zhentao Zuo (2)(3) Feng Liu (1) Adnan Trakic (1) Ewald Weber (1) Yan Zhuo (3) Rong Xue (3) Stuart Crozier (1)  Co-corresponding authors (1) School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia…”

Section: In Vivo Sensitivity Estimation and Imaging Acceleration Withmentioning

confidence: 99%

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

Jin

Zuo

et al. 2015

Journal of Magnetic Resonance

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Using a new rotating SENSitivity Encoding (rotating-SENSE) algorithm, we have successfully demonstrated that the rotating radiofrequency coil array (RRFCA) is capable of achieving a significant reduction in scan time and a uniform image reconstruction for a homogeneous phantom at 7 Tesla. However, at 7 Tesla the in vivo with the same number of coil elements, the RRFCA was able to reconstruct good quality images at a high reduction factor. It is hoped that the proposed sensitivity estimation algorithm and the acceleration ability of the RRFCA will be particularly useful for ultra high field MRI.

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Section: In Vivo Sensitivity Estimation and Imaging Acceleration Withmentioning

confidence: 99%

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

Jin

Zuo

et al. 2015

Journal of Magnetic Resonance

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“…In traditional rapid MRI techniques, two main streams have been developed for imaging acceleration. The first one is based on full k-space scanning which uses a combination of fast gradients and RF coil pulse sequence [1][2][3]. While these methods have some drawbacks, such as (1) technological challenges associated with fast gradient coils and intensive sequences in hardware; (2) degraded image quality caused by eddy current distortions and nonlinear gradient magnetic fields;…”

Section: Motivationmentioning

confidence: 99%

“…Provided that objective function is convex, Equation (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) has the computational complexity which is polynomial with the signal length [53]. Furthermore, when ( ) = { : = } , the optimization problem can be adapted as a linear program [38].…”

Section: Recovery Algorithmmentioning

confidence: 99%

“…Recently, a fast composite splitting algorithm (FCSA) was proposed as an efficient algorithm to shorten the optimization time for reconstruction [83]. FCSA separated equation (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) into the terms of a least square data fitting and l1 norm regularization. By existing techniques, the two sub-problems can be solved efficiently.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…However, artefacts are difficult to distinguish from real signals at larger reduction factors (the ratio between the amount of subsampled data (D) and fully sampled data (N), i.e., = ) [2]. This is because the final reconstructed image quality not only relies on the limited randomness of the k-space sampling, but it is also largely determined by another major factor, that is the total amount and property of the collected signal [105][106][107][108].…”

Section: Introductionmentioning

confidence: 99%

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Improving the image quality in compressed sensing MRI by the exploitation of data properties

Yang¹

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Minimizing scan time without compromising image quality has been the main thrust of magnetic resonance imaging (MRI) since the establishment of the field. Tremendous effort has been put into MRI systems in pursuit of faster MR imaging techniques, which can substantially facilitate clinical diagnoses and improve the patient experience. Traditional MRI accelerated approaches mainly focused on hardware improvements to improve the speed of scanning. These methods are still governed by Nyquist-Shannon sampling rate. Hence, when the acceleration rate is pushed beyond the theoretical limit, aliasing artefacts are introduced which degrade the reconstructed image quality and are difficult to eliminate via current methods. Recently, it has been found that a newly developed signal processing theory, compressed sensing (CS), can be used to create high-resolution signal data sets from sparse samples, despite violating the classical Nyquist-Shannon sampling criteria. The application of CS to MRI opens up an appealing avenue to offer potentially significant scan time reductions. CS reconstructs MR images from incomplete k-space measurements. Taking advantage of the fact that MR images have sparse representations under certain mathematical transformations, CS overcomes the distortions resulting from the under-sampled k-space data. Significant progress has been made in the development of CS MRI. However, there are two major remaining challenges to its full adoption in clinical applications: (1) the small amount of under-sampled data (for example, less than 1/8 of the whole k-space data) will inherently introduce artefacts in the reconstructed image and compromise diagnosis accuracy for the reconstructed images; (2) owing to hardware limits, it is difficult to realise two-dimension random under-sampling, which is favoured by CS operation.Practically, the random phase-encode under-sampling pattern has been widely implemented.However, it introduces coherent aliasing artefacts that are hard to eliminate using the CS theory.In this thesis, several techniques are proposed that can improve the quality of images reconstructed by CS MRI. These techniques have the potential to facilitate faithful reconstruction of CS MRI in clinical applications. A novel two-stage reconstruction scheme is introduced in Chapter 3. In this method, the under-sampled k-space data is segmented into low-frequency and high-frequency parts.Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the under-sampled high-frequency k-space data to complete the second stage reconstruction of the whole image. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate than conventional approaches. Because the restricted isometric property is easier to satisfy than conventional CS method, the reconstruction consequently ii produces lower residual errors in each step...

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Multidimensional k-space model for analysis of flow-related phenomena in MR imaging

Petersson

Christoffersson²

1999

Int. J. Imaging Syst. Technol.

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The multidimensional partition model, which combines the k‐space description of magnetic resonance imaging pulse sequences with a partition concept, has been developed to model flow‐related effects. A partition represents the magnetization created due to excitation by a given radiofrequency pulse. In the present model, it is visualized as a set of parameters rather than a vector sum taken over a collection of spins. The phase dispersion due to velocity and higher orders of motion is described by adding new dimensions to the three spatial k‐space dimensions. The computer implementation of the model is capable of simulating the outcome from pulse sequences used in clinical routines and it does not need assumptions regarding pulse sequence parameters. Furthermore, flow‐induced effects due to phase dispersion, caused by movements in a magnetic field gradient, as well as the inflow of unsaturated spins, are handled by the model. Constant or varying (pulsatile) flow velocity may be assigned together with different velocity profiles (e.g., plug flow, laminar flow) over a simulated vessel. The model makes it possible to simulate images of an object containing vessels with pulsatile laminar flow pattern, scanned using a three‐dimensional angiography pulse sequence, in <10 min on a Pentium‐based PC system. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 10, 115–127, 1999

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Very fast MR imaging by field echoes and small angle excitation

Cited by 116 publications

References 2 publications

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

Improving the image quality in compressed sensing MRI by the exploitation of data properties

Multidimensional k-space model for analysis of flow-related phenomena in MR imaging

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