Wavelet‐encoded MR imaging

Weaver, John B.; Xu, Yansun; Healy, Dennis M.; Driscoll, James R.

doi:10.1002/mrm.1910240209

Cited by 99 publications

(69 citation statements)

References 8 publications

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“…Rather than defining the transform matrix explicitly, it is much easier to describe the underlying decomposition algorithm, which uses the two complementary filters and . In the orthogonal case, the low-pass filter satisfies the so-called quadrature mirror filter (QMF) conditions (7) (8) where is the transfer function ( -transform) of . The high-pass filter is the modulated version of given by (9) The wavelet decomposition is implemented iteratively as in Fig.…”

Section: A Multiresolution and Wavelet Decomposition Of A One-dimensmentioning

confidence: 99%

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Statistical analysis of functional MRI data in the wavelet domain

Ruttimann

Unser

Rawlings

et al. 1998

IEEE Trans. Med. Imaging

135

110

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Abstract-The use of the wavelet transform is explored for the detection of differences between brain functional magnetic resonance images (fMRI's) acquired under two different experimental conditions. The method benefits from the fact that a smooth and spatially localized signal can be represented by a small set of localized wavelet coefficients, while the power of white noise is uniformly spread throughout the wavelet space. Hence, a statistical procedure is developed that uses the imposed decomposition orthogonality to locate wavelet-space partitions with large signal-to-noise ratio (SNR), and subsequently restricts the testing for significant wavelet coefficients to these partitions. This results in a higher SNR and a smaller number of statistical tests, yielding a lower detection threshold compared to spatial-domain testing and, thus, a higher detection sensitivity without increasing type I errors. The multiresolution approach of the wavelet method is particularly suited to applications where the signal bandwidth and/or the characteristics of an imaging modality cannot be well specified. The proposed method was applied to compare two different fMRI acquisition modalities. Differences of the respective useful signal bandwidths could be clearly demonstrated; the estimated signal, due to the smoothness of the wavelet representation, yielded more compact regions of neuroactivity than standard spatial-domain testing.

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Section: A Multiresolution and Wavelet Decomposition Of A One-dimensmentioning

confidence: 99%

“…reconstruction [6], [7], and in the design of new acquisition schemes for magnetic resonance imaging (MRI) [8]- [11]. Wavelet representations are also well suited for a variety of data processing tasks.…”

mentioning

confidence: 99%

Statistical analysis of functional MRI data in the wavelet domain

Ruttimann

Unser

Rawlings

et al. 1998

IEEE Trans. Med. Imaging

135

110

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show abstract

“…For example, instead of completing the k-space traversal for every measurement, accelerated MR data acquisition can be achieved by various alterations of the k-space trajectories and the associated image reconstruction algorithms, such as partial k-space sampling (10). Alternatively, a priori information-based methods can also improve the temporal resolution of MR dynamic measurements (11) with various implementations, such as keyhole imaging (12), singular value decomposition (SVD) MRI (13), and wavelet-encoded MRI (14). Rather than a direct measurement, it has been demonstrated that manipulating experimental design can obtain fMRI with millisecond temporal resolution (15).…”

mentioning

confidence: 99%

Dynamic magnetic resonance inverse imaging of human brain function

Lin

Wald

Ahlfors

et al. 2006

Magnetic Resonance in Med

114

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MRI is widely used for noninvasive hemodynamic-based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order-of-magnitude speedup in generating time-resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time-consuming gradient-encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography ( Key words: MRI; inverse; parallel MRI; magnetoencephalography; minimum-norm; MNE; dSPM; InI; electroencephalgraphy Single-shot fast MRI with 1-3-s time resolution has been the principal technology for functional MRI (fMRI) (1-4). This time resolution is adequate for observing hemodynamic responses (HDRs), which are secondary to the much faster neural activity (5). Because of its high spatial resolution, noninvasiveness, and flexibility of contrast preparation, MRI has also been considered for its potential to further detect fast, nonhemodynamic functional changes related to human brain activity (6,7). However, efforts to identify suitable MRI contrast mechanisms have been hampered by the lack of required temporal resolution. Ultrafast MRI (100 frames per second or more) is a mandatory tool for testing any novel direct neuronal contrast mechanisms. It may not be possible to discern a contrast change that occurs and then disappears on a 10-ms scale without an imaging technique with similar temporal resolution. Thus, a fast imaging technique with millisecond temporal resolution is indispensable as the prerequisite for allowing MR to probe direct neural contrast mechanisms.In traditional Fourier MRI, the temporal resolution is constrained by the k-space traversing scheme used to spatially encode the data. Echo-planar imaging (EPI) (8) and spiral imaging (9) utilize switching gradients to achieve k-space traversing at a faster imaging rate than gradientecho or spin-echo imaging. With state-of-the-art technology, 2D single-slice T 2 *-weighted images can be obtained in approximately 80 ms by EPI or spiral imaging. This allows whole-head coverage with 3-mm isotropic resolution in 2-3 s. Modest improvements can be made by optimizing the k-space sampling and reconstruction methods. For example, instead of completing the k-space traversal for every measurement, accelerated MR data acquisition can be achieved by various alterations of the k-space trajectories and the associated image reconstruction algorithms, such as partial k-space sampling (10). Alternatively, a priori information-based methods can also improve the temporal resolution of MR dynamic measurements (11) with various implementations, such as keyhole imaging (12), singu...

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“…As such, the imaging equation is in the form of a Fourier integral. For notational convenience, we define here the dynamic imaging problem as the acquisition of a sequence of images, denoted as To overcome this problem, several data-sharing methods have been proposed for efficient dynamic imaging [1][2][3][4][5][6], two examples of which are the Keyhole and RIGR (Reduced-encoding Imaging by Generalized-series Reconstruction) techniques [1][2][3]. This paper presents an extension on the RIGR technique so that multiple references can be used to achieve high spatiotemporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Generalized Series Imaging with Multiple References

Liang

2001

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Wavelet‐encoded MR imaging

Cited by 99 publications

References 8 publications

Statistical analysis of functional MRI data in the wavelet domain

Statistical analysis of functional MRI data in the wavelet domain

Dynamic magnetic resonance inverse imaging of human brain function

Generalized Series Imaging with Multiple References

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