Single breath‐hold whole‐heart MRA using variable‐density spirals at 3t
Juan M. SantosCharles H. CunninghamMichael LustigBrian A. HargreavesBob S. HuDwight G. NishimuraJohn M. Pauly
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Abstract Multislice breath‐held coronary imaging techniques conventionally lack the coverage of free‐breathing 3D acquisitions but use a considerably shorter acquisition window during the cardiac cycle. This produces images with significantly less motion artifact but a lower signal‐to‐noise ratio (SNR). By using the extra SNR available at 3 T and undersampling k ‐space without introducing significant aliasing artifacts, we were able to acquire high‐resolution fat‐suppressed images of the whole heart in 17 heartbeats (a single breath‐hold). The basic pulse sequence consists of a spectral‐spatial excitation followed by a variable‐density spiral readout. This is combined with real‐time localization and a real‐time prospective shim correction. Images are reconstructed with the use of gridding, and advanced techniques are used to reduce aliasing artifacts. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.Keywords:
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Abstract The undersampled radial acquisition has been widely employed for accelerated (by a factor R = N r / N p ) cardiac imaging, but the resulting reduction in image quality has not been well characterized. This investigation presents a method of measuring these artifacts through synthetic undersampling of high SNR images (SNR ≥ 30). After validating the method in phantoms, the method was applied to a study of short‐axis, long‐axis, and coronary MRI imaging in healthy subjects. For 60 projections (60 N p ), the total artifact is ∼10% for short and long‐axis imaging ( R = 2.1) and ∼15% for coronary MRI ( R = 3.7). For 60 N p , the SD of artifact in the region of the heart is 2% for short‐ and long‐axis imaging ( R = 2.1) and 3.5% for coronary MRI ( R = 3.7). The artifact content is less in the region of the heart than in the periphery. The artifact is very reproducible among subjects for standard views. A study of coronary MRI at progressively fewer projections (at constant scan time) showed that right coronary MRI images were acceptable if total artifact was <6.5% of image content ( N p > 120, R = 2.1). Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.
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Conventional parallel imaging methods mostly utilize the spatial encoding by array of receiver coils to unfold the periodic aliasing artifact resulted from uniformly undersampled k-space data. In scenarios such as low- or ultra-low-field MRI where effective receiver arrays do not exist and SNRs are low, these methods are not generally applicable. This study presents a U-Net based deep learning approach to single-channel MRI acceleration that unfolds the aliasing by exploiting its periodicity. The results demonstrate the aliasing unfolding capability of this method for single-channel MRI even at very high acceleration and in presence of pathologies.
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We propose a novel compressive sensing algorithm for cognitive radio networks, based on non-uniform under-sampling. It is known that the spectrum of uniformly under-sampled signals exhibit frequency aliasing, whereby the frequency location is impossible. To alleviate aliasing, non-uniform sampli
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Sampling is an inherent feature of all staring arrays. The sampling process creates new frequencies that were not present in the scene. Aliasing (caused by undersampling) occurs if these new frequencies appear in the reconstructed image. Whether aliasing is bothersome (it is almost always present) depends on the scene and the degree of undesirability cannot be predicted in advance. We have become accustomed to undersampling (we love our TV). An image must be significantly degraded before we object to the image quality. However, this does not mean that aliasing should be neglected.
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To obtain whole-brain high-resolution T2 maps in 2 minutes by combining simultaneous multislice excitation and low-power PINS (power independent of number of slices) refocusing pulses with undersampling and a model-based reconstruction.A multi-echo spin-echo sequence was modified to acquire multiple slices simultaneously, ensuring low specific absorption rate requirements. In addition, the acquisition was undersampled to achieve further acceleration. Data were reconstructed by subsequently applying parallel imaging to separate signals from different slices, and a model-based reconstruction to estimate quantitative T2 from the undersampled data. The signal model used is based on extended phase graph simulations that also account for nonideal slice profiles and B1 inhomogeneity. In vivo experiments with 3 healthy subjects were performed to compare accelerated T2 maps to fully sampled single-slice acquisitions. The accuracy of the T2 values was assessed with phantom experiments by comparing the T2 values to single-echo spin-echo measurements.In vivo results showed that conventional multi-echo spin-echo, simultaneous multislice, and undersampling result in similar mean T2 values within regions of interest. However, combining simultaneous multislice and undersampling results in higher SDs (about 7 ms) in comparison to a conventional sequence (about 3 ms). The T2 values were reproducible between scan and rescan (SD < 1.2 ms) within subjects and were in similar ranges across subjects (SD < 4.5 ms).The proposed method is a fast T2 mapping technique that enables whole-brain acquisitions at 0.7-mm in-plane resolution, 3-mm slice thickness, and low specific absorption rate in 2 minutes.
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Calculation of small angle, near field Fresnel diffraction patterns of a propagating light beam is a common task in the system analysis of various high energy laser systems. The basic mathematical principles which cause spatial domain aliasing and undersampling errors are well understood. However, the work to develop these fundamental principles into concise expressions of intensity errors has not been addressed previously . Based on new intensity error estimates of aliasing and rough estimates of undersampling errors, we attempt to clarify the different nature of these two phenomena by illustrating their different functional dependencies. Further, we argue that in most cases of practical interest, aliasing errors completely dominate undersampling errors.
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One of parts of digital signal processing is processing with undersampling. In this case, the sampling frequency is lower, than it is required by the sampling theorem. At undersampling, the spectrum of signals is distorted by an aliasing. Various ways of correction of the arising distortions are known. These ways are known under the name the unaliasing. In this article, one more method of the unaliasing is offered. It is offered to carry out multirate digital signal processing in parallel in two channels. Sampling frequencies in these channels are various. Both sampling frequencies are less, than the theorem of counting demands. The method is based on consecutive performance of several stages. At each stage on the next interval of frequencies, the part of a range is restored. Length of an interval is equal to a difference of two sampling frequencies. Therefore, the number of steps is defined by a relationship of width of the restored range and the interval size. For calculations, only subtraction operations are required. In paper, application of the offered way for restoration of a power range is shown. The same way can be applied also to complex Fourier-spectrum.
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We developed new calibration kernels with an alternative undersampling scheme (CASS) for parallel imaging to reduce coherent aliasing artifacts and noises. By sampling k-space lines with irregular and blockwise patterns, incoherent aliasing patterns and noise signals were spread in reconstructed CASS images. Noteworthily, the CASS method outperformed the conventional GRAPPA method at higher acceleration factors.
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As is well known, the energy of an imageis mostly concentrated in the low frequency components of k-space. In this paper, variable density Cartesian trajectory and variable density square spiral trajectory are proposed, oversampling the low frequency area while undersampling the high frequency area in k-space, in order to respectively reduce the aliasing artifact and shorten the scan time. Experimentatl results illustrated that aliasing artifact is almost imperceptible due to low frequency components oversampling and scan time is shortened due to high frequency area undersampling.
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