Adaptive sensitivity encoding incorporating temporal filtering (TSENSE) †

By Peter Kellman, F. Epstein, Elliot R. McVeigh. Magnetic Resonance in Medicine, 2001

441 citations recorded by the source corpus.

A number of different methods have been demonstrated which increase the speed of MR acquisition by decreasing the number of sequential phase encodes. The UNFOLD technique is based on time interleaving of k‐space lines in sequential images and exploits the property that the outer portion of the field‐of‐view is relatively static. The differences in spatial sensitivity of multiple receiver coils may be exploited using SENSE or SMASH techniques to eliminate the aliased component that results from undersampling k‐space. In this article, an adaptive method of sensitivity encoding is presented which incorporates both spatial and temporal filtering. Temporal filtering and spatial encoding may be combined by acquiring phase encodes in an interleaved manner. In this way the aliased components are alternating phase. The SENSE formulation is not altered by the phase of the alias artifact; however, for imperfect estimates of coil sensitivities the residual artifact will have alternating phase using this approach. This is the essence of combining temporal filtering (UNFOLD) with spatial sensitivity encoding (SENSE). Any residual artifact will be temporally frequency‐shifted to the band edge and thus may be further suppressed by temporal low‐pass filtering. By combining both temporal and spatial filtering a high degree of alias artifact rejection may be achieved with less stringent requirements on accuracy of coil sensitivity estimates and temporal low‐pass filter selectivity than would be required using each method individually. Experimental results that demonstrate the adaptive spatiotemporal filtering method (adaptive TSENSE) with acceleration factor R = 2, for real‐time nonbreath‐held cardiac MR imaging during exercise induced stress are presented. Magn Reson Med 45:846–852, 2001. Published 2001 Wiley‐Liss, Inc.

Subfields: Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9e080856215cf64b0e1ca4fba8882b443d929099

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Citations in the corpus

Real‐time MRI at a resolution of 20 ms (2010, methodology): “In a similar manner, the proposed method does not depend on the specific nature of the k–t space, as required, for example, for UNFOLD (unaliasing by Fourier-encoding the overlaps using the temporal dimension) (28) and TSENSE (adaptive sensitivity encoding incorporating temporal filtering) (29).”
A tour of accelerated parallel MR imaging from a linear systems perspective (2005, neutral): “[8]”
Gadgetron: An open source framework for medical image reconstruction (2013, methodology): “Data are assumed to be acquired in a timeinterleaved fashion, so that a number of neighboring frames can be averaged and used for calibration data (19).”
Parallel MR imaging (2012, methodology): “A similar method, known as TSENSE (81), can be used to determine coil sensitivity maps in the same way, also eliminating the need for an additional calibration scan.”
Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR (2011, positive): “Several authors have used the spatial diversity of the coil sensitivity profiles to accelerate cardac MRI [44], [45].”
Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible Dynamic Volumetric MRI (2013, methodology): “Temporal PI techniques, such as TSENSE (6) or TGRAPPA (7), remove the need to acquire extra coil reference data, by combining different temporal frames acquired with shifted undersampling patterns.”
Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) (2007, background): “[9] with respect to the coefficients a given f:”