Frequency‐domain simulation of MR tagging

Abstract

Simulation of MR images is a useful tool for offline sequence development and as an aid to understanding image formation. One particular application of simulation is MR tagging, which is used for tracking myocardial motion. Simple spatial-domain methods cannot adequately represent effects common in these images, such as motion artifact and signal wrap. An existing frequency-domain model is shown to be inappropriate for tagged images, and an extension based on the Bloch equations and Fourier shift theorem is described to correct this. Software incorporating the new model is used to generate ideal tag intensity profiles and to accurately simulate tagged images. The shifted k-space patterns associated with tagged images, and their dependence on the order of the binomial tagging sequence, are explained. An application of the Fourier shift theorem is suggested that allows more rapid simulation of static tagged images.

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

“…The wave vector of this spatial sine wave is equal to k 5cGs, where c is the proton gyromagnetic ratio, G is the tagging gradient, and s is its duration. 30,31 Therefore, acquiring MRI data after a spoiled SPAMM sequence yields images that are the superposition of the object magnetization distribution and the sinusoidal tagging pattern. 32,33 Since the sinusoidal tagging pattern created by a SPAMM sequence deforms with the patient during respiration, gating a SPAMM þ readout acquisition reveals the complex deformation of tissues during breathing.…”

“…Velocity encoded phase contrast MRI (VEPC-MRI) allows estimation of velocity vectors for every pixel of an MRI image by subtracting two phase images acquired with different velocity encoding gradients but otherwise identical acquisition parameters. 28,29 Tagged-MRI is another MRI technique that allows estimation of the complex deformation of biological tissues by superimposing a regular tagging pattern on the object magnetization distribution [30][31][32][33][34] (for a review of MR-based motion estimation approaches, see Ref. 35).…”

Whole-body FDG PET-MR oncologic imaging: pitfalls in clinical interpretation related to inaccurate MR-based attenuation correction

“…VEPC-MRI consists of the acquisition of two otherwise identical pulse sequences with different velocity-encoding gradients, the subtraction of the two resulting images providing for pixel-by-pixel estimations of velocity vectors [31,43,44]. Tagged-MRI refers to superimposing a tagging pattern on the magnetization distribution of a subject, thus yielding displacement vectors which can be utilized for motion correction [31,[45][46][47][48][49]. MRI motion correction reduces motion-induced image blur and leads to a significantly increased SNR relative to standard gating methods by decreasing motion artifacts of up to 60% however, have not demonstrated robust translation into the clinic because of signal to noise and/or field of view problems within the larger human.…”

A k-Space Analysis of MR Tagging

“…,␣ NϪ1 . For example, the rather daunting analytic expression for a SPAMM pattern generated with only three RF pulses is M z ͑ x͒ ϭ ͓cos 1 cos 2 cos 3 ϩ 1 2 ͑1 Ϫ cos 2 ͒sin 1 sin 3 ͔ Ϫ ͓sin 2 sin͑ 1 ϩ 3 ͔͒cos͑⌽x͒ Ϫ ͓ 1 2 ͑1 ϩ cos 2 ͒sin 1 sin 3 ͔cos͑2⌽x͒, [4] where 1 , 2 , and 3 are the tip angles associated with the three RF pulses (13). Such complicated relationships make it difficult to specify and utilize optimality criteria.…”