Rapid B1 field mapping at 3 T using the 180° signal null method with extended flip angle

Abstract

The EA-NSM permits accurate, fast, and practical B mapping in a 3 T clinical setting.

Four-angle method for practical ultra-high-resolution magnetic resonance mapping of brain longitudinal relaxation time and apparent proton density

“…Figure2shows examples of derived T 1 and APD maps obtained from the phantom imaging data using FAM. As expected, the ζ map exhibits a smooth decrease in ζ as a function of distance from the center of the phantom, in accordance with previous observations[39][40][41]. More importantly, derived T 1 and APD maps with ζ correction exhibit homogeneity across the phantom, reflecting the homogeneity of the phantom itself.…”

“…Assuming a longitudinal steady-state has been reached, the magnitude of the SPGR signal is conventionally given by the Ernst equation through S SPGR (ζθ, TR) = APDsin(ζθ) 1 − exp −TR/T 1 / 1 − exp −TR/T 1 cos(ζθ) [1] where the dimensionless quantity ζ denotes the local, voxel-wise, B 1 + scaling factor, APD is the apparent proton density representing the signal amplitude and is proportional to the true PD, reception field amplitude B [33,[38][39][40][41][42][43][44], only modest spatial resolution imaging is required.…”

Spatially adaptive unsupervised multispectral nonlocal filtering for improved cerebral blood flow mapping using arterial spin labeling magnetic resonance imaging

“…The same threshold value was successfully used to improve myelin water fraction (MWF) mapping from MSE imaging datasets (Bouhrara et al, 2017b; Bouhrara et al, 2018b). Of note, as both B1 and noise SD vary smoothly throughout the images (Aja-Fernández and Vegas-Sánchez-Ferrero, 2016; Bouhrara and Bonny, 2012; Bouhrara and Spencer, 2016; Rejimon et al, 2018), we found that a neighborhood of R = 15 mm x 15 mm x 15 mm provided excellent results through a range of spatial resolution, SNR, and CBF values.…”

A simple and fast adaptive nonlocal multispectral filtering algorithm for efficient noise reduction in magnetic resonance imaging

“…d ( i,j ) between i and each candidate voxel j belonging to W is calculated and arranged in ascending order, with the R voxels having the smallest distance being incorporated into the MLE of its true intensity through trueA^k(i) = argmaxAk{∑i=1RlogSk(i)σ2−∑iRSk2(i)+Ak22σ2+∑i=1Rlog I0[Sk(i) ⋅ Akσ2]}. where trueA^k(i) is the estimated true intensity of spectral image k and index voxel i . The size of the window W must be sufficiently large to ensure inclusion of an adequate number of similar voxels, and sufficiently restricted to ensure that the transmission and reception B 1 fields is approximately constant within the window (25, 26).…”