Quantitative susceptibility mapping of the spine using in‐phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat‐water separation

Abstract

Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi‐echo gradient‐echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat‐water separation (R2*‐IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in‐phase (IP) echo initialization of R2*‐IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi‐echo GRE sequence was implemented to acquire out‐phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*‐IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO‐GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi‐peak fat model. There was no substantial difference on QSM between the single peak and multi‐peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO‐GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

“… corrected water/fat separation estimating water, fat, and inhomogeneous field from gradient‐recalled echo (GRE) signal is a necessary step in quantitative susceptibility mapping to remove the associated chemical shift contribution to the field 1‐5 . Several algorithms, including hierarchical multiresolution separation, multi‐step adaptive fitting, and ‐IDEAL, have been proposed to decompose the water/fat separation problem into linear (water and fat) and nonlinear (field and ) subproblems and solve these problems iteratively 6‐8 .…”

Early detection of increased marrow adiposity with age in rats using Z‐spectral MRI at ultra‐high field (7 T)

“…For Dixon MRI data, the post‐processing algorithm was developed in MATLAB (r2016a, MathWorks, Natick, Massachusetts). Multiple echo complex images were rebuilt from real and imaginary images, then the water and fat images were estimated according to the iterative least‐squares algorithm (IDEAL, iterative decomposition of water and fat with echo asymmetry and least‐squares estimation) 21,30 . Complex signal over echo time S ( T E n ) extracted pixel by pixel, containing water and fat species, can be expressed as 31,32 …”

“…Multiple echo complex images were rebuilt from real and imaginary images, then the water and fat images were estimated according to the iterative leastsquares algorithm (IDEAL, iterative decomposition of water and fat with echo asymmetry and least-squares estimation). 21,30 Complex signal over echo time S(T En ) extracted pixel by pixel, containing water and fat species, can be expressed as 31,32 S…”

Quantitative susceptibility mapping of the head‐and‐neck using SMURF fat‐water imaging with chemical shift and relaxation rate corrections

“…The effects of relaxation time differences are known to bias the estimation of proton density fat-fraction (PDFF), 37 , 38 but are generally neglected in QSM. 12 , 23 , 24 , 28 To minimize the bias caused by T 1 relaxation rate differences, small FAs have to be used, resulting in poor SNR, or a correction based on known T 1 values 38 can be applied. In the case of PDFF mapping of the iron overloaded liver, the differences in are assumed to be negligible 39 due to the shortening of the values being dominated by the presence of iron.…”

“…Although a single-peak fat signal model is assumed in generating the spectrally selective radiofrequency (RF) pulses in SMURF, it has been shown that this approximation does not lead to significant bias in QSM outside the brain. 23 Susceptibility values of fatty tissues vary, however, quite widely in the literature, 23 e.g., 0.29 ppm 7 and 0.57 ppm 9 for the fat in the liver, 0.19 ppm 21 for the fat in the knee, and 0.29 ppm 24 for the fatty fascia in the head-and-neck. Nevertheless, in all cases was the fat assessed as being more paramagnetic than the surrounding water-based tissues.…”