Optimized inversion‐prepared gradient echo imaging

Abstract

Purpose: To implement a method using an extended phase graph (EPG)-based simulation to optimize inversion-prepared gradient echo sequences with respect to signal and contrast within the shortest acquisition time.Materials and Methods: A critical issue in rapid gradient-echo imaging is the effect of residual transverse magnetization between consecutive data acquisition windows. Various spoiling schemes have been proposed to mitigate this problem, and while spoiling is often considered to be perfect, imaging can be more truthfully described using the EPG. An EPG-based simulation is used to analyze and predict the image signal and contrast to serve as a basis for sequence optimization.Results: Fourteen biological phantom experiments and five brain imaging experiments on each of five healthy volunteers was performed to validate and verify the accuracy of the EPG-based simulation. In addition, two experiments on an in-cranial cadaver brain were performed to show the ability of the proposed method for improving overall image quality. Conclusion:From the experiment results, it is demonstrated that optimization of 3D magnetization-prepared rapid gradient-echo imaging sequences can be performed with an EPG-based simulation to manipulate the sequence parameters for generating images with highly specific signal and contrast characteristics for quantitative T1-weighted human brain imaging.

Improved gray‐white matter contrast using magnetization prepared fast imaging with steady‐state free precession (MP‐FISP) brain imaging at 0.55 T

“…The simulation was done for all combinations of a few values of $$$ {T}_{1,\mathrm{GM}}=\left\{700,750,800,850,900\right\} $$$ ms and $$$ {T}_{1,\mathrm{WM}}=\left\{400,450,500\right\} $$$ ms. $$$ {T}_{2,\mathrm{GM}}=112 $$$ ms, $$$ {T}_{2,\mathrm{WM}}=89 $$$ ms, 11 $$$ {\rho}_{\mathrm{GM}}=1 $$$ and $$$ {\rho}_{\mathrm{WM}}=0.92 $$$ 16 were assumed. Because the sequence is $$$ {T}_1 $$$‐weighted, the used $$$ {T}_1 $$$ value is of much greater importance than the $$$ {T}_2 $$$ value, which is why a range of $$$ {T}_1 $$$ values was used, while $$$ {T}_2 $$$ was kept constant at the reported literature value.…”

“…To have some guidance in finding the optimal parameters for maximum GM/WM contrast, a simulation was performed. For this, the MP‐RAGE and MP‐FISP signal were computed, using the EPG method, 13‐15 as described by Kir et al 16 …”

“…The MP‐RAGE signal of a tissue with a given proton density $$$ \rho $$$ and relaxation times $$$ {\mathrm{T}}_1 $$$ and $$$ {\mathrm{T}}_2 $$$ depends on the delay TD1 after the inversion pulse, before acquisition starts, the delay TD2 after acquisition, before the next inversion, the flip angle $$$ \alpha $$$, the “small” repetition time $$$ \tau $$$ between $$$ \alpha $$$ pulses (echo‐spacing) and the number of $$$ \alpha $$$ pulses $$$ N $$$ in one outer loop 16 (see Figure 1 for a visual depiction).…”

“…The EPG simulation keeps track of the state evolution by updating the expansion coefficients for each RF pulse $$$ i $$$ via matrix operations (see Kir et al, 16 equations [1] and [2]). Plugging in the tissue parameters ($$$ {\mathrm{T}}_1 $$$, $$$ {\mathrm{T}}_2 $$$, $$$ \rho $$$) and sequence parameters ($$$ \alpha $$$, TD1 and TD2, $$$ \tau $$$, $$$ N $$$ and RF phase $$$ {\Psi}_i $$$) into these equations yields the signal evolution during data acquisition.…”

“…While an MP‐RAGE sequence is RF spoiled by using a nonzero $$$ \phi $$$, the MP‐FISP does not employ RF spoiling, $$$ \phi ={0}^{\circ } $$$. For the MP‐RAGE, a typical choice for $$$ \phi $$$ is 50°, 16,17 which is used by Siemens 18 and, therefore, for our calculations as well.…”