Optimization of Gradient-Echo Echo-Planar Imaging for T 2 * Contrast in the Brain at 0.5 T.
Arjama HalderChad T HarrisCurtis N WiensAndrea SodduBlaine A ChronikPublished in: Sensors (Basel, Switzerland) (2023)
Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T 2 * and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric "T 2 * contrast efficiency" for this evaluation, which minimizes dead time in the EPI sequence while maximizing T 2 * contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T 2 * measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T 2 * contrast efficiency is achieved. T 2 * for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T 2 * of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.
Keyphrases
- contrast enhanced
- magnetic resonance
- diffusion weighted
- diffusion weighted imaging
- magnetic resonance imaging
- computed tomography
- resting state
- high resolution
- functional connectivity
- randomized controlled trial
- blood pressure
- machine learning
- electronic health record
- mass spectrometry
- data analysis
- current status
- quantum dots
- artificial intelligence
- subarachnoid hemorrhage