Synthetic MRI with T₂-based Water Suppression to Reduce Hyperintense Artifacts due to CSF-Partial Volume Effects in...

Our purpose was to assess our proposed new synthetic MRI (synMRI) technique, combined with T₂-based water suppression (T₂wsup), to reduce cerebral spinal fluid (CSF)-partial volume effects (PVEs). These PVEs are problematic in the T₂-weighted fluid-attenuation inversion recovery (FLAIR) images obtained by conventional synMRI techniques.

Our T₂wsup was achieved by subtracting additionally acquired long TE spin echo (SE) images of water signals dominant from the originally acquired images after T₂ decay correction and a masking on the long TE image using the water volume (V_w) map to preserve tissue SNR, followed by quantitative mapping and then calculation of the synthetic images. A simulation study based on a two-compartment model including tissue and water in a voxel and a volunteer MR study were performed to assess our proposed method. Parameters of long TE and a threshold value in the masking were assessed and optimized experimentally. Quantitative parameter maps of standard and with T₂wsup were generated, then wsup-synthetic FLAIR and SE images were calculated using those suitable combinations and compared.

Our simulation clarified that the CSF-PVE artifacts in the standard synthetic FLAIR increase T₂ as the water volume increases in a voxel, and the volunteer MR brain study demonstrated that the hyperintense artifacts on synthetic images were reduced to <10% of V_w in those with the standard synMRI while keeping the tissue SNR by selecting optimal masking parameters on additional long TE images of TE = 300 ms. In addition, the wsup-synthetic SE provided better gray-white matter contrasts compared with the wsup-synthetic FLAIR while keeping CSF suppression.

Our proposed T₂wsup-synMRI technique makes it easy to reduce the CSF-PVE artifacts problematic in the synthetic FLAIR images using the current synMRI technique by adding long TE images and simple processing. Although further optimizations in data acquisition and processing techniques are required before actual clinical use, we expect our technique to become clinically useful.