Ultrafast multi-slice spatiotemporally encoded MRI with slice-selective dimension segmented.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Biomedical Engineering
• Medical Physics

Background:

• Spatiotemporally encoded (SPEN) MRI is a robust imaging technique resistant to field inhomogeneity and chemical shift.
• Existing multi-slice SPEN methods face challenges with specific absorption rate (SAR) and image quality.
• Advancements are needed to improve efficiency and quality in multi-slice MRI.

Purpose of the Study:

• To propose and evaluate a novel multi-slice SPEN MRI method (SeSPEN) with segmented slice-selective encoding.
• To assess the SeSPEN method's performance regarding SAR, image quality, and signal-to-noise ratio (SNR).
• To compare SeSPEN with existing spin-echo EPI, spin-echo SPEN, and multi-slice global SPEN (GlSPEN) methods.

Main Methods:

• Developed the segmented SPEN (SeSPEN) method by segmenting the slice-selective dimension.
• Conducted theoretical analysis of the SeSPEN method.
• Performed experimental validation using phantoms, lemons, and in vivo rat brain imaging.
• Utilized super-resolution reconstruction with deconvolution for all SPEN images.

Main Results:

• The SeSPEN method demonstrated robustness to field inhomogeneity, a key advantage of SPEN MRI.
• SeSPEN achieved a better signal-to-noise ratio (SNR) compared to the multi-slice GlSPEN method.
• The SeSPEN method exhibited comparable SAR to GlSPEN while mitigating T1 signal attenuation.
• Experimental results confirmed the efficacy of SeSPEN in phantom, lemon, and rat brain scans.

Conclusions:

• The SeSPEN method effectively enhances multi-slice SPEN MRI by improving image quality and SNR.
• SeSPEN offers a solution for reducing SAR and T1 signal attenuation in multi-slice imaging.
• This novel approach facilitates faster multi-slice SPEN MRI scans with superior image quality.