Quantitative DLA-based compressed sensing for T₁-weighted acquisitions.

Area of Science:

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Biophysics

Background:

• High-resolution Manganese Enhanced Magnetic Resonance Imaging (MEMRI) offers potential for single-neuron scale functional imaging.
• Long acquisition times in high-resolution MEMRI can degrade image quality due to sample deterioration and hardware instability.
• Compressed Sensing (CS) techniques can accelerate MRI acquisition, reducing scan times.

Purpose of the Study:

• To evaluate the feasibility of CS acquisitions using Diffusion Limited Aggregation (DLA) sampling patterns for high-resolution quantitative T1-weighted imaging.
• To assess the accuracy of signal quantification and impact on spatial resolution using DLA-CS in MEMRI.
• To compare DLA sampling with conventional polynomial undersampling for MEMRI.

Main Methods:

• Acquisition of fully encoded and DLA-CS T1-weighted images of Aplysia californica neural tissue using a 17.2T MRI system.
• Quantification of MR signal from single neurons in both fully encoded and DLA-CS T1-weighted images.
• Comparison of DLA sampling performance against conventional polynomial undersampling schemes.

Main Results:

• DLA-CS with 50% undersampling accurately quantified signal intensities in T1-weighted images, showing only 1.37% difference compared to fully encoded data.
• Minimal impact on image spatial resolution was observed with DLA-CS.
• DLA sampling outperformed conventional polynomial undersampling for the acquired data.

Conclusions:

• DLA-CS is a feasible technique for accelerating high-resolution quantitative T1-weighted MEMRI.
• This method enables accurate signal quantification and maintains spatial resolution, crucial for functional imaging of neuronal tissue.
• Higher undersampling ratios can be employed to further reduce acquisition times in MEMRI studies, depending on signal-to-noise ratio.