Quantitative DLA-based compressed sensing for T1-weighted acquisitions

• Published in: Journal of magnetic resonance (1997) Vol. 281; pp. 26 - 30
• Main Authors: Svehla, Pavel, Nguyen, Khieu-Van, Li, Jing-Rebecca, Ciobanu, Luisa
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2017; Elsevier
• Subjects: Aplysia californica; Bioengineering; Compressed sensing (CS); Computer Science; Diffusion limited aggregation (DLA); Imaging; Life Sciences; Magnetic resonance microscopy (MRM); Manganese-enhanced magnetic resonance imaging (MEMRI); Modeling and Simulation; Manganese-enhanced magnetic resonance imaging (MEMRI); Magnetic resonance microscopy (MRM); Aplysia californica; Compressed sensing (CS); Diffusion limited aggregation (DLA)
• ISSN: 1090-7807; 1096-0856
• DOI: 10.1016/j.jmr.2017.05.002

[Display omitted] •DLA-based CS is implemented for quantitative T1-weighted imaging.•An acceleration factor of two is appropriate for maintaining image resolution.•The technique is adequate for accurate signal intensity quantification. High resolution Manganese Enhanced Magnetic Resonance Imaging (MEMRI), which uses manganese as a T1 contrast agent, has great potential for functional imaging of live neuronal tissue at single neuron scale. However, reaching high resolutions often requires long acquisition times which can lead to reduced image quality due to sample deterioration and hardware instability. Compressed Sensing (CS) techniques offer the opportunity to significantly reduce the imaging time. The purpose of this work is to test the feasibility of CS acquisitions based on Diffusion Limited Aggregation (DLA) sampling patterns for high resolution quantitative T1-weighted imaging. Fully encoded and DLA-CS T1-weighted images of Aplysia californica neural tissue were acquired on a 17.2T MRI system. The MR signal corresponding to single, identified neurons was quantified for both versions of the T1 weighted images. For a 50% undersampling, DLA-CS can accurately quantify signal intensities in T1-weighted acquisitions leading to only 1.37% differences when compared to the fully encoded data, with minimal impact on image spatial resolution. In addition, we compared the conventional polynomial undersampling scheme with the DLA and showed that, for the data at hand, the latter performs better. Depending on the image signal to noise ratio, higher undersampling ratios can be used to further reduce the acquisition time in MEMRI based functional studies of living tissues.