Whole Brain Myelin Water Mapping in One Minute Using Tensor Dictionary Learning With Low-Rank Plus Sparse...

Area of Science:

• Neuroimaging
• Medical Physics
• Biomedical Engineering

Background:

• Myelin water content quantification is crucial for brain imaging.
• Multi-echo T2-weighted images (T2WIs) are used but require long acquisition times.
• Accelerating acquisition is essential for clinical feasibility.

Purpose of the Study:

• To develop a novel algorithm for rapid reconstruction of T2WIs.
• To enable high-quality myelin water fraction (MWF) mapping with accelerated acquisition.
• To improve the efficiency of brain imaging for myelin quantification.

Main Methods:

• A tensor dictionary learning algorithm with low-rank and sparse regularization (TDLLS) was proposed.
• The algorithm leverages local/nonlocal similarity and temporal redundancy in complex relaxation signals.
• Parallel imaging (pTDLLS) was integrated for further acceleration, with a novel pulse sequence for prospective undersampling.

Main Results:

• The pTDLLS algorithm successfully reconstructed high-quality T2WIs from undersampled data.
• Whole-brain myelin water fraction (MWF) maps were obtained within 1 minute.
• High-quality MWF maps were achieved at an acceleration factor (R) of 6.

Conclusions:

• The proposed pTDLLS algorithm significantly accelerates myelin water content quantification.
• This method allows for rapid, high-quality MWF mapping, improving clinical applicability.
• The technique holds promise for efficient neuroimaging of myelin-related pathologies.