Keywords: Fat & Fat/Water Separation, Fat
Motivation: Despite recent advances, chemical shift-encoded MRI (CSE-MRI) remains a challenging problem and many algorithms are computationally expensive, leading to interest in deep learning-based methods. However, initial attempts have used convolutional neural networks (CNNs), which are limited by data requirements, poor generalisability across different anatomies (‘anatomy-dependence’) and training time.
Goal(s): To address these limitations, we propose a deep learning-based method known as RAIDER.
Approach: RAIDER uses two multilayer perceptrons (MLPs), each trained separately with simulated single-voxel data, to achieve ultrafast parameter estimation.
Results: RAIDER is several orders of magnitude faster than conventional fitting, with similar/better performance, and avoids the inherent limitations of CNN-based methods.
Impact: RAIDER delivers ‘ultrafast’ CSE-MRI processing whilst avoiding the data and training-time requirements and anatomy-dependence of CNN-based methods. It could simplify, accelerate and reduce the cost of CSE-MRI processing in both research and clinical practice.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords