Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Implicit neural representation, Relaxation and self-consistency priors, Parameter mapping
Motivation: Magnetic resonance $$$\text {T}_{1\rho}$$$ mapping provides critical insights into tissue properties for early disease detection, but its clinical use is hindered by long scan times needed for acquiring multiple $$$\text {T}_{1\rho}$$$-weighted images.
Goal(s): This study proposes an unsupervised implicit neural representation (INR) framework for precise $$$\text{T}_{1\rho}$$$ map generation.
Approach: A subject-specific unsupervised method that learns an implicit neural representation of the $$$\text {T}_{1\rho}$$$-weighted images, simultaneously capturing the relationships among $$$\text {T}_{1\rho}$$$-weighted images and multi-channel $$$k$$$-space data.
Results: LINEAR achieves 14-fold acceleration with high accuracy in $$$\text {T}_{1\rho}$$$ map generation, outperforming state-of-the-art unsupervised and traditional methods in artifact suppression and error reduction.
Impact: This study enables accelerated, high-quality $$$\text {T}_{1\rho}$$$ mapping, improving diagnostic efficiency and providing a foundation for future advancements in rapid quantitative imaging, with potential applications across diverse clinical and research fields.
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