Keywords: Motion Correction, Motion Correction, Implicit Neural Representation
Motivation: Dynamic Contrast-Enhanced MRI (DCE-MRI) requires high temporal resolution to effectively capture motion and contrast dynamics. Current methods rely on pre-estimating temporal information from calibration data, limiting imaging performance.
Goal(s): We aim to develop a novel reconstruction method for achieving sub-second temporal resolution in DCE-MRI without the need for calibration data.
Approach: We introduce an unsupervised Implicit Neural Representation (INR) framework, where spatial coordinates and learnable temporal latent variables are used to model and reconstruct the DCE-MRI data directly.
Results: Our method reconstructs artifact-free images, with even one spoke per frame, and the learned latent variables accurately capture both contrast changes and respiratory motion.
Impact: The proposed method paves the way for artifact-free, high-temporal-resolution DCE-MRI for clinical applications. Moreover, the proposed INR framework makes calibrationless MRI reconstruction feasible and interpretable, offering a powerful tool for a wide range of imaging challenges.
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