Keywords: AI Diffusion Models, AI/ML Image Reconstruction, Lower-field, Neonatal, Clinical Translation
Motivation: Accelerating lower-field MRI in the neonatal-intensive-care-unit may reduce motion artifacts and increase accessibility to a wider range of patients. However, parallel imaging and machine learning reconstruction models trained on adult MRI do not apply in the neonatal setting.
Goal(s): This work accelerates single-channel neonatal MRI with diffusion-probabilistic-generative models trained from limited and noisy data collected on a permanent magnet system.
Approach: The proposed training method combines datasets from multiple contrasts and orientations with class embeddings and applies a self-supervised denoiser before training. Diffusion posterior sampling reconstructs images from under-sampled k-space.
Results: Our method enables 1.5x reduction in scan-time using a single-channel.
Impact: The improvement in acquisition speed of T1 and T2 weighted lower field neonatal MRI protocols using diffusion-probabilistic-generative models, trained with methods designed to handle the noisy, limited data, improves accessibility of MRI to patients in the neonatal-intensive-care-unit.
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