Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis
arXiv:2606.17989v1 Announce Type: cross Abstract: Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis. However, acquiring all MRI sequences is often time-consuming and costly. Recent generative models perform cross-contrast synthesis to address this issue by inferring absent contrasts from the available ones. Nevertheless, synthesizing 3D MRI presents significant challenges. Due to the massive volume sizes, operating directly in the pixel space is computationally prohibitive; therefore, a common approach is to first compress the 3D volumes
Advances in generative AI, particularly in latent space modeling, are enabling more efficient processing of complex medical imaging data.
Improved 3D MRI reconstruction and cross-contrast synthesis can significantly reduce the cost and time associated with medical diagnostics, making advanced imaging more accessible.
The ability to infer missing MRI contrasts and reconstruct high-quality 3D images from compressed data streamlines medical workflows and potentially reduces patient scan times.
- · Medical diagnostic companies
- · Hospitals and clinics
- · Generative AI developers
- · Patients
Faster and cheaper access to comprehensive medical imaging, particularly for multi-contrast MRI.
Increased reliance on AI for medical image interpretation and diagnosis, potentially leading to new regulatory frameworks.
Democratization of advanced medical diagnostics, improving healthcare outcomes in resource-limited settings.
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Read at arXiv cs.AI