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cMeta-INR: cohort-informed meta-learning-based implicit neural representation for deformable registration-driven

Xiaoxue Qian1, Hua-Chieh Shao1, Jing Cai2

  • 1The Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

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This study introduces cohort-informed meta-learning (cMeta-INR) for fast and accurate 3D magnetic resonance (MR) image reconstruction from limited k-space data. The novel framework enhances implicit neural representations for patient-specific deformable image registration, improving anatomical detail preservation.

Keywords:
deformable image registrationimplicit neural representation (INR)meta learningpopulation priortest-time adaptation

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computational Anatomy

Background:

  • Reconstructing high-quality 3D MR images from undersampled k-space data is challenging.
  • Deformable image registration is crucial for aligning medical images, especially in image-guided treatments.
  • Implicit Neural Representations (INRs) offer potential for detailed image reconstruction but struggle with limited data.

Purpose of the Study:

  • To develop a novel cohort-informed meta-learning (cMeta-INR) framework.
  • To enhance the accuracy and efficiency of INRs for patient-specific deformable image registration using limited k-space data.
  • To enable rapid adaptation of INRs for undersampled MR imaging scenarios.

Main Methods:

  • Proposed cMeta-INR framework incorporating token-aware modulation and population-level deformation priors.
  • Utilized a pre-trained population-based registration network (KS-RegNet) for meta-learning.
  • Employed INR template-based meta-learning for rapid adaptation to new registration cases with undersampled k-space data.

Main Results:

  • cMeta-INR outperformed state-of-the-art methods on abdominal and cardiac 4D MRIs.
  • Achieved superior Dice similarity coefficients (0.778 for abdominal, 0.894 for cardiac) and center-of-mass errors.
  • Demonstrated rapid test-time adaptation in approximately 35 seconds on an NVIDIA H100 GPU.

Conclusions:

  • The cohort-informed meta-learning framework significantly improves INR adaptation for individual patients with highly undersampled k-space data.
  • cMeta-INR shows strong potential for fast and accurate patient-specific deformable registration in medical imaging.
  • This approach addresses key challenges in real-time image-guided interventions and MR image reconstruction.