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Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior.

Cheng Che Tsai1, Xiaoyang Chen2,3, Sahar Ahmad2,3

  • 1Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.

Machine Learning in Medical Imaging. MLMI (Workshop)
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Summary
This summary is machine-generated.

This study introduces a dual-modal Deep Image Prior (dmDIP) method to enhance infant Magnetic Resonance Imaging (MRI) quality without extra scanning. The technique improves image resolution and reduces sensitivity to early stopping during processing.

Keywords:
Dual-ModalityInfant MRISuper-ResolutionUnsupervised Learning

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Infant brain development studies rely on Magnetic Resonance Imaging (MRI), but acquisition challenges like long scan times and motion artifacts limit data quality.
  • Existing super-resolution (SR) methods often require paired low-resolution (LR) and high-resolution (HR) images, which are impractical for infant MRI.
  • Deep Image Prior (DIP) offers unsupervised single-image SR but struggles with automated early stopping criteria.

Purpose of the Study:

  • To develop an unsupervised super-resolution technique for enhancing infant MRI quality without additional imaging burden.
  • To address the challenge of automated early stopping in Deep Image Prior (DIP) for single-image SR.
  • To leverage multi-modal MRI data (T1-weighted and T2-weighted) for improved image reconstruction.

Main Methods:

  • A novel dual-modal Deep Image Prior (dmDIP) framework was designed, integrating information from both T1-weighted and T2-weighted MRI scans.
  • The method constrains the low-frequency k-space of the super-resolved image to match that of the input low-resolution image.
  • The unsupervised approach optimizes the image reconstruction using only the low-resolution input, eliminating the need for paired high-resolution data.

Main Results:

  • The dmDIP model successfully enhanced the image quality of infant MRI scans.
  • The proposed method demonstrated substantially reduced sensitivity to the critical early stopping parameter in DIP training.
  • Evaluation on infant MRI data from birth to one year confirmed the effectiveness of the dual-modal approach.

Conclusions:

  • Dual-modal Deep Image Prior (dmDIP) provides an effective solution for unsupervised super-resolution of infant MRI.
  • The technique improves image quality and robustness to training parameters, facilitating automated processing.
  • dmDIP holds promise for advancing the study of early brain development using high-quality MRI data.