Convolutional Framework for Accelerated Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation. The clinical application of MRI may be limited by long data acquisition times; therefore, MR image reconstruction from highly undersampled k-space data has been an active area of research. Many works exploit rank deficiency in a Hankel data matrix to recover unobserved k-space samples; the resulting problem is non-convex, so the choice of numerical algorithm can significantly affect performance, computation, and memory. We present a simple, scalable approach called Convolutional Framework (CF). We demonstrate the feasibility and versatility of CF using measured data from 2D, 3D, and dynamic applications.

• Publication year

  2020

• Venue

  IEEE International Symposium on Biomedical Imaging

• Publication date

  2020-02-08

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/ISBI45749.2020.9098393arXiv 2002.03225PMID 35211242PMCID PMC8865187
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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