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Jointly learning non-cartesian k-space trajectories and reconstruction networks for 2D and 3D MR imaging through projection

January 2023 Chaithya Giliyar Radhakrishna, Philippe Ciuciu Bioengineering · Journal article
#Non-Cartesian Sampling #k-space Sampling #Deep Learning #Compressed Sensing #MRI #compressed sensing

Abstract

Compressed sensing in magnetic resonance imaging essentially involves the optimization of (1) the sampling pattern in k-space under MR hardware constraints and (2) image reconstruction from undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems simultaneously, especially in the non-Cartesian acquisition setting. This work aims to contribute to this field by tackling some major concerns in existing approaches. Particularly, current state-of-the-art learning methods seek hardware compliant k-space sampling trajectories by enforcing the hardware constraints through additional penalty terms in the training loss. Through ablation studies, we rather show the benefit of using a projection step to enforce these constraints and demonstrate that the resulting k-space trajectories are more flexible under a projection-based scheme, which results in superior performance in reconstructed image quality. In 2D studies, our novel method trajectories present an improved image reconstruction quality at a 20-fold acceleration factor on the fastMRI data set with SSIM scores of nearly 0.92–0.95 in our retrospective studies as compared to the corresponding Cartesian reference and also see a 3–4 dB gain in PSNR as compared to earlier state-of-the-art methods. Finally, we extend the algorithm to 3D and by comparing optimization as learning-based projection schemes, we show that data-driven joint learning-based method trajectories outperform model-based methods such as SPARKLING through a 2 dB gain in PSNR and 0.02 gain in SSIM.

Details

  • Type of work: Journal article
  • Venue: Bioengineering
  • Published: 24 January 2023
  • Volume: 10
  • Pages: 158
  • DOI: 10.3390/bioengineering10020158
  • Authors: Chaithya Giliyar Radhakrishna, and Philippe Ciuciu
  • Cited by: 20 times (OpenAlex)
  • Data sources: ref.bib, OpenAlex

BibTeX

@article{radhakrishna2023jointly,
  title={Jointly learning non-cartesian k-space trajectories and reconstruction networks for 2D and 3D MR imaging through projection},
  author={Radhakrishna, Chaithya Giliyar and Ciuciu, Philippe},
  journal={Bioengineering},
  volume={10},
  number={2},
  pages={158},
  year={2023},
  publisher={MDPI}
}