Quantitative MRI: Rationale and Challenges

@inproceedings{Gulani2020QuantitativeMR,
  title={Quantitative MRI: Rationale and Challenges},
  author={Vikas Gulani and Nicole Seiberlich},
  year={2020}
}
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3 Citations
Background Citations
2
Methods Citations
1

3 Citations

Quantitative Imaging Principles Improves Medical Image Learning
TLDR
This work proposes incorporating quantitative imaging principles during generative SSL to improve image quality and quantitative biological accuracy and shows that this training schema results in better starting states for downstream supervised training on limited data.
Timing the Ischemic Stroke by Multiparametric Quantitative Magnetic Resonance Imaging
TLDR
Exploiting advanced technologies such as Magnetic Resonance Fingerprinting, artificial intelligence (AI), and machine learning (ML) for the post-processing of MRI data, combined with fast MRI techniques, is expected to speed up the translation of objective stroke timing procedures into patient management.
Low‐rank inversion reconstruction for through‐plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T
TLDR
A low-rank inversion method is proposed to resolve MRF contrast dynamics from through-plane accelerated Cartesian/radial measurements applied to quantitative relaxation-time mapping on a 0.35T system.

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