[Formula: see text] Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing.

[Formula: see text] Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing.

Utzschneider, Matthias;Behl, Nicolas G R;Lachner, Sebastian;Gast, Lena V;Maier, Andreas;Uder, Michael;Nagel, Armin M;
magma (new york, ny) 2020
237
utzschneider2020formulamagma

Abstract

To accelerate tissue sodium concentration (TSC) quantification of skeletal muscle using Na MRI and 3D dictionary-learning compressed sensing (3D-DLCS).Simulations and in vivo Na MRI examinations of calf muscle were performed with a nominal spatial resolution of [Formula: see text]. Fully sampled and three undersampled Na MRI data sets (undersampling factors (USF) = 3, 4.4, 6.7) were evaluated. Ten healthy subjects were examined on a 3 Tesla MRI system. Results of the simulation study and the in vivo measurements were compared to the ground truth (GT) and the fully sampled fast Fourier transform (NUFFT) reconstruction, respectively.Reconstruction results of simulated data with optimized 3D-DLCS yielded a lower deviation (< 4%) from the GT than results of the NUFFT reconstruction (> 5%) and a lower standard deviation (SD). For in vivo measurements, a TSC of [Formula: see text] was observed. The mean deviation from the reference is lower for the undersampled 3D-DLCS reconstructions (3.4%) than for NUFFT reconstructions (4.6%). SD is reduced using 3D-DLCS. Compared to a fully sampled NUFFT reconstruction, acquisition time could be reduced by a factor of 4.4 while maintaining similar quantitative accuracy.The optimized 3D-DLCS reconstruction enables accelerated TSC measurements with high quantification accuracy.

Citation

ID: 89341
Ref Key: utzschneider2020formulamagma
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
89341
Unique Identifier:
10.1007/s10334-019-00819-2
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet