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A Weighted Stochastic Conjugate Direction Algorithm for Quantitative Magnetic Resonance Images-A Pattern in Ruptured Achilles Tendon T2-Mapping Assessment.

Piotr A RegulskiJakub ZielinskiBartosz BoruckiKrzysztof Nowinski
Published in: Healthcare (Basel, Switzerland) (2022)
This study presents an accurate biexponential weighted stochastic conjugate direction (WSCD) method for the quantitative T2-mapping reconstruction of magnetic resonance images (MRIs), and this approach was compared with the non-negative-least-squares Gauss-Newton (GN) numerical optimization method in terms of accuracy and goodness of fit of the reconstructed images from simulated data and ruptured Achilles tendon (AT) MRIs. Reconstructions with WSCD and GN were obtained from data simulating the signal intensity from biexponential decay and from 58 MR studies of postrupture, surgically repaired ATs. Both methods were assessed in terms of accuracy (closeness of the means of calculated and true simulated T2 values) and goodness of fit (magnitude of mean squared error (MSE)). The lack of significant deviation in correct T2 values for the WSCD method was demonstrated for SNR ≥ 20 and for GN-SNR ≥ 380. The MSEs for WSCD and GN were 287.52 ± 224.11 and 2553.91 ± 1932.31, respectively. The WSCD reconstruction method was better than the GN method in terms of accuracy and goodness of fit.
Keyphrases
  • magnetic resonance
  • high resolution
  • deep learning
  • contrast enhanced
  • convolutional neural network
  • machine learning
  • optical coherence tomography
  • cancer therapy
  • subarachnoid hemorrhage
  • big data
  • high density