DCE-Qnet: Deep network quantification of dynamic contrast enhanced (DCE) MRI Journal Article

   


Authors: Cohen, O.; Kargar, S.; Woo, S.; Vargas, A.; Otazo, R.
Article Title: DCE-Qnet: Deep network quantification of dynamic contrast enhanced (DCE) MRI
Abstract: Introduction: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. Methods: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test–retest experiments were used to assess reproducibility of the parameter maps in the tumor. Results: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. Conclusion: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification. © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2024.
Keywords: neural network; dce; deep learning; drone
Journal Title: Magnetic Resonance Materials in Physics, Biology and Medicine
Volume: 37
Issue: 6
ISSN: 0968-5243
Publisher: ESMRMB  
Date Published: 2024-12-01
Start Page: 1077
End Page: 1090
Language: English
DOI: 10.1007/s10334-024-01189-0
PROVIDER: scopus
PUBMED: 39112813
PMCID: PMC11996236
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200574816&doi=10.1007%2fs10334-024-01189-0&partnerID=40&md5=1dbd04c534ebf5992813019984e9943a
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in PubMed and PDF -- Corresponding authors is MSK author: Ouri Cohen -- Source: Scopus
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MSK Authors
  1. Hebert Alberto Vargas Alvarez
    267 Vargas Alvarez
  2. Jose Ricardo Otazo Torres
    47 Otazo Torres
  3. Sungmin Woo
    62 Woo
  4. Ouri Cohen
    11 Cohen
  5. Soudabeh Kargar
    1 Kargar
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