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Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T.

Publication ,  Journal Article
Moore, J; Jimenez, J; Lin, W; Powers, W; Zong, X
Published in: Magn Reson Med
November 2022

PURPOSE: To develop a prospective motion correction (MC) method for phase contrast (PC) MRI of penetrating arteries (PAs) in centrum semiovale at 7 T and to evaluate its performance using automatic PA segmentation. METHODS: Head motion was monitored and corrected during the scan based on fat navigator images. Two convolutional neural networks (CNN) were developed to automatically segment PAs and exclude surface vessels. Real-life scans with MC and without MC (NoMC) were performed to evaluate the MC performance. Motion score was calculated from the ranges of translational and rotational motion parameters. MC versus NoMC pairs with similar motion scores during MC and NoMC scans were compared. Data corrupted by motion were reacquired to further improve PA visualization. RESULTS: PA counts (NPA ) and PC and magnitude contrasts (MgC) relative to neighboring tissue were significantly correlated with motion score and were higher in MC than NoMC images at motion scores above 0.5-0.8 mm. Data reacquisition further increased PC but had no significant effect on NPA and MgC. CNNs had higher sensitivity and Dice similarity coefficient for detecting PAs than a threshold-based method. CONCLUSIONS: Prospective MC can improve the count and contrast of segmented PAs in the presence of severe motion. CNN-based PA segmentation has improved performance in delineating PAs than the threshold-based method.

Duke Scholars

Published In

Magn Reson Med

DOI

EISSN

1522-2594

Publication Date

November 2022

Volume

88

Issue

5

Start / End Page

2088 / 2100

Location

United States

Related Subject Headings

  • Prospective Studies
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Motion
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Arteries
  • 4003 Biomedical engineering
  • 0903 Biomedical Engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Moore, J., Jimenez, J., Lin, W., Powers, W., & Zong, X. (2022). Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T. Magn Reson Med, 88(5), 2088–2100. https://doi.org/10.1002/mrm.29364
Moore, Julia, Jordan Jimenez, Weili Lin, William Powers, and Xiaopeng Zong. “Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T.Magn Reson Med 88, no. 5 (November 2022): 2088–2100. https://doi.org/10.1002/mrm.29364.
Moore J, Jimenez J, Lin W, Powers W, Zong X. Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T. Magn Reson Med. 2022 Nov;88(5):2088–100.
Moore, Julia, et al. “Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T.Magn Reson Med, vol. 88, no. 5, Nov. 2022, pp. 2088–100. Pubmed, doi:10.1002/mrm.29364.
Moore J, Jimenez J, Lin W, Powers W, Zong X. Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T. Magn Reson Med. 2022 Nov;88(5):2088–2100.
Journal cover image

Published In

Magn Reson Med

DOI

EISSN

1522-2594

Publication Date

November 2022

Volume

88

Issue

5

Start / End Page

2088 / 2100

Location

United States

Related Subject Headings

  • Prospective Studies
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Motion
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Arteries
  • 4003 Biomedical engineering
  • 0903 Biomedical Engineering