Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network.
arterial spin labeling
arterial transit time
cerebral blood flow
convolutional neural network
deep learning
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
revised:
13
03
2023
received:
27
09
2022
accepted:
30
03
2023
medline:
29
5
2023
pubmed:
24
4
2023
entrez:
24
04
2023
Statut:
ppublish
Résumé
To reduce the total scan time of multiple postlabeling delay (multi-PLD) pseudo-continuous arterial spin labeling (pCASL) by developing a hierarchically structured 3D convolutional neural network (H-CNN) that estimates the arterial transit time (ATT) and cerebral blow flow (CBF) maps from the reduced number of PLDs as well as averages. A total of 48 subjects (38 females and 10 males), aged 56-80 years, compromising a training group (n = 45) and a validation group (n = 3) underwent MRI including multi-PLD pCASL. We proposed an H-CNN to estimate the ATT and CBF maps using a reduced number of PLDs and a separately reduced number of averages. The proposed method was compared with a conventional nonlinear model fitting method using the mean absolute error (MAE). The H-CNN provided the MAEs of 32.69 ms for ATT and 3.32 mL/100 g/min for CBF estimations using a full data set that contains six PLDs and six averages in the 3 test subjects. The H-CNN also showed that the smaller number of PLDs can be used to estimate both ATT and CBF without significant discrepancy from the reference (MAEs of 231.45 ms for ATT and 9.80 mL/100 g/min for CBF using three of six PLDs). The proposed machine learning-based ATT and CBF mapping offers substantially reduced scan time of multi-PLD pCASL.
Substances chimiques
Spin Labels
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
583-595Subventions
Organisme : NIH HHS
ID : P30AG049638
Pays : United States
Organisme : NIH HHS
ID : RF1NS110043
Pays : United States
Informations de copyright
© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
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