In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 4 ( 2023-4-14), p. e0284440-
Abstract:
Automated brain volumetric analysis based on high-resolution T1-weighted MRI datasets is a frequently used tool in neuroimaging for early detection, diagnosis, and monitoring of various neurological diseases. However, image distortions can corrupt and bias the analysis. The aim of this study was to explore the variability of brain volumetric analysis due to gradient distortions and to investigate the effect of distortion correction methods implemented on commercial scanners. Material and methods 36 healthy volunteers underwent brain imaging using a 3T magnetic resonance imaging (MRI) scanner, including a high-resolution 3D T1-weighted sequence. For all participants, each T1-weighted image was reconstructed directly on the vendor workstation with (DC) and without (nDC) distortion correction. For each participant’s set of DC and nDC images, FreeSurfer was used for the determination of regional cortical thickness and volume. Results Overall, significant differences were found in 12 cortical ROIs comparing the volumes of the DC and nDC data and in 19 cortical ROIs comparing the thickness of the DC and nDC data. The most pronounced differences for cortical thickness were found in the precentral gyrus, the lateral occipital and postcentral ROI (2.69, -2.91% and -2.79%, respectively) while cortical volumes differed most prominently in the paracentral, the pericalcarine and lateral occipital ROI (5.52%, -5.40% and -5.11%, respectively). Conclusion Correcting for gradient non-linearities can have significant influence on volumetric analysis of cortical thickness and volume. Since the distortion correction is an automatic feature of the MR scanner, it should be stated by each study that applies volumetric analysis which images were used.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0284440
DOI:
10.1371/journal.pone.0284440.g001
DOI:
10.1371/journal.pone.0284440.g002
DOI:
10.1371/journal.pone.0284440.g003
DOI:
10.1371/journal.pone.0284440.g004
DOI:
10.1371/journal.pone.0284440.g005
DOI:
10.1371/journal.pone.0284440.t001
DOI:
10.1371/journal.pone.0284440.t002
DOI:
10.1371/journal.pone.0284440.s001
DOI:
10.1371/journal.pone.0284440.s002
DOI:
10.1371/journal.pone.0284440.s003
DOI:
10.1371/journal.pone.0284440.s004
DOI:
10.1371/journal.pone.0284440.s005
DOI:
10.1371/journal.pone.0284440.s006
DOI:
10.1371/journal.pone.0284440.r001
DOI:
10.1371/journal.pone.0284440.r002
DOI:
10.1371/journal.pone.0284440.r003
DOI:
10.1371/journal.pone.0284440.r004
DOI:
10.1371/journal.pone.0284440.r005
DOI:
10.1371/journal.pone.0284440.r006
DOI:
10.1371/journal.pone.0284440.r007
DOI:
10.1371/journal.pone.0284440.r008
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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