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• Technical Article •
Optimization of artificial intelligence-assisted compressed sensing sequences for cerebral and carotid 3D-TOF-MRA
YUAN Chang  ZHANG Yukun  CAO Jiajun  SONG Qingwei  MIAO Yanwei 

Cite this article as: YUAN C, ZHANG Y K, CAO J J, et al. Optimization of artificial intelligence-assisted compressed sensing sequences for cerebral and carotid 3D-TOF-MRA[J]. Chin J Magn Reson Imaging, 2024, 15(4): 139-144, 152. DOI:10.12015/issn.1674-8034.2024.04.022.


[Abstract] Objective By comparing the application of different acceleration factors (AF) in parallel imaging (PI), compressed sensing (CS), and artificial intelligence-compressed sensing (ACS) techniques for three-dimensional time-of-flight magnetic resonance angiography (3D-TOF-MRA) in the cerebral and carotid region, to select an optimized acceleration factor for ACS.Materials and Methods Twenty-four healthy volunteers were prospectively recruited and underwent cerebral and carotid 3D-TOF-MRA scanning with AF of 3 in PI (PI 3), 4 and 6 in CS (CS 4 and CS 6), and 4, 6, 8 and 10 in ACS (ACS 4, ACS 6, ACS 8 and ACS 10). Scan durations were: PI 3=8 min 40 s; CS 4=6 min 38 s; CS 6=4 min 9 s; ACS 4=5 min 24 s; ACS 6=4 min 30 s; ACS 8=4 min 13 s; ACS 10=3 min 24 s. Regions of interest (ROIs) were delineated in bilateral middle cerebral artery (MCA) at M1 segment, bilateral internal carotid artery (ICA) at C4 segment, five levels below the bifurcation of the carotid artery in bilateral common carotid artery (CCA), and temporal white matter at the same level as MCA and ICA, with the sternocleidomastoid muscle at the same level as CCA serving as the background region. Signal intensity (SI) and standard deviation (SD) were recorded to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality was assessed using four-point and five-point scales for overall image quality, intracranial arteries, and major neck arteries. Intra-class correlation coefficient (ICC) was used to compare the consistency of objective evaluations between two radiologists and within the same radiologist, while Kappa test was employed to compare the consistency of subjective evaluations. If consistency was satisfactory, objective and subjective ratings from one radiologist were selected for subsequent analysis. One-way ANOVA or Kruskal-Wallis test was used to compare overall differences in objective and subjective ratings, with post hoc tests conducted for statistically significant differences.Results Compared with PI, there were statistically significant differences (P<0.05) in SNRL-MCA of ACS 4-ACS 8, CNRL-MCA of ACS 8 and ACS 10, CNRR-MCA of ACS 4, ACS 6, and ACS 10, SNRR-MCA, SNRL-CCA, CNRL-CCA of ACS 4-ACS 10, SNRR-CCA, CNRR-CCA, compared with PI 3, all of which were higher than PI 3. Compared with CS, statistically significant differences (P<0.05) were observed in SNRR-MCA, CNRL-MCA, and CNRR-MCA of ACS 4-ACS 10, SNRL-MCA, SNRL-CCA, SNRR-CCA, CNRL-CCA, CNRR-CCA of ACS 4-ACS 8, compared with CS 4, as well as SNRL-MCA, SNRR-MCA, CNRL-MCA, CNRR-MCA, SNRL-ICA, SNRR-ICA, CNRL-ICA, SNRL-CCA, SNRR-CCA, CNRL-CCA, CNRR-CCA of ACS 4-ACS 10, compared with CS 6, all of which were superior to CS 4 and CS 6. In pairwise comparisons among ACS, SNRL-MCA of ACS 8 was higher than ACS 10 (P<0.05), SNRR-CCA and CNRR-CCA of ACS 8 and ACS 10 were both higher than ACS4 (P<0.05), while other differences were not statistically significant (P>0.05). Except for the images of the carotid arteries, subjective scoring results showed statistically significant differences (P<0.05) between ACS 4-ACS 10 and CS 6, all of which were superior to CS 6.Conclusions Compared with PI and CS, ACS has shorter scanning time and better image quality. ACS 8 is the optimal sequence, with a 51% reduction in scanning time compared with PI 3.
[Keywords] parallel imaging；compressed sensing；artificial intelligence-compressed sensing；cerebral and carotid combined time-of-flight magnetic resonance angiography；magnetic resonance imaging

YUAN Chang   ZHANG Yukun   CAO Jiajun   SONG Qingwei   MIAO Yanwei*  

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

Corresponding author: MIAO Y W, E-mail: ywmiao716@163.com

Conflicts of interest   None.

Received  2023-11-12
Accepted  2024-03-22
DOI: 10.12015/issn.1674-8034.2024.04.022
Cite this article as: YUAN C, ZHANG Y K, CAO J J, et al. Optimization of artificial intelligence-assisted compressed sensing sequences for cerebral and carotid 3D-TOF-MRA[J]. Chin J Magn Reson Imaging, 2024, 15(4): 139-144, 152. DOI:10.12015/issn.1674-8034.2024.04.022.

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