Share：
Share this content in WeChat
X
[Chinese][PDF]3464
• 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.

[1]
PU Y H, ZOU X Y, WANG Y L, et al. Difference of one year death and stroke recurrence in ischemic stroke patients with anterior and posterior circulation intracranial atherosclerosis[J]. Zhonghua Yi Xue Za Zhi, 2018, 98(7): 502-507. DOI: 10.3760/cma.j.issn.0376-2491.2018.07.004.
[2]
ZHAO D L, LI R Y, LI C, et al. Assessment of the degree of arterial stenosis in intracranial atherosclerosis using 3D high-resolution MRI: comparison with time-of-flight MRA, contrast-enhanced MRA, and DSA[J]. Clin Radiol, 2023, 78(2): e63-e70. DOI: 10.1016/j.crad.2022.08.132.
[3]
NIU J X, RAN Y C, CHEN R, et al. Use of PETRA-MRA to assess intracranial arterial stenosis: comparison with TOF-MRA, CTA, and DSA[J/OL]. Front Neurol, 2022, 13: 1068132 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/36726752/. DOI: 10.3389/fneur.2022.1068132.
[4]
MUNIO M, DARCOURT J, GOLLION C, et al. Large artery intracranial stenosis in young adults with ischaemic stroke[J]. Rev Neurol, 2022, 178(3): 206-212. DOI: 10.1016/j.neurol.2021.10.008.
[5]
GWAK D S, KIM B K, CHUNG I, et al. The usefulness of Time-of-Flight MR angiography in detection of intraplaque hemorrhage in patients with acute ischemic stroke with symptomatic carotid stenosis[J/OL]. PLoS One, 2020, 15(2): e0229024 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/32053703/. DOI: 10.1371/journal.pone.0229024.
[6]
YU J F, PUNG L, MINAMI H, et al. Virtual 2D angiography from four-dimensional digital subtraction angiography (4D-DSA): a feasibility study[J]. Interv Neuroradiol, 2021, 27(2): 307-313. DOI: 10.1177/1591019920961604.
[7]
HUANG T, ZOU S L, SHEN X, et al. Evaluation of the level of carotid bifurcation and the morphology of extracranial internal carotid artery in patients with carotid stenosis by color Doppler ultrasound and digital subtraction angiography[J]. Zhonghua Yi Xue Za Zhi, 2022, 102(11): 781-786. DOI: 10.3760/cma.j.cn112137-20210605-01285.
[8]
SAMARZIJA K, MILOSEVIC P, JURJEVIC Z, et al. Grading of carotid artery stenosis with computed tomography angiography: whether to use the narrowest diameter or the cross-sectional area[J]. Insights Imaging, 2018, 9(4): 527-534. DOI: 10.1007/s13244-018-0622-5.
[9]
WU C H, CHEN S T, CHEN J H, et al. Diagnosis of extracranial carotid stenosis by MRA of the brain[J/OL]. Sci Rep, 2021, 11(1): 12010 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/34103593/. DOI: 10.1038/s41598-021-91511-w.
[10]
YOKUŞ A, TOPRAK N, GÜNDÜZ A M, et al. Anterior cerebral artery and anterior communicating artery variations: assessment with magnetic resonance angiography[J/OL]. World Neurosurg, 2021, 155: e203-e209 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/34400324/. DOI: 10.1016/j.wneu.2021.08.027.
[11]
FUJITA H. New horizons in MR technology: RF coil designs and trends[J]. Magn Reson Med Sci, 2007, 6(1): 29-42. DOI: 10.2463/mrms.6.29.
[12]
ARUN A, THOMAS T J, RANI J S, et al. Efficient directionality-driven dictionary learning for compressive sensing magnetic resonance imaging reconstruction[J/OL]. J Med Imaging, 2020, 7(1): 014002 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/32042856/. DOI: 10.1117/1.JMI.7.1.014002.
[13]
SAKATA A, FUSHIMI Y, OKADA T, et al. Evaluation of cerebral arteriovenous shunts: a comparison of parallel imaging time-of-flight magnetic resonance angiography (TOF-MRA) and compressed sensing TOF-MRA to digital subtraction angiography[J]. Neuroradiology, 2021, 63(6): 879-887. DOI: 10.1007/s00234-020-02581-y.
[14]
ZHANG H N, SONG Q W, WANG J Z, et al. A preliminary study on the effect of compressed sensing with multiple acceleration factors on carotid 3D-TOF-MRA[J]. J Clin Radiol, 2021, 40(6): 1079-1083. DOI: 10.13437/j.cnki.jcr.2021.06.010.
[15]
HUANG C, HU J W, LI L, et al. Artificial intelligence-assisted compressed sensing technology for lumbar MR scanning[J]. Chin J Med Imag Technol, 2023, 39(7): 1080-1083. DOI: 10.13929/j.issn.1003-3289.2023.07.029.
[16]
CHEN C Y, YU Y J, WANG M X, et al. Improvement of intracranial 3D TOF MRA image quality based on artificial intelligence reconstruction[J]. Chin J CT MRI, 2023, 21(10): 60-63. DOI: 10.3969/j.issn.1672-5131.2023.10.019.
[17]
LI B B, ZHANG H N, FANG X, et al. Impacts of compressed sensing acceleration factors on imaging quality of head 3D-T1WI and voxel-based morphometry quantitative parameters[J]. Chin J Med Imag Technol, 2022, 38(11): 1730-1734. DOI: 10.13929/j.issn.1003-3289.2022.11.031.
[18]
NIU E B, CHEN H L, ZHOU X M, et al. Application of compressed sensing combined with parallel imaging in intracranial MRA[J]. J Pract Med Imag, 2023, 24(2): 90-93. DOI: 10.16106/j.cnki.cn14-1281/r.2023.02.002.
[19]
ZHANG Y K, LIU N, CAO J J, et al. Optimization of MR sequences of craniocerebral three-dimensional phase-contrast magnetic resonance venogram based on sensitivity coding and compressed sensing technology[J]. Chin J Med Imag Technol, 2023, 39(6): 915-919. DOI: 10.13929/j.issn.1003-3289.2023.06.026.
[20]
WANG Q, WANG G, SUN Q, et al. Application of MAGnetic resonance imaging compilation in acute ischemic stroke[J]. World J Clin Cases, 2021, 9(35): 10828-10837. DOI: 10.12998/wjcc.v9.i35.10828.
[21]
HAN L M, YU Q. Optimization of parallel acquisition technique in 3DTOF MRA of head with 8-receiving channels phased-array coil in1.5TMR system: a phantom study[J]. Chin J Med Imag Technol, 2019, 35(3): 423-427. DOI: 10.13929/j.1003-3289.201807146.
[22]
PAN Z Y, DING X W, WANG S W, et al. Application of 32-channel head coil with integrated parallel acquisition technique(iPAT)in the skull of MRA[J]. J Med Imag, 2013, 23(11): 1673-1675, 1682.
[23]
DING J, DUAN Y, ZHUO Z, et al. Acceleration of brain TOF-MRA with compressed sensitivity encoding: a multicenter clinical study[J]. AJNR Am J Neuroradiol, 2021, 42(7): 1208-1215. DOI: 10.3174/ajnr.A7091.
[24]
REN S J, WU W, SU C Q, et al. High-resolution compressed sensing time-of-flight MR angiography outperforms CT angiography for evaluating patients with Moyamoya disease after surgical revascularization[J]. BMC Med Imaging, 2022, 22(1): 64. DOI: 10.1186/s12880-022-00790-w.
[25]
ZHANG X, CAO Y Z, MU X H, et al. Highly accelerated compressed sensing time-of-flight magnetic resonance angiography may be reliable for diagnosing head and neck arterial steno-occlusive disease: a comparative study with digital subtraction angiography[J]. Eur Radiol, 2020, 30(6): 3059-3065. DOI: 10.1007/s00330-020-06682-3.
[26]
GAO T Y, LU Z, WANG F Z, et al. Using the compressed sensing technique for lumbar vertebrae imaging: comparison with conventional parallel imaging[J]. Curr Med Imaging, 2021, 17(8): 1010-1017. DOI: 10.2174/1573405617666210126155814.
[27]
WANG Q Z, ZHAO W L, XING X Y, et al. Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study[J]. Eur Radiol, 2023, 33(12): 8585-8596. DOI: 10.1007/s00330-023-09823-6.
[28]
DRATSCH T, SIEDEK F, ZÄSKE C, et al. Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers[J/OL]. Eur Radiol Exp, 2023, 7(1): 66 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/37880546/. DOI: 10.1186/s41747-023-00377-2.
[29]
FERVERS P, ZAESKE C, RAUEN P, et al. Conventional and deep-learning-based image reconstructions of undersampled K-space data of the lumbar spine using compressed sensing in MRI: a comparative study on 20 subjects[J/OL]. Diagnostics, 2023, 13(3): 418 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/36766523/. DOI: 10.3390/diagnostics13030418.
[30]
ZHAO Q, XU J J, YANG Y X, et al. AI-assisted accelerated MRI of the ankle: clinical practice assessment[J/OL]. Eur Radiol Exp, 2023, 7(1): 62 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/37857868/. DOI: 10.1186/s41747-023-00374-5.
[31]
NI M, HE M, YANG Y X, et al. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective[J/OL]. Eur Radiol, 2023 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/37932390/. DOI: 10.1007/s00330-023-10368-x.
[32]
IUGA A I, RAUEN P S, SIEDEK F, et al. A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers[J/OL]. Br J Radiol, 2023, 96(1146): 20220074 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/37086077/. DOI: 10.1259/bjr.20220074.
[33]
ZHONG M M, CAO J J, AN Q, et al. Impact of AI-assisted compressed sensing on quality and phase value of nuclei of brain susceptibility weighted imaging[J]. Chin J Magn Reson Imag, 2023, 14(12): 91-97. DOI: 10.12015/issn.1674-8034.2023.12.015.
[34]
SUI H, GONG Y, LIU L, et al. Comparison of artificial intelligence-assisted compressed sensing (ACS) and routine two-dimensional sequences on lumbar spine imaging[J/OL]. J Pain Res, 2023, 16: 257-267 [2023-11-27]. https://pubmed.ncbi.nlm.nih.gov/36744117/. DOI: 10.2147/JPR.S388219.

PREV Application of head enhanced T1WI sequences based on deep learning reconstruction technology in the transformation of pituitary neuroendocrine neoplasms
NEXT Research on multi-source cardiac image segmentation method based on modal interaction learning
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn