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Magnetic resonance multitasking for multidimensional assessment of cardiovascular system: Development and feasibility study on the thoracic aorta.
Hu, Zhehao; Christodoulou, Anthony G; Wang, Nan; Shaw, Jaime L; Song, Shlee S; Maya, Marcel M; Ishimori, Mariko L; Forbess, Lindsy J; Xiao, Jiayu; Bi, Xiaoming; Han, Fei; Li, Debiao; Fan, Zhaoyang.
Afiliação
  • Hu Z; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.
  • Christodoulou AG; Department of Bioengineering, University of California, Los Angeles, California.
  • Wang N; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.
  • Shaw JL; Department of Medicine, University of California, Los Angeles, California.
  • Song SS; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.
  • Maya MM; Department of Bioengineering, University of California, Los Angeles, California.
  • Ishimori ML; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.
  • Forbess LJ; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California.
  • Xiao J; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California.
  • Bi X; Department of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California.
  • Han F; Department of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California.
  • Li D; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.
  • Fan Z; Siemens Healthcare, Los Angeles, California.
Magn Reson Med ; 84(5): 2376-2388, 2020 11.
Article em En | MEDLINE | ID: mdl-32301164
ABSTRACT

PURPOSE:

To develop an MR multitasking-based multidimensional assessment of cardiovascular system (MT-MACS) with electrocardiography-free and navigator-free data acquisition for a comprehensive evaluation of thoracic aortic diseases.

METHODS:

The MT-MACS technique adopts a low-rank tensor image model with a cardiac time dimension for phase-resolved cine imaging and a T2 -prepared inversion-recovery dimension for multicontrast assessment. Twelve healthy subjects and 2 patients with thoracic aortic diseases were recruited for the study at 3 T, and both qualitative (image quality score) and quantitative (contrast-to-noise ratio between lumen and wall, lumen and wall area, and aortic strain index) analyses were performed in all healthy subjects. The overall image quality was scored based on a 4-point scale 3, excellent; 2, good; 1, fair; and 0, poor. Statistical analysis was used to test the measurement agreement between MT-MACS and its corresponding 2D references.

RESULTS:

The MT-MACS images reconstructed from acquisitions as short as 6 minutes demonstrated good or excellent image quality for bright-blood (2.58 ± 0.46), dark-blood (2.58 ± 0.50), and gray-blood (2.17 ± 0.53) contrast weightings, respectively. The contrast-to-noise ratios for the three weightings were 49.2 ± 12.8, 20.0 ± 5.8 and 2.8 ± 1.8, respectively. There were good agreements in the lumen and wall area (intraclass correlation coefficient = 0.993, P < .001 for lumen; intraclass correlation coefficient = 0.969, P < .001 for wall area) and strain (intraclass correlation coefficient = 0.947, P < .001) between MT-MACS and conventional 2D sequences.

CONCLUSION:

The MT-MACS technique provides high-quality, multidimensional images for a comprehensive assessment of the thoracic aorta. Technical feasibility was demonstrated in healthy subjects and patients with thoracic aortic diseases. Further clinical validation is warranted.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aorta Torácica / Doenças da Aorta Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Magn Reson Med Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aorta Torácica / Doenças da Aorta Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Magn Reson Med Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2020 Tipo de documento: Article