Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion

• Verfasst von: Ilicak, Efe [VerfasserIn]
• Verfasst von: Özdemir, Safa [VerfasserIn]
• Verfasst von: Zapp, Jascha [VerfasserIn]
• Verfasst von: Schad, Lothar R. [VerfasserIn]
• Verfasst von: Zöllner, Frank G. [VerfasserIn]
• Titel: Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion
• Titelzusatz: technical note
• Verf.angabe: Efe Ilicak, Safa Ozdemir, Jascha Zapp, Lothar R. Schad, Frank G. Zöllner
• E-Jahr: 2023
• Jahr: August 2023
• Umfang: 9 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 29. März 2023 ; Gesehen am 02.04.2024
• Titel Quelle: Enthalten in: Magnetic resonance in medicine
• Ort Quelle: New York, NY [u.a.] : Wiley-Liss, 1984
• Jahr Quelle: 2023
• Band/Heft Quelle: 90(2023), 2 vom: Aug., Seite 761-769
• ISSN Quelle: 1522-2594
• DOI: doi:10.1002/mrm.29656
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: dynamic mode decomposition
• Sach-SW: Fourier decomposition
• Sach-SW: lung
• Sach-SW: lung function
• Sach-SW: matrix pencil
• Sach-SW: pulmonary MRI
• K10plus-PPN: 188470123X

Abstract

Purpose To introduce dynamic mode decomposition (DMD) as a robust alternative for the assessment of pulmonary functional information from dynamic non-contrast-enhanced acquisitions. Methods Pulmonary fractional ventilation and normalized perfusion maps were obtained using DMD from simulated phantoms as well as in vivo dynamic acquisitions of healthy volunteers at 1.5T. The performance of DMD was compared with conventional Fourier decomposition (FD) and matrix pencil (MP) methods in estimating functional map values. The proposed method was evaluated based on estimated signal amplitude in functional maps across varying number of measurements. Results Quantitative assessments performed on phantoms and in vivo measurements indicate that DMD is capable of successfully obtaining pulmonary functional maps. Specifically, compared to FD and MP methods, DMD is able to reduce variations in estimated amplitudes across different number of measurements. This improvement is evident in the fractional ventilation and normalized perfusion maps obtain from phantom simulations with frequency variations and noise, as well as in the maps obtained from in vivo measurements. Conclusions A robust method for accurately estimating pulmonary ventilation and perfusion related signal changes in dynamic acquisitions is presented. The proposed method uses DMD to obtain functional maps reliably, while reducing amplitude variations caused by differences in number of measurements.