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

Abstract

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.

Functional lung imaging of 2-year-old children after congenital diaphragmatic hernia repair using dynamic mode decomposition MRI

“…In this work, a publicly available method based on DMD is utilised to analyse the time-series data to obtain functional maps robustly from dynamic acquisitions [ 28 ]. In essence, DMD is a data-driven modal decomposition algorithm capable of identifying dominant spatiotemporal structures latent within time-series data [ 29 – 31 ].…”

“…As such, DMD can determine the underlying oscillation frequencies and amplitudes in dynamic MR acquisitions, rendering it suitable for the identification of ventilation- and perfusion-related signal changes. With DMD MRI, dynamic MR images are analysed to identify ventilation and perfusion modes, and the ensuing fractional ventilation and normalised perfusion maps are calculated as previously explained [ 28 ]. The overall workflow is illustrated in Fig.…”

Estimating ventilation correlation coefficients in the lungs using PREFUL‐MRI in chronic obstructive pulmonary disease patients and healthy adults

“…Similar methods for pulmonary functional imaging using proton MRI are also available, mainly the Fourier decomposition (FD) MRI, along with a few related techniques such as matrix pencil decomposition, nonuniform FD, and dynamic mode decomposition. [5][6][7][8] These methods implement multi-step post-processing with multiple frequency filtering operations on the signal of a dynamic MRI dataset to estimate ventilation and perfusion components at each lung voxel. The post-processing in PREFUL relies more on the calculation of amplitudes in the time domain without a strict low-pass/high-pass filtering; thus, there is no need to define specific frequency thresholds corresponding to ventilation and perfusion components making PREFUL less prone to measurement artifacts in case of respiration variability.…”

“…Similar methods for pulmonary functional imaging using proton MRI are also available, mainly the Fourier decomposition (FD) MRI, along with a few related techniques such as matrix pencil decomposition, nonuniform FD, and dynamic mode decomposition 5–8 . These methods implement multi‐step post‐processing with multiple frequency filtering operations on the signal of a dynamic MRI dataset to estimate ventilation and perfusion components at each lung voxel.…”