Quantitative Analysis of Pulmonary Perfusion using Time-Resolved Parallel 3D MRI - Initial results

doi:10.1055/s-2004-817624

Cited by 48 publications

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“…For COPD, the lack of relevant correlations in this study is most likely due to a limited range of disease severity in the evaluated COPD patients. Furthermore, emphysema, pulmonary hypertension, or peripheral obstructions have an influence on QDP in COPD, whereas FEV1%predicted is mainly driven by large airway obstructions, which may cause disagreement between QDP and FEV1%predicted ( 12 , 48 ). In a previous COPD study, with a broader disease severity range, a moderate correlation between QDP and the ratio between FEV1 and forced vital capacity (FEV1/FVC) was observed ( 12 ).…”

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confidence: 99%

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases

et al. 2022

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BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the assessment of pulmonary perfusion, which may play a key role in the development of muco-obstructive lung disease. One problem with quantifying pulmonary perfusion is the high variability of metrics. Quantifying the extent of abnormalities using unsupervised clustering algorithms in residue function maps leads to intrinsic normalization and could reduce variability.PurposeWe investigated the reproducibility of perfusion defects in percent (QDP) in clinically stable patients with cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD).Methods15 CF (29.3 ± 9.3y, FEV1%predicted = 66.6 ± 15.8%) and 20 COPD (66.5 ± 8.9y, FEV1%predicted = 42.0 ± 13.3%) patients underwent DCE-MRI twice 1 month apart. QDP, pulmonary blood flow (PBF), and pulmonary blood volume (PBV) were computed from residue function maps using an in-house quantification pipeline. A previously validated MRI perfusion score was visually assessed by an expert reader.ResultsOverall, mean QDP, PBF, and PBV did not change within 1 month, except for QDP in COPD (p < 0.05). We observed smaller limits of agreement (± 1.96 SD) related to the median for QDP (CF: ± 38%, COPD: ± 37%) compared to PBF (CF: ± 89%, COPD: ± 55%) and PBV (CF: ± 55%, COPD: ± 51%). QDP correlated moderately with the MRI perfusion score in CF (r = 0.46, p < 0.05) and COPD (r = 0.66, p < 0.001). PBF and PBV correlated poorly with the MRI perfusion score in CF (r =−0.29, p = 0.132 and r =−0.35, p = 0.067, respectively) and moderately in COPD (r =−0.57 and r =−0.57, p < 0.001, respectively).ConclusionIn patients with muco-obstructive lung diseases, QDP was more robust and showed a higher correlation with the MRI perfusion score compared to the traditionally used perfusion metrics PBF and PBV.

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confidence: 99%

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases

et al. 2022

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“…Focal areas of wedge-shaped perfusion defects allow for an indirect diagnosis of PE. In these areas of decreased perfusion, the mean PBF and PBV are decreased, and mean MTT is prolonged [61,62]. The acute PTE index, defined as the ratio between the volume of perfusion defects and the total lung volume as determined by DCE-MR, has been shown to correlate with clinical severity and has similar accuracy to the right ventricular /left ventricular (RV/LV) diameter ratio for the prediction of outcome after acute PE [63].…”

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confidence: 99%

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Gopalan

Gibbs

2020

Diagnostics

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Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure–function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.

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Dynamic pulmonary perfusion and flow quantification with MR imaging, 3.0T vs. 1.5T: Initial results

Nael

Michaely

Lee

et al. 2006

Magnetic Resonance Imaging

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Purpose: To prospectively evaluate the technical feasibility and relative performance of pulmonary time-resolved MR angiography (MRA) and pulmonary artery (PA) flow quantification at 3.0T vs. 1.5T. Materials and Methods:Time-resolved contrast-enhanced (CE) MRA of the pulmonary circulation, and flow quantification of the main PA (MPA) were performed in 14 consecutive adult healthy volunteers at both 1.5 and 3.0 Tesla with nearly identical sequence parameters. Image quality, signal-to-noise ratio (SNR), and quantitative indices of pulmonary perfusion, flow, and velocity were evaluated and compared at both field strengths.Results: Time-resolved pulmonary MRA, perfusion, and flow quantification were successfully performed at both magnetic fields. The results of pulmonary perfusion and flow indices were comparable at both magnetic fields, with no statistically significant difference. The SNR values for vascular structures were higher at 3.0T vs. 1.5T (P ϭ 0.001). The SNR values and the definition scores for parenchymal enhancement were significantly lower (P ϭ 0.008 and 0.001, respectively) at 3.0T. Conclusion:Time-resolved pulmonary MRA, perfusion, and flow quantification at 3.0T was feasible, with comparable results to 1.5T. The lower parenchymal enhancement at 3.0T is believed to reflect increased susceptibility effects at higher magnetic fields. Further work is needed to fully exploit the potential of pulmonary perfusion imaging at 3.0T and to address the current limitations.Key Words: pulmonary perfusion with MRI; time-resolved pulmonary MRA at 3.0T; pulmonary flow at 3.0T; comparison of 3.0T vs. MRI IS INCREASINGLY being applied to the study of pulmonary vascular anatomy, perfusion, and blood flow. Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) in particular is well suited for studying the pulmonary circulation because it offers the potential for simultaneous evaluation of vascular anatomy and dynamic microvascular parenchymal enhancement.Recently, parallel imaging with k-space undersampling has improved the temporal and spatial resolution of time-resolved MRA (1,2) with positive results at 1.5T. Phase-sensitive velocity imaging has been successfully applied to the study of pulmonary artery (PA) blood flow for some time (3,4).With the advent of whole-body, 3.0T MR systems, the promise is that performance and image quality can be even further enhanced. The higher available signal-tonoise ratio (SNR) can be used to support fast imaging tools, improving both spatial and temporal resolution. Also, since the longitudinal relaxation time (T1) of unenhanced blood increases with field strength (5), sensitivity to injected gadolinium (Gd) agents for CE-MRA may be heightened. There are, however, substantial technical challenges presented by MRI at 3.0T. Specific absorption rates (SAR) set lower limits on nominal flip angles, and dielectric resonances and radiofrequency (RF) eddy currents may result in B 1 inhomogeneity, which can negatively impact image quality (6 -8). Magnetic susceptibility eff...

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Quantitative Analysis of Pulmonary Perfusion using Time-Resolved Parallel 3D MRI - Initial results

Abstract: Time-resolved parallel 3D MRI allows at least a semi-quantitative assessment of lung perfusion. Future studies will have to assess the clinical value of this quantitative information for the diagnosis and management of cardiopulmonary disease.

Cited by 48 publications

References 12 publications

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Dynamic pulmonary perfusion and flow quantification with MR imaging, 3.0T vs. 1.5T: Initial results

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