Application of model‐free analysis in the MR assessment of pulmonary perfusion dynamics

Abstract

Dynamic contrast-enhanced (DCE) MRI has been used to quantitatively evaluate pulmonary perfusion based on the assumption of a gamma-variate function and an arterial input function (AIF) for deconvolution. However, these assumptions may be too simplistic and may not be valid in pathological conditions, especially in patients with complex inflow patterns (such as in congenital heart disease). Exploratory data analysis methods make minimal assumptions on the data and could overcome these pitfalls. In this work, two temporal clustering methodsKohonen clustering network (KCN) and Fuzzy C-Means (FCM)-were concatenated to identify pixel time-course patterns. The results from seven normal volunteers show that this technique is superior for discriminating vessels and compartments in the pulmonary circulation. Patient studies with five cases of acquired or congenital pulmonary perfusion disorders demonstrate that pathologies can be highlighted in a concise map that combines information of the mean transit time (MTT) and pulmonary blood volume (PBV). The method was found to provide greater insight into the perfusion dynamics that might be over- Proper regulation of pulmonary perfusion and ventilation is required for efficient gas exchange. Therefore, it is essential to accurately estimate pulmonary perfusion to assess the pathophysiology of the lung. In current clinical practice, pulmonary perfusion is evaluated with the use of nucleotide scintigraphic methods. However, these techniques are limited by poor spatial resolution and artifacts introduced by long imaging times (1). Magnetic resonance imaging (MRI) has been demonstrated to be a promising tool for evaluating brain perfusion with a spatial resolution of less than 1.5 mm and temporal resolution of about 1 s using Gd-DTPA as a contrast agent (2). When the same strategy is extended to image pulmonary perfusion, the poor magnetic field homogeneity caused by the complex air-tissue interfaces in the lung reduces the T* 2 of the lung tissue to only a few milliseconds. The resulting low signalto-noise ratio (SNR) limits the application of MRI in the lungs. Recent developments in short-TE imaging sequences have overcome the T* 2 decay and made it feasible to assess pulmonary perfusion by dynamic contrast-enhanced (DCE) MRI, as used in brain perfusion studies (1,3-7).Assuming that the contrast agent is a nondiffusible, intravascular tracer, quantitative indices, such as the relative pulmonary blood volume (PBV), relative mean transit time (MTT), and relative pulmonary blood flow (PBF), can be derived from the time courses of signal intensity (SI) in DCE-MRI (1,8,9). To eliminate the SI change from recirculation of the tracer, the time course is fitted to an assumed bolus-shaped function (typically a gamma-variate function) before the calculation is performed. Summary parameters, such as time-to-peak and gamma-fitting parameters, can be obtained as well (10). Furthermore, to achieve the absolute quantification of PBV, PBF, and MTT, a suitable arterial input function (AIF) is ...

Semi-automatic lung segmentation of DCE-MRI data sets of 2-year old children after congenital diaphragmatic hernia repair: Initial results

“…Ray et al (15) implemented an active counter approach to segment from hyperpolarized 3 He MR images while Ingrisch et al (16) performed correlation analysis on the signal time course of the DCE-MRI of healthy volunteers. Also taking the shape information of the signal time course into account, Chuang et al employed self-organizing maps andFuzzy k-means clustering to segment the lung(17).…”

Regional pulmonary perfusion using model‐free analysis of contrast‐enhanced MRI in meconium‐aspirated piglets

“…The interaction of these two time‐response curves (bolus delivery and bolus retention in the microvascular bed) is described by the mathematical process of convolution of the feeding pulmonary artery input C a (t) and the regional impulse response function R(t). Note that the model‐free deconvolution approach used in the present study is only valid when the contrast agent is considered to be confined within the intravascular compartment; this may become too simplistic for validation pathological conditions in the lung (10). During the first passage of Gd‐DTPA, we considered the contrast agent to reside within the intravascular compartment, but as recirculation takes place, the signal intensity is considerably influenced by an extravascular uptake.…”

“…These abnormalities were shown by histology to be associated with capillary destruction (8). Moreover, patients with pulmonary arterial hypertension were found to have a decreased peripheral pulmonary microvascular enhancement concomitant with an increase in central pulmonary microvascular enhancement evaluated by analysis of dynamic contrast‐enhanced MRI (9, 10).…”

Temporal correlation‐based dynamic contrast‐enhanced MR imaging improves assessment of complex pulmonary circulation in congenital heart disease

“…We did this because a high negative correlation represents an inversion of the template curve, which is physiologically meaningless and is not helpful for diagnostic purposes. Others have used a similar approach (18,22). Another point to be noted is that in our TC analysis we did not implement the amplitudemultiplication approach (in which the SI amplitude is multiplied by a correlation coefficient (19,22)) because this would have highlighted the major vessels while suppressing our target organ (the lung parenchyma).…”

“…To our knowledge, however, DCE-MRI has not been applied to the evaluation of complex pulmonary circulation (CPC) associated with congenital heart disease, for at least three reasons: First, it is often difficult to distinguish washout of the first-pass transit of the contrast agent in children because they have a fast recirculation time. Second, the gamma-variate function is often invalid for hemodynamic assumptions because of the CPC (15)(16)(17)(18). Third, physiologic motion due to heartbeat and breathing causes severe fluctuations in the signal intensity (SI), which makes perfusion measurements imprecise.…”