Pulmonary perfusion imaging in the rodent lung using dynamic contrast-enhanced MRI

Abstract

With the development of various models of pulmonary disease, there is tremendous interest in quantitative regional assessment of pulmonary function. While ventilation imaging has been addressed to a certain extent, perfusion imaging for small animals has not kept pace. In humans and large animals perfusion can be assessed using dynamic contrast-enhanced (DCE) MRI with a single bolus injection of a gadolinium (Gd)-based contrast agent. But the method developed for the clinic cannot be translated directly to image the rodent due to the combined requirements of higher spatial and temporal resolution. This work describes a novel image acquisition technique staggered over multiple, repeatable bolus injections of contrast agent using an automated microinjector, synchronized with image acquisition to achieve dynamic first-pass contrast enhancement in the rat lung. This allows dynamic first-pass imaging that can be used to quantify pulmonary perfusion. Further improvements are made in the spatial and temporal resolution by combining the multiple injection acquisition method with Interleaved Radial Imaging and "Sliding window-keyhole" reconstruction (IRIS). The results demonstrate a simultaneous increase in spatial resolution (<200 mum) and temporal resolution (<200 ms) over previous methods, with a limited loss in signal-to-noise-ratio.

Fig 1

Dynamic RA acquisition and reconstruction scheme for DCE-MRI. Data are acquired over multiple injections (Inj1-InjN). During each injection, the same lines of k-space are acquired across multiple time-points (Tp1-TpN). The missing views for each time-point are acquired during the remaining injections. Reconstruction can be carried out by combining data (shown in last row) from the N injections and using a standard regridding algorithm.

Fig 2

IRIS acquisition scheme for DCE-MRI. Data is acquired over multiple injections (Inj1-InjN). The sequence differs from that in Figure 1, since the trajectories for each time-point for a given injection are no longer identical. Instead, each set of radial lines is rotated by a small increment (Δϕ) with respect to the trajectories of the previous time-point. Last row shows the k-space sampling pattern created when information from multiple injections is combined. Time-compressed k-space sampling distribution created by combining all k-space lines acquired throughout the acquisition over multiple injections and multiple time-points is shown the extreme right bottom. The different colored lines indicate the different time-points. Each radial line acquired throughout the acquisition is unique.

Fig 3

IRIS reconstruction updates the core of the time-compressed k-space. Tp1-TpN are the number of time-points that are dynamically created. In this example, the time resolution is improved by a factor of 2. The first row shows the core with a single color (keyhole), while the second row shows the core with colors from the two closest time-points (sliding window).

Fig 4

Dynamic contrast enhanced MRI images acquired using dynamic RA (8 from a series of 16) in a rat showing the wash-in/wash-out behavior of the contrast agent at a spatial resolution of ∼780 µm and a temporal resolution of 400 ms using two 20 µl injections of Gd-DTPA.

Fig 5

(a) Dynamic first-pass curves created from a dynamic RA dataset using 8 injections to improve temporal resolution. First-pass curves are created by selecting regions of interest over various parts of the cardio-pulmonary circuit. The curves show subtle variations in different regions such as the pulmonary artery, the parenchyma, the pulmonary veins and the descending aorta. (b) Dynamic first-pass curves shown in 5a, fitted with gamma variate curves providing quantitative information about pulmonary perfusion.

Fig 6

(a) Dynamic Shepp-Logan simulation data at 6 uniformly spaced time-points out of the 500 views showing the modulation of the left ellipse by the wash-in/wash-out curve. (b) Dynamic Shepp-Logan reconstructed at 6 time-points using undersampled PR. Note the undersampling artifacts that are spread all over the image affecting all the regions. (c) Dynamic Shepp-Logan reconstructed at 6 time-points using IRIS. Note the reduction in artifacts as compared to the undersampled PR reconstruction using the same data shown in (b).

Fig 7

Comparison of similar time-points of the undersampled PR (a, b) (series of 16) and IRIS (c, d) (series of 31). Points (a) and (c) are at the peak of contrast bolus and points (b) and (d) are at the end of wash-out part of the curve.

Fig 8

Post-contrast injection scan in a rat lung, scanned using dynamic RA (inset) and IRIS in a rat lung. The dynamic RA images are acquired using two 20 µl injections of Gd-DTPA and reconstructed at a spatial resolution of ∼780 µm and a temporal resolution of 200 ms. IRIS images are reconstructed at a spatial resolution of ∼195 µm and a temporal resolution of 200 ms using four 20 µl injections of Gd-DTPA. The improvement in the spatial and the temporal resolution is achieved primarily by IRIS reconstruction. Only 8 images from a series of 31 are shown.

Fig 9

(a) Dynamic first-pass curves, scanned using dynamic RA, created by selecting regions of interest over various parts of the cardio-pulmonary circuit. (b) Dynamic first-pass curves, scanned for the same animal using IRIS, created by selecting regions of interest over various parts of the cardio-pulmonary circuit. The curves are comparable as far as their times to peak are concerned. The variability is attributed to the difficulty in selecting regions for the dynamic RA dataset due to its very coarse resolution.