Temporally constrained reconstruction of dynamic cardiac perfusion MRI

Abstract

Dynamic contrast-enhanced (DCE) MRI is a powerful technique to probe an area of interest in the body. Here a temporally constrained reconstruction (TCR) technique that requires less k-space data over time to obtain good-quality reconstructed images is proposed. This approach can be used to improve the spatial or temporal resolution, or increase the coverage of the object of interest. The method jointly reconstructs the space-time data iteratively with a temporal constraint in order to resolve aliasing. The method was implemented and its feasibility tested on DCE myocardial perfusion data with little or no motion. The results obtained from sparse k-space data using the TCR method were compared with results obtained with a sliding-window (SW) method and from full data using the standard inverse Fourier transform (IFT) reconstruction. Acceleration factors of 5 (R ‫؍‬ 5) were achieved without a significant loss in image quality. Mean improvements of 28 ؎ 4% in the signal-to-noise ratio (SNR) and 14 ؎ 4% in the contrast-to-noise ratio (CNR) were observed in the images reconstructed using the TCR method on sparse data (R ‫؍‬ 5) compared to the standard IFT reconstructions from full data for the perfusion datasets. Dynamic contrast-enhanced (DCE) MRI is used to track changes over time in an object of interest by acquiring a series of images. A contrast agent is injected and the data are acquired in k-space for each time frame. Rapid acquisitions are required to track the quickly changing contrast in the object. One application of DCE-MRI is myocardial perfusion, which is an important tool for assessing coronary artery disease. In DCE-MRI for myocardial perfusion, contrast agents such as gadolinium (Gd)-DTPA are injected and images are acquired using ECG-gated sequences to track the uptake of the contrast agent by the myocardium at high temporal resolution.To reduce the data acquisition time of dynamic MRI, a number of techniques have been developed. These methods acquire a fraction of k-space in each time frame and reconstruct images based on a priori information about the dynamic data. Methods such as keyhole imaging (1,2) and reduced-encoding MR imaging with generalized-series reconstruction (RIGR) (3Ϫ5) assume that in a dynamic sequence only the low-frequency data change and the high-frequency data remain static. Thus full data can be acquired for a single frame in the sequence and only lowfrequency data need to be acquired for the remaining frames. This assumption of static high frequencies is not always accurate.View-sharing-type methods (6 -9) assume that the dynamics in an image sequence change only by a small amount from frame to frame. Thus only a fraction of data can be acquired for each frame and the missing data can be obtained from the adjacent frames. Such data-sharing is equivalent to linear interpolation in time and can reduce temporal resolution.More recently, Madore et al. (10) proposed the unaliasing by Fourier-encoding the overlaps using the temporal dimension (UNFOLD) method for cardiac cine imagin...

Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI

“…Temporally constrained reconstruction [15,16] and, subsequently, a spatiotemporal constrained reconstruction (STCR) [17] have been shown to give good quality reconstructions of undersampled cardiac perfusion images with some respiratory motion using radial data with 24 rays. Here we propose to extend STCR in two ways.…”

Accelerated Dynamic Cardiac MRI Using Motion-Guided Compressed Sensing with Regional Sparsity

“…Dynamic contrast-enhanced MRI of the heart is well-suited for acceleration with compressed sensing (44) due to its spatiotemporal sparsity; however, respiratory motion can degrade sparsity and lead to image artifacts (15,16,19,21,23,(45)(46)(47). A novel compressed sensing method, Block LOw-rank Sparsity with Motion-guidance (BLOSM), was developed to accelerate first-pass cardiac MRI, even in the presence of respiratory motion.…”

“…RMSE measures the direct difference between the two images and is widely used for the assessment of CS methods (15,16,19,23,45,68,69). In this study, we used the relative RMSE (rRMSE) defined as…”

“…Several compressed sensing (32) methods have been proposed to accelerate first-pass perfusion imaging (15,19,25,26,45). However, patient motion due to imperfect breathholding and other factors leads to degraded quality of CSreconstructed images.…”

“…However, patient motion due to respiratory or other factors reduces the spatiotemporal redundancy of the data and, if not corrected, leads to image artifacts (15,16,(19)(20)(21)23,(45)(46)(47). The problem of imperfect breathholding and associated respiratory motion presents a major challenge to CS-acceleration of first-pass cardiac MRI, where, even when patients are instructed to suspend respiration for 15-20 seconds, they are often unable to comply fully with instructions and they breathe during the scan.…”