Synthetic T1-weighted brain image generation with incorporated coil intensity correction using DESPOT1

Abstract

The increased use of phased-array and surface coils in magnetic resonance imaging, the push toward increased field strength and the need for standardized imaging across multiple sites during clinical trials have resulted in the need for methods that can ensure consistency of intensity both within the image and across multiple subjects/sites. Here, we describe a means of addressing these concerns through an extension of the rapid T(1) mapping technique - driven equilibrium single-pulse observation of T(1). The effectiveness of the proposed approach was evaluated using human brain T(1) maps acquired at 1.5 T with a multichannel phased-array coil. Corrected "synthetic" T(1)-weighted images were reconstructed by substituting the T(1) values back into the governing signal intensity equation while assuming a constant value for the equilibrium magnetization. To demonstrate signal normalization across a longitudinal study, we calculated synthetic T(1)-weighted images from data acquired from the same healthy subject at four different time points. Signal intensity profiles between the acquired and synthetic images were compared to determine the improvements with our proposed approach. Following correction, the images demonstrate obvious qualitative improvement with increased signal uniformity across the image. Near-perfect signal normalization was also observed across the longitudinal study, allowing direct comparison between the images. In addition, we observe an increase in contrast-to-noise ratio (compared with regular T(1)-weighted images) for synthetic images created, assuming uniform proton density throughout the volume. The proposed approach permits rapid correction for signal intensity inhomogeneity without significantly lengthening exam time or reducing image signal-to-noise ratio. This technique also provides a robust method for signal normalization, which is useful in multicenter longitudinal MR studies of disease progression, and allows the user to reconstruct T(1)-weighted images with arbitrary T(1) weighting.