K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application

Abstract

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

Arterial Spin Labeling Blood Flow MRI: Its Role in the Early Characterization of Alzheimer's Disease

“…Faster scans and reduced motion sensitivity by using parallel imaging techniques can also improve the sensitivity and resolution of ASL images. Improved analysis methods for atrophy correction, for identifying spatial patterns of diffuse disease [43,62], or potentially using higher resolution structural images to guide high resolution reconstructions of low resolution ASL images [63] may further enhance studies. Finally, using modified ASL approaches to characterize arterial transit times [64], arterial blood volume [46], capillary permeability [65] and oxygenation [66], and vascular reactivity [20] could provide new insights into early AD pathophysiology.…”

Using Anatomic Magnetic Resonance Image Information to Enhance Visualization and Interpretation of Functional Images: A Comparison of Methods Applied to Clinical Arterial Spin Labeling Images

“…Segmentation of anatomic images, along with a priori information about the expected functional signal from different tissue types can be used to correct for volume loss of particular tissue types [11], [12] or to isolate the information of the functional image that is not explained by the spatial distribution of tissue types [13]. Alternatively, anatomic information can be used to guide the reconstruction of images using Bayesian [14], [15] or potentially multi-contrast compressed sensing methods [16]–[18]. Though such methods are promising, they require specialized acquisitions or reconstructions and may make assumptions about the functional image signal, which are not general enough for a broad clinical population.…”

Bayesian $k$-Space–Time Reconstruction of MR Spectroscopic Imaging for Enhanced Resolution

“…First developed for (non-dynamic) perfusion MRI [22, 23], the K-Bayes approach is here expanded considerably to accommodate the additional spectral fitting problem inherent to MRSI. K-Bayes combines information from high-resolution tissue segmented structural MRIs with low-resolution k -space-time MRSI data.…”