Unifying linear prior‐information‐driven methods for accelerated image acquisition

Abstract

In the ongoing quest for faster imaging and higher spatial resolution, several methods have been developed to speed up data acquisition by incorporating prior information about the object being imaged. This study shows that many of these methods can be integrated into a single common equation

Minimum‐norm reconstruction for sensitivity‐encoded magnetic resonance spectroscopic imaging

“…[9]), the reconstructed image is generated as a linear combination of the complex conjugates of the encoding functions. Equation [9] illustrates that the achievable resolution depends directly upon the encoding functions.…”

“…[9]), the reconstructed image is generated as a linear combination of the complex conjugates of the encoding functions. Equation [9] illustrates that the achievable resolution depends directly upon the encoding functions. In particular, it shows that coil sensitivity gradients can enhance the resolution by adding to the spatial signal variation induced by the common B z gradients.…”

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

“…Small improvements in temporal resolution can be achieved by optimizing k-space sampling schemes and reconstruction methods: e.g., instead of completing the k-space traversal for every measurement, MRI data acquisition can be accelerated by coordinated alterations of in k-space trajectories and their associated image reconstruction algorithms, as in partial-k space sampling (McGibney et al, 1993). Alternatively, a priori information-based methods can improve the temporal resolution of MR dynamic measurements (Tsao et al, 2001).…”

Dynamic Magnetic Resonance Imaging

“…Due to its nature, MRI has a physical limitation, which is related to the speed and quality of the gradients. Some static MRI methods try to push this limit by inserting spatial prior information [16] into the imaging algorithm. However, common factors in different cardiac images which can be used for prior information are based on temporal information rather than spatial.…”

“…K-space correlation was used by different methods such as partial Fourier [3], [14], reduced field of view [15], parallel imaging [9], [4] and prior information inclusion [3], [16]. K-space methods reconstruct each time frame independently using the spatial correlation between the data points to fill in the missing information.…”