Proc. SPIE. 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics
KEYWORDS: Magnetic resonance imaging, Picosecond phenomena, Data acquisition, Data centers, Image restoration, Data modeling, Temporal resolution, Fourier transforms, Computer programming, Data analysis
This paper presents a new approach to highly accelerated dynamic parallel MRI using low rank matrix completion, partial separability (PS) model. In data acquisition, <i>k</i>-space data is moderately randomly undersampled at the center kspace navigator locations, but highly undersampled at the outer <i>k</i>-space for each temporal frame. In reconstruction, the navigator data is reconstructed from undersampled data using structured low-rank matrix completion. After all the unacquired navigator data is estimated, the partial separable model is used to obtain partial <i>k-t</i> data. Then the parallel imaging method is used to acquire the entire dynamic image series from highly undersampled data. The proposed method has shown to achieve high quality reconstructions with reduction factors up to 31, and temporal resolution of 29ms, when the conventional PS method fails.
In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.
Dynamic contrast enhanced MRI requires high spatial resolution for morphological information and high temporal
resolution for contrast pharmacokinetics. The current techniques usually have to compromise the spatial information for
the required temporal resolution. This paper presents a novel method that effectively integrates sparse sampling, parallel
imaging, partial separable (PS) model, and sparsity constraints for highly accelerated DCE-MRI. Phased array coils were
used to continuously acquire data from a stack of variable-density spiral trajectory with a golden angle. In reconstruction,
the sparsity constraints, the coil sensitivities, spatial and temporal bases of the PS model are jointly estimated through
alternating optimization. Experimental results from in vivo DCE liver imaging data show that the proposed method is
able to achieve high spatial and temporal resolutions at the same time.
Compressive sensing (CS) is a new technique for reconstructing essentially sparse signals from a number of
measurements smaller than the Nyquist-Shannon criterion. The application of CS to hyperspectral imaging has the
potential for significantly reducing the sampling rate and hence the cost of the analog-to-digital sensors. In this paper a
novel approach for hyperspectral compressive sensing is proposed where each band of hyperspectral imagery is sampled
under the same measurement matrix. It is shown that the correlation between two neighboring band compressive sample
values is consistent with that between two neighboring band pixel values. Our hyperspectral compressive sensing
experimental results show that the proposed joint reconstruction method yields smaller reconstruction errors than the
individual reconstruction method at various sampling rates.