Compressed sensing (CS) comprises a set of relatively new techniques that exploit the underlying structure of data sets allowing their reconstruction from compressed versions or incomplete information. CS reconstruction algorithms are essentially nonlinear, demanding heavy computation overhead and large storage memory, especially in the case of multidimensional signals. Excellent review papers ...

In both lensless Fourier transform holography (FTH) and coherent diffraction imaging (CDI), a beamstop is used to block strong intensities which exceed the limited dynamic range of the sensor, causing a loss in low-frequency information, making high quality reconstructions difficult or even impossible. In this paper, we show that an image can be recovered from high-frequencies alone, thereby ov...

Compressed sensing (CS) using sparse measurement matrices and iterative messagepassing reconstruction algorithms have been recently investigated as a low-complexity alternative to traditional CS methods. In this paper, we investigate the adaptive version of well-known Sudocodes scheme, where the sparse measurement matrix is progressively created based on the outcomes of previous measurements. I...

Background Imaging with large coil arrays is desirable for rapid imaging and high signal to noise ratio. Compressed sensing (CS) is a promising way to accelerate myocardial perfusion imaging [1]. However with increasing number of coils CS is costly in terms of memory and computation time. Coil compression methods for reconstructing cardiac cine data with parallel imaging have been proposed [2,3...

Introduction Adaptive imaging allows multiple image sets, each having a different spatial-temporal balance, to be retrospectively reconstructed from the same dataset. High temporal resolution image sets from radial sampling schemes are typically undersampled, and suffer from streak artifacts that degrade image quality. It has been shown that a compressed sensing (CS) L1-penalized reconstruction...

Introduction Dynamic imaging with high spatial and temporal resolution is a demanding task in clinical MR tomography. In case of undersampling in dynamic imaging, radial trajectories are advantageous due to their incoherent artifact behavior. Compressed Sensing (CS) [1,2] is a new technique for reconstructing accelerated datasets without utilizing parallel imaging methods. First applications of...

A large number of pilots are utilized to acquire channel information in traditional channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) system, which leads to lower spectrum efficiency. For exploiting the sparse channel characteristics of 3GPP multipath channels, we employ the Compressed Sensing (CS) approach for channel estimation. Two CS-based recovery algorithms, Orthogo...

Background CMR is generally accepted as the gold standard for left ventricular (LV) volumes and function assessment. The conventional CMR approach involves several breathholds to cover the entire heart with short-axis acquisitions. Recently, compressed sensing (CS) techniques emerged as a means to considerably accelerate data acquisition. CS principally relies on: 1) transform sparsity, 2) inco...

Background First-pass perfusion imaging using CMR is an important tool for diagnosing coronary artery disease (CAD), but most clinical techniques are limited in their spatial coverage. While compressed-sensing (CS) holds promise for highly accelerated perfusion spiral imaging, CS techniques suffer from blurring artifacts in the setting of respiratory motion. Spiral pulse sequences have multiple...

We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD camera pixels. OCT images are reconstructed by solving an optimization problem that minimizes the...