Compressive sensing (CS) offers compression of data below the Nyquist rate, making it an attractive solution in the field of medical imaging, and has been extensively used for ultrasound (US) compression and sparse recovery. In practice, CS offers a reduction in data sensing, transmission, and storage. Compressive sensing relies on the sparsity of data; i.e., data should be sparse in original or in some transformed domain. A look at the literature reveals that rich variety of algorithms have been suggested to recover data using compressive sensing from far fewer samples accurately, but with tradeoffs for efficiency. This paper reviews a number of significant CS algorithms used to recover US images from the undersampled data along with the discussion of CS in 3D US images. In this paper, sparse recovery algorithms applied to US are classified in five groups. Algorithms in each group are discussed and summarized based on their unique technique, compression ratio, sparsifying transform, 3D ultrasound, and deep learning. Research gaps and future directions are also discussed in the conclusion of this paper. This study is aimed to be beneficial for young researchers intending to work in the area of CS and its applications, specifically to US.

Compress sensing has advantage of simultaneous sensing and data compression. In recent years block compressive sensing (BCS) is celebrated because of its low encoding complexity. In this paper FOCUSS reconstruction algorithm based on BCS frame work is proposed utilizing iterative re-weighted l ₁ norm minimization. In proposed approach the required sparsity was attained using l ₁ norm minimization and 2D-Median filter is implemented as smoothing operator for removing blocking artifacts. The performance of reconstruction algorithm is verified on different data sets and results obtained from analysis reveal that the suggested approach for image reconstruction offers significant compression performance in contrast to existing techniques.

This paper presents the design and comparison of single band rectangular, cylindrical and triangular dielectric resonator antenna (DRA). Three shapes of DRA are designed with similar feeding technique i.e. Slot Aperture for C and X-Band frequency operations. C and X band are microwave band lies between frequency range 4 to 8 GHz and 8 to 12 GHz. Antenna constraints such as dimensions and frequencies are determined using mathematical formulation. The three shapes of DRA are simulated by electro-magnetic simulator CST microwave studio. The bandwidth, and gain of three shapes of DRA are evaluated and it is finally observed that the Triangular DRA has much wider bandwidth and have high value of gain than rectangular and cylindrical DRA at same resonant frequency. The results of the present work may provide design guidelines for the development of efficient DRA at C and X Band frequency operations.