What is the underlying concept for compressed sensing?
It states that if a real signal’s highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly by means of sinc interpolation. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal.
What is the use of compressive sensing?
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.
Who invented compressive sensing?
David Donoho
More recent results include Fadil Santosa and William Symes in 1986 [36], but it was not until 2006 that David Donoho coined the term “compressive sensing” [37]. Since then, Donoho and others have advanced CS theory and championed its use in the measurement process.
What is compressed sensing in MRI?
Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. Studies have successfully accelerated MRI with this technology, with varying degrees of success.
What is sensing matrix?
One of the most important aspects of compressed sensing (CS) theory is an efficient design of sensing matrices. These sensing matrices are accountable for the required signal compression at the encoder end and its exact or approximate reconstruction at the decoder end.
What is compressed signal?
Signal compression is the use of various techniques to increase the quality or quantity of signal parameters transmitted through a given telecommunications channel.
What is a sensing matrix?
What is parallel imaging in MRI?
Parallel imaging is a widely used technique where the known placement and sensitivities of receiver coils are used to assist spatial localization of the MR signal. Having this additional information about the coils allows reduction in number of phase-encoding steps during image acquisition.
What does it mean for a signal to be sparse?
1. Is a signal which contains only a small number of non-zero elements compared to its dimension. Analog to Information Converter: AIC is the front end of compressive sampling systems that is able to capture linear combinations of signal measurements at sub Nyquist rate.
What are the types of compression?
There are two main types of compression: lossy and lossless.
What is spin echo sequence?
The spin echo sequence is made up of a series of events : 90° pulse – 180° rephasing pulse at TE/2 – signal reading at TE. This series is repeated at each time interval TR (Repetition time). With each repetition, a k-space line is filled, thanks to a different phase encoding.
What is compressive sensing?
Recently, compressive sensing or compressed sensing (will be referred as CS henceforth) has been an active research area in the field of signal processing and communication. It has been applied to Wireless sensor networks, video processing and image processing and up to some extent on speech signal processing also.
What is the difference between traditional sampling and compressive sensing?
In a traditional sampling theorem, the signal is sampled using Nyquist rate, whereas with the help of compressive sensing the signal is sampled below the Nyquist rate. This is possible because the signal is transformed into a domain in which it has a sparse representation.
Can compressed sensing reduce image acquisition energy?
Compressed sensing is used in a mobile phone camera sensor. The approach allows a reduction in image acquisition energy per image by as much as a factor of 15 at the cost of complex decompression algorithms; the computation may require an off-device implementation.
Does compressed sensing violate the sampling theorem?
At first glance, compressed sensing might seem to violate the sampling theorem, because compressed sensing depends on the sparsity of the signal in question and not its highest frequency. This is a misconception, because the sampling theorem guarantees perfect reconstruction given sufficient, not necessary, conditions.