Compressed Sensing (CS) \ Hyper Sense MRI

Compressed Sensing (CS) MRI is a groundbreaking technique that aims to accelerate the acquisition of MRI data while maintaining image quality. Traditional MRI methods require collecting a large amount of raw data to reconstruct high-resolution images. CS takes advantage of the fact that MRI scan images can be represented using fewer data points in certain domains without a significant loss of information.

The physics behind CS MRI involves exploiting the sparsity or compressibility of the signal in a specific domain. The following section will provide a simple explanation of how it works.

Sparse Representation: Many natural images, including medical images like MRI scans, can be represented using a small number of meaningful features or coefficients in a certain domain (e.g., wavelet, Fourier, or total variation domain). Most coefficients are close to zero, indicating that only a few coefficients carry essential information.

Sub-Nyquist Sampling: In traditional MRI, data points are collected in k-space in a grid-like pattern. CS MRI, however, employs a randomized or non-uniform sampling pattern in k-space. This allows for the acquisition of fewer data points compared to traditional methods.

Reconstruction: The core challenge of CS MRI is accurately reconstructing a high-resolution image from the undersampled k-space data. This is achieved by leveraging the sparse representation of the image. The reconstruction process involves finding the sparsest representation of the image that matches the acquired data.

Optimization: Reconstruction is often formulated as an optimization problem aiming to minimize the difference between the acquired data and the estimated data based on the sparse representation. Advanced algorithms are used to leverage the sparsity properties of the image and solve this optimization problem.

The benefits of Compressed Sensing MRI are significant. By acquiring fewer data points, CS MRI substantially reduces scan time. This acceleration is particularly valuable for capturing dynamic processes or imaging patients who struggle to remain still during longer scans.

Compressed Sensing has transformed various fields of medical imaging: