Compressive sensing in medical imaging

Abstract

The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

Robust compressive sensing of sparse signals: a review

“…In such cases, the signal is acquired taking a few linear measurements and subsequently accurately recovered using nonlinear iterative algorithms [4,5]. CS has proven particularly effective in imaging applications due to the inherent sparsity, e.g., in medical imaging [6], astronomical imaging [7], radar imaging [8], and hyperspectral imaging [9].…”

Feasibility of high spatiotemporal resolution for an abbreviated 3D radial breast MRI protocol

“…Our results show preliminary experience with an ultrafast volumetric bilateral breast MRI exam with submillimeter isotropic resolution, surpassing the clinical standard while providing temporal resolutions 6‐to‐15‐fold faster than typical clinical protocols. Although a judicious combination of CS and view sharing can produce high spatiotemporal resolution in various DCE MRI applications, the imaging performance of these methods is object dependent . The local spatially variant temporal constraint is able to further exploit data redundancy by learning and modeling temporal behavior rather than the assumption of consistency over a period of time .…”

Fusing multiple existing space‐time land cover products

“…While the primary motivation of this work is to fuse LC products, SCaMF can also be applied to other geospatial categorical data or in fields where the synthesis of multiple images with different spatial and/or temporal resolutions is needed, such as medical imaging (Graff & Sidky, ; Hill, Batchelor, Holden & Hawkes, ; Wald, ). In the case of a single LC product being available or suitable for a specific application, this methodology can be applied to down or up‐scale the spatial and temporal resolution of the product.…”