Review ArticleSparse Reconstruction Techniques in Magnetic Resonance Imaging Methods, Applications, and Challenges to Clinical AdoptionYang, Alice C. BS; Kretzler, Madison MS; Sudarski, Sonja MD; Gulani, Vikas MD, PhD; Seiberlich, Nicole PhDAuthor Information From the Departments of *Biomedical Engineering and †Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH; ‡Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; and §Department of Radiology, University Hospitals of Cleveland, Cleveland, OH. Received for publication December 8, 2015; and accepted for publication, after revision, February 3, 2016. Conflicts of interest and sources of funding: Supported by the National Science Foundation Graduate Research Fellowship Program under grant number DGE-1451075, NIH Interdisciplinary Biomedical Imaging Training Program, NIH T32EB007509 administered by the Department of Biomedical Engineering, Case Western Reserve University, NHLBI R01HL094557, NIDDK R01DK098503, NIBIB R01EB016728, NIBIB R01BB017219, and Siemens Medical Solutions. A.C.Y.Y., M.K., V.G., and N.S. wish to disclose that their group receives research support from Siemens Medical Solutions. S.S. declares no conflict of interest. Correspondence to: Nicole Seiberlich, PhD, Department of Biomedical Engineering, University Hospitals of Cleveland, Wickenden Bldg, Room 428, 10900 Euclid Ave, Cleveland, OH 44106. E-mail: email@example.com. Investigative Radiology: June 2016 - Volume 51 - Issue 6 - p 349-364 doi: 10.1097/RLI.0000000000000274 Buy Metrics Abstract The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed. Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.