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Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction.
Phys Med Biol 2015; 60(21):R297-322PM

Abstract

MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI.

Authors+Show Affiliations

Newcastle Magnetic Resonance Centre, Institute of Cellular Medicine, Newcastle University, NE 4 5PL, UK.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26448064

Citation

Hollingsworth, Kieren Grant. "Reducing Acquisition Time in Clinical MRI By Data Undersampling and Compressed Sensing Reconstruction." Physics in Medicine and Biology, vol. 60, no. 21, 2015, pp. R297-322.
Hollingsworth KG. Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Phys Med Biol. 2015;60(21):R297-322.
Hollingsworth, K. G. (2015). Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Physics in Medicine and Biology, 60(21), pp. R297-322. doi:10.1088/0031-9155/60/21/R297.
Hollingsworth KG. Reducing Acquisition Time in Clinical MRI By Data Undersampling and Compressed Sensing Reconstruction. Phys Med Biol. 2015 Nov 7;60(21):R297-322. PubMed PMID: 26448064.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. A1 - Hollingsworth,Kieren Grant, Y1 - 2015/10/08/ PY - 2015/10/9/entrez PY - 2015/10/9/pubmed PY - 2016/6/9/medline SP - R297 EP - 322 JF - Physics in medicine and biology JO - Phys Med Biol VL - 60 IS - 21 N2 - MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI. SN - 1361-6560 UR - https://www.unboundmedicine.com/medline/citation/26448064/Reducing_acquisition_time_in_clinical_MRI_by_data_undersampling_and_compressed_sensing_reconstruction_ L2 - https://doi.org/10.1088/0031-9155/60/21/R297 DB - PRIME DP - Unbound Medicine ER -