Literature DB >> 26402216

Compressed sensing MRI: a review of the clinical literature.

Oren N Jaspan1, Roman Fleysher2, Michael L Lipton3.   

Abstract

MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. 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. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed.

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Mesh:

Year:  2015        PMID: 26402216      PMCID: PMC4984938          DOI: 10.1259/bjr.20150487

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  42 in total

1.  Parallel imaging reconstruction using automatic regularization.

Authors:  Fa-Hsuan Lin; Kenneth K Kwong; John W Belliveau; Lawrence L Wald
Journal:  Magn Reson Med       Date:  2004-03       Impact factor: 4.668

2.  Sparse MRI: The application of compressed sensing for rapid MR imaging.

Authors:  Michael Lustig; David Donoho; John M Pauly
Journal:  Magn Reson Med       Date:  2007-12       Impact factor: 4.668

3.  Carotid blood flow measurement accelerated by compressed sensing: validation in healthy volunteers.

Authors:  Yuehui Tao; Gabriel Rilling; Mike Davies; Ian Marshall
Journal:  Magn Reson Imaging       Date:  2013-07-03       Impact factor: 2.546

4.  Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays.

Authors:  D K Sodickson; W J Manning
Journal:  Magn Reson Med       Date:  1997-10       Impact factor: 4.668

5.  Noise in MRI.

Authors:  A Macovski
Journal:  Magn Reson Med       Date:  1996-09       Impact factor: 4.668

6.  High spatial and temporal resolution dynamic contrast-enhanced magnetic resonance angiography using compressed sensing with magnitude image subtraction.

Authors:  Stanislas Rapacchi; Fei Han; Yutaka Natsuaki; Randall Kroeker; Adam Plotnik; Evan Lehrman; James Sayre; Gerhard Laub; J Paul Finn; Peng Hu
Journal:  Magn Reson Med       Date:  2013-06-25       Impact factor: 4.668

Review 7.  Compressed sensing MRI: a review.

Authors:  Sairam Geethanath; Rashmi Reddy; Amaresha Shridhar Konar; Shaikh Imam; Rajagopalan Sundaresan; Ramesh Babu D R; Ramesh Venkatesan
Journal:  Crit Rev Biomed Eng       Date:  2013

8.  Rapid 3D-imaging of phosphocreatine recovery kinetics in the human lower leg muscles with compressed sensing.

Authors:  Prodromos Parasoglou; Li Feng; Ding Xia; Ricardo Otazo; Ravinder R Regatte
Journal:  Magn Reson Med       Date:  2012-09-28       Impact factor: 4.668

9.  Compressive sensing could accelerate 1H MR metabolic imaging in the clinic.

Authors:  Sairam Geethanath; Hyeon-Man Baek; Sandeep K Ganji; Yao Ding; Elizabeth A Maher; Robert D Sims; Changho Choi; Matthew A Lewis; Vikram D Kodibagkar
Journal:  Radiology       Date:  2012-03       Impact factor: 11.105

10.  ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA.

Authors:  Martin Uecker; Peng Lai; Mark J Murphy; Patrick Virtue; Michael Elad; John M Pauly; Shreyas S Vasanawala; Michael Lustig
Journal:  Magn Reson Med       Date:  2014-03       Impact factor: 4.668

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  52 in total

1.  Deep Leaning Based Multi-Modal Fusion for Fast MR Reconstruction.

Authors:  Lei Xiang; Yong Chen; Weitang Chang; Yiqiang Zhan; Weili Lin; Qian Wang; Dinggang Shen
Journal:  IEEE Trans Biomed Eng       Date:  2018-11-29       Impact factor: 4.538

2.  Compressed sensing MRI of different organs: ready for clinical daily practice?

Authors:  Bénédicte Marie Anne Delattre; Sana Boudabbous; Catrina Hansen; Angeliki Neroladaki; Anne-Lise Hachulla; Maria Isabel Vargas
Journal:  Eur Radiol       Date:  2019-07-01       Impact factor: 5.315

Review 3.  Principles of the magnetic resonance imaging movie method for articulatory movement.

Authors:  Midori Yoshida; Eiichi Honda; Erika Ozawa; Sayuri Maristela Inoue-Arai; Hiroko Ohmori; Keiji Moriyama; Takashi Ono; Tohru Kurabayashi; Hozumi Yoshihara; Kulthida Nunthayanon Parakonthun
Journal:  Oral Radiol       Date:  2018-09-14       Impact factor: 1.852

4.  Feasibility of accelerated 3D T1-weighted MRI using compressed sensing: application to quantitative volume measurements of human brain structures.

Authors:  Uten Yarach; Suwit Saekho; Kawin Setsompop; Atita Suwannasak; Ratthaporn Boonsuth; Kittichai Wantanajittikul; Salita Angkurawaranon; Chaisiri Angkurawaranon; Prapatsorn Sangpin
Journal:  MAGMA       Date:  2021-06-28       Impact factor: 2.310

Review 5.  Compressed sensing for body MRI.

Authors:  Li Feng; Thomas Benkert; Kai Tobias Block; Daniel K Sodickson; Ricardo Otazo; Hersh Chandarana
Journal:  J Magn Reson Imaging       Date:  2016-12-16       Impact factor: 4.813

6.  3D true-phase polarity recovery with independent phase estimation using three-tier stacks based region growing (3D-TRIPS).

Authors:  Haining Liu; Gregory J Wilson; Niranjan Balu; Jeffrey H Maki; Daniel S Hippe; Wei Wu; Hiroko Watase; Jinnan Wang; Martin L Gunn; Chun Yuan
Journal:  MAGMA       Date:  2017-12-07       Impact factor: 2.310

7.  Comparison of a fast 5-min knee MRI protocol with a standard knee MRI protocol: a multi-institutional multi-reader study.

Authors:  Erin FitzGerald Alaia; Alex Benedick; Nancy A Obuchowski; Joshua M Polster; Luis S Beltran; Jean Schils; Elisabeth Garwood; Christopher J Burke; I-Yuan Joseph Chang; Soterios Gyftopoulos; Naveen Subhas
Journal:  Skeletal Radiol       Date:  2017-09-26       Impact factor: 2.199

8.  Efficient directionality-driven dictionary learning for compressive sensing magnetic resonance imaging reconstruction.

Authors:  Anupama Arun; Thomas James Thomas; J Sheeba Rani; R K Sai Subrahmanyam Gorthi
Journal:  J Med Imaging (Bellingham)       Date:  2020-01-24

9.  Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Authors:  Mehmet Akçakaya; Steen Moeller; Sebastian Weingärtner; Kâmil Uğurbil
Journal:  Magn Reson Med       Date:  2018-09-18       Impact factor: 4.668

10.  In vivo magnetic resonance imaging and spectroscopy. Technological advances and opportunities for applications continue to abound.

Authors:  Peter van Zijl; Linda Knutsson
Journal:  J Magn Reson       Date:  2019-07-09       Impact factor: 2.229

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