Magnetic Resonance Fingerprinting-An Overview.
Ananya Panda,Bhairav Bipin Mehta,Simone Coppo,Yun Jiang,Dan Ma,Nicole Seiberlich,Mark A. Griswold,Vikas Gulani +7 more
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TLDR
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition as discussed by the authors.About:
This article is published in Current Opinion in Biomedical Engineering.The article was published on 2017-09-01 and is currently open access. It has received 78 citations till now. The article focuses on the topics: Magnetic resonance imaging.read more
Citations
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An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Journal ArticleDOI
An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: This paper indicates how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction, and provides a starting point for people interested in experimenting and contributing to the field of deep learning for medical imaging.
Journal ArticleDOI
Low‐field MRI: An MR physics perspective
TL;DR: The current state‐of‐the‐art of low‐field systems (defined as 0.25–1T), both with respect to its low cost, low foot‐print, and subject accessibility and how low field could potentially benefit from many of the developments that have occurred in higher‐field MRI are described.
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Quantitative magnetic resonance imaging of brain anatomy and in vivo histology
Nikolaus Weiskopf,Nikolaus Weiskopf,Luke J. Edwards,Gunther Helms,Gunther Helms,Siawoosh Mohammadi,Siawoosh Mohammadi,Evgeniya Kirilina,Evgeniya Kirilina +8 more
TL;DR: Advances in concepts, instrumentation, biophysical models and validation approaches facilitating this rapidly developing field are discussed, pointing out challenges and the latest advances in this field.
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Erratum to “Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting”
TL;DR: A spatially constrained quantification method that uses the signals at multiple neighboring pixels to better estimate tissue properties at the central pixel is proposed and a unique two-step deep learning model is designed that learns the mapping from the observed signals to the desired properties for tissue quantification.
References
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Journal ArticleDOI
Magnetic resonance fingerprinting
Dan Ma,Vikas Gulani,Vikas Gulani,Nicole Seiberlich,Kecheng Liu,Jeffrey L. Sunshine,Jeffrey L. Duerk,Jeffrey L. Duerk,Mark A. Griswold,Mark A. Griswold +9 more
TL;DR: An approach to data acquisition, post-processing and visualization that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue is introduced—which is termed ‘magnetic resonance fingerprinting’ (MRF).
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NMR relaxation times in the human brain at 3.0 tesla.
TL;DR: Relaxation time measurements at 3.0 T are reported for both gray and white matter in normal human brain in normal adults with no clinical evidence of neurological disease, and there were no significant differences in T1 from one location in the brain to another.
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Imaging biomarker roadmap for cancer studies.
James P B O'Connor,Eric O. Aboagye,Judith E. Adams,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Sally F. Barrington,Ambros J. Beer,Ronald Boellaard,Sarah E. Bohndiek,J. Michael Brady,Gina Brown,David L. Buckley,Thomas L. Chenevert,Laurence P. Clarke,Sandra Collette,Gary Cook,Nandita M. deSouza,John Dickson,Caroline Dive,Jeffrey L. Evelhoch,Corinne Faivre-Finn,Ferdia A. Gallagher,Fiona J. Gilbert,Robert J. Gillies,Vicky Goh,John R. Griffiths,Ashley M. Groves,Steve Halligan,Adrian L. Harris,David J. Hawkes,Otto S. Hoekstra,Erich P. Huang,Brian Hutton,Edward F. Jackson,Gordon C Jayson,Andrew J. I. Jones,Dow-Mu Koh,Denis Lacombe,Philippe Lambin,Nathalie Lassau,Martin O. Leach,Ting-Yim Lee,Edward Leen,Jason S. Lewis,Yan Liu,Mark F. Lythgoe,Prakash Manoharan,Ross J. Maxwell,Kenneth A. Miles,Bruno Morgan,Steve Morris,Tony Ng,Anwar R. Padhani,Geoff J M Parker,Mike Partridge,Arvind P. Pathak,Arvind P. Pathak,Andrew C. Peet,Shonit Punwani,Andrew R. Reynolds,Simon P. Robinson,Lalitha K. Shankar,Ricky A. Sharma,Dmitry Soloviev,Sigrid Stroobants,Daniel C. Sullivan,Stuart A. Taylor,Paul S. Tofts,Gillian M. Tozer,Marcel van Herk,Simon Walker-Samuel,James Wason,Kaye J. Williams,Paul Workman,Thomas E. Yankeelov,Kevin M. Brindle,Lisa M. McShane,Alan Jackson,John C. Waterton +78 more
TL;DR: Experts assembled to review, debate and summarize the challenges of IB validation and qualification produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical validation, biological/clinical validation and assessment of cost-effectiveness.
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MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results.
TL;DR: T1 relaxation times are generally higher and T2 relaxation time are generally lower at 3.0 T than at 1.5 T, but the magnitude of change varies greatly in different tissues, including liver, kidney, spleen, and muscle tissue.
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B1 mapping by Bloch-Siegert shift.
TL;DR: In vivo Bloch‐Siegert B1 mapping with 25 sec/slice is demonstrated to be quantitatively comparable to a 21‐min double‐angle map, enabling robust, high‐resolution B 1+ mapping in a clinically acceptable time frame.