A systematic review of the utility of 1.5 versus 3 Tesla magnetic resonance brain imaging in clinical practice and research.
OBJECTIVE
MRI at 3 T is said to be more accurate than 1.5 T MR, but costs and other practical differences mean that it is unclear which to use.


METHODS
We systematically reviewed studies comparing diagnostic accuracy at 3 T with 1.5 T. We searched MEDLINE, EMBASE and other sources from 1 January 2000 to 22 October 2010 for studies comparing diagnostic accuracy at 1.5 and 3 T in human neuroimaging. We extracted data on methodology, quality criteria, technical factors, subjects, signal-to-noise, diagnostic accuracy and errors according to QUADAS and STARD criteria.


RESULTS
Amongst 150 studies (4,500 subjects), most were tiny, compared old 1.5 T with new 3 T technology, and only 22 (15 %) described diagnostic accuracy. The 3 T images were often described as "crisper", but we found little evidence of improved diagnosis. Improvements were limited to research applications [functional MRI (fMRI), spectroscopy, automated lesion detection]. Theoretical doubling of the signal-to-noise ratio was not confirmed, mostly being 25 %. Artefacts were worse and acquisitions took slightly longer at 3 T.


CONCLUSIONS
Objective evidence to guide MRI purchasing decisions and routine diagnostic use is lacking. Rigorous evaluation accuracy and practicalities of diagnostic imaging technologies should be the routine, as for pharmacological interventions, to improve effectiveness of healthcare.


CONCLUSIONS
• Higher field strength MRI may improve image quality and diagnostic accuracy.                 • There are few direct comparisons of 1.5 and 3 T MRI.                 • Theoretical doubling of the signal-to-noise ratio in practice was only 25 %.                 • Objective evidence of improved routine clinical diagnosis is lacking.                 • Other aspects of technology improved images more than field strength.
Scottish Imaging Network, A Platform for Scientific Excellence Collaboration, Edinburgh, UK. joanna.wardlaw@ed.ac.uk
Joanna M
JM
Wardlaw
Will
W
Brindle
Ana M
AM
Casado
Kirsten
K
Shuler
Moira
M
Henderson
Brenda
B
Thomas
Jennifer
J
Macfarlane
Susana
S
Muñoz Maniega
Katherine
K
Lymer
Zoe
Z
Morris
Cyril
C
Pernet
William
W
Nailon
Trevor
T
Ahearn
Abdul Nashirudeen
AN
Mumuni
Carlos
C
Mugruza
John
J
McLean
Goultchira
G
Chakirova
Yuehui Terry
YT
Tao
Johanna
J
Simpson
Andrew C
AC
Stanfield
Harriet
H
Johnston
Jehill
J
Parikh
Natalie A
NA
Royle
Janet
J
De Wilde
Mark E
ME
Bastin
Nick
N
Weir
Andrew
A
Farrall
Maria C
MC
Valdes Hernandez
D035843:Biomedical Research / Q000639:trends; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis / Q000473:pathology; D009369:Neoplasms / Q000175:diagnosis / Q000473:pathology; D059906:Neuroimaging / Q000379:methods; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012107:Research Design; D059629:Signal-To-Noise Ratio