TY - GEN
T1 - Improving magnetic resonance resolution with supervised learning
AU - Jog, Amod
AU - Carass, Aaron
AU - Prince, Jerry L.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/29
Y1 - 2014/7/29
N2 - Despite ongoing improvements in magnetic resonance (MR) imaging (MRI), considerable clinical and, to a lesser extent, research data is acquired at lower resolutions. For example 1 mm isotropic acquisition of T1-weighted (T1-w) Magnetization Prepared Rapid Gradient Echo (MPRAGE) is standard practice, however T2-weighted (T2-w) - because of its longer relaxation times (and thus longer scan time) - is still routinely acquired with slice thicknesses of 2-5 mm and in-plane resolution of 2-3 mm. This creates obvious fundamental problems when trying to process T1-w and T2-w data in concert. We present an automated supervised learning algorithm to generate high resolution data. The framework is similar to the brain hallucination work of Rousseau, taking advantage of new developments in regression based image reconstruction. We present validation on phantom and real data, demonstrating the improvement over state-of-the-art super-resolution techniques.
AB - Despite ongoing improvements in magnetic resonance (MR) imaging (MRI), considerable clinical and, to a lesser extent, research data is acquired at lower resolutions. For example 1 mm isotropic acquisition of T1-weighted (T1-w) Magnetization Prepared Rapid Gradient Echo (MPRAGE) is standard practice, however T2-weighted (T2-w) - because of its longer relaxation times (and thus longer scan time) - is still routinely acquired with slice thicknesses of 2-5 mm and in-plane resolution of 2-3 mm. This creates obvious fundamental problems when trying to process T1-w and T2-w data in concert. We present an automated supervised learning algorithm to generate high resolution data. The framework is similar to the brain hallucination work of Rousseau, taking advantage of new developments in regression based image reconstruction. We present validation on phantom and real data, demonstrating the improvement over state-of-the-art super-resolution techniques.
KW - Brain
KW - Image reconstruction
KW - MRI
KW - Regression
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=84927929648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84927929648&partnerID=8YFLogxK
U2 - 10.1109/isbi.2014.6868038
DO - 10.1109/isbi.2014.6868038
M3 - Conference contribution
AN - SCOPUS:84927929648
T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
SP - 987
EP - 990
BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Y2 - 29 April 2014 through 2 May 2014
ER -