TY - GEN
T1 - Compressed sensing based intensity non-uniformity correction
AU - Roy, Snehashis
AU - Carass, Aaron
AU - Prince, Jerry L.
PY - 2011
Y1 - 2011
N2 - We present a compressed sensing based approach to remove gain field from magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in the radio-frequency (RF) coil appears as shading artifact in the intensity image. The inhomogeneity poses problem in any automatic algorithm that uses intensity as a feature. It has been shown that at low field strength, the shading can be assumed to be a smooth field that is composed of low frequency components. Thus most inhomogeneity correction algorithms assume some kind of explicit smoothness criteria on the field. This sometimes limits the performance of the algorithms if the actual inhomogeneity is not smooth, which is the case at higher field strength. We describe a model-free, nonparametric patch-based approach that uses compressed sensing for the correction. We show that these features enable our algorithm to perform comparably with a current state of the art method N3 on images acquired at low field, while outperforming N3 when the image has non-smooth inhomogeneity, such as 7T images.
AB - We present a compressed sensing based approach to remove gain field from magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in the radio-frequency (RF) coil appears as shading artifact in the intensity image. The inhomogeneity poses problem in any automatic algorithm that uses intensity as a feature. It has been shown that at low field strength, the shading can be assumed to be a smooth field that is composed of low frequency components. Thus most inhomogeneity correction algorithms assume some kind of explicit smoothness criteria on the field. This sometimes limits the performance of the algorithms if the actual inhomogeneity is not smooth, which is the case at higher field strength. We describe a model-free, nonparametric patch-based approach that uses compressed sensing for the correction. We show that these features enable our algorithm to perform comparably with a current state of the art method N3 on images acquired at low field, while outperforming N3 when the image has non-smooth inhomogeneity, such as 7T images.
KW - 7T
KW - MRI
KW - bias correction
KW - bias field
KW - intensity inhomogeneity
KW - intensity non-uniformity
UR - http://www.scopus.com/inward/record.url?scp=80055052633&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80055052633&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2011.5872364
DO - 10.1109/ISBI.2011.5872364
M3 - Conference contribution
C2 - 24443667
AN - SCOPUS:80055052633
SN - 9781424441280
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 101
EP - 104
BT - 2011 8th IEEE International Symposium on Biomedical Imaging
T2 - 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Y2 - 30 March 2011 through 2 April 2011
ER -