TY - GEN
T1 - A compressed sensing approach for MR tissue contrast synthesis
AU - Roy, Snehashis
AU - Carass, Aaron
AU - Prince, Jerry
PY - 2011
Y1 - 2011
N2 - The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multi-scanner neuroimaging studies.
AB - The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multi-scanner neuroimaging studies.
KW - compressed sensing
KW - histogram equalization
KW - histogram matching
KW - image synthesis
KW - intensity normalization
KW - magnetic resonance imaging (MRI)
KW - phantom
KW - segmentation
KW - standardization
UR - http://www.scopus.com/inward/record.url?scp=80052334094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052334094&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22092-0_31
DO - 10.1007/978-3-642-22092-0_31
M3 - Conference contribution
C2 - 21761671
AN - SCOPUS:80052334094
SN - 9783642220913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 371
EP - 383
BT - Information Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings
T2 - 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011
Y2 - 3 July 2011 through 8 July 2011
ER -