TY - GEN
T1 - Atlas based intensity transformation of brain MR images
AU - Roy, Snehashis
AU - Jog, Amod
AU - Carass, Aaron
AU - Prince, Jerry L.
PY - 2013/9/5
Y1 - 2013/9/5
N2 - Magnetic resonance imaging (MRI) is a noninvasive modality that has been widely used to image the structure of the human brain. Unlike reconstructed x-ray computed tomography images, MRI intensities do not possess a calibrated scale, and the images suffer from wide variability in intensity contrasts due to scanner calibration and pulse sequence variations. Most MR image processing tasks use intensities as the principal feature and therefore the results can vary widely according to the actual tissue intensity contrast. Since it is difficult to control the MR scanner acquisition protocols in multi-scanner cross-sectional studies, results achieved using image processing tools are often difficult to compare in such studies. Similar issues can happen in longitudinal studies, as scanners undergo upgrades or improvements in pulse sequences, leading to new imaging sequences. We propose a novel probabilistic model to transform image contrasts by matching patches of a subject image to a set of patches from a multi-contrast atlas. Although the transformed images are not for diagnostic purpose, the use of such contrast transforms is shown for two applications, (a) to improve segmentation consistency across scanners and pulse sequences, (b) to improve registration accuracy between multi-contrast image pairs by transforming the subject image to the contrast of the reference image and then registering the transformed subject image to the reference image. Contrary to previous intensity transformation methods, our technique does not need any information about landmarks, pulse sequence parameters or imaging equations. It is shown to provide more consistent segmentation across scanners compared to state-of-the-art methods.
AB - Magnetic resonance imaging (MRI) is a noninvasive modality that has been widely used to image the structure of the human brain. Unlike reconstructed x-ray computed tomography images, MRI intensities do not possess a calibrated scale, and the images suffer from wide variability in intensity contrasts due to scanner calibration and pulse sequence variations. Most MR image processing tasks use intensities as the principal feature and therefore the results can vary widely according to the actual tissue intensity contrast. Since it is difficult to control the MR scanner acquisition protocols in multi-scanner cross-sectional studies, results achieved using image processing tools are often difficult to compare in such studies. Similar issues can happen in longitudinal studies, as scanners undergo upgrades or improvements in pulse sequences, leading to new imaging sequences. We propose a novel probabilistic model to transform image contrasts by matching patches of a subject image to a set of patches from a multi-contrast atlas. Although the transformed images are not for diagnostic purpose, the use of such contrast transforms is shown for two applications, (a) to improve segmentation consistency across scanners and pulse sequences, (b) to improve registration accuracy between multi-contrast image pairs by transforming the subject image to the contrast of the reference image and then registering the transformed subject image to the reference image. Contrary to previous intensity transformation methods, our technique does not need any information about landmarks, pulse sequence parameters or imaging equations. It is shown to provide more consistent segmentation across scanners compared to state-of-the-art methods.
KW - brain
KW - histogram matching
KW - intensity normalization
KW - intensity transformation
KW - magnetic resonance imaging (MRI)
UR - http://www.scopus.com/inward/record.url?scp=84883287536&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883287536&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-02126-3_6
DO - 10.1007/978-3-319-02126-3_6
M3 - Conference contribution
AN - SCOPUS:84883287536
SN - 9783319021256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 51
EP - 62
BT - Multimodal Brain Image Analysis - Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Proceedings
T2 - 3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 22 September 2013
ER -