TY - GEN
T1 - Tumor Growth Estimation via Registration of DCE-MRI Derived Tumor Specific Descriptors
AU - Roque, Thais
AU - Papiez, Bartlomiej W.
AU - Kersemans, Veerle
AU - Smart, Sean
AU - Allen, Danny
AU - Chappell, Michael
AU - Schnabel, Julia A.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides information on changes occurring during tumor growth in the tumor micro-environment and vasculature. In the present paper, tumor voxel-wise estimates of tumor descriptors including total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level derived from DCE-MRI data are used to guide the deformable registration of subsequent time points over the tumor growth cycle, evaluating their predictive value for tumor growth. The analysis of three preclinical colon carcinoma longitudinal cases shows that using physiologically meaningful measures of tumor as guidance information can improve non-rigid registration of longitudinal tumor imaging data when compared to a stateof-the-art local correlation coefficient Demons approach. Moreover, using the determinant of the Jacobian of the estimated displacement field as an indicator of volume change allows us to observe a correlation between the tumor descriptor values and tumor growth, especially when maps of hypoxic cells and level of oxygen were used to aid registration. To the best of our knowledge, this work demonstrates for the first time the feasibility of using biologically meaningful tumor descriptors (total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level) derived from DCE-MRI to aid non-rigid registration of longitudinal tumor data as well as to estimate tumor growth.
AB - Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides information on changes occurring during tumor growth in the tumor micro-environment and vasculature. In the present paper, tumor voxel-wise estimates of tumor descriptors including total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level derived from DCE-MRI data are used to guide the deformable registration of subsequent time points over the tumor growth cycle, evaluating their predictive value for tumor growth. The analysis of three preclinical colon carcinoma longitudinal cases shows that using physiologically meaningful measures of tumor as guidance information can improve non-rigid registration of longitudinal tumor imaging data when compared to a stateof-the-art local correlation coefficient Demons approach. Moreover, using the determinant of the Jacobian of the estimated displacement field as an indicator of volume change allows us to observe a correlation between the tumor descriptor values and tumor growth, especially when maps of hypoxic cells and level of oxygen were used to aid registration. To the best of our knowledge, this work demonstrates for the first time the feasibility of using biologically meaningful tumor descriptors (total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level) derived from DCE-MRI to aid non-rigid registration of longitudinal tumor data as well as to estimate tumor growth.
UR - http://www.scopus.com/inward/record.url?scp=85010213733&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2016.70
DO - 10.1109/CVPRW.2016.70
M3 - Conference contribution
AN - SCOPUS:85010213733
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 507
EP - 515
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
PB - IEEE Computer Society
T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
Y2 - 26 June 2016 through 1 July 2016
ER -