TY - CONF
T1 - Classifying Hemodynamics of MR Brain Perfusion Images Using Independent Component Analysis (ICA)
AU - Wu, Yu Te
AU - Kao, Yi Hsuan
AU - Guo, Wan Yuo
AU - Yeh, Tzu Chen
AU - Hsieh, Jen Chuen
AU - Teng, Michael Mu Huo
PY - 2003/7
Y1 - 2003/7
N2 - Dynamic-susceptibility-contrast MR imaging is a widely used perfusion imaging technique that records signal changes on images caused by the passage of contrast-agent particles in the human brain after a bolus injection of contrast agent. The signal changes over time on different brain tissues represent distinct blood supply patterns and are crucial for studying cerebral hemodynamics. By assuming the spatial independence among these patterns, independent component analysis (ICA) was applied to classify different tissues, i.e., artery, gray matter, white matter, vein and sinus and choroid plexus, so that the spatio-temporal hemodynamics of these tissues were decomposed and analyzed. An arterial input function was modeled using the concentration-time curve of the arterial area for the deconvolution calculation of relative cerebral blood flow. The cerebral hemodynamic parameters, such as relative cerebral blood volume (CBV), relative cerebral blood flow (CBF), and relative mean transit time (MTT), were computed and their averaged ratios between gray matter and white matter were in good agreement with those in the literature.
AB - Dynamic-susceptibility-contrast MR imaging is a widely used perfusion imaging technique that records signal changes on images caused by the passage of contrast-agent particles in the human brain after a bolus injection of contrast agent. The signal changes over time on different brain tissues represent distinct blood supply patterns and are crucial for studying cerebral hemodynamics. By assuming the spatial independence among these patterns, independent component analysis (ICA) was applied to classify different tissues, i.e., artery, gray matter, white matter, vein and sinus and choroid plexus, so that the spatio-temporal hemodynamics of these tissues were decomposed and analyzed. An arterial input function was modeled using the concentration-time curve of the arterial area for the deconvolution calculation of relative cerebral blood flow. The cerebral hemodynamic parameters, such as relative cerebral blood volume (CBV), relative cerebral blood flow (CBF), and relative mean transit time (MTT), were computed and their averaged ratios between gray matter and white matter were in good agreement with those in the literature.
KW - Hemodynamics
KW - Independent Component Analysis (ICA)
KW - Perfusion image
UR - http://www.scopus.com/inward/record.url?scp=0141461550&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2003.1223433
DO - 10.1109/IJCNN.2003.1223433
M3 - Paper
AN - SCOPUS:0141461550
SP - 616
EP - 621
T2 - International Joint Conference on Neural Networks 2003
Y2 - 20 July 2003 through 24 July 2003
ER -