TY - GEN
T1 - Spectral separation resolves partial volume effect in MRSI
T2 - 33rd Annual Northeast Bioengineering Conference, NEBC
AU - Su, Yuzhuo
AU - Thakur, Sunitha B.
AU - Sasan, Karimi
AU - Du, Shuyan
AU - Sajda, Paul
AU - Huang, Wei
AU - Parra, Lucas C.
PY - 2007
Y1 - 2007
N2 - Magnetic resonance spectroscopic imaging (MRSI) is utilized clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability due to partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity and measurement noise. This study investigates spectral separation as a novel quantification tool, addressing these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. Present results on 20 clinical cases of brain tumors show reduced cross-subject variability. This reduced variability leads to improved discrimination between high and low-grade gliomas, confirming the physiological relevance of the extracted spectra. Further validation on phantom data demonstrates the accuracy of the estimated abundances. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool.
AB - Magnetic resonance spectroscopic imaging (MRSI) is utilized clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability due to partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity and measurement noise. This study investigates spectral separation as a novel quantification tool, addressing these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. Present results on 20 clinical cases of brain tumors show reduced cross-subject variability. This reduced variability leads to improved discrimination between high and low-grade gliomas, confirming the physiological relevance of the extracted spectra. Further validation on phantom data demonstrates the accuracy of the estimated abundances. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool.
UR - http://www.scopus.com/inward/record.url?scp=48749087382&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48749087382&partnerID=8YFLogxK
U2 - 10.1109/NEBC.2007.4413293
DO - 10.1109/NEBC.2007.4413293
M3 - Conference contribution
AN - SCOPUS:48749087382
SN - 1424410339
SN - 9781424410330
T3 - Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
SP - 90
EP - 91
BT - 33rd Annual Northeast Bioengineering Conference - Engineering Innovations in Life Sciences and Healthcare, NEBC
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 March 2007 through 11 March 2007
ER -