Time-domain semi-parametric estimation based on a metabolite basis set.
A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.
Ratiney
H
H
Laboratoire de RMN, CNRS UMR 5012, Université Claude Bernard Lyon I-CPE, Villeurbanne, France.
Sdika
M
M
Coenradie
Y
Y
Cavassila
S
S
van Ormondt
D
D
Graveron-Demilly
D
D
D000465:Algorithms; D001921:Brain / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D009419:Nerve Tissue Proteins / Q000378:metabolism*; D019047:Phantoms, Imaging; D011522:Protons; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D009419:Nerve Tissue Proteins; D011522:Protons