title: Quantification of prostate MRSI data by model-based time domain fitting and frequency domain analysis.
abstract: This paper compares two spectral processing methods for obtaining quantitative measures from in vivo prostate spectra, evaluates their effectiveness, and discusses the necessary modifications for accurate results. A frequency domain analysis (FDA) method based on peak integration was compared with a time domain fitting (TDF) method, a model-based nonlinear least squares fitting algorithm. The accuracy of both methods at estimating the choline + creatine + polyamines to citrate ratio (CCP:C) was tested using Monte Carlo simulations, empirical phantom MRSI data and in vivo MRSI data. The paper discusses the different approaches employed to achieve the quantification of the overlapping choline, creatine and polyamine resonances. Monte Carlo simulations showed induced biases on the estimated CCP:C ratios. Both methods were successful in identifying tumor tissue, provided that the CCP:C ratio was greater than a given (normal) threshold. Both methods predicted the same voxel condition in 94% of the in vivo voxels (68 out of 72). Both TDF and FDA methods had the ability to identify malignant voxels in an artifact-free case study using the estimated CCP:C ratio. Comparing the ratios estimated by the TDF and the FDA, the methods predicted the same spectrum type in 17 out of 18 voxels of the in vivo case study (94.4%).
authors.affiliation: ESAT-SCD, Katholieke Universiteit Leuven, Leuven-Heverlee, Belgium. pieter.pels@mrsc.ucsf.edu
authors.forename: Pieter
authors.initials: P
authors.lastname: Pels
authors.forename: Esin
authors.initials: E
authors.lastname: Ozturk-Isik
authors.forename: Mark G
authors.initials: MG
authors.lastname: Swanson
authors.forename: Leentje
authors.initials: L
authors.lastname: Vanhamme
authors.forename: John
authors.initials: J
authors.lastname: Kurhanewicz
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Nelson
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003198:Computer Simulation; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D008954:Models, Biological; D019047:Phantoms, Imaging; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors
chemical_list: D014408:Biomarkers, Tumor