Quantification of prostate MRSI data by model-based time domain fitting and frequency domain analysis

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Spectroscopy

Background:

• Accurate quantification of in vivo prostate spectra is crucial for diagnosing prostate cancer.
• Existing spectral processing methods, frequency domain analysis (FDA) and time domain fitting (TDF), have varying effectiveness.

Purpose of the Study:

• To compare the effectiveness of FDA and TDF methods for quantitative analysis of in vivo prostate spectra.
• To evaluate the accuracy of both methods in estimating the choline + creatine + polyamines to citrate ratio (CCP:C).
• To discuss modifications needed for accurate spectral quantification.

Main Methods:

• Comparison of a peak integration-based FDA method with a model-based nonlinear least squares TDF algorithm.
• Validation using Monte Carlo simulations, empirical phantom MRSI data, and in vivo MRSI data.
• Analysis of quantification approaches for overlapping choline, creatine, and polyamine resonances.

Main Results:

• Monte Carlo simulations indicated potential biases in estimated CCP:C ratios.
• Both FDA and TDF methods successfully identified tumor tissue when CCP:C exceeded a normal threshold.
• High agreement between methods: 94% voxel condition prediction in vivo and 94.4% spectrum type prediction.

Conclusions:

• Both FDA and TDF methods are effective for quantitative analysis of in vivo prostate spectra.
• The CCP:C ratio is a reliable indicator for identifying malignant prostate tissue.
• Further modifications may enhance the accuracy of spectral quantification for clinical applications.