PURPOSE: To examine the sensitivity of quantitative dynamic contrast enhanced MRI (DCE-MRI) perfusion maps to errors in the various source images and to determine optimal imaging parameters for reducing this sensitivity. MATERIALS AND METHODS: A detailed analysis of the precision of a DCE-MRI protocol was performed using the "propagation of errors" technique to investigate the effect of errors in the source images on errors in K(trans). Optimal parameter values and interactions between parameters were examined. The propagation of errors analysis was validated by Monte-Carlo simulations. RESULTS: The precision of K(trans) was found to be most sensitive to artifacts in the tissue portion of the baseline images and least sensitive to noise in the arterial portion of the dynamic images. The tip-angle strongly affected the precision, with the optimum being a function of tissue T1(0). CONCLUSION: Protocol optimization requires matching the tip-angle to the anticipated T1(0) of the tissue of interest; however such optimization yields a relatively small improvement. Future developmental efforts would be most productively focused on minimizing the artifact level. Copyright 2003 Wiley-Liss, Inc.
PURPOSE: To examine the sensitivity of quantitative dynamic contrast enhanced MRI (DCE-MRI) perfusion maps to errors in the various source images and to determine optimal imaging parameters for reducing this sensitivity. MATERIALS AND METHODS: A detailed analysis of the precision of a DCE-MRI protocol was performed using the "propagation of errors" technique to investigate the effect of errors in the source images on errors in K(trans). Optimal parameter values and interactions between parameters were examined. The propagation of errors analysis was validated by Monte-Carlo simulations. RESULTS: The precision of K(trans) was found to be most sensitive to artifacts in the tissue portion of the baseline images and least sensitive to noise in the arterial portion of the dynamic images. The tip-angle strongly affected the precision, with the optimum being a function of tissue T1(0). CONCLUSION: Protocol optimization requires matching the tip-angle to the anticipated T1(0) of the tissue of interest; however such optimization yields a relatively small improvement. Future developmental efforts would be most productively focused on minimizing the artifact level. Copyright 2003 Wiley-Liss, Inc.
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