PURPOSE: Liver iron quantification by MRI has become routine. Pixelwise (PW) fitting to the iron-mediated signal decay has some advantages but is slower and more vulnerable to noise than region-based techniques. We present a fast, pseudo-pixelwise mapping (PPWM) algorithm. MATERIALS AND METHODS: The PPWM algorithm divides the entire liver into non-contiguous groups of pixels sorted by rapid relative relaxivity estimates. Pixels within each group of like-relaxivity were binned and fit using a Levenberg-Marquadt algorithm. RESULTS: The developed algorithm worked about 30 times faster than the traditional PW approach and generated R2* maps qualitatively and quantitatively similar. No systematic difference was observed in median R2* values with a coefficient of variability (CoV) of 2.4%. Intra-observer and inter-observer errors were also under 2.5%. Small systematic differences were observed in the right tail of the R2* distribution resulting in slightly lower mean R2* values (CoV of 4.2%) and moderately lower SD of R2* values for the PPWM algorithm. Moreover, the PPWM provided the best accuracy, giving a lower error of R2* estimates. CONCLUSION: The PPWM yielded comparable reproducibility and higher accuracy than the TPWM. The method is suitable for relaxivity maps in other organs and applications.
PURPOSE: Liver iron quantification by MRI has become routine. Pixelwise (PW) fitting to the iron-mediated signal decay has some advantages but is slower and more vulnerable to noise than region-based techniques. We present a fast, pseudo-pixelwise mapping (PPWM) algorithm. MATERIALS AND METHODS: The PPWM algorithm divides the entire liver into non-contiguous groups of pixels sorted by rapid relative relaxivity estimates. Pixels within each group of like-relaxivity were binned and fit using a Levenberg-Marquadt algorithm. RESULTS: The developed algorithm worked about 30 times faster than the traditional PW approach and generated R2* maps qualitatively and quantitatively similar. No systematic difference was observed in median R2* values with a coefficient of variability (CoV) of 2.4%. Intra-observer and inter-observer errors were also under 2.5%. Small systematic differences were observed in the right tail of the R2* distribution resulting in slightly lower mean R2* values (CoV of 4.2%) and moderately lower SD of R2* values for the PPWM algorithm. Moreover, the PPWM provided the best accuracy, giving a lower error of R2* estimates. CONCLUSION: The PPWM yielded comparable reproducibility and higher accuracy than the TPWM. The method is suitable for relaxivity maps in other organs and applications.
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