Regression error estimation significantly improves the region-of-interest statistics of noisy MR images

Medical Physics
Vladimir JurasSiegfried Trattnig

Abstract

The region-of-interest (ROI) selection and evaluation is one of the key factors in the successful evaluation of radiological images. However, the presence of noise in images may lead to incorrect diagnosis. The aim of this study was to test the hypothesis that the weighting by error estimation in ROI assessment might significantly improve the validity of the results. As a model, the data maps of the transverse relaxation time constants (T2) from patients who underwent a matrix-associated chondrocyte transplantation procedure on the femoral condyle were analyzed. Artificial noise with a Rician density probability distribution was added to each TE image. ROIs were processed either as a regular arithmetic mean or as a weighted mean, in which weighted coefficients were calculated with regard to fitting error estimates [coefficient of determination (R2); root mean squared error (RMSE), mean absolute error (MSE), mean squared error (MAE), and chi-squared error (chi2)]. The global T2 values in repair tissue (mean +/- standard deviation, 62 +/- 7 ms; range 51-70 ms) and in healthy cartilage (mean +/- SD, 49 +/- 6 ms; range 40-60 ms) were significantly different (p < 0.001). With a 45% or greater decrease from the original SNR value (co...Continue Reading

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Citations

Oct 31, 2013·Knee Surgery, Sports Traumatology, Arthroscopy : Official Journal of the ESSKA·Rachel K SurowiecCharles P Ho
Aug 2, 2012·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Vladimir JurasSiegfried Trattnig
Apr 23, 2013·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Vladimir JurasSiegfried Trattnig

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