RESUMO
PURPOSE: The structural similarity index measure (SSIM) has become a popular quality metric to evaluate QSM in a way that is closer to human perception than RMS error (RMSE). However, SSIM may overpenalize errors in diamagnetic tissues and underpenalize them in paramagnetic tissues, resulting in biasing. In addition, extreme artifacts may compress the dynamic range, resulting in unrealistically high SSIM scores (hacking). To overcome biasing and hacking, we propose XSIM: SSIM implemented in the native QSM range, and with internal parameters optimized for QSM. METHODS: We used forward simulations from a COSMOS ground-truth brain susceptibility map included in the 2016 QSM Reconstruction Challenge to investigate the effect of QSM reconstruction errors on the SSIM, XSIM, and RMSE metrics. We also used these metrics to optimize QSM reconstructions of the in vivo challenge data set. We repeated this experiment with the QSM abdominal phantom. To validate the use of XSIM instead of SSIM for QSM quality assessment across a range of different reconstruction techniques/algorithms, we analyzed the reconstructions submitted to the 2019 QSM Reconstruction Challenge 2.0. RESULTS: Our experiments confirmed the biasing and hacking effects on the SSIM metric applied to QSM. The XSIM metric was robust to those effects, penalizing the presence of streaking artifacts and reconstruction errors. Using XSIM to optimize QSM reconstruction regularization weights returned less overregularization than SSIM and RMSE. CONCLUSION: XSIM is recommended over traditional SSIM to evaluate QSM reconstructions against a known ground truth, as it avoids biasing and hacking effects and provides a larger dynamic range of scores.
RESUMO
Background: Visceral fat accumulation is associated with the development of metabolic diseases. Anthropometry is one of the methods used to quantify it. aim: to evaluate the relationship between visceral adipose tissue volume (VAT), measured with magnetic resonance imaging (MRI), and anthropometric indexes, such as body mass index (BMI) and waist circumference (WC), in type 2 diabetic patients (DM2). Patients and Methods: Twenty four type 2 diabetic patients aged 55 to 78 years (15 females) and weighting 61.5 to 97 kg, were included. The patients underwent MRI examination on a Philips Intera® 1.5T MR scanner. The MRI protocol included a spectral excitation sequence centered at the fat peak. The field of view included from L4-L5 to the diaphragmatic border. VAT was measured using the software Image J®. Weight, height, BMI, WC and body fat percentage (BF%), derived from the measurement offour skinfolds with the equation of Durnin and Womersley, were also measured. The association between MRIVAT measurement and anthropometry was evaluated using the Pearson's correlation coefficient. Results: Mean VAT was 2478 ± 758 ml, mean BMI29.5 ± 4.7 kg/m², and mean WC was 100 ± 9.7 cm. There was a poor correlation between VAT, BMI (r = 0.18) and WC (r = 0.56). Conclusions: BMI and WC are inaccurate predictors of VAT volume in type 2 diabetic patients.