RESUMO
Purpose To test the hypothesis that synthetic MRI of the knee generates accurate and repeatable quantitative maps and produces morphologic MR images with similar quality and detection rates of structural abnormalities than does conventional MRI. Materials and Methods Data were collected prospectively between January 2017 and April 2018 and were retrospectively analyzed. An International Society for Magnetic Resonance in Medicine-National Institute of Standards and Technology phantom was used to determine the accuracy of T1, T2, and proton density (PD) quantification. Statistical models were applied for correction. Fifty-four participants (24 men, 30 women; mean age, 40 years; range, 18-62 years) underwent synthetic and conventional 3-T MRI twice on the same day. Fifteen of 54 participants (28%) repeated the protocol within 9 days. The intra- and interday agreements of quantitative cartilage measurements were assessed. Contrast-to-noise (CNR) ratios, image quality, and structural abnormalities were assessed on corresponding synthetic and conventional images. Statistical analyses included the Wilcoxon test, χ2 test, and Cohen Kappa. P values less than or equal to .01 were considered to indicate a statistically significant difference. Results Synthetic MRI quantification of T1, T2, and PD values had an overall model-corrected error margin of 0.8%. The synthetic MRI interday repeatability of articular cartilage quantification had native and model-corrected error margins of 3.3% and 3.5%, respectively. The cartilage-to-fluid CNR and menisci-to-fluid CNR was higher on synthetic than conventional MR images (P ≤ .001, respectively). Synthetic MRI improved short-tau inversion recovery fat suppression (P Ë .01). Intermethod agreements of structural abnormalities were good (kappa, 0.621-0.739). Conclusion Synthetic MRI of the knee is accurate for T1, T2, and proton density quantification, and simultaneously generated morphologic MR images have detection rates of structural abnormalities similar to those of conventional MR images, with similar acquisition time. © RSNA, 2018.