Measuring signal‐to‐noise ratio (SNR) for parallel MRI reconstructions is difficult due to spatially dependent noise amplification. Existing approaches for measuring parallel MRI SNR are limited because they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A new SNR estimation approach is proposed, a hybrid of the repeated image acquisitions method detailed in the National Electrical Manufacturers Association (NEMA) standard and the Monte Carlo based pseudo‐multiple replica method, in which the difference between images reconstructed from the unaltered acquired data and that same data reconstructed after the addition of calibrated pseudo‐noise is used to estimate the noise in the parallel MRI image reconstruction. This new noise estimation method can be used to rapidly compute the pixel‐wise SNR of the image generated from any parallel MRI reconstruction of a single acquisition. SNR maps calculated with the new method are validated against existing SNR calculation techniques. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.

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Purpose: Attempts by magnetic resonance (MR) manufacturers to help imaging centres improve patient throughput has led to the development of more automated acquisition. This software is capable of customizing individual scan alignment; potentially improving imaging efficiency and standardizing protocols. However, substantial investments are required to introduce such systems, potentially deterring their widespread application. This study assessed the implementation costs and reduction in examination durations for automated knee MR imaging (MRI) software. Materials and Methods: Research activities were performed at a community-based academic centre on a 3-Tesla (3-T) system using Siemens' Day Optimizing Throughput (Dot) knee software. Examination acquisition times were extracted from the system before and after software implementation. Fiscal year 2012/13 finances were used to determine the average hourly cost of MRI utilization. Costs associated with automated software implementation were also calculated. Finally, the number of knee scans required to achieve a positive return on investment using the software was established. Results and Discussion: The mean (standard deviation, sample size) pre-and post-Dot software scan times were 23.20 (4.18, n ¼ 266) and 21.94 (4.51, n ¼ 59) minutes, respectively, for a routine knee scan and 11.88 (1.60, n ¼ 74) and 11.24 (1.51, n ¼ 27) minutes, respectively, for a fast knee scan. The overall weighted average resulted in a 64-second time savings per automated knee examination. This negligible time savings would be extremely difficult to make use of clinically. Dot simplified 29 unique knee protocols to two, improving the consistency of knee examinations. Current Dot software is not compatible with all patients and therefore has limitations that are a concern among MR technologists. Conclusion: Adoption of automated knee systems could assist in standardizing protocols; however, the cost of implementation and difficulty in modifying patient scheduling to reflect the minimal time savings would make a financial return unlikely to occur at smalland medium-sized institutions. The author(s) has no financial disclosures or conflicts of interest to declare.