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Quantitative sodium magnetic resonance imaging permits noninvasive measurement of the tissue sodium concentration (TSC) bioscale in the brain. Computing the TSC bioscale requires reconstructing and combining multiple datasets acquired with a non-Cartesian acquisition that highly oversamples the center of k-space. Even with an optimized implementation of the algorithm to compute TSC, the overall processing time exceeds the time required to collect data from the human subject. Such a mismatch presents a challenge for sustained sodium imaging to avoid a growing data backlog and provide timely results. The most computationally intensive portions of the TSC calculation have been identified and accelerated using a consumer graphics processing unit (GPU) in addition to a conventional central processing unit (CPU). A recently developed data organization technique called Compact Binning was used along with several existing algorithmic techniques to maximize the scalability and performance of these computationally intensive operations. The resulting GPU+CPU TSC bioscale calculation is more than 15 times faster than a CPU-only implementation when processing 256 × 256 × 256 data and 2.4 times faster when processing 128 × 128 × 128 data. This eliminates the possibility of a data backlog for quantitative sodium imaging. The accelerated quantification technique is suitable for general three-dimensional non-Cartesian acquisitions and may enable more sophisticated imaging techniques that acquire even more data to be used for quantitative sodium imaging. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 29-35, 2013. [ABSTRACT FROM AUTHOR]

Aims and objectives Most nursing research using quantitative empirical data involves entering information collected on data collection forms into a computer. This paper brings to attention issues related to the introduction of errors during this transfer of data and makes some recommendations as to how this might be dealt with. Background Beyond concerns with the reliability and validity of data collection instruments, the issue of data accuracy and the introduction of errors in research data sets receive little mention in the nursing research literature. Given that studies have reported up to a 26·9 per cent error rate in data transfer (Goldberg et al. Proceedings American Medical Informatics Association, Annual Symposium November 2008), it is important that this potential problem is addressed in the context of nursing research. Design Discursive paper. Conclusions An approach to checking the accuracy of data entered onto a computer via the keyboard is suggested, and a method for estimating the level of confidence that could be claimed regarding the number of errors introduced is outlined. Relevance to clinical practice If nursing is to be investigated by quantitative methods, it would seem that the accuracy of study findings is paramount. This paper identifies one area of research where errors are prone to arise and suggests a method for containing these. [ABSTRACT FROM AUTHOR]

Abstract: The rapid biexponential transverse relaxation of the sodium MR signal from brain tissue requires efficient k-space sampling for quantitative imaging in a time that is acceptable for human subjects. The flexible twisted projection imaging (flexTPI) sequence has been shown to be suitable for quantitative sodium imaging with an ultra-short echo time to minimize signal loss. The fidelity of the k-space center location is affected by the readout gradient timing errors on the three physical axes, which is known to cause image distortion for projection-based acquisitions. This study investigated the impact of these timing errors on the voxel-wise accuracy of the tissue sodium concentration (TSC) bioscale measured with the flexTPI sequence. Our simulations show greater than 20% spatially varying quantification errors when the gradient timing errors are larger than 10μs on all three axes. The quantification is more tolerant of gradient timing errors on the Z-axis. An existing method was used to measure the gradient timing errors with <1μs error. The gradient timing error measurement is shown to be RF coil dependent, and timing error differences of up to ∼16μs have been observed between different RF coils used on the same scanner. The measured timing errors can be corrected prospectively or retrospectively to obtain accurate TSC values. [Copyright &y& Elsevier]

An introduction is presented in which the editor discusses various reports within the issue on topics of issues in quantitative healthcare research such as issues concerning the generation of robust data sets, and different approaches in data analysis.