A standard system phantom for magnetic resonance imaging.

PURPOSE: A standard MRI system phantom has been designed and fabricated to assess scanner performance, stability, comparability and assess the accuracy of quantitative relaxation time imaging. The phantom is unique in having traceability to the International System of Units, a high level of precision, and monitoring by a national metrology institute. Here, we describe the phantom design, construction, imaging protocols, and measurement of geometric distortion, resolution, slice profile, signal-to-noise ratio (SNR), proton-spin relaxation times, image uniformity and proton density. METHODS: The system phantom, designed by the International Society of Magnetic Resonance in Medicine ad hoc committee on Standards for Quantitative MR, is a 200 mm spherical structure that contains a 57-element fiducial array; two relaxation time arrays; a proton density/SNR array; resolution and slice-profile insets. Standard imaging protocols are presented, which provide rapid assessment of geometric distortion, image uniformity, T1 and T2 mapping, image resolution, slice profile, and SNR. RESULTS: Fiducial array analysis gives assessment of intrinsic geometric distortions, which can vary considerably between scanners and correction techniques. This analysis also measures scanner/coil image uniformity, spatial calibration accuracy, and local volume distortion. An advanced resolution analysis gives both scanner and protocol contributions. SNR analysis gives both temporal and spatial contributions. CONCLUSIONS: A standard system phantom is useful for characterization of scanner performance, monitoring a scanner over time, and to compare different scanners. This type of calibration structure is useful for quality assurance, benchmarking quantitative MRI protocols, and to transition MRI from a qualitative imaging technique to a precise metrology with documented accuracy and uncertainty.

Assessing effects of scanner upgrades for clinical studies.

BACKGROUND: Scanner upgrades due to software and hardware changes are an inevitable part of MR research and, without quality assurance protocols, can jeopardize studies. PURPOSE: To evaluate changes in T1 relaxation time by inversion recovery (IR) and variable flip angle (VFA) measurements on a 3T system that underwent an "everything but the magnet" upgrade. STUDY TYPE: Longitudinal. PHANTOM: An International Society of Magnetic Resonance in Medicine / National Institute of Standards and Technology (ISMRM/NIST) system phantom with repeated measurements across multiple (n = 3) days. FIELD STRENGTH/SEQUENCE: T1 IR, VFA at 3T. ASSESSMENT: The T1 measurements by IR and VFA were compared with the nuclear magnetic resonance (NMR) measurements, which constitute the known values for the ISMRM/NIST system phantom, to determine the measurement error. STATISTICAL TESTS: Descriptive. RESULTS: The T1 VFA measurement errors were distributed around zero (-15% to +10%) on the original system and then the errors were distributed entirely above zero post-upgrade (+5% to 30%). The T1 IR results had a dramatic increase in error distribution (±5% original, ±20% post-upgrade) prior to the identification of signal saturation as an issue. Once the signal saturation was accounted for, the T1 IR errors decreased to ±10% post-upgrade. DATA CONCLUSION: The T1 VFA measurement differences between the original and post-upgrade systems can be entirely attributed to contributions from B1 . The T1 IR measurements demonstrate the need for quantitative quality assurance (QA) processes. The site under study passed the QA protocols in place, which did not identify the increased T1 error, nor the signal saturation issue. To improve on this study, we would make systematic, quantitative measurements at intervals less than a year and following any hardware or software upgrade. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1948-1954.

Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom.

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.

A FAST SIMPLE ALGORITHM FOR COMPUTING THE POTENTIAL OF CHARGES ON A LINE.

We present a fast method for evaluating expressions of the form u j = ∑ i = 1 , i ≠ j n α i x i - x j , for j = 1 , … , n , where αi are real numbers, and xi are points in a compact interval of R . This expression can be viewed as representing the electrostatic potential generated by charges on a line in R 3 . While fast algorithms for computing the electrostatic potential of general distributions of charges in R 3 exist, in a number of situations in computational physics it is useful to have a simple and extremely fast method for evaluating the potential of charges on a line; we present such a method in this paper, and report numerical results for several examples.

Evaluation of Abramowitz functions in the right half of the complex plane.

A numerical scheme is developed for the evaluation of Abramowitz functions Jn in the right half of the complex plane. For n = - 1, … , 2, the scheme utilizes series expansions for ∣z∣ < 1, asymptotic expansions for ∣z∣ > R with R determined by the required precision, and least squares Laurent polynomial approximations on each sub-region in the intermediate region 1 ≤ ∣z∣ ≤ R. For n > 2, Jn is evaluated via a forward recurrence relation. The scheme achieves nearly machine precision for n = -1, … , 2 at a cost that is competitive as compared with software packages for the evaluation of other special functions in the complex domain.

A Fast Summation Method for Oscillatory Lattice Sums.

We present a fast summation method for lattice sums of the type which arise when solving wave scattering problems with periodic boundary conditions. While there are a variety of effective algorithms in the literature for such calculations, the approach presented here is new and leads to a rigorous analysis of Wood's anomalies. These arise when illuminating a grating at specific combinations of the angle of incidence and the frequency of the wave, for which the lattice sums diverge. They were discovered by Wood in 1902 as singularities in the spectral response. The primary tools in our approach are the Euler-Maclaurin formula and a steepest descent argument. The resulting algorithm has super-algebraic convergence and requires only milliseconds of CPU time.