Stephen E. Russek1, Michael A. Boss2, H. Cecil Charles3, Andrew M. Dienstfrey1, Jeffrey L. Evelhoch4, Jeffrey L. Gunter5, Derek L. G. Hill6, Edward F. Jackson7, Kathryn E. Keenan1, Guoying Liu8, Michele Martin1, Nikki S. Rentz1, Karl F. Stupic1, Chun Yuan9, and Zydrunas Gimbutas1
1National Institute of Standards and Technology, Boulder, CO, United States, 2American College of Radiology, Philadelphia, PA, United States, 3Duke Image Analysis Laboratory, Durham, NC, United States, 4Merck Research Laboratories, West Point, PA, United States, 5Mayo Clinic, Rochester, MN, United States, 6University College London, London, United Kingdom, 7University of Wisconsin, Madison, WI, United States, 8National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, United States, 9University of Washington, Seattle, WA, United States
Synopsis
We describe
basic scanner characterization and determination of MR-parameter measurement accuracy using the
ISMRM/NIST system phantom. The phantom provides a convenient method to measure
geometric distortion; the efficacy of non-linear gradient corrections; slice
profiles and associated measurement uncertainties; protocol and system dependent
resolution contributions; SNR according to NEMA protocols; accuracy of
relaxation time; and proton density measurements. The phantom is unique in
having SI-traceability, a high level of precision, long-term stability, and
monitoring by a national metrology institute.
Introduction
The
ISMRM ad hoc Committee on Standards for Quantitative Magnetic Resonance and
NIST, have designed and commercialized a standard MRI system phantom(1) to assess scanner performance, scanner
stability, scanner inter-comparability and accuracy of quantitative relaxation
time imaging. The phantom is unique in having SI-traceability(2), a high level of precision, long-term
stability, and monitoring by a national metrology institute. The
system phantom, or modified versions of it, have been used for several studies
including repeatability of MR fingerprinting (3),
assessing changes occurring during scanner upgrades (4), and assessing
variation in multi-site T1 measurements (5). Many other uses are
ongoing, including monitoring of scanners for quality assurance and
homogenizing scan protocols across multi-vendor scanners. Here, we describe the use of the phantom to
measure fundamental MRI parameters including geometric distortion, resolution,
slice profile, and proton density. We present standard measurement and analysis
protocols. The standard measurement
protocols are not meant to be the best measurement methods, rather they are
meant to form a common baseline upon which improved protocols can be
benchmarked. We show that the phantom provides important information on scanner
performance including the accuracy of nonlinear gradient corrections, protocol
and scanner contributions to image resolution, slice profiles, and signal to
noise ratio (SNR) metrics.METHODS
The NIST/ISMRM system phantom (Fig. 1) consists
of a 200mm diameter water-filled sphere with a 57-element fiducial array for
assessing geometric distortion and image homogeneity; two 14-element arrays for
characterizing T1 and T2; a 14-element
array for characterizing proton density and SNR; slice profile; and resolution
insets. The proton spin relaxation time values for the two relaxation-time
arrays, one based on NiCl2 and the other on MnCl2
solutions, are shown in Fig. 1d, along with some reference values for tissue(6). Relaxation time measurements have been
discussed previously(7). Here, we focus on
geometric distortion, slice profile, resolution, proton density and SNR. Three-dimensional geometric distortion
analysis is done by isolating the 57 high-contrast fiducial spheres and
determining the sphere center locations using cross correlation and center of
mass techniques(8). The scanner-to-phantom
coordinate transformation and image uniformity are determined in the same
analysis. The slice profile inset consists of a pair of oppositely oriented
wedges and conforms to the National Equipment Manufacturers Association (NEMA)
standards(9). The resolution inset, based
on the ACR phantom(10), consists of hole arrays
with hole sizes ranging from 0.8mm to 0.4mm diameter. RESULTS
Fig.
2a,b shows the process for determining geometric distortion due to gradient
nonlinearity. A 3D gradient echo image is obtained, the fiducial spheres are isolated, and sphere centers are determined. Fig. 2c shows the results of geometric
distortion analysis with and without the nonlinear gradient corrections,
showing maximal distortion of approximately 2% without corrections and 1% with
corrections. Fig. 3 shows results of slice thickness and slice profile
measurements using the NEMA protocol and a modified automated analysis. Here,
the measured slice thickness was 3.15mm and 3.16mm ±0.2 mm for the two analyses, while the prescribed
slice thickness was 3.0mm. Fig. 4 shows automated resolution inset analysis,
which consists of generating a synthetic k-space image and applying a k-space point
spread function until the difference between the real-space image and synthetic
image is minimized. For the scan shown, with a voxel size of 0.35mm, the
manual ACR method yields a resolution of 0.5mm, while the automated analysis reports
a protocol-based resolution contribution, due to finite k-space sampling, of
0.35 mm, and an additional scanner-based contribution of 0.075 mm. Fig. 5 shows
results from a proton density measurement and an SNR analysis. Using a local
background reference, the proton density measurement accuracy is less than 10%.
Two SNR analyses, one measuring temporal and the other spatial noise, are shown.
The first calculates noise from the difference of two identical sequential images,
the second from the standard deviation within a uniform region
of interest (ROI).DISCUSSION
Geometric distortion analysis is important to
provide uncertainty in distance and volume measurements. Typical measured
distortions from nonlinear gradients are 2% over a 200mm spherical volume,
going below 1% with accurate corrections. Verifying and recording nonlinear
gradient corrections is required for use in further corrections to gradient
based measurements, such as diffusion and flow. The slice thickness and profile
measurements are important to verify that the scanner is operating according to
specification, and knowledge of the slice profile is required for implementing
corrections to flip angle dependent measurements such as for many T1
and T2 protocols. For the scan shown in Fig. 3, 30% of the signal
comes from the slice edges, where the flip angles may be non-ideal.CONCLUSION
Here we have shown how the ISMRM/NIST
system phantom can be used for basic scanner diagnostics assessing geometric
distortion, image homogeneity, SNR, resolution and slice profile. The resulting
data can provide both parameters to benchmark scanner performance and insight
on what is determining these parameters. For example, resolution analysis can
separate the pulse-sequence component from the intrinsic scanner component of
the point spread function. The SNR protocol can separate temporal and spatial
noise components, which contribute in different ways to SNR. Finally, the
MR-parameter arrays provide a broad range of properties to allow
validation of both standard and advanced quantitative protocols. Acknowledgements
We thank Michael Snow
and William Hollander at High Precision Devices for the computer aided design
and manufacturing guidance. We thank Elizabeth Mirowski from Verellium and
Joshua Levy from Phantom Labs for useful discussions on phantom design and
implementation. We thank Dr. Paul Finn from UCLA for his continued encouragement and suggestions. We thank all
of the ISMRM SQMR committee members, past and present, for their advice and
support.
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