Is Your System Calibrated? MRI Gradient System Calibration for Pre-Clinical, High-Resolution Imaging

O’Callaghan, James M.; Wells, Jack A.; Richardson, S.; Holmes, Holly; Yu, Yichao; Walker‐Samuel, Simon; Siow, Bernard; Lythgoe, Mark F.

doi:10.1371/journal.pone.0096568

Cited by 26 publications

(37 citation statements)

References 19 publications

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“…Each MRI vendor provides unique hardware configurations, pulse sequences and reconstruction algorithms, and these cause variations in quantitation of T1 values, tissue contrast concentrations and perfusion parameters. Besides, the original settings may drift owing to hardware instability, thus an external phantom should be small enough to be imaged concurrently in the bore of an MR scanner with a patient for on‐site quality assurance, but large enough not to suffer from partial volume effect . The use of a static phantom comprised of multiple objects with different contrast concentrations was suggested to correct the variability in quantitating tissue contrast concentration .…”

Section: Introductionmentioning

confidence: 99%

Portable perfusion phantom for quantitative DCE‐MRI of the abdomen

et al. 2017

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Purpose To develop a portable perfusion phantom and validate its utility in quantitative dynamic contrast-enhanced magnetic resonance imaging of the abdomen. Methods A portable perfusion phantom yielding a reproducible contrast enhancement curve (CEC) was developed. A phantom package including perfusion and static phantoms were imaged simultaneously with each of three healthy human volunteers in two different 3T MR scanners. Look-up tables correlating reference (known) contrast concentrations with measured ones were created using either the static or perfusion phantom. Contrast maps of image slices showing four organs (liver, spleen, pancreas, and paravertebral muscle) were generated before and after data correction using the look-up tables. The contrast concentrations at 4.5 minutes after dosing in each of the four organs were averaged for each volunteer. The mean contrast concentrations (4 organs x 3 volunteers = 12) were compared for the two scanners, and the intra-class correlation coefficient (ICC) was calculated. Also, the ICC of the mean Ktrans values between the two scanners was calculated before and after data correction. Results The repeatability coefficient of CECs of perfusion phantom was higher than 0.997 in all measurements. The ICC of the tissue contrast concentrations between the two scanners was 0.693 before correction, but increased to 0.974 after correction using the look-up tables (LUTs) of perfusion phantom. However, the ICC was not increased after correction using static phantom (ICC: 0.617). Similarly, the ICC of the Ktrans values was 0.899 before correction, but increased to 0.996 after correction using perfusion phantom LUTs. The ICC of the Ktrans values, however, was not increased when static phantom LUTs were used (ICC: 0.866). Conclusions The perfusion phantom reduced variability in quantifying contrast concentration and Ktrans values of human abdominal tissues across different MR units, but static phantom did not. The perfusion phantom has the potential to facilitate multi-institutional clinical trials employing quantitative DCE-MRI to evaluate various abdominal malignancies.

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Section: Introductionmentioning

confidence: 99%

Portable perfusion phantom for quantitative DCE‐MRI of the abdomen

et al. 2017

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“…Current methods for correcting gradient nonlinearities include deformation mapping based on high‐resolution geometric information 1, 2, 3, and modeling of the gradient field with spherical harmonics 26, 27, truncated linear distributions 28, and exponentials of power series 25. The use of deformation mapping approaches requires manufacture of typically 3D grid phantoms to high tolerances.…”

Section: Discussionmentioning

confidence: 99%

“…Calibration is usually performed by vendors at installation and during routine servicing, based on anatomical scans of a phantom of known dimensions. Phantoms with more complex geometries, typically grid structures over an extended field of view (FOV), have also been proposed for gradient calibration, with the added benefit of addressing gradient nonlinearities 1, 2, 3. The accuracy of these approaches is governed by the spatial resolution of the scan, and the geometric accuracy of the phantom dimensions.…”

Section: Introductionmentioning

confidence: 99%

Efficient gradient calibration based on diffusion MRI

Teh

Maguire

Schneider

2016

Magnetic Resonance in Med

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PurposeTo propose a method for calibrating gradient systems and correcting gradient nonlinearities based on diffusion MRI measurements.MethodsThe gradient scaling in x, y, and z were first offset by up to 5% from precalibrated values to simulate a poorly calibrated system. Diffusion MRI data were acquired in a phantom filled with cyclooctane, and corrections for gradient scaling errors and nonlinearity were determined. The calibration was assessed with diffusion tensor imaging and independently validated with high resolution anatomical MRI of a second structured phantom.ResultsThe errors in apparent diffusion coefficients along orthogonal axes ranged from −9.2% ± 0.4% to + 8.8% ± 0.7% before calibration and −0.5% ± 0.4% to + 0.8% ± 0.3% after calibration. Concurrently, fractional anisotropy decreased from 0.14 ± 0.03 to 0.03 ± 0.01. Errors in geometric measurements in x, y and z ranged from −5.5% to + 4.5% precalibration and were likewise reduced to −0.97% to + 0.23% postcalibration. Image distortions from gradient nonlinearity were markedly reduced.ConclusionPeriodic gradient calibration is an integral part of quality assurance in MRI. The proposed approach is both accurate and efficient, can be setup with readily available materials, and improves accuracy in both anatomical and diffusion MRI to within ±1%. Magn Reson Med 77:170–179, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

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“…Recently, other larger and more complex phantom designs have been proposed for monitoring geometric accuracy or performing 3D geometric distortion correction in preclinical MRI [18, 29–31], but their size inhibits their use with various imaging coils, they generally have lower construction precision and are currently not as cost-effective; as such, they were not used in this multicenter study. LEGO bricks were also used successfully for the development of clinical phantoms before [25].…”

Section: Discussionmentioning

confidence: 99%

Multicenter Evaluation of Geometric Accuracy of MRI Protocols Used in Experimental Stroke

et al. 2016

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It has recently been suggested that multicenter preclinical stroke studies should be carried out to improve translation from bench to bedside, but the accuracy of magnetic resonance imaging (MRI) scanners routinely used in experimental stroke has not yet been evaluated. We aimed to assess and compare geometric accuracy of preclinical scanners and examine the longitudinal stability of one scanner using a simple quality assurance (QA) protocol. Six 7 Tesla animal scanners across six different preclinical imaging centers throughout Europe were used to scan a small structural phantom and estimate linear scaling errors in all orthogonal directions and volumetric errors. Between-scanner imaging consisted of a standard sequence and each center’s preferred sequence for the assessment of infarct size in rat models of stroke. The standard sequence was also used to evaluate the drift in accuracy of the worst performing scanner over a period of six months following basic gradient calibration. Scaling and volumetric errors using the standard sequence were less variable than corresponding errors using different stroke sequences. The errors for one scanner, estimated using the standard sequence, were very high (above 4% scaling errors for each orthogonal direction, 18.73% volumetric error). Calibration of the gradient coils in this system reduced scaling errors to within ±1.0%; these remained stable during the subsequent 6-month assessment. In conclusion, despite decades of use in experimental studies, preclinical MRI still suffers from poor and variable geometric accuracy, influenced by the use of miscalibrated systems and various types of sequences for the same purpose. For effective pooling of data in multicenter studies, centers should adopt standardized procedures for system QA and in vivo imaging.

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Is Your System Calibrated? MRI Gradient System Calibration for Pre-Clinical, High-Resolution Imaging

Cited by 26 publications

References 19 publications

Portable perfusion phantom for quantitative DCE‐MRI of the abdomen

Portable perfusion phantom for quantitative DCE‐MRI of the abdomen

Efficient gradient calibration based on diffusion MRI

Multicenter Evaluation of Geometric Accuracy of MRI Protocols Used in Experimental Stroke

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