Quantitative Evaluation of apparent diffusion coefficient in a large multi-unit institution using the QIBA diffusion phantom.
Yung, J. P.; Ding, Y.; Hwang, K.-P.; Cardenas, C. E.; Ai, H.; Fuller, C. D.; Stafford, R. J.
Show abstract
PurposeThe purpose of this study was to determine the quantitative variability of diffusion weighted imaging and apparent diffusion coefficient values across a large fleet of MR systems. Using a NIST traceable magnetic resonance imaging diffusion phantom, imaging was reproducible and the measurements were quantitatively compared to known values. MethodsA fleet of 23 clinical MRI scanners was investigated in this study. A NIST/QIBA DWI phantom was imaged with protocols provided with the phantom. The resulting images were analyzed and ADC maps were generated. User-directed region-of-interests on each of the different vials provided ADC measurements among a wide range of known ADC values. ResultsThree diffusion phantoms were used in this study and compared to one another. From the one-way analysis of the variance, the mean and standard deviation of the percent errors from each phantom were not significantly different from one another. The low ADC vials showed larger errors and variation and appear directly related to SNR. Across all the MR systems and data, the coefficient of variation was calculated and Bland-Altman analysis was performed. ADC measurements were similar to one another except for the vials with the lower ADC values, which had a higher coefficient of variation. ConclusionADC values among the three phantoms showed good agreement and were not significantly different from one another. The large percent errors seen primarily at the low ADC values were shown to be a consequence of the SNR dependence and very little bias was observed between magnetic strengths and manufacturers. ADC values between diffusion phantoms were not statistically significant. Future investigations will be performed to study differences in magnetic field strength, vendor, MR system models, gradients, and bore size. More data across different MR platforms would facilitate quantitative measurements for multi-platform and multi-site imaging studies. With the increasing usage of diffusion weighted imaging in the clinic, the characterization of ADC variability for MR systems provides an improved quality control over the MR systems.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Increased Brain Volumetric Measurement Precision from Multi-Site 3D T1-weighted 3T Magnetic Resonance Imaging by Correcting Geometric Distortions 95%
- Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI 94%
- Noise decorrelation optimizes SNR of GABA-edited MRS data: A comparison of RF coil combination methods 94%
Similar papers in this journal
Similar papers in this journal
- A Phantom System Designed to Assess the Effects of Membrane Lipids on Water Proton Relaxation 96%
- Age and gender dependence of liver diffusion parameters and the evidence of intravoxel incoherent motion modelling of perfusion component is constrained by diffusion component. 94%
- Non-invasive Real-time Detection of Potassium level Changes in Skeletal Muscles during Exercise by Magnetic Resonance Spectroscopy 94%
Similar papers in this journal
- Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI using Reduced Field-of-View Echo-Planar Imaging and Enhanced Reverse Polarity Gradient Method 95%
- Test-retest reproducibility of in vivo magnetization transfer ratio and saturation index in mice at 9.4 Tesla 93%
- AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI): a Comprehensive Multiparametric Neurological Protocol from a Single MRI Scan 93%
Similar papers in this journal
- Increased brain coverage and efficiency when measuring current-induced magnetic fields by use of simultaneous multi-slice echo-planar MRI 95%
- Analysis of physiological noise in quantitative cardiac magnetic resonance 94%
- Optimizing the intrinsic parallel diffusivity in NODDI: an extensive empirical evaluation 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.