ArticlePhysics and imaging in radiation oncology2025
Quantitative evaluation of apparent diffusion coefficient in a large multi-unit institution.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study.European radiology experimental · 2026Article
- Multi-center and multi-vendor evaluation study across 1.5 T and 3 T scanners (part 1): apparent diffusion coefficient standardization in a diffusion MRI phantom.Magma (New York, N.Y.) · 2025Article
- Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors.Cancers · 2024Article
- Whole Process of Standardization of Diffusion-Weighted Imaging: Phantom Validation and Clinical Application According to the QIBA Profile.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation inDiagnostic and interventional radiology (Ankara, Turkey) · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
Background and purpose: Diffusion weighted imaging (DWI) and their calculated apparent diffusion coefficient (ADC) values in magnetic resonance imaging (MRI) has grown with applications across multiple anatomical sites and settings across diagnostic and oncologic therapy settings, necessitating characterizing its quantitative performance across multiple scanners. Therefore, the purpose of this study was to determine the quantitative accuracy of DWI and their calculated ADC values across a fleet of MRI systems using a NIST-traceable diffusion phantom. Materials and methods: Three NIST/QIBA DWI phantoms were imaged with protocols provided with the phantom on a multi-vendor fleet of 23 clinical MRI scanners. User-directed regions-of-interest on each vial provided ADC measurements among a wide range of NIST-traceable ADC values. Across all the MRI systems and data, the coefficient-of-variation was calculated, and Bland-Altman analysis was performed. The measurements between phantoms were analyzed to assess their agreement to determine if multiple phantoms can be used for quality assurance across a large institution. Results: Lower ADC values and the sagittal orientation exhibited the largest error range and coefficient-of-variation (CoV) and was directly related to SNR. From a one-way analysis of the variance (ANOVA), the mean and standard deviation of the percent errors from each phantom were not significantly different from one another. No strong differences in Bland-Altman plots were seen between 1.5T and 3T scanners, however confidence intervals were different among vendors. The ADC values among the three phantoms were not significantly different from one another. Conclusions: A comprehensive analysis of institution-wide MRI scanners was conducted, motivating further work to utilize these results to implement a comprehensive quality assurance program and advanced scheduling system to better match similarly performing MRI scanners.
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