ArticleMagnetic resonance in medicine2023
Truly reproducible uniform estimation of the ADC with multi-b diffusion data- Application in prostate diffusion imaging.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Self-Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi-b Prostate Diffusion MRI.Magnetic resonance in medicine · 2026Article
- Optimization of Diffusion MRI With Consideration of the Signal Decay in Biological Tissues.Magnetic resonance in medicine · 2026Article
- Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data.European radiology · 2026Article
- Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.Radiology. Artificial intelligence · 2025Article
- Human prostate MRI at ultrahigh-performance gradient: A feasibility study.Magnetic resonance in medicine · 2024Article
- Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment.European journal of radiology · 2023Review
- Truly reproducible uniform estimation of the ADC with multi-b diffusion data- Application in prostate diffusion imaging.Magnetic resonance in medicine · 2023Article
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Authors and funding
3 authors.
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Abstract
purposeThe ADC is a well-established parameter for clinical diagnostic applications, but lacks reproducibility because it is also influenced by the choice diffusion weighting level. A framework is evaluated that is based on multi-b measurement over a wider range of diffusion-weighting levels and higher order tissue diffusion modeling with retrospective, fully reproducible ADC calculation.
methodsAveraging effect from curve fitting for various model functions at 20 linearly spaced b-values was determined by means of simulations and theoretical calculations. Simulation and patient multi-b image data were used to compare the new approach for diffusion-weighted image and ADC map reconstruction with and without Rician bias correction to an active clinical trial protocol probing three non-zero b-values.
resultsAveraging effect at a certain b-value varies for model function and maximum b-value used. Images and ADC maps from the novel procedure are on-par with the clinical protocol. Higher order modeling and Rician bias correction is feasible, but comes at the cost of longer computation times.
conclusionsApplication of the new framework makes higher order modeling more feasible in a clinical setting while still providing patient images and reproducible ADC maps of adequate quality.
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