ArticleEuropean radiology2015
Intravoxel incoherent motion diffusion-weighted imaging in the liver: comparison of mono-, bi- and tri-exponential modelling at 3.0-T.
Article in European radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Value of intravoxel incoherent motion in detecting and staging liver fibrosis: A meta-analysis.World journal of gastroenterology · 2020Pooled it
- Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation.Medical physics · 2026Article
- A New Approach for Estimating Liver IVIM Fast Diffusion Coefficient in Two-Compartment Model and Fast Diffusion Fraction in Three-Compartment Model: Theory and In Vivo Validation of Two Centers' Data.NMR in biomedicine · 2026Article
- An explanation for the triphasic dependency of apparent diffusion coefficient (ADC) on T2 relaxation time: the multiple T2 compartments model.Quantitative imaging in medicine and surgery · 2025Article
- MRI signal simulation of liverQuantitative imaging in medicine and surgery · 2025Article
- Reproducibility of intravoxel incoherent motion quantification in the liver across field strengths and gradient hardware.Magnetic resonance in medicine · 2024Article
- Article
- A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status.European radiology · 2023Article
- Article
- The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2023Article
- Precision of region of interest-based tri-exponential intravoxel incoherent motion quantification and the role of the Intervoxel spatial distribution of flow velocities.Magnetic resonance in medicine · 2022Article
- Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology.Magnetic resonance in medicine · 2022Article
- b value and first-order motion moment optimized data acquisition for repeatable quantitative intravoxel incoherent motion DWI.Magnetic resonance in medicine · 2022Article
- Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease.Frontiers in physiology · 2022Article
- Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations.Diagnostics (Basel, Switzerland) · 2021Review
- Advanced diffusion imaging of abdominal organs in different hydration states of the human body: stability of biomarkers.Heliyon · 2021Article
- Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model.Frontiers in oncology · 2021Article
- Diffusion processes modeling in magnetic resonance imaging.Insights into imaging · 2020Article
- Topics on quantitative liver magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2019Review
- Living tissue intravoxel incoherent motion (IVIM) diffusion MR analysis withoutQuantitative imaging in medicine and surgery · 2019Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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Abstract
purposeTo determine whether a mono-, bi- or tri-exponential model best fits the intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) signal of normal livers. MATERIALS AND
methodsThe pilot and validation studies were conducted in 38 and 36 patients with normal livers, respectively. The DWI sequence was performed using single-shot echoplanar imaging with 11 (pilot study) and 16 (validation study) b values. In each study, data from all patients were used to model the IVIM signal of normal liver. Diffusion coefficients (Di ± standard deviations) and their fractions (fi ± standard deviations) were determined from each model. The models were compared using the extra sum-of-squares test and information criteria.
resultsThe tri-exponential model provided a better fit than both the bi- and mono-exponential models. The tri-exponential IVIM model determined three diffusion compartments: a slow (D1 = 1.35 ± 0.03 × 10(-3) mm(2)/s; f1 = 72.7 ± 0.9 %), a fast (D2 = 26.50 ± 2.49 × 10(-3) mm(2)/s; f2 = 13.7 ± 0.6 %) and a very fast (D3 = 404.00 ± 43.7 × 10(-3) mm(2)/s; f3 = 13.5 ± 0.8 %) diffusion compartment [results from the validation study]. The very fast compartment contributed to the IVIM signal only for b values ≤15 s/mm(2)
conclusionThe tri-exponential model provided the best fit for IVIM signal decay in the liver over the 0-800 s/mm(2) range. In IVIM analysis of normal liver, a third very fast (pseudo)diffusion component might be relevant. KEY POINTS: • For normal liver, tri-exponential IVIM model might be superior to bi-exponential • A very fast compartment (D = 404.00 ± 43.7 × 10 (-3) mm (2) /s; f = 13.5 ± 0.8 %) is determined from the tri-exponential model • The compartment contributes to the IVIM signal only for b ≤ 15 s/mm(2).
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