An optimized b‐value distribution for triexponential intravoxel incoherent motion (IVIM) in the liver

Riexinger, Andreas; Martin, Jan; Wetscherek, Andreas; Kuder, Tristan Anselm; Uder, Michael; Hensel, Bernhard; Laun, Frederik Bernd

doi:10.1002/mrm.28582

Cited by 14 publications

(38 citation statements)

References 37 publications

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“…As for bi-exponential fitting, full fitting [22,24] and segmented fitting [24][25][26] methods have been used for the tri-exponential model. The full fitting method was performed using nonlinear least squares regression model-based algorithm [22,24].…”

Section: Tri-exponential Model Fitting Methodsmentioning

confidence: 99%

“…By definition, the sum of the fraction of each compartment is equal to 1 (F' slow + F' fast + F' Vfast = 1). Therefore, tri-exponential function can be reduced to five rather than six parameters to solve [24][25][26]. For instance, the parameter F' Vfast can be removed from the equation, leading to:…”

Section: Tri-exponential Model Fitting Methodsmentioning

confidence: 99%

“…Segmented fitting method consists of a two-step approach. It is commonly accepted that only the slow diffusion compartment contributes significantly to the DWI signal for b-values higher than 100-200 s/mm 2 [9,[24][25][26]37,[40][41][42][43]. Therefore, the estimation of D' slow can first be obtained by a linear fit using only b-values above a certain b-value threshold (generally, 100-200 s/mm 2 ).…”

Section: Tri-exponential Model Fitting Methodsmentioning

confidence: 99%

“…Another study was conducted in order to find optimized b-values that reduce the fit uncertainty in all tri-exponential parameters [26]. With simulated data, AIC comparison favored the bi-exponential model if the number of b-values was small, whereas it favored the tri-exponential model if more b-values were added to the b-values distribution.…”

Section: Evidence For Tri-exponential Decay Modelmentioning

confidence: 99%

“…Since flowing or moving spins are present in these compartments, which are directly or indirectly connected together, it can be hypothesized that more than two compartments of diffusion exist in the liver and could be analyzed through the recorded DWI signal decay. Recently, evidence of the existence of an additional very fast component of diffusion in the liver has been published [20,[22][23][24][25][26]. Therefore, a tri-exponential decay model has been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations

Chevallier¹,

Wáng

Guillen³

et al. 2021

Diagnostics

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Diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) have been explored to assess liver tumors and diffused liver diseases. IVIM reflects the microscopic translational motions that occur in voxels in magnetic resonance (MR) DWI. In biologic tissues, molecular diffusion of water and microcirculation of blood in the capillary network can be assessed using IVIM DWI. The most commonly applied model to describe the DWI signal is a bi-exponential model, with a slow compartment of diffusion linked to pure molecular diffusion (represented by the coefficient Dslow), and a fast compartment of diffusion, related to microperfusion (represented by the coefficient Dfast). However, high variance in Dfast estimates has been consistently shown in literature for liver IVIM, restricting its application in clinical practice. This variation could be explained by the presence of another very fast compartment of diffusion in the liver. Therefore, a tri-exponential model would be more suitable to describe the DWI signal. This article reviews the published evidence of the existence of this additional very fast diffusion compartment and discusses the performance and limitations of the tri-exponential model for liver IVIM in current clinical settings.

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Section: Tri-exponential Model Fitting Methodsmentioning

confidence: 99%

Section: Tri-exponential Model Fitting Methodsmentioning

confidence: 99%

Section: Tri-exponential Model Fitting Methodsmentioning

confidence: 99%

Section: Evidence For Tri-exponential Decay Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations

Chevallier¹,

Wáng

Guillen³

et al. 2021

Diagnostics

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Two‐Compartment Perfusion MR IVIM Model to Investigate Normal and Pathological Placental Tissue

Maiuro

Ercolani

Stadio

et al. 2023

Magnetic Resonance Imaging

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BackgroundPerfusion and diffusion coexist in the placenta and can be altered by pathologies. The two‐perfusion model, where f1 and, f2 are the perfusion‐fraction of the fastest and slowest perfusion compartment, respectively, and D is the diffusion coefficient, may help differentiate between normal and impaired placentas.PurposeInvestigate the potential of the two‐perfusion IVIM model in differentiating between normal and abnormal placentas.Study‐TypeRetrospective, case–control.Population43 normal pregnancy, 9 fetal‐growth‐restriction (FGR), 6 small‐for‐gestational‐age (SGA), 4 accreta, 1 increta and 2 percreta placentas.Field Strength/SequenceDiffusion‐weighted‐echo planar imaging sequence at 1.5 T.AssessmentVoxel‐wise signal‐correction and fitting‐controls were used to avoid overfitting obtaining that two‐perfusion model fitted the observed data better than the IVIM model (Akaike weight: 0.94). The two‐perfusion parametric‐maps were quantified from ROIs in the fetal and maternal placenta and in the accretion zone of accreta placentas. The diffusion coefficient D was evaluated using a b ≥ 200 sec/mm2‐mono‐exponential decay fit. IVIM metrics were quantified to fix f1 + f2 = fIVIM.Statistical‐TestsANOVA with Dunn‐Sidák's post‐hoc correction and Cohen's d test were used to compare parameters between groups. Spearman's coefficient was evaluated to study the correlation between variables. A P‐value<0.05 indicated a statistically significant difference.ResultsThere was a significant difference in f1 between FGR and SGA, and significant differences in f2 and fIVIM between normal and FGR. The percreta + increta group showed the highest f1 values (Cohen's d = −2.66). The f2 between normal and percreta + increta groups showed Cohen's d = 1.12. Conversely, fIVIM had a small effective size (Cohen's d = 0.32). In the accretion zone, a significant correlation was found between f2 and GA (ρ = 0.90) whereas a significant negative correlation was found between fIVIM and D (ρ = −0.37 in fetal and ρ = −0.56 in maternal side) and f2 and D (ρ = −0.38 in fetal and ρ = −0.51 in maternal side) in normal placentas.ConclusionThe two‐perfusion model provides complementary information to IVIM parameters that may be useful in identifying placenta impairment.Level of Evidence2Technical Efficacy Stage1

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Reproducibility and Repeatability of Intravoxel Incoherent Motion MRI Acquisition Methods in Liver

Vasquez,

Brown,

Woolsey

et al. 2024

Magnetic Resonance Imaging

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BackgroundIntravoxel incoherent motion (IVIM) diffusion weighted MRI (DWI) has potential for evaluating hepatic fibrosis but image acquisition technique influence on diffusion parameter estimation bears investigation.PurposeTo minimize variability and maximize repeatably in abdominal DWI in terms of IVIM parameter estimates.Study TypeProspective test–retest and image quality comparison.SubjectsHealthy volunteers (3F/7M, 29.9 ± 12.9 years) and Family Study subjects (18F/12M, 51.7 ± 16.7 years), without and with liver steatosis.Field Strength/SequenceAbdominal single‐shot echo‐planar imaging (EPI) and simultaneous multi‐slice (SMS) DWI sequences with respiratory triggering (RT), breath‐holding (BH), and navigator echo (NE) at 3 Tesla.AssessmentSMS‐BH, EPI‐NE, and SMS‐RT data from twice‐scanned healthy volunteers were analyzed using 6 × b‐values (0–800 s⋅mm−2) and lower (LO) and higher (HI) b‐value ranges. Family Study subjects were scanned using SMS and standard EPI sequences. The biexponential IVIM model was used to estimate fast‐diffusion coefficient (Df), fraction of fast diffusion (f), and slow‐diffusion coefficient (Ds). Scan time, estimated signal‐to‐noise ratio (eSNR), eSNR per acquisition, and distortion ratio were compared.Statistical TestsCoefficients of variation (CoV) and Bland Altman analyses were performed for test–retest repeatability. Interclass correlation coefficient (ICC) assessed interobserver agreement with P < 0.05 deemed significant.ResultsWithin‐subject CoVs among volunteers (N = 10) for f and Ds were lowest in EPI‐NE‐LO (11.6%) and SMS‐RT‐HI (11.1%). Inter‐observer ICCs for f and Ds were highest for EPI‐NE‐LO (0.63) and SMS‐RT‐LO (0.76). Df could not be estimated for most subjects. Estimated eSNR (EPI = 21.9, SMS = 4.7) and eSNR time (EPI = 6.7, SMS = 16.6) were greater for SMS, with less distortion in the liver region (DR‐PE: EPI = 23.6, SMS = 13.1).Data ConclusionSimultaneous multislice acquisitions had significantly less variability and higher ICCs of Ds, higher eSNR, less distortion, and reduced scan time compared to EPI.Evidence Level2Technical EfficacyStage 1

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An optimized b‐value distribution for triexponential intravoxel incoherent motion (IVIM) in the liver

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 14 publications

References 37 publications

Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations

Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations

Two‐Compartment Perfusion MR IVIM Model to Investigate Normal and Pathological Placental Tissue

Reproducibility and Repeatability of Intravoxel Incoherent Motion MRI Acquisition Methods in Liver

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