Impact of prior distributions and central tendency measures on Bayesian intravoxel incoherent motion model fitting

Gustafsson, Oscar; Montelius, Mikael; Starck, Göran; Ljungberg, Maria

doi:10.1002/mrm.26783

Cited by 49 publications

(74 citation statements)

References 23 publications

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“…Choice of prior distribution for Bayesian IVIM model fitting has previously been shown to have a substantial effect on parameter estimates from the full IVIM model [21]. Due to the exclusion of D* in the sIVIM model, the model is considerably less flexible and therefore is likely less susceptible to noise.…”

Section: Discussionmentioning

confidence: 99%

“…3 was performed using a previously published MATLAB function for Bayesian IVIM model fitting 1 [21], which was adapted to the sIVIM model. The implementation uses a Markov chain Monte Carlo setup to sample the posterior parameter distribution from which the marginal posterior mode or posterior mean was estimated.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…However, evaluation of approaches for estimating D and f only is, to our knowledge, limited to a subanalysis in a single study [16]. Furthermore, while Bayesian estimation techniques have shown great promise for the full IVIM model, evaluation of the potential improvement in estimating D and f only is lacking [21][22][23]. When the performance of a set of estimators is compared, general measures such as the bias and variability of the estimates can be used.…”

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confidence: 99%

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Comparison of methods for estimation of the intravoxel incoherent motion (IVIM) diffusion coefficient (D) and perfusion fraction (f)

Jalnefjord

Andersson

Montelius

et al. 2018

Magn Reson Mater Phy

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Objective Intravoxel incoherent motion (IVIM) shows great potential in many applications, e.g., tumor tissue characterization. To reduce image-quality demands, various IVIM analysis approaches restricted to the diffusion coefficient (D) and the perfusion fraction (f) are increasingly being employed. In this work, the impact of estimation approach for D and f is studied. Materials and methods Four approaches for estimating D and f were studied: segmented IVIM fitting, least-squares fitting of a simplified IVIM model (sIVIM), and Bayesian fitting of the sIVIM model using marginal posterior modes or posterior means. The estimation approaches were evaluated in terms of bias and variability as well as ability for differentiation between tumor and healthy liver tissue using simulated and in vivo data. Results All estimation approaches had similar variability and ability for differentiation and negligible bias, except for the Bayesian posterior mean of f, which was substantially biased. Combined use of D and f improved tumor-to-liver tissue differentiation compared with using D or f separately. Discussion The similar performance between estimation approaches renders the segmented one preferable due to lower numerical complexity and shorter computational time. Superior tissue differentiation when combining D and f suggests complementary biologically relevant information.

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Section: Discussionmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

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Comparison of methods for estimation of the intravoxel incoherent motion (IVIM) diffusion coefficient (D) and perfusion fraction (f)

Jalnefjord

Andersson

Montelius

et al. 2018

Magn Reson Mater Phy

Self Cite

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“…It has been found by previous studies that the choice of Bayesian methods for fitting IVIM data can give lower variability, especially for f and D* , but runs the potential risk of obscuring real features in regions where there is a high uncertainty associated with the parameters . Similarly, when the choice of posterior summary statistic can influence the resulting estimations, it is critical that analysis is not performed blind to these details. Additionally, Bayesian methods require substantially longer time for analysis, and some studies argue that simpler analysis can provide similar results .…”

Section: Discussionmentioning

confidence: 99%

“…Thus, it is critical when applying Bayesian estimation to consider whether the chosen prior reflects the true behavior and properties of the tissue; to this end, additional and independent information about the tissue is required to act as the reference and gold standard. Additionally, parameter estimates are typically derived from Bayesian methods by reporting specific metrics of the marginalized posterior distribution, such as the mean or mode, and the possible influence of the choice of metric on the final output needs to be explored …”

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confidence: 99%

Accuracy of breast cancer lesion classification using intravoxel incoherent motion diffusion‐weighted imaging is improved by the inclusion of global or local prior knowledge with bayesian methods

Vidić

Jerome

Bathen

et al. 2019

Magnetic Resonance Imaging

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Background Diffusion‐weighted MRI (DWI) has potential to noninvasively characterize breast cancer lesions; models such as intravoxel incoherent motion (IVIM) provide pseudodiffusion parameters that reflect tissue perfusion, but are dependent on the details of acquisition and analysis strategy. Purpose To examine the effect of fitting algorithms, including conventional least‐squares (LSQ) and segmented (SEG) methods as well as Bayesian methods with global shrinkage (BSP) and local spatial (FBM) priors, on the power of IVIM parameters to differentiate benign and malignant breast lesions. Study Type Prospective patient study. Subjects 61 patients with confirmed breast lesions. Field Strength/Sequence DWI (bipolar SE‐EPI, 13 b values) was included in a clinical MR protocol including T2‐weighted and dynamic contrast‐enhanced MRI on a 3T scanner. Assessment The IVIM model was fitted voxelwise in lesion regions of interest (ROIs), and derived parameters were compared across methods within benign and malignant subgroups (correlation, coefficients of variation). Area under receiver operator characteristic curves (ROC AUCs) were calculated to determine discriminatory power of parameter combinations from all fitting methods. Statistical Tests Kruskal–Wallis, Mann–Whitney, Pearson correlation. Results All methods provided useful IVIM parameters; D was well‐correlated across all methods (r > 0.8), with a wider range for f and D* (0.3–0.7). Fitting methods gave detectable differences in parameters, but all showed increased f and decreased D in malign lesions. D was the most discriminatory single parameter, with LSQ performing least well (AUC 0.83). In general, ROC AUCs were maximized by the inclusion of pseudodiffusion parameters, and by the use of Bayesian methods incorporating prior information (maximum AUC of 0.92 for BSP). Data Conclusion DWI performs well at classifying breast lesions, but careful consideration of analysis procedure can improve performance. D is the most discriminatory single parameter, but including pseudodiffusion parameters (f and D*) increases ROC AUC. Bayesian methods outperformed conventional least‐squares and segmented fitting methods for breast lesion classification. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1478–1488.

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Non‐contrast based approach for liver function quantification using Bayesian‐based intravoxel incoherent motion diffusion weighted imaging: A pilot study

Phonlakrai,

Ramadan,

Simpson

et al. 2023

J Applied Clin Med Phys

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PurposeLiver cirrhosis disrupts liver function and tissue perfusion, detectable by magnetic resonance imaging (MRI). Assessing liver function at the voxel level with 13‐b value intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) could aid in radiation therapy liver‐sparing treatment for patients with early impairment. This study aimed to evaluate the feasibility of IVIM‐DWI for liver function assessment and correlate it with other multiparametric (mp) MRI methods at the voxel level.MethodThis study investigates the variability of apparent diffusion coefficient (ADC) derived from 13‐b value IVIM‐DWI and B1‐corrected dual flip angle (DFA) T1 mapping. Experiments were conducted in‐vitro with QIBA and NIST phantoms and in 10 healthy volunteers for IVIM‐DWI. Additionally, 12 patients underwent an mp‐MRI examination. The imaging protocol included a 13‐b value IVIM‐DWI sequence for generating IVIM parametric maps. B1‐corrected DFA T1 pulse sequence was used for generating T1 maps, and Gadoxatate low temporal resolution dynamic contrast‐enhanced (LTR‐DCE) MRI was used for generating the Hepatic extraction fraction (HEF) map. The Mann‐Whitney U test was employed to compare IVIM‐DWI parameters (Pure Diffusion, Dslow; Pseudo diffusion, Dfast; and Perfusion Fraction, Fp) between the healthy volunteer and patient groups. Furthermore, in the patient group, statistical correlations were assessed at a voxel level between LTR‐DCE MRI‐derived HEF, T1 post‐Gadoxetate administration, ΔT1%, and various IVIM parameters using Pearson correlation.ResultsFor‐vitro measurements, the maximum coefficient of variation of the ADC and T1 parameters was 12.4% and 16.1%, respectively. The results also showed that Fp and Dfast were able to distinguish between healthy liver function and mild liver function impairment at the global level, with p = 0.002 for Fp and p < 0.001 for Dfast. Within the patient group, these parameters also exhibited a moderate correlation with HEF at the voxel level.ConclusionOverall, the study highlighted the potential of Dfast and Fp for detecting liver function impairment at both global and pixel levels.

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Impact of prior distributions and central tendency measures on Bayesian intravoxel incoherent motion model fitting

Cited by 49 publications

References 23 publications

Comparison of methods for estimation of the intravoxel incoherent motion (IVIM) diffusion coefficient (D) and perfusion fraction (f)

Comparison of methods for estimation of the intravoxel incoherent motion (IVIM) diffusion coefficient (D) and perfusion fraction (f)

Accuracy of breast cancer lesion classification using intravoxel incoherent motion diffusion‐weighted imaging is improved by the inclusion of global or local prior knowledge with bayesian methods

Non‐contrast based approach for liver function quantification using Bayesian‐based intravoxel incoherent motion diffusion weighted imaging: A pilot study

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