Intravoxel incoherent motion diffusion‐weighted MRI of the abdomen: The effect of fitting algorithms on the accuracy and reliability of the parameters

Park, Hyo Jung; Sung, Yu Sub; Lee, Seung Soo; Lee, Yedaun; Cheong, Hyunhee; Kim, Yeong Jae; Lee, Moon-Gyu

doi:10.1002/jmri.25535

Cited by 56 publications

(96 citation statements)

References 39 publications

(125 reference statements)

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“…In the real cases, the CV of the LSQ‐FULL approach was the worst for f and D ∗ estimation. From these results, and in agreement with a previous work, we may conclude that, if a least square approach is chosen, the segmented fitting should be preferred. Regarding the choice of constraints boundaries, here they were selected as adopted by While, in order to have a more reliable comparison between the results presented here and those reported in the literature.…”

Section: Discussionmentioning

confidence: 99%

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

et al. 2019

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The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification. The two segmented Bayesian approaches, with and without CAR specification, were compared with two standard least-square and a direct Bayesian fitting methods. All approaches were tested on simulated images and real data of patients with head-and-neck and rectal cancer. Estimation accuracy and maps noisiness were quantified on simulated images, whereas the coefficient of variation and the goodness of fit were evaluated for real data.Both versions of the segmented Bayesian approach outperformed the standard methods on simulated images for pseudo-diffusion (D * ) and perfusion fraction (f), whilst the segmented least-square fitting remained the less biased for the diffusion coefficient (D). On real data, Bayesian approaches provided the less noisy maps, and the two Bayesian methods without CAR generally estimated lower values for f and D * coefficients with respect to the other approaches.The proposed segmented Bayesian approaches were superior, in terms of estimation accuracy and maps quality, to the direct Bayesian model and the least-square fittings. The CAR method improved the estimation accuracy, especially for D * . KEYWORDS Bayesian Segmented Approach, Conditional Autoregressive Model, Diffusion-Weighted MRI, IVIM 1 INTRODUCTION Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a non-invasive technique that quantitatively characterizes the diffusion properties of water molecules. 1 Typically, the standard analysis of DW-MRI signals provides the estimation of the Apparent Diffusion Coefficient (ADC), which is estimated using a mono-exponential decay model of the signal intensity over b values. The Intra-Voxel Incoherent Motion (IVIM) model describes the incoherent motion of the water molecules in function of diffusion and perfusionproperties of a tissue at the same time. 2 In the IVIM model, the signal intensity in each voxel is modeled via a bi-exponential decay over b, characterized by a first fast component, which is related to tissue perfusion (the blood flow in the capillaries), followed by a second slow component, which is related to tissue molecular diffusion. Specifically, the IVIM model allows to estimate the diffusion coefficient (D), the pseudo-diffusion coefficient (D * ) and the perfusion volume fraction (f) within the tissue. The estimation of these coefficients can be made within a region of interest, by averaging the signal intensities over the voxels, or voxel-by-voxel to obtain parametric maps.NMR in Biomedicine. 2020;33:e4201. wil...

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Section: Discussionmentioning

confidence: 99%

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

et al. 2019

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“…The physiological motions, particularly those associated with respiration, complicate data acquisition for the long scan duration with multiple b ‐values . For the bi‐exponential IVIM model, a segmented approach, that is to calculate first D slow by linear regression, then perfusion fraction (PF) by extrapolation, and then D fast by nonlinear least squares fitting, has commonly been used, since simultaneous fitting of all diffusion parameters usually gives less stable results . However, segmented fitting remains an approximating approach that emphasizes the accuracy of D slow estimation while allowing less flexibility for D fast calculation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of tri‐exponential decay versus bi‐exponential decay and full fitting versus segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI

et al. 2019

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Objectives To determine whether bi‐ or tri‐exponential models, and full or segmented fittings, better fit the intravoxel incoherent motion (IVIM) imaging signal of healthy livers. Methods Diffusion‐weighted images were acquired with a 3 T scanner using a respiratory‐triggered echo‐planar sequence and 16 b‐values (0–800 s/mm2). Eighteen healthy volunteers had their livers scanned twice in the same session, and then once in another session. Liver parenchyma region‐of‐interest‐based measurements were processed with bi‐exponential and tri‐exponential models, with both full fitting and segmented fitting (threshold b‐value = 200 s/mm2). Results With the signal of all scans averaged, bi‐exponential model full fitting showed Dslow = 1.14 × 10−3 mm2/s, Dfast = 193.6 × 10−3 mm2/s, and perfusion fraction (PF) = 16.9%, and segmented fitting showed Dslow = 0.98 × 10−3 mm2/s, Dfast = 42.2 × 10−3 mm2/s, and PF = 23.3%. IVIM parameters derived from the tri‐exponential model were similar for full fitting and segmented fitting, with slow (D'slow = 0.98 × 10−3 mm2/s; F'slow = 76.4 or 76.6%), fast (D'fast = 15.1 or 15.4 × 10−3 mm2/s; F'fast = 11.8 or 11.7%) and very fast (D'Vfast = 445.0 or 448.8 × 10−3 mm2/s; F'Vfast = 11.8 or 11.7%) diffusion compartments. The tri‐exponential model provided an overall better fit than the bi‐exponential model. For the bi‐exponential model, full fitting provided a better fit at very low and low b‐values compared with segmented fitting, with the latter tending to underestimate Dfast; however, the segmented method demonstrated lower error in signal prediction for high b‐values. Compared with full fitting, tri‐exponential segmented fitting offered better scan‐rescan reproducibility. Conclusion For healthy liver, tri‐exponential modeling is preferred to bi‐exponential modeling. For the bi‐exponential model, segmented fitting underestimates Dfast, but offers a more accurate estimation of Dslow.

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“…[15,16,26]) and more recently for estimation limited to D and f [27,28]. However, comparison of estimation approaches for D and f only is, to our knowledge, limited to a single study in which the segmented approach was compared with least-squares fitting of the sIVIM model [16].…”

Section: Discussionmentioning

confidence: 99%

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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Intravoxel incoherent motion diffusion‐weighted MRI of the abdomen: The effect of fitting algorithms on the accuracy and reliability of the parameters

Abstract: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1637-1647.

Cited by 56 publications

References 39 publications

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

Comparison of tri‐exponential decay versus bi‐exponential decay and full fitting versus segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI

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

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