Spatially-constrained probability distribution model of incoherent motion (SPIM) for abdominal diffusion-weighted MRI

Kurugol, Sila; Freiman, Moti; Afacan, Onur; Peréz-Rosselló, Jeannette M.; Callahan, Michael J.; Warfield, Simon K.

doi:10.1016/j.media.2016.03.009

Cited by 17 publications

(29 citation statements)

References 33 publications

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“…Instead, we solve it as a simultaneous optimization problem, where registration, reconstruction of the high SNR DW-MRI images, and estimation of the signal decay model parameters are iteratively performed (TABLE 1). We used the recently proposed spatially-constrained probability distribution model of diffusion (Kurugol et al, 2016) as the signal decay model ( g (Θ , i )), and the non-rigid diffeomorphic block-matching algorithm by Commowick et al (2012) for registration.…”

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

“…Instead of the bi-exponential IVIM signal decay model, we use the recently proposed spatially-constrained probability distribution model of slow and fast diffusion (SPIM) (Kurugol et al, 2016) to robustly estimate the fast and slow diffusion parameters (Θ) of the signal decay model ( g (Θ , i )).…”

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

“…Gamma pdf has 2 parameters, one shape parameter k and a scale parameter s . The signal decay model using the mixture of Gamma pdfs can be simplified into this form (Kurugol et al, 2016):

g false(normalΘ, i false) = S o false(f \exp false(- k_{1} \log false(1 + b_{i} s_{1} false) false) + false(1 - f false) false(\exp false(- k_{2} \log false(1 + b_{i} s_{2} false) false) false) false) .

where the parameters Θ={ So , f , k 1 , s 1 , k 2 , s 2 } can be estimated by solving Eqn. 2.…”

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

“…Specifically, the mean values of the Gamma distributions were initialized using the D and D* parameters of the IVIM model, respectively and their mixing proportion was initialized using the f parameter. The standard deviation of the Gamma distributions were initialized to be 0.1 for slow and 1 for fast diffusion component as described in (Kurugol et al, 2016). …”

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

“…A spatial homogeneity prior has been added to this model (Kurugol et al, 2014) to obtain reliable estimates of parameters using the Fusion Bootstrap Moves (FBM) algorithm proposed by Freiman et al (Freiman et al, 2013) for the spatially-constrained IVIM model. This spatially-constrained probability model of incoherent motion (SPIM) has been shown to characterize the entire scale of diffusion reflecting the tissue microstructure while increasing the precision and reproducibility of parameter estimation in low signal-to-noise ratio (SNR) DW-MRI images (Kurugol et al, 2016). None of these signal decay models, however, consider the presence of the respiratory, cardiac and peristalsis motion, which causes misalignment between image volumes, acquired at multiple b-values and deteriorates the accuracy and robustness of parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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Motion-robust parameter estimation in abdominal diffusion-weighted MRI by simultaneous image registration and model estimation

Kurugol

Freiman

Afacan

et al. 2017

Medical Image Analysis

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Quantitative body DW-MRI can detect abdominal abnormalities as well as monitor response-to-therapy for applications including cancer and inflammatory bowel disease with increased accuracy. Parameter estimates are obtained by fitting a forward model of DW-MRI signal decay to the observed data acquired with several b-values. The DW-MRI signal decay models typically used do not account for respiratory, cardiac and peristaltic motion, however, which may deteriorate the accuracy and robustness of parameter estimates. In this work, we introduce a new model of DW-MRI signal decay that explicitly accounts for motion. Specifically, we estimated motion-compensated model parameters by simultaneously solving image registration and model estimation (SIR-ME) problems utilizing the interdependence of acquired volumes along the diffusion-weighting dimension. To accomplish this, we applied the SIR-ME model to the in-vivo DW-MRI data sets of 26 Crohn’s disease (CD) patients and achieved improved precision of the estimated parameters by reducing the coefficient of variation by 8%, 24% and 8% for slow diffusion (D), fast diffusion (D*) and fast diffusion fraction (f) parameters respectively, compared to parameters estimated with independent registration in normal-appearing bowel regions. Moreover, the parameters estimated with the SIR-ME model reduced the error rate in classifying normal and abnormal bowel loops to 12% for D and 10% for f parameter with a reduction in error rate by 13% and 11% for D and f parameters, respectively, compared to the error rate in classifying parameter estimates obtained with independent registration. The experiments in DW-MRI of liver in 20 subjects also showed that the SIR-ME model improved the precision of parameter estimation by reducing the coefficient of variation to 7% for D, 23% for D*, and 8% for the f parameter. Using the SIR-ME model, the coefficient of variation was reduced by 4%, 14% and 6% for D, D* and f parameters, respectively, compared to parameters estimated with independent registration. These results demonstrate that the proposed SIR-ME model improves the accuracy and robustness of quantitative body DW-MRI in characterizing tissue microstructure.

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Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

“…Gamma pdf has 2 parameters, one shape parameter k and a scale parameter s . The signal decay model using the mixture of Gamma pdfs can be simplified into this form (Kurugol et al, 2016):

g false(normalΘ, i false) = S o false(f \exp false(- k_{1} \log false(1 + b_{i} s_{1} false) false) + false(1 - f false) false(\exp false(- k_{2} \log false(1 + b_{i} s_{2} false) false) false) false) .

where the parameters Θ={ So , f , k 1 , s 1 , k 2 , s 2 } can be estimated by solving Eqn. 2.…”

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

Section: Simultaneous Image Registration and Model Estimation (Sirmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Motion-robust parameter estimation in abdominal diffusion-weighted MRI by simultaneous image registration and model estimation

Kurugol

Freiman

Afacan

et al. 2017

Medical Image Analysis

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Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration

Coll‐Font

Afacan

Hoge

et al. 2020

Magnetic Resonance Imaging

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Background Diffusion‐weighted MRI (DW‐MRI) of the kidneys is a technique that provides information about the microstructure of renal tissue without requiring exogenous contrasts such as gadolinium, and it can be used for diagnosis in cases of renal disease and assessing response‐to‐therapy. However, physiological motion and large geometric distortions due to main B0 field inhomogeneities degrade the image quality, reduce the accuracy of quantitative imaging markers, and impede their subsequent clinical applicability. Purpose To retrospectively correct for geometric distortion for free‐breathing DW‐MRI of the kidneys at 3T, in the presence of a nonstatic distortion field due to breathing and bulk motion. Study Type Prospective. Subjects Ten healthy volunteers (ages 29–38, four females). Field Strength/Sequence 3T; DW‐MR dual‐echo echo‐planar imaging (EPI) sequence (10 b‐values and 17 directions) and a T2 volume. Assessment The distortion correction was evaluated subjectively (Likert scale 0–5) and numerically with cross‐correlation between the DW images at b = 0 s/mm2 and a T2 volume. The intravoxel incoherent motion (IVIM) and diffusion tensor (DTI) model‐fitting performance was evaluated using the root‐mean‐squared error (nRMSE) and the coefficient of variation (CV%) of their parameters. Statistical Tests Statistical comparisons were done using Wilcoxon tests. Results The proposed method improved the Likert scores by 1.1 ± 0.8 (P < 0.05), the cross‐correlation with the T2 reference image by 0.13 ± 0.05 (P < 0.05), and reduced the nRMSE by 0.13 ± 0.03 (P < 0.05) and 0.23 ± 0.06 (P < 0.05) for IVIM and DTI, respectively. The CV% of the IVIM parameters (slow and fast diffusion, and diffusion fraction for IVIM and mean diffusivity, and fractional anisotropy for DTI) was reduced by 2.26 ± 3.98% (P = 6.971 × 10−2), 11.24 ± 26.26% (P = 6.971 × 10−2), 4.12 ± 12.91% (P = 0.101), 3.22 ± 0.55% (P < 0.05), and 2.42 ± 1.15% (P < 0.05). Data Conclusion The results indicate that the proposed Di + MoCo method can effectively correct for time‐varying geometric distortions and for misalignments due to breathing motion. Consequently, the image quality and precision of the DW‐MRI model parameters improved. Level of Evidence 2 Technical Efficacy Stage 1

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Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Stabinska,

Wittsack,

Lerman

et al. 2023

Magnetic Resonance Imaging

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Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre‐urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion‐weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion‐weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non‐Gaussian diffusion, and hybrid IVIM‐DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI.Level of Evidence5Technical EfficacyStage 2

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Spatially-constrained probability distribution model of incoherent motion (SPIM) for abdominal diffusion-weighted MRI

Cited by 17 publications

References 33 publications

Motion-robust parameter estimation in abdominal diffusion-weighted MRI by simultaneous image registration and model estimation

Motion-robust parameter estimation in abdominal diffusion-weighted MRI by simultaneous image registration and model estimation

Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

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