Quantitative Analysis of Intravoxel Incoherent Motion (IVIM) Diffusion <scp>MRI</scp> using Total Variation and Huber Penalty Function

Kayal, Esha Baidya; Kandasamy, Devasenathipathy; Khare, Kedar; Alampally, Jayendra Tiru; Bakhshi, Sameer; Sharma, Raju; Mehndiratta, Amit

doi:10.1002/mp.12520

Cited by 16 publications

(25 citation statements)

References 40 publications

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“…The conventional IVIM model is prone to produce high amount of noise in estimating perfusion parameters. Therefore, different advanced IVIM models have been proposed in recent years (30)(31)(32), which have improved the diagnostic accuracy of IVIM perfusion parameters. However, these advanced models were unfortunately not used in this study.…”

Section: Discussionmentioning

confidence: 99%

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions

Liu

Yang

et al. 2023

Front. Oncol.

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ObjectiveTo investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs).Materials and MethodsPatients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs.ResultsMost of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs.ConclusionsDKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.

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Section: Discussionmentioning

confidence: 99%

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions

Liu

Yang

et al. 2023

Front. Oncol.

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“…The IVIM bi‐exponential (BE) model 37 is defined as:

S_{b} / S_{0} = bold-italicf \cdot e^{- bold-italicb \cdot D^{*}} + ((), bold1 - bold-italicf) \cdot e^{- bold-italicb \cdot bold-italicD}

where D is the diffusion coefficient, D* is the perfusion coefficient and f is the perfusion fraction. Quantitative IVIM parameters were evaluated using a state‐of‐the‐art IVIM analysis method BE model with adaptive total variation (TV) penalty function (BE+TV) 43 . The BE+TV method uses nonlinear least‐square optimization for data fitting with adaptive penalty function, TV, for IVIM parametric reconstruction, leading to a good signal‐to‐noise ratio and reducing nonphysiological spatial inhomogeneity in estimated parametric images.…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative IVIM parameters were evaluated using a state-of-the-art IVIM analysis method BE model with adaptive total variation (TV) penalty function (BE+TV). 43 The BE+TV method uses nonlinear least-square optimization for data fitting with adaptive penalty function, TV, for IVIM parametric reconstruction, leading to a good signal-to-noise ratio and reducing nonphysiological spatial inhomogeneity in estimated parametric images.…”

Section: Quantitative Parameter Evaluationmentioning

confidence: 99%

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

et al. 2020

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The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tonedifference matrix (NGTDM) and run-length matrix (RLM). Receiver-operatingcharacteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/ terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D*

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“…There are several different methods with which to improve the robustness of parameter estimates in the presence of high noise. For example, constrained fitting methods, total variation‐based methods and Bayesian probability‐based methods were shown to provide improved parameter estimates. Several recent studies have also shown that denoising DW‐MRI data can improve the quality of parametric images .…”

Section: Introductionmentioning

confidence: 99%

Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

Huang

2020

NMR in Biomedicine

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In this study, we evaluate whether diffusion‐weighted magnetic resonance imaging (DW‐MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW‐MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b‐values ranging from 0 to 1000 s/mm2. DW‐MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo‐diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW‐MRI data using the biexponential model with the same b‐values. Rician noise was added to generate DW‐MRI data with various signal‐to‐noise ratio (SNR) levels. The experimental results showed that the denoised DW‐MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were <12% for PF, D and D*) in the presence of different levels of simulated Rician noise (SNRb=0 = 20‐40). Simulations and experiments show that brain DW‐MRI data after denoising can provide a reliable estimation of IVIM parameters.

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Quantitative Analysis of Intravoxel Incoherent Motion (IVIM) Diffusion MRI using Total Variation and Huber Penalty Function

Abstract: Bi-exponential model with penalty function showed quantitatively and qualitatively improved IVIM parameter estimation for both simulated and clinical dataset of bone tumors, thus potentially making this approach suitable for clinical applications in future.

Cited by 16 publications

References 40 publications

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

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