Calculation of intravoxel incoherent motion parameter maps using a kernelized total difference-based method

Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.

Radiologists' Rating for Comparative Qualitative Assessment of Intravoxel Incoherent Motion Using Novel Analysis Methods

OBJECTIVE

The objective was to assess qualitative interpretability and quantitative precision and reproducibility of intravoxel incoherent motion ( IVIM) parametric images evaluated using novel IVIM analysis methods for diagnostic accuracy.

METHODS

Intravoxel incoherent motion datasets of 55 patients (male/female = 41:14; age = 17.8 ± 5.5 years) with histopathology-proven osteosarcoma were analyzed. Intravoxel incoherent motion parameters-diffusion coefficient ( D ), perfusion fraction ( f ), and perfusion coefficient ( D* )-were estimated using 5 IVIM analysis methods-(i) biexponential (BE) model, (ii) BE-segmented fitting 2-parameter (BESeg-2), (iii) BE-segmented fitting 1-parameter (BESeg-1), (iv) BE model with total variation penalty function (BE + TV), and (v) BE model with Huber penalty function (BE + HPF). Qualitative scoring in a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) was performed by 2 radiologists for 4 criteria: (a) tumor shape and margin, (b) morphologic correlation, (c) noise suppression, and (d) overall interpretability. Interobserver agreement was evaluated using Spearman rank-order correlation ( rs ). Precision and reproducibility were evaluated using within-subject coefficient of variation (wCV) and between-subject coefficient of variation (bCV).

RESULTS

BE + TV and BE + HPF produced significantly ( P < 10 -3 ) higher qualitative scores for D (fair-good [3.3-3.8]) than BE (poor [2.3]) and for D* (poor-fair [2.2-2.7]) and f (fair-good [3.2-3.8]) than BE, BESeg-2, and BESeg-1 ( D* : uninterpretable-poor [1.3-1.9] and f : poor-fair [1.5-3]). Interobserver agreement for qualitative scoring was rs = 0.48-0.59, P < 0.009. BE + TV and BE + HPF showed significantly ( P < 0.05) improved reproducibility in estimating D (wCV: 24%-31%, bCV: 21%-31% improvement) than the BE method and D* (wCV: 4%-19%, bCV: 5%-19% improvement) and f (wCV: 25%-49%, bCV: 25%-47% improvement) than BE, BESeg-2, and BESeg-1 methods.

CONCLUSIONS

BE + TV and BE + HPF demonstrated qualitatively and quantitatively improved IVIM parameter estimation and may be considered for clinical use further.

Measuring Perfusion: Intravoxel Incoherent Motion MR Imaging

The signal acquired in vivo using a diffusion-weighted MR imaging (DWI) sequence is influenced by blood motion in the tissue. This means that perfusion information from a DWI sequence can be obtained in addition to thermal diffusion, if the appropriate sequence parameters and postprocessing methods are applied. This is commonly regrouped under the denomination intravoxel incoherent motion (IVIM) perfusion MR imaging. Of relevance, the perfusion information acquired with IVIM is essentially local, quantitative and acquired without intravenous injection of contrast media. The aim of this work is to review the IVIM method and its clinical applications.

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

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 (SNR_b=0 = 20-40). Simulations and experiments show that brain DW-MRI data after denoising can provide a reliable estimation of IVIM parameters.

A kernel-based image denoising method for improving parametric image generation

One of the main challenges in the pixel-wise modeling analysis is the presence of high noise levels. Wang and Qi proposed a kernel-based method for dynamic positron emission tomgraphy reconstruction. Inspired by this method, we propose a kernel-based image denoising method based on the minimization of a kernel-based lp-norm regularized problem. To solve the kernel-based image denoising problem, we used the general-threshold filtering algorithm in combination with total difference. In the present study, we investigated whether diffusion-weighted magnetic resonance imaging (DW-MRI) data denoised using the proposed method can provide improved intravoxel incoherent motion (IVIM) parametric images. We also compared the proposed method with the method using the local principal component analysis (LPCA). The simulated DW-MR magnitude images are assumed to have Rician distributed noise. Computer simulations show that the proposed image denoising method can achieve a better bias-variance trade-off than the LPCA method. Moreover, the proposed method can reduce variance while simultaneously preserving edges in the parametric images. We tested our image denoising method on in vivo DW-MRI data, and the result showed that the denoised DWI-MRI data obtained using the proposed method can substantially improve the quality of IVIM parametric images.

A general-threshold filtering method for improving intravoxel incoherent motion parameter estimates

In this study, we present an image denoising method for diffusion-weighted magnetic resonance imaging (DW-MRI) data. Our aim is to improve the estimation of intravoxel incoherent motion (IVIM) parameters using denoised DW-MRI data. A general-threshold filtering (GTF) reconstruction via total variation minimization has been proposed to improve image quality in few-view computed tomography. Here, we applied the combination of GTF and total difference to image denoising. Voxel-wise IVIM analysis was performed using both real and simulated DW-MRI data. Using an institutional review board-approved protocol with written informed consent, DW-MRI imaging was performed at a 3 T hybrid PET/MR system in 10 patients with Hodgkin lymphoma lesions. A simulated phantom consisting of four organs (liver, pancreas, spleen and kidney) was used to generate noisy DW-MRI data according to the IVIM model at different noise levels. DW-MRI data were denoised before IVIM parameter estimation. The proposed image denoising method was compared with the image denoising method using joint rank and edge constraints (JREC). The results of simulated data show that at the lower signal-to-noise ratios the proposed image denoising method outperformed the JREC method in terms of the accuracy and precision of the IVIM parameter estimates. The experimental results also show that the proposed image denoising method could yield better parametric images than the JREC method in terms of noise reduction and edge preservation.

Using 1H2O MR to measure and map sodium pump activity in vivo

The cell plasma membrane Na⁺,K⁺-ATPase [NKA] is one of biology's most [if not the most] significant enzymes. By actively transporting Na⁺ out [and K⁺ in], it maintains the vital trans-membrane ion concentration gradients and the membrane potential. The forward NKA reaction is shown in the Graphical Abstract [which is elaborated in the text]. Crucially, NKA does not operate in isolation. There are other transporters that conduct K⁺ back out of [II, Graphical Abstract] and Na⁺ back into [III, Graphical Abstract] the cell. Thus, NKA must function continually. Principal routes for ATP replenishment include mitochondrial oxidative phosphorylation, glycolysis, and creatine kinase [CrK] activity. However, it has never been possible to measure, let alone map, this integrated, cellular homeostatic NKA activity in vivo. Active trans-membrane water cycling [AWC] promises a way to do this with ¹H₂O MR. Inthe Graphical Abstract, the AWC system is characterized by active contributions totheunidirectional rate constants for steady-state water efflux and influx, respectively, k_io(a) and k_oi(a). The discovery, validation, and initial exploration of active water cycling are reviewed here. Promising applications in cancer, cardiological, and neurological MRI are covered. This initial work employed paramagnetic Gd(III)chelate contrast agents [CAs]. However, the significant problems associated with in vivo CA use are also reviewed. A new analysis of water diffusion-weighted MRI [DWI] is presented. Preliminary results suggest a non-invasive way to measure the cell number density [ρ (cells/μL)], the mean cell volume [V (pL)], and the cellular NKA metabolic rate [^cMR_NKA(fmol(ATP)/s/cell)] with high spatial resolution. These crucial cell biology properties have not before been accessible invivo. Furthermore, initial findings indicate their absolute values can be determined.