Minimizing the Acquisition Time for Intravoxel Incoherent Motion Magnetic Resonance Imaging Acquisitions in the Liver and Pancreas:

Intravoxel incoherent motion diffusion-weighted imaging of pancreas: Probing evidence of β-cell dysfunction in asymptomatic adults with hyperglycemia in vivo

PURPOSE

Early evaluation of β-cell dysfunction of hyperglycemic patients in asymptomatic adults would be valuable for timely prevention of the diabetes. This study aimed to evaluate functional changes in the pancreas using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and determine whether it could be used as a non-invasive method of assessing β-cell dysfunction.

METHODS

This prospective cohort study was conducted from August 2022 to November 2022 in Jinan University Affiliated Guangdong Second General Hospital. Three groups were enrolled and underwent IVIM-DWI: confirmed patients with type 2 diabetes (T2DM); hyperglycemic patients in asymptomatic adults; and the volunteers with normal glucose tolerance (NGT). Imaging parameters were obtained: apparent diffusion coefficient (ADC), the true diffusion coefficient (Dt), the pseudo-diffusion coefficient (Dp), and the perfusion fraction (f). The β-cell function indexes were calculated from blood examinations: composite insulin sensitivity index (ISI), 60-min insulinogenic index (IGI60), and the disposition index (DI). We compared imaging parameters among three groups, calculated the diagnostic performance of them for differentiating different groups, and the reproducibility of them was evaluated using intraclass correlation coefficient (ICC).

RESULTS

The imaging parameters except f gradually decreased among the groups with significant differences for ADC (p < 0.0001), Dt (p < 0.0001), and Dp (p = 0.013). Dt demonstrated the best diagnostic performance for differentiating asymptomatic patients from NGT (Area Under Curve [AUC] = 0.815, p < 0.0001). IVIM-DWI parameters correlated with composite ISI and DI, of which, Dt has the highest correlation with DI (Pearson correlation coefficient [r] = 0.546, p < 0.0001). The ICC of IVIM-DWI parameters was very good, Dt was highest (Interobserver ICC = 0.938, 95% Confidence Interval [CI], 0.899-0.963; Intraobserver ICC = 0.941, 95% CI, 0.904-0.965).

CONCLUSION

IVIM-DWI is a non-invasive quantitative method that can identify β-cell dysfunction in the pancreas.

Application of Intravoxel Incoherent Motion in the Evaluation of Hepatocellular Carcinoma after Transarterial Chemoembolization

(1) Background: To assess the efficacy of the quantitative parameters of intravoxel incoherent motion (IVIM) diffusion-weighted imaging for hepatocellular carcinoma (HCC) diagnosis after transarterial chemoembolization (TACE). (2) Methods: Fifty HCC patients after TACE were included and underwent MRI. All of the patients were scanned with the IVIM-DWI sequence and underwent TACE retreatment within 1 week. Referring to digital subtraction angiography (DSA) and MR enhanced images, two readers measured the f, D, and D* values of the tumor active area (TAA), tumor necrotic area (TNA), and adjacent normal hepatic parenchyma (ANHP). Then, the distinctions of the TAA, TNA, and ANHP were compared and we analyzed the differential diagnosis of the parameters in three tissues. (3) Results: For values of f and D, there were significant differences between any of the TAA, TNA, and ANHP (p < 0.05). The values of f and D were the best indicators for identifying the TAA and TNA, with AUC values of 0.959 and 0.955, respectively. The values of f and D performed well for distinguishing TAA from ANHP, with AUC values of 0.835 and 0.753, respectively. (4) Conclusions: Quantitative IVIM-DWI was effective for evaluating tumor viability in HCC patients treated with TACE and may be helpful for non-invasive monitoring of the tumor viability.

89Zr-Labeled High-Density Lipoprotein Nanoparticle PET Imaging Reveals Tumor Uptake in Patients with Esophageal Cancer

Nanomedicine holds promise for the delivery of therapeutic and imaging agents to improve cancer treatment outcomes. Preclinical studies have demonstrated that high-density lipoprotein (HDL) nanoparticles accumulate in tumor tissue on intravenous administration. Whether this HDL-based nanomedicine concept is feasible in patients is unexplored. Using a multimodal imaging approach, we aimed to assess tumor uptake of exogenously administered HDL nanoparticles in patients with esophageal cancer. Methods: The HDL mimetic CER-001 was radiolabeled using ⁸⁹Zr to allow for PET/CT imaging. Patients with primary esophageal cancer staged T2 and above were recruited for serial ⁸⁹Zr-HDL PET/CT imaging before starting chemoradiation therapy. In addition, patients underwent routine ¹⁸F-FDG PET/CT and 3-T MRI scanning (diffusion-weighted imaging/intravoxel incoherent motion imaging and dynamic contrast-enhanced MRI) to assess tumor glucose metabolism, tumor cellularity and microcirculation perfusion, and tumor vascular permeability. Tumor biopsies were analyzed for the expression of HDL scavenger receptor class B1 and macrophage marker CD68 using immunofluorescence staining. Results: Nine patients with adenocarcinoma or squamous cell carcinoma underwent all study procedures. After injection of ⁸⁹Zr-HDL (39.2 ± 1.2 [mean ± SD] MBq), blood-pool SUV_mean decreased over time (11.0 ± 1.7, 6.5 ± 0.6, and 3.3 ± 0.5 at 1, 24, and 72 h, respectively), whereas liver and spleen SUV_mean remained relatively constant (4.1 ± 0.6, 4.0 ± 0.8, and 4.3 ± 0.8 at 1, 24, and 72 h, respectively, for the liver; 4.1 ± 0.3, 3.4 ± 0.3, and 3.1 ± 0.4 at 1, 24, and 72 h, respectively, for the spleen) and kidney SUV_mean markedly increased over time (4.1 ± 0.9, 9.3 ± 1.4, and 9.6 ± 2.0 at 1, 24, and 72 h, respectively). Tumor uptake (SUV_peak) increased over time (3.5 ± 1.1 and 5.5 ± 2.1 at 1 and 24 h, respectively [P = 0.016]; 5.7 ± 1.4 at 72 h [P = 0.001]). The effective dose of ⁸⁹Zr-HDL was 0.523 ± 0.040 mSv/MBq. No adverse events were observed after the administration of ⁸⁹Zr-HDL. PET/CT and 3-T MRI measures of tumor glucose metabolism, tumor cellularity and microcirculation perfusion, and tumor vascular permeability did not correlate with tumor uptake of ⁸⁹Zr-HDL, suggesting that a specific mechanism mediated the accumulation of ⁸⁹Zr-HDL. Immunofluorescence staining of clinical biopsies demonstrated scavenger receptor class B1 and CD68 positivity in tumor tissue, establishing a potential cellular mechanism of action. Conclusion: To our knowledge, this was the first ⁸⁹Zr-HDL study in human oncology. ⁸⁹Zr-HDL PET/CT imaging demonstrated that intravenously administered HDL nanoparticles accumulated in tumors of patients with esophageal cancer. The administration of ⁸⁹Zr-HDL was safe. These findings may support the development of HDL nanoparticles as a clinical delivery platform for drug agents. ⁸⁹Zr-HDL imaging may guide drug development and serve as a biomarker for individualized therapy.

Directional and inter-acquisition variability in diffusion-weighted imaging and editing for restricted diffusion

Purpose

To evaluate and quantify inter‐directional and inter‐acquisition variation in diffusion‐weighted imaging (DWI) and emphasize signals that report restricted diffusion to enhance cancer conspicuity, while reducing the effects of local microscopic motion and magnetic field fluctuations.

Methods

Ten patients with biopsy‐proven prostate cancer were studied under an Institutional Review Board‐approved protocol. Individual acquisitions of DWI signal intensities were reconstructed to calculate inter‐acquisition distributions and their statistics, which were compared for healthy versus cancer tissue. A method was proposed to detect and filter the acquisitions affected by motion‐induced signal loss. First, signals that reflect restricted diffusion were separated from the acquisitions that suffer from signal loss, likely due to microscopic motion, by imposing a cutoff value. Furthermore, corrected apparent diffusion coefficient maps were calculated by employing a weighted sum of the multiple acquisitions, instead of conventional averaging. These weights were calculated by applying a soft‐max function to the set of acquisitions per‐voxel, making the analysis immune to acquisitions with significant signal loss, even if the number of such acquisitions is high.

Results

Inter‐acquisition variation is much larger than the Rician noise variance, local spatial variations, and the estimates of diffusion anisotropy based on the current data, as well as the published values of anisotropy. The proposed method increases the contrast for cancers and yields a sensitivity of 98.8% with a false positive rate of 3.9%.

Conclusion

Motion‐induced signal loss makes conventional signal‐averaging suboptimal and can obscure signals from areas with restricted diffusion. Filtering or weighting individual acquisitions prior to image analysis can overcome this problem.

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Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

PURPOSE

Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients.

METHOD

In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.

RESULTS

In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations.

CONCLUSION

IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Age and gender dependence of liver diffusion parameters and the possibility that intravoxel incoherent motion modeling of the perfusion component is constrained by the diffusion component

The aim of this study was to establish reference values for middle-aged subjects and to investigate the age and gender dependence of liver diffusion MRI parameters. The IVIM type of liver diffusion scan was based on a single-shot spin-echo-type echo-planar sequence using a 1.5 T magnet with 16 b-values. Diffusion-derived vessel density (DDVD)(b0b2) or DDVD(b0b10) was the signal difference between b = 0 and b = 2 (or b = 10) s/mm² images after removing visible vessels. IVIM analysis was performed with full fitting and segmented fitting, and with a threshold b-value of 60 or 200 s/mm² , and fitting started from b = 2 s/mm² . Thirty-one men (age range: 25-71 years) and 26 men (age: 22-69 years) had DDVD and IVIM analysis, respectively, while 37 women (age: 20-71 years) and 36 women (age: 20-71 years) had DDVD and IVIM analysis, respectively. DDVD results showed a significant age-related reduction for women. IVIM results for full fitting showed excellent agreement with those for segmented fitting using a threshold b of 60 s/mm² , but this was less good for results with a threshold b of 200 s/mm² . As age increased, female subjects' D_slow measure showed a significant reduction, while their PF and D_fast measures showed a significant increase. For the age group of 40-55 years, DDVD(b0b2), DDVD(b0b10), D_slow , PF and D_fast were 12.26 ± 3.90 au/pixel, 16.95 ± 5.45 au/pixel, 1.072 ± 0.067 (10^-3 mm² /s), 0.141 ± 0.025 and 61.0 ± 14.0 (10^-3 mm² /s) for men, and 13.35 ± 3.6 au/pixel, 17.20 ± 3.62 au/pixel, 1.069 ± 0.074 (10^-3 mm² /s), 0.119 ± 0.014 and 57.1 ± 13.2 (10^-3 mm² /s) for women, respectively. DDVD measure of this study suggest that aging is associated with a reduction in liver perfusion. There is a possibility that a lower D_slow measure is associated with artificially higher PF and D_fast measures, and that IVIM modeling of the perfusion component is constrained by the diffusion component.

Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations

Diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) have been explored to assess liver tumors and diffused liver diseases. IVIM reflects the microscopic translational motions that occur in voxels in magnetic resonance (MR) DWI. In biologic tissues, molecular diffusion of water and microcirculation of blood in the capillary network can be assessed using IVIM DWI. The most commonly applied model to describe the DWI signal is a bi-exponential model, with a slow compartment of diffusion linked to pure molecular diffusion (represented by the coefficient D_slow), and a fast compartment of diffusion, related to microperfusion (represented by the coefficient D_fast). However, high variance in D_fast estimates has been consistently shown in literature for liver IVIM, restricting its application in clinical practice. This variation could be explained by the presence of another very fast compartment of diffusion in the liver. Therefore, a tri-exponential model would be more suitable to describe the DWI signal. This article reviews the published evidence of the existence of this additional very fast diffusion compartment and discusses the performance and limitations of the tri-exponential model for liver IVIM in current clinical settings.

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network

Purpose

The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least‐squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM‐NET, and a version of the neural network modified to increase consistency, IVIM‐NET_mod.

Methods

Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient Dt, perfusion fraction fp, and pseudo‐diffusion coefficient Dp) from each fit method were determined in the tonsils and in the pterygoid muscles. Within‐subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance.

Results

The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of Dt in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM‐NET, and 11.2% for IVIM‐NET_mod. However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM‐NET were 15% for both Dt and fp, and 94% for Dp; for IVIM‐NET_mod, these values improved to 5%, 9%, and 62%, respectively.

Conclusion

Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck.

Spectral diffusion analysis of kidney intravoxel incoherent motion MRI in healthy volunteers and patients with renal pathologies

PURPOSE

To assess the feasibility of measuring tubular and vascular signal fractions in the human kidney using nonnegative least-square (NNLS) analysis of intravoxel incoherent motion data collected in healthy volunteers and patients with renal pathologies.

METHODS

MR imaging was performed at 3 Tesla in 12 healthy subjects and 3 patients with various kidney pathologies (fibrotic kidney disease, failed renal graft, and renal masses). Relative signal fractions f and mean diffusivities of the diffusion components in the cortex, medulla, and renal lesions were obtained using the regularized NNLS fitting of the intravoxel incoherent motion data. Test-retest repeatability of the NNLS approach was tested in 5 volunteers scanned twice.

RESULTS

In the healthy kidneys, the NNLS method yielded diffusion spectra with 3 distinguishable components that may be linked to the slow tissue water diffusion, intermediate tubular and vascular flow, and fast blood flow in larger vessels with the relative signal fractions, f_slow , f_interm and f_fast , respectively. In the pathological kidneys, the diffusion spectra varied substantially from those acquired in the healthy kidneys. Overall, the renal cyst showed substantially higher f_interm and lower f_slow , whereas the fibrotic kidney, failed renal graft, and renal cell carcinoma demonstrated the opposite trend.

CONCLUSION

NNLS-based intravoxel incoherent motion could potentially become a valuable tool in assessing changes in tubular and vascular volume fractions under pathophysiological conditions.

An optimized b-value distribution for triexponential intravoxel incoherent motion (IVIM) in the liver

PURPOSE

To find an optimized b-value distribution for reproducible triexponential intravoxel incoherent motion (IVIM) exams in the liver.

METHODS

A numeric optimization of b-value distributions was performed using the triexponential IVIM equation and 27 different IVIM parameter sets. Starting with an initially optimized distribution of 6 b-values, the number of b-values was increased stepwise. Each new b-value was chosen from a set of 64 predefined b-values based on the computed summed relative mean error of the fitted triexponential IVIM parameters. This process was repeated for up to 100 b-values. In simulations and in vivo measurements, optimized b-value distributions were compared to 4 representative distributions found in literature.

RESULTS

The first 16 optimized b-values were 0, 0.3, 0.3, 70, 200, 800, 70, 1, 3.5, 5, 70, 1.2, 6, 45, 1.5, and 60 in units of s/mm² . Low b-values were much more frequent than high b-values. The optimized b-value distribution resulted in a higher fit stability compared to distributions used in literature in both, simulation and in vivo measurements. Using more than 6 b-values, ideally 16 or more, increased the fit stability considerably.

CONCLUSION

Using optimized b-values, the fit uncertainty in triexponential IVIM can be largely reduced. Ideally, 16 or more b-values should be acquired.

Pathological validation and prognostic potential of quantitative MRI in the characterization of pancreas cancer: preliminary experience

Patient stratification based on biological variation in pancreatic ductal adenocarcinoma (PDAC) subtypes could help to improve clinical outcome. However, noninvasive assessment of the entire tumor microenvironment remains challenging. In this study, we investigate the biological basis of dynamic contrast-enhanced (DCE), intravoxel incoherent motion (IVIM), and R2*-derived magnetic resonance imaging (MRI) parameters for the noninvasive characterization of the PDAC tumor microenvironment and evaluate their prognostic potential in PDAC patients. Patients diagnosed with treatment-naïve resectable PDAC underwent MRI. After resection, a whole-mount tumor slice was analyzed for collagen fraction, vessel density, and hypoxia and matched to the MRI parameter maps. MRI parameters were correlated to immunohistochemistry-derived tissue characteristics and evaluated for prognostic potential. Thirty patients were included of whom 21 underwent resection with whole-mount histology available in 15 patients. DCE Ktrans and ve , ADC, and IVIM D correlated with collagen fraction. DCE kep and IVIM f correlated with vessel density and R2* with tissue hypoxia. Based on MRI, two main PDAC phenotypes could be distinguished; a stroma-high phenotype demonstrating high vessel density and high collagen fraction and a stroma-low phenotype demonstrating low vessel density and low collagen fraction. Patients with the stroma-high phenotype (high kep and high IVIM D, n = 8) showed longer overall survival (not reached vs. 14 months, P = 0.001, HR = 9.1, P = 0.004) and disease-free survival (not reached vs. 2 months, P < 0.001, HR 9.3, P = 0.003) compared to the other patients (n = 22). Median follow-up was 41 (95% CI: 36-46) months. MRI was able to accurately characterize tumor collagen fraction, vessel density, and hypoxia in PDAC. Based on imaging parameters, a subgroup of patients with significantly better prognosis could be identified. These first results indicate that stratification-based MRI-derived biomarkers could help to tailor treatment and improve clinical outcome and warrant further research.

An optimal acquisition and post-processing pipeline for hybrid IVIM-DKI in head and neck

Purpose

To optimize the diffusion‐weighting b values and postprocessing pipeline for hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region.

Methods

Optimized diffusion‐weighting b value sets ranging between 5 and 30 b values were constructed by optimizing the Cramér‐Rao lower bound of the hybrid intravoxel incoherent motion diffusion kurtosis imaging model. With this model, the perfusion fraction, pseudodiffusion coefficient, diffusion coefficient, and kurtosis were estimated. Sixteen volunteers were scanned with a reference b value set and 3 repeats of the optimized sets, of which 1 with volunteers swallowing on purpose.

The effects of (1) b value optimization and number of b values, (2) registration type (none vs. intervolume vs. intra‐ and intervolume registration), and (3) manual swallowing artifact rejection on the parameter precision were assessed.

Results

The SD was higher in the reference set for perfusion fraction, diffusion coefficient, and kurtosis by a factor of 1.7, 1.5, and 2.3 compared to the optimized set, respectively. A smaller SD (factor 0.7) was seen in pseudodiffusion coefficient. The sets containing 15, 20, and 30 b values had comparable repeatability in all parameters, except pseudodiffusion coefficient, for which set size 30 was worse. Equal repeatability for the registration approaches was seen in all parameters of interest. Swallowing artifact rejection removed the bias when present.

Conclusion

To achieve optimal hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region, b value optimization and swallowing artifact image rejection are beneficial. The optimized set of 15 b values yielded the optimal protocol efficiency, with a precision comparable to larger b value sets and a 50% reduction in scan time.

Optimal acquisition scheme for flow-compensated intravoxel incoherent motion diffusion-weighted imaging in the abdomen: An accurate and precise clinically feasible protocol

PURPOSE

Flow-compensated (FC) diffusion-weighted MRI (DWI) for intravoxel-incoherent motion (IVIM) modeling allows for a more detailed description of tissue microvasculature than conventional IVIM. The long acquisition time of current FC-IVIM protocols, however, has prohibited clinical application. Therefore, we developed an optimized abdominal FC-IVIM acquisition with a clinically feasible scan time.

METHODS

Precision and accuracy of the FC-IVIM parameters were assessed by fitting the FC-IVIM model to signal decay curves, simulated for different acquisition schemes. Diffusion-weighted acquisitions were added subsequently to the protocol, where we chose the combination of b-value, diffusion time and gradient profile (FC or bipolar) that resulted in the largest improvement to its accuracy and precision. The resulting two optimized FC-IVIM protocols with 25 and 50 acquisitions (FC-IVIMopt25 and FC-IVIMopt50 ), together with a complementary acquisition consisting of 50 diffusion-weighting (FC-IVIMcomp ), were acquired in repeated abdominal free-breathing FC-IVIM imaging of seven healthy volunteers. Intersession and intrasession within-subject coefficient of variation of the FC-IVIM parameters were compared for the liver, spleen, and kidneys.

RESULTS

Simulations showed that the performance of FC-IVIM improved in tissue with larger perfusion fraction and signal-to-noise ratio. The scan time of the FC-IVIMopt25 and FC-IVIMopt50 protocols were 8 and 16 min. The best in vivo performance was seen in FC-IVIMopt50 . The intersession within-subject coefficients of variation of FC-IVIMopt50 were 11.6%, 16.3%, 65.5%, and 36.0% for FC-IVIM model parameters diffusivity, perfusion fraction, characteristic time and blood flow velocity, respectively.

CONCLUSIONS

We have optimized the FC-IVIM protocol, allowing for clinically feasible scan times (8-16 min).

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

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 D_slow  = 1.14 × 10^-3  mm² /s, D_fast  = 193.6 × 10^-3  mm² /s, and perfusion fraction (PF) = 16.9%, and segmented fitting showed D_slow  = 0.98 × 10^-3  mm² /s, D_fast  = 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 D_fast ; 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 D_fast , but offers a more accurate estimation of D_slow .

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy

Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)—known as Microstructure Imaging—has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a “building block”-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.

Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images

Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method.

On the Field Strength Dependence of Bi- and Triexponential Intravoxel Incoherent Motion (IVIM) Parameters in the Liver

BACKGROUND

Studies on intravoxel incoherent motion (IVIM) imaging are carried out with different acquisition protocols.

PURPOSE

To investigate the dependence of IVIM parameters on the B₀ field strength when using a bi- or triexponential model.

STUDY TYPE

Prospective.

STUDY POPULATION

20 healthy volunteers (age: 19-28 years).

FIELD STRENGTH/SEQUENCE

Volunteers were examined at two field strengths (1.5 and 3T). Diffusion-weighted images of the abdomen were acquired at 24 b-values ranging from 0.2 to 500 s/mm² .

ASSESSMENT

ROIs were manually drawn in the liver. Data were fitted with a bi- and a triexponential IVIM model. The resulting parameters were compared between both field strengths.

STATISTICAL TESTS

One-way analysis of variance (ANOVA) and Kruskal-Wallis test were used to test the obtained IVIM parameters for a significant field strength dependency.

RESULTS

At b-values below 6 s/mm² , the triexponential model provided better agreement with the data than the biexponential model. The average tissue diffusivity was D = 1.22/1.00 μm² /msec at 1.5/3T. The average pseudodiffusion coefficients for the biexponential model were D^* = 308/260 μm² /msec at 1.5/3T; and for the triexponential model D 1 * = 81.3/65.9 μm² /msec, D 2 * = 2453/2333 μm² /msec at 1.5/3T. The average perfusion fractions for the biexponential model were f = 0.286/0.303 at 1.5/3T; and for the triexponential model f₁ = 0.161/0.174 and f₂ = 0.152/0.159 at 1.5/3T. A significant B₀ dependence was only found for the biexponential pseudodiffusion coefficient (ANOVA/KW P = 0.037/0.0453) and tissue diffusivity (ANOVA/KW: P < 0.001).

DATA CONCLUSION

Our experimental results suggest that triexponential pseudodiffusion coefficients and perfusion fractions obtained at different field strengths could be compared across different studies using different B₀ . However, it is recommended to take the field strength into account when comparing tissue diffusivities or using the biexponential IVIM model. Considering published values for oxygenation-dependent transversal relaxation times of blood, it is unlikely that the two blood compartments of the triexponential model represent venous and arterial blood.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1883-1892.

Quantitative diffusion magnetic resonance imaging in head and neck tumors

In patients with head and neck cancer, conventional anatomical magnetic resonance imaging (MRI) scans are commonly used for identification of primary lesion, assessment of structural distortion, and presence of metastatic lymph nodes. However, quantitative analysis of diffusion MRI can provide added value to structural and anatomical evaluation of head and neck tumors (HNT), by differentiation of primary malignant process, prognostic prediction, and treatment monitoring. In this article, we will review the applications of quantitative diffusion MRI in identification of primary malignant tissue, differentiation of tumor pathology, prediction of molecular phenotype, monitoring of treatment response, and evaluation of posttreatment changes in patient with HNT.

Removal of evidential motion-contaminated and poorly fitted image data improves IVIM diffusion MRI parameter scan-rescan reproducibility

Background It has been reported that intravoxel incoherent motion (IVIM) diffusion magnetic resonance imaging (MRI) scan-rescan reproducibility is unsatisfactory. Purpose To study IVIM MRI parameter reproducibility for liver parenchyma after the removal of motion-contaminated and/or poorly fitted image data. Material and Methods Eighteen healthy volunteers had liver scans twice in the same session to assess scan-rescan repeatability, and again in another session after an average interval of 13 days to assess reproducibility. Diffusion-weighted images were acquired with a 3-T scanner using respiratory-triggered echo-planar sequence and 16 b-values (0-800 s/mm²). Measurement was performed on the right liver with segment-unconstrained least square fitting. Image series with evidential anatomical mismatch, apparent artifacts, and poorly fitted signal intensity vs. b-value curve were excluded. A minimum of three slices was deemed necessary for IVIM parameter estimation. Results With a total 54 examinations, six did not satisfy inclusion criteria, leading to a success rate of 89%, and 14 volunteers were finally included for the repeatability/reproducibility study. A total of 3-10 slices per examination (mean = 5.3 slices, median = 5 slices) were utilized for analysis. Using threshold b-value = 80 s/mm², the coefficient of variation and within-subject coefficient of variation for repeatability were 2.86% and 3.36% for D_slow, 3.81% and 4.24% for perfusion fraction (PF), 18.16% and 24.88% for D_fast; and those for reproducibility were 2.48% and 3.24% for D_slow, 4.91% and 5.38% for PF, and 21.18% and 30.89% for D_fast. Conclusion Removal of motion-contaminated and/or poorly fitted image data improves IVIM parameter reproducibility.

Evaluation of Six Diffusion-weighted MRI Models for Assessing Effects of Neoadjuvant Chemoradiation in Pancreatic Cancer Patients

PURPOSE

To compare 6 diffusion-weighted imaging (DWI) MRI models for response evaluation in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

DWI images were acquired at 3T for b = 0-600 s/mm² in fourteen patients with advanced PDAC during 2 separate pretreatment sessions and 9 patients with (borderline) resectable PDAC pre and post neoadjuvant chemoradiation. Data was fitted with a mono-exponential (ADC), double mono-exponential to b = 0 and 100 s/mm² (ADC_fast), and b = 100 and 600 s/mm² (ADC_slow), IVIM model with D* free (D, f, D*) and fixed (D, f), tri-exponent (D, f₁, f₂), and stretched exponent model (DDC, α). Goodness of fit (adjusted R²), tumor to normal tissue contrast, repeatability (coefficient of variation), and parameter correlations (Spearman's rho) were assessed for the repeated measures. Treatment induced changes were assessed and compared to the repeatability.

RESULTS

The mono-exponential model had the lowest goodness of fit in both tumor (R² = 0.94) and normal-appearing pancreas (R² = 0.88). Tumour to normal tissue contrast was higher for the 'non-diffusion' parameters (ADC_fast, f, D*, f₁, f₂, α), with better repeatability for the diffusion parameters (ADC, ADC_slow, D, DDC). Diffusion parameters were strongly correlated between the models (rho ≥0.81) and showed a general treatment associated increase. All models were able to identify individual treatment effects, showing a change greater than the repeatability in 5 out of 9 patients for at least one of the parameters.

CONCLUSIONS

Individual treatment evaluation is possible with all investigated DWI models, with treatment associated changes exceeding the repeatability. The double monoexponential fit with ADC_fast and ADC_slow is able to discriminate between non-diffusion and diffusion related effects, is measured fast and can be performed on most commercial scanners, making it an attractive alternative for the more advanced multiparametric models in radiotherapy treatment evaluation.

Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients

The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman’s rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.

Cardiac-gated intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for the investigation of intracranial cerebrospinal fluid dynamics in the lateral ventricle: a feasibility study

PURPOSE

Intravoxel incoherent motion (IVIM) in diffusion-weighted magnetic resonance imaging (DW-MRI) attributes the signal attenuation to the molecular diffusion and to a faster pseudo-diffusion. Purpose of the study was to demonstrate the feasibility of IVIM for the investigation of intracranial cerebrospinal fluid (CSF) dynamics.

METHODS

Cardiac-gated DW-MRI images with fifteen b-values (0-1300s/mm²) along three orthogonal directions (mediolateral (ML), anteroposterior (AP), and craniocaudal (CC)) were acquired during maximum systole and diastole in 10 healthy volunteers (6 males, mean age 36 ± 15 years). A pixel-wise bi-exponential fitting with an iterative nonparametric algorithm was carried out to calculate the following parameters: diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast diffusion (f). Region of interest measurements were performed in both lateral ventricles. Comparison of IVIM parameters was performed among two cardiac cycle acquisitions and among the diffusion-encoding directions using a paired Student's t test.

RESULTS

f significantly (p < 0.05) depended on the diffusion-encoding direction and on the cardiac cycle (diastole AP 0.30 ± 0.13, ML 0.22 ± 0.12, CC 0.26 ± 0.17; systole AP 0.45 ± 0.17, ML 0.34 ± 0.15, CC 0.40 ± 0.21). Neither a cardiac cycle nor a direction dependency was found among mean D values (which is in line with the expected intraventricular isotropic diffusion) and D* values (p > 0.05 each).

CONCLUSION

The fraction of fast diffusion from IVIM is feasible to detect a direction-dependent and cardiac-dependent pulsatile CSF flow within the lateral ventricles allowing for quantitative monitoring of CSF dynamics. This technique might provide opportunities to further investigate the pathophysiology of various neurological disorders involving altered CSF dynamics.

A Combined Use of Intravoxel Incoherent Motion MRI Parameters Can Differentiate Early-Stage Hepatitis-b Fibrotic Livers from Healthy Livers

This study investigated a combined use of intravoxel incoherent motion (IVIM) parameters, Dslow ( D), PF ( f), and Dfast ( D*), for liver fibrosis evaluation. Sixteen healthy volunteers (F0) and 33 hepatitis-b patients (stage F1 = 15, stage F2-4 = 18) were included. With a 1.5 T MR scanner and respiration gating, IVIM diffusion-weighted imaging was acquired using a single-shot echo-planar imaging sequence with 10 b values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm². Signal measurement was performed on right liver parenchyma. With a three-dimensional tool, Dslow, PF, and Dfast values were placed along the x axis, y axis, and z axis, and a plane was defined to separate healthy volunteers from patients. The three-dimensional tool demonstrated that healthy volunteers and all patients with liver fibrosis could be separated. Classification and regression tree showed that a combination of PF (PF < 12.55%), Dslow (Dslow < 1.152 × 10^-3 mm²/s), and Dfast (Dfast < 13.36 × 10^-3 mm²/s) could differentiate healthy subjects and all fibrotic livers (F1-4) with an area under the curve of logistic regression (AUC) of 0.986. The AUC for differentiation of healthy livers versus F2-4 livers was 1. PF offered the best diagnostic value, followed by Dslow; however, all three parameters of PF, Dslow, and Dfast contributed to liver fibrosis detection.

Quantitative assessment of biliary stent artifacts on MR images: Potential implications for target delineation in radiotherapy

PURPOSE

Biliary stents may cause susceptibility artifacts, gradient-induced artifacts, and radio frequency (RF) induced artifacts on magnetic resonance images, which can hinder accurate target volume delineation in radiotherapy. In this study, the authors investigated and quantified the magnitude of these artifacts for stents of different materials.

METHODS

Eight biliary stents made of nitinol, platinum-cored nitinol, stainless steel, or polyethylene from seven vendors, with different lengths (57-98 mm) and diameters (3.0-11.7 mm), were placed in a phantom. To quantify the susceptibility artifacts sequence-independently, ΔB0-maps and T2^∗-maps were acquired at 1.5 and 3 T. To study the effect of the gradient-induced artifacts at 3 T, signal decay in images obtained with maximum readout gradient-induced artifacts was compared to signal decay in reference scans. To quantify the RF induced artifacts at 3 T, B1-maps were acquired. Finally, ΔB0-maps and T2^∗-maps were acquired at 3 T of two pancreatic cancer patients who had received platinum-cored nitinol biliary stents.

RESULTS

Outside the stent, susceptibility artifacts dominated the other artifacts. The stainless steel stent produced the largest susceptibility artifacts. The other stents caused decreased T2^∗ up to 5.1 mm (1.5 T) and 8.5 mm (3 T) from the edge of the stent. For sequences with a higher bandwidth per voxel (1.5 T: BW_vox > 275 Hz/voxel; 3 T: BW_vox > 500 Hz/voxel), the B0-related susceptibility artifacts were negligible (<0.2 voxels). The polyethylene stent showed no artifacts. In vivo, the changes in B0 and T2^∗ induced by the stent were larger than typical variations in B0 and T2^∗ induced by anatomy when the stent was at an angle of 30° with the main magnetic field.

CONCLUSIONS

Susceptibility artifacts were dominating over the other artifacts. The magnitudes of the susceptibility artifacts were determined sequence-independently. This method allows to include additional safety margins that ensure target irradiation.

Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation

A comprehensive literature review was performed on liver intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) technique and its applications. Heterogeneous data have been reported. IVIM parameters are magnetic field strength dependent to a mild extent. A lower Dslow (D) value at 3 T than at 1.5 T and higher perfusion fraction (PF) value at 3 T than at 1.5 T were noted. An increased number of b values are associated with increased IVIM parameter measurement accuracy. With the current status of art, IVIM technique is not yet capable of detecting early stage liver fibrosis and diagnosing liver fibrosis grades, nor can it differentiate liver tumors. Though IVIM parameters show promise for tumor treatment monitoring, till now how PF and Dfast (D*) add diagnostic value to Dslow or apparent diffusion coefficient (ADC) remains unclear. This paper shows the state-of-art IVIM MR technique is still not able to offer reliable measurement for liver. More works on the measurement robustness are warranted as they are essential to justify follow-up clinical studies on patients.

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

PURPOSE

To evaluate the influence of fitting methods on the accuracy and reliability of intravoxel incoherent motion (IVIM) parameters, with a particular emphasis on the constraint function.

MATERIALS AND METHODS

Diffusion-weighted (DW) imaging data were analyzed using IVIM-based full-fitting (simultaneous fit of all parameters) and segmented-fitting (step-by-step fit of each parameter), each with and without the constraint function, to estimate the molecular diffusion coefficient (D_slow ), perfusion fraction (f), and flow-related diffusion coefficient (D_fast ). Computational simulations were performed at variable signal-to-noise ratios to evaluate the relative error (RE) and coefficient of variation (CV) of the estimated IVIM parameters. DW imaging of the abdomen was performed twice at 1.5 Tesla using nine b-values (0-900 s/mm² ) in 12 health volunteers (6 men and 6 women; mean age: 30 years). The measurement repeatability of IVIM parameters in the liver and the pancreas was evaluated using the within-subject coefficient of variation (_w CV).

RESULTS

In simulations, full-fitting without the constraint function yielded the largest RE (P < 0.001 for D_slow and f; P ≤ 0.044 for D_fast ) and CV (P ≤ 0.033 for D_slow and f; P ≤ 0.473 for D_fast ) for IVIM parameters among all four algorithms. In volunteer imaging, full-fitting without the constraint function also resulted in the poorest repeatability for D_slow (_w CV, 17.12%-65.45%) and f (_w CV, 19.35%-42.84%) in the liver and pancreas, while the other algorithms had similar repeatability values (_w CV, 4.05%-11.99% for D_slow and 9.65%-18.66% for f). Measurement repeatability of D_fast (_w CV, 29.52%-85.01%) was the poorest among the IVIM parameters.

CONCLUSION

For accurate and reliable measurement of IVIM parameters, segmented fitting or full-fitting with the constraint function should be used for IVIM-based analysis of DW imaging.

LEVEL OF EVIDENCE

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