Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling

Reproducibility of intravoxel incoherent motion quantification in the liver across field strengths and gradient hardware

PURPOSE

To evaluate reproducibility and interlobar agreement of intravoxel incoherent motion (IVIM) quantification in the liver across field strengths and MR scanners with different gradient hardware.

METHODS

Cramer-Rao lower bound optimization was performed to determine optimized monopolar and motion-robust 2D (b-value and first-order motion moment [M1]) IVIM-DWI acquisitions. Eleven healthy volunteers underwent diffusion MRI of the liver, where each optimized acquisition was obtained five times across three MRI scanners. For each data set, IVIM estimates (diffusion coefficient (D), pseudo-diffusion coefficients (d 1 * $$ {d}_1^{\ast } $$ andd 2 * $$ {d}_2^{\ast } $$ ), blood velocity SDs (Vb1 and Vb2), and perfusion fractions [f1 and f2]) were obtained in the right and left liver lobes using two signal models (pseudo-diffusion and M1-dependent physical) with and without T2 correction (fc1 and fc2) and three fitting techniques (tri-exponential region of interest-based full and segmented fitting and blood velocity SD distribution fitting). Reproducibility and interlobar agreement were compared across methods using within-subject and pairwise coefficients of variation (CVw and CVp), paired sample t-tests, and Bland-Altman analysis.

RESULTS

Using a combination of motion-robust 2D (b-M1) data acquisition, M1-dependent physical signal modeling with T2 correction, and blood velocity SD distribution fitting, multiscanner reproducibility with median CVw = 5.09%, 11.3%, 9.20%, 14.2%, and 12.6% for D, Vb1, Vb2, fc1, and fc2, respectively, and interlobar agreement with CVp = 8.14%, 11.9%, 8.50%, 49.9%, and 42.0%, respectively, was achieved.

CONCLUSION

Recently proposed advanced IVIM acquisition, signal modeling, and fitting techniques may facilitate reproducible IVIM quantification in the liver, as needed for establishment of IVIM-based quantitative biomarkers for detection, staging, and treatment monitoring of diseases.

Comparison of methods for intravoxel incoherent motion parameter estimation in the brain from flow-compensated and non-flow-compensated diffusion-encoded data

PURPOSE

Joint analysis of flow-compensated (FC) and non-flow-compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain.

METHODS

Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non-linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b-values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning-based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b-values 0-200 s/mm2 and corresponding flow weighting factors 0-2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis.

RESULTS

Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning-based algorithm for IVIM parametersD $$ D $$ andf $$ f $$ , and for the Bayesian algorithm only forv d $$ {v}_d $$ , relative to the other methods.

CONCLUSION

A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning-based algorithms appear promising.

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.

Multisite MRI Intravoxel Incoherent Motion Repeatability and Reproducibility across 3 T Scanners in a Breast Diffusion Phantom: A BReast Intravoxel Incoherent Motion Multisite (BRIMM) Study

BACKGROUND

Monoexponential apparent diffusion coefficient (ADC) and biexponential intravoxel incoherent motion (IVIM) analysis of diffusion-weighted imaging is helpful in the characterization of breast tumors. However, repeatability/reproducibility studies across scanners and across sites are scarce.

PURPOSE

To evaluate the repeatability and reproducibility of ADC and IVIM parameters (tissue diffusivity (Dt), perfusion fraction (Fp) and pseudo-diffusion (Dp)) within and across sites employing MRI scanners from different vendors utilizing 16-channel breast array coils in a breast diffusion phantom.

STUDY TYPE

Phantom repeatability.

PHANTOM

A breast phantom containing tubes of different polyvinylpyrrolidone (PVP) concentrations, water, fat, and sponge flow chambers, together with an MR-compatible liquid crystal (LC) thermometer.

FIELD STRENGTH/SEQUENCE

Bipolar gradient twice-refocused spin echo sequence and monopolar gradient single spin echo sequence at 3 T.

ASSESSMENT

Studies were performed twice in each of two scanners, located at different sites, on each of 2 days, resulting in four studies per scanner. ADCs of the PVP and water were normalized to the vendor-provided calibrated values at the temperature indicated by the LC thermometer for repeatability/reproducibility comparisons.

STATISTICAL TESTS

ADC and IVIM repeatability and reproducibility within and across sites were estimated via the within-system coefficient of variation (wCV). Pearson correlation coefficient (r) was also computed between IVIM metrics and flow speed. A P value <0.05 was considered statistically significant.

RESULTS

ADC and Dt demonstrated excellent repeatability (<2%; <3%, respectively) and reproducibility (both <5%) at the two sites. Fp and Dp exhibited good repeatability (mean of two sites 3.67% and 5.59%, respectively) and moderate reproducibility (mean of two sites 15.96% and 13.3%, respectively). The mean intersite reproducibility (%) of Fp/Dp/Dt was 50.96/13.68/5.59, respectively. Fp and Dt demonstrated high correlations with flow speed while Dp showed lower correlations. Fp correlations with flow speed were significant at both sites.

DATA CONCLUSION

IVIM reproducibility results were promising and similar to ADC, particularly for Dt. The results were reproducible within both sites, and a progressive trend toward reproducibility across sites except for Fp.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method

BACKGROUND

Accurately fitting diffusion-time-dependent diffusion MRI (td-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition.

PURPOSE

Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework.

STUDY TYPE

Retrospective.

POPULATION

Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum.

FIELD STRENGTH/SEQUENCE

3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE).

ASSESSMENT

The Bayesian method's performance in fitting cell diameter (d $$ d $$ ), intracellular volume fraction (f in $$ {f}_{in} $$ ), and extracellular diffusion coefficient (D ex $$ {D}_{ex} $$ ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology.

STATISTICAL TESTS

T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05.

RESULTS

Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively ford $$ d $$ ,f in $$ {f}_{in} $$ , andD ex $$ {D}_{ex} $$ compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 forf in $$ {f}_{in} $$ compared to r = 0.698 using NLLS, P = 0.5764).

DATA CONCLUSION

The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling

PURPOSE

Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods.

METHODS

To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise.

RESULTS

Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction.

CONCLUSION

The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

Towards detection of early response in neoadjuvant chemotherapy of breast cancer using Bayesian intravoxel incoherent motion

Introduction

The early identification of good responders to neoadjuvant chemotherapy (NACT) holds a significant potential in the optimal treatment of breast cancer. A recent Bayesian approach has been postulated to improve the accuracy of the intravoxel incoherent motion (IVIM) model for clinical translation. This study examined the prediction and early sensitivity of Bayesian IVIM to NACT response.

Materials and methods

Seventeen female patients with breast cancer were scanned at baseline and 16 patients were scanned after Cycle 1. Tissue diffusion and perfusion from Bayesian IVIM were calculated at baseline with percentage change at Cycle 1 computed with reference to baseline. Cellular proliferative activity marker Ki-67 was obtained semi-quantitatively with percentage change at excision computed with reference to core biopsy.

Results

The perfusion fraction showed a significant difference (p = 0.042) in percentage change between responder groups at Cycle 1, with a decrease in good responders [-7.98% (-19.47-1.73), n = 7] and an increase in poor responders [10.04% (5.09-28.93), n = 9]. There was a significant correlation between percentage change in perfusion fraction and percentage change in Ki-67 (p = 0.042). Tissue diffusion and pseudodiffusion showed no significant difference in percentage change between groups at Cycle 1, nor was there a significant correlation against percentage change in Ki-67. Perfusion fraction, tissue diffusion, and pseudodiffusion showed no significant difference between groups at baseline, nor was there a significant correlation against Ki-67 from core biopsy.

Conclusion

The alteration in tumour perfusion fraction from the Bayesian IVIM model, in association with cellular proliferation, showed early sensitivity to good responders in NACT.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT03501394, identifier NCT03501394.

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors

PURPOSE

To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

METHODS

We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping.

RESULTS

We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior.

CONCLUSION

DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.

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

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 EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Non-contrast based approach for liver function quantification using Bayesian-based intravoxel incoherent motion diffusion weighted imaging: A pilot study

PURPOSE

Liver cirrhosis disrupts liver function and tissue perfusion, detectable by magnetic resonance imaging (MRI). Assessing liver function at the voxel level with 13-b value intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) could aid in radiation therapy liver-sparing treatment for patients with early impairment. This study aimed to evaluate the feasibility of IVIM-DWI for liver function assessment and correlate it with other multiparametric (mp) MRI methods at the voxel level.

METHOD

This study investigates the variability of apparent diffusion coefficient (ADC) derived from 13-b value IVIM-DWI and B1-corrected dual flip angle (DFA) T1 mapping. Experiments were conducted in-vitro with QIBA and NIST phantoms and in 10 healthy volunteers for IVIM-DWI. Additionally, 12 patients underwent an mp-MRI examination. The imaging protocol included a 13-b value IVIM-DWI sequence for generating IVIM parametric maps. B1-corrected DFA T1 pulse sequence was used for generating T1 maps, and Gadoxatate low temporal resolution dynamic contrast-enhanced (LTR-DCE) MRI was used for generating the Hepatic extraction fraction (HEF) map. The Mann-Whitney U test was employed to compare IVIM-DWI parameters (Pure Diffusion, Dslow ; Pseudo diffusion, Dfast ; and Perfusion Fraction, Fp ) between the healthy volunteer and patient groups. Furthermore, in the patient group, statistical correlations were assessed at a voxel level between LTR-DCE MRI-derived HEF, T1 post-Gadoxetate administration, ΔT1%, and various IVIM parameters using Pearson correlation.

RESULTS

For-vitro measurements, the maximum coefficient of variation of the ADC and T1 parameters was 12.4% and 16.1%, respectively. The results also showed that Fp and Dfast were able to distinguish between healthy liver function and mild liver function impairment at the global level, with p = 0.002 for Fp and p < 0.001 for Dfast . Within the patient group, these parameters also exhibited a moderate correlation with HEF at the voxel level.

CONCLUSION

Overall, the study highlighted the potential of Dfast and Fp for detecting liver function impairment at both global and pixel levels.

Estimation of intravoxel incoherent motion (IVIM) parameters in vertebral bone marrow: a comparative study of five algorithms

OBJECTIVES

To access the performances of different algorithms for quantification of Intravoxel incoherent motion (IVIM) parameters D, f, [Formula: see text] in Vertebral Bone Marrow (VBM).

MATERIALS AND METHODS

Five algorithms were studied: four deterministic algorithms (the One-Step and three segmented methods: Two-Step, Three-Step, and Fixed-[Formula: see text] algorithm) based on the least-squares (LSQ) method and a Bayesian probabilistic algorithm. Numerical simulations and quantification of IVIM parameters D, f, [Formula: see text] in vivo in vertebral bone marrow, were done on six healthy volunteers. The One-way repeated-measures analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test (p value = 0.05) was applied.

RESULTS

In numerical simulations, the Bayesian algorithm provided the best estimation of D, f, [Formula: see text] compared to the deterministic algorithms. In vivo VBM-IVIM, the values of D and f estimated by the Bayesian algorithm were close to those of the One-Step method, in contrast to the three segmented methods.

DISCUSSION

The comparison of the five algorithms indicates that the Bayesian algorithm provides the best estimation of VBM-IVIM parameters, in both numerical simulations and in vivo data.

Quantitative muscle MRI displays clinically relevant myostructural abnormalities in long-term ICU-survivors: a case-control study

BACKGROUND

Long-term data on ICU-survivors reveal persisting sequalae and a reduced quality-of-life even after years. Major complaints are neuromuscular dysfunction due to Intensive care unit acquired weakness (ICUAW). Quantitative MRI (qMRI) protocols can quantify muscle alterations in contrast to standard qualitative MRI-protocols.

METHODS

Using qMRI, the aim of this study was to analyse persisting myostructural abnormalities in former ICU patients compared to controls and relate them to clinical assessments. The study was conducted as a cohort/case-control study. Nine former ICU-patients and matched controls were recruited (7 males; 54.8y ± 16.9; controls: 54.3y ± 11.1). MRI scans were performed on a 3T-MRI including a mDTI, T2 mapping and a mDixonquant sequence. Water T2 times, fat-fraction and mean values of the eigenvalue (λ₁), mean diffusivity (MD), radial diffusivity (RD) and fractional anisotropy (FA) were obtained for six thigh and seven calf muscles bilaterally. Clinical assessment included strength testing, electrophysiologic studies and a questionnaire on quality-of-life (QoL). Study groups were compared using a multivariate general linear model. qMRI parameters were correlated to clinical assessments and QoL questionnaire using Pearson´s correlation.

RESULTS

qMRI parameters were significantly higher in the patients for fat-fraction (p < 0.001), water T2 time (p < 0.001), FA (p = 0.047), MD (p < 0.001) and RD (p < 0.001). Thighs and calves showed a different pattern with significantly higher water T2 times only in the calves. Correlation analysis showed a significant negative correlation of muscle strength (MRC sum score) with FA and T2-time. The results were related to impairment seen in QoL-questionnaires, clinical testing and electrophysiologic studies.

CONCLUSION

qMRI parameters show chronic next to active muscle degeneration in ICU survivors even years after ICU therapy with ongoing clinical relevance. Therefore, qMRI opens new doors to characterize and monitor muscle changes of patients with ICUAW. Further, better understanding on the underlying mechanisms of the persisting complaints could contribute the development of personalized rehabilitation programs.

Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies

PURPOSE

The development of advanced estimators for intravoxel incoherent motion (IVIM) modeling is often motivated by a desire to produce smoother parameter maps than least squares (LSQ). Deep neural networks show promise to this end, yet performance may be conditional on a myriad of choices regarding the learning strategy. In this work, we have explored potential impacts of key training features in unsupervised and supervised learning for IVIM model fitting.

METHODS

Two synthetic data sets and one in-vivo data set from glioma patients were used in training of unsupervised and supervised networks for assessing generalizability. Network stability for different learning rates and network sizes was assessed in terms of loss convergence. Accuracy, precision, and bias were assessed by comparing estimations against ground truth after using different training data (synthetic and in vivo).

RESULTS

A high learning rate, small network size, and early stopping resulted in sub-optimal solutions and correlations in fitted IVIM parameters. Extending training beyond early stopping resolved these correlations and reduced parameter error. However, extensive training resulted in increased noise sensitivity, where unsupervised estimates displayed variability similar to LSQ. In contrast, supervised estimates demonstrated improved precision but were strongly biased toward the mean of the training distribution, resulting in relatively smooth, yet possibly deceptive parameter maps. Extensive training also reduced the impact of individual hyperparameters.

CONCLUSION

Voxel-wise deep learning for IVIM fitting demands sufficiently extensive training to minimize parameter correlation and bias for unsupervised learning, or demands a close correspondence between the training and test sets for supervised learning.

The quantification of IntraVoxel incoherent motion - MRI maps cannot preserve texture information: An evaluation based on simulated and in-vivo images

BACKGROUND

Radiomics can be applied on parametric maps obtained from IntraVoxel Incoherent Motion (IVIM) MRI to characterize heterogeneity in diffusion and perfusion tissue properties. The purpose of this work is to assess the accuracy and reproducibility of radiomic features computed from IVIM maps using different fitting methods.

METHODS

200 digitally simulated IVIM-MRI images with various SNR containing different combinations of texture patterns were generated from ground truth maps of true diffusion D, pseudo-diffusion D* and perfusion fraction f. Four different methods (segmented least-square LSQ, Bayesian, supervised and unsupervised deep learning DL) were adopted to quantify IVIM maps from simulations and from two real images of liver tumor. Radiomic features were computed from ground truth and estimated maps. Accuracy and reproducibility among quantification methods were assessed.

RESULTS

Almost 50% of radiomic features computed from D maps using DL approaches, 36% using Bayes and 27% using LSQ presented errors lower than 50%. Radiomic features from f and D* maps were accurate only if computed using DL methods from histogram. High reproducibility (ICC>0.8) was found only for D maps among DL and Bayes methods, whereas features from f and D* maps were less reproducible, with LSQ approach in lower agreement with the others.

CONCLUSIONS

Texture patterns were preserved and correctly estimated only on D maps, except for LSQ approach. We suggest limiting radiomic analysis only to histogram and some texture features from D maps, to histogram features from f maps, and to avoid it on D* maps.

Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney

PURPOSE

To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach.

METHODS

The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting).

RESULTS

The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method.

CONCLUSION

The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.

Diffusion-weighted MRI to determine response and long-term clinical outcomes in muscle-invasive bladder cancer following neoadjuvant chemotherapy

Objective

This study aims to determine local treatment response and long-term survival outcomes in patients with localised muscle-invasive bladder cancer (MIBC) patients receiving neoadjuvant chemotherapy (NAC) using diffusion-weighted MRI (DWI) and apparent diffusion coefficient (ADC) analysis.

Methods

Patients with T2-T4aN0-3M0 bladder cancer suitable for NAC were recruited prospectively. DWI was performed prior to NAC and was repeated following NAC completion. Conventional response assessment was performed with cystoscopy and tumour site biopsy. Response was dichotomised into response (

Results

Forty-eight patients (96 DWI) were evaluated. NAC response was associated with significant increase in mean ΔADC and %ΔADC compared to poor response (ΔADCall 0.32×10-3 versus 0.11×10-3 mm2/s; p=0.009, and %ΔADCall 21.70% versus 8.23%; p=0.013). Highest specificity predicting response was seen at 75th percentile ADC (AUC, 0.8; p=0.01). Sensitivity, specificity, positive predictive power, and negative predictive power of %ΔADCb100 75th percentile was 73.7%, 90.0%, 96.6%, and 52.9%, respectively. %ΔADCb100 75th percentile >15.5% was associated with significant improvement in OS (HR, 0.40; 95% CI, 0.19-0.86; p=0.0179), bCSS (HR, 0.26; 95% CI, 0.08-0.82; p=0.0214), PFS (HR, 0.16; 95% CI, 0.05-0.48; p=0.0012), and time to cystectomy (HR, 0.19; 95% CI, 0.07-0.47; p=0.0004).

Conclusions

Quantitative ADC analysis can successfully identify NAC response and improved long-term clinical outcomes. Multi-centre validation to assess reproducibility and repeatability is required before testing within clinical trials to inform MIBC treatment decision making.

Advances in knowledge

We successfully demonstrated that measured change in DWI can successfully identify NAC response and improved long-term survival outcomes.

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Key Words

• MRI
• diffusion weighted magnetic resonance imaging (DWI)
• imaging biomakers
• muscle invasive bladder cancer (MIBC)
• neoadjuavant chemotherapy

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MESH Headings Collapse

Grants

• 28724 Cancer Research UK

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Affiliation(s)

Shaista Hafeez Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom Department of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, United Kingdom
Mu Koh Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Department of Diagnostic Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
Kelly Jones Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
Amir El Ghzal Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
James D’Arcy Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
Pardeep Kumar Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
Vincent Khoo Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom Department of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, United Kingdom
Susan Lalondrelle Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Department of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, United Kingdom
Fiona McDonald Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Department of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, United Kingdom
Alan Thompson Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
Erica Scurr Department of Diagnostic Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
Aslam Sohaib Department of Diagnostic Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
Robert Anthony Huddart Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom Urology Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom Department of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, United Kingdom

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An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters

Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T

PURPOSE

The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value.

MATERIALS AND METHODS

Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm² ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm² ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2_p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times.

RESULTS

Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2_p = 100 ms) and 60 % (T2_p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm² was around 8% lower than at b = 0 s/mm² (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2_p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01).

CONCLUSION

The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

Evaluation of crossed cerebellar diaschisis after cerebral infarction in MCAO rats based on DKI

OBJECTIVE

To observe the expression of N-methyl-D-aspartate (NMDA), apoptosis and the effect on neurological function recovery in rat model with middle cerebral artery occlusion (MCAO). Diffusion kurtosis imaging (DKI) was used to evaluate crossed cerebellar diaschisis (CCD) and to provide experimental and theoretical basis for the clinical treatment.

MATERIALS AND METHODS

The MCAO models were established in rats. Eighty-four rats were randomly and evenly divided into 7 groups, including control group, 6-h group, 12-h group, 24-h group, 48-h group, 7-day group and 14-day group. The rats were scanned by MRI at the above time points. Then, rats were sacrificed for H&E staining, immunohistochemical staining and TUNEL staining to detect the expression of NMDA in the core infarct area and cerebellum. At the end, the discussion of relationships between molecular biology and MRI parameters (ADC derived from DWI, and MD, MK and FA derived from DKI) was performed.

RESULTS

The values of MD, ADC and FA in MCAO rats were all lower than those in the control group. All MRI parameters of the contralateral cerebellum were lower than those of the ipsilateral cerebellum (p < .05). The parameters reached the lowest value at 12 h, except that the MK reached the highest at 12 h. The expression of NMDA showed a fluctuation along time in the MCAO group. Overall, it is higher in the MCAO group than in the control group, reaching the maximum at 24 h (p < .05). At the same time, the expression of NMDA in the contralateral cerebellum was higher than in the ipsilateral cerebellum.

CONCLUSION

It is found that NMDA and DKI of CCD have the same changing trend, which indicates that the intervention of NMDA receptor apoptosis may become a new target for the treatment of cerebral infarction, and MRI parameters can predict the occurrence and development of CCD.

A Clustering Approach to Improve IntraVoxel Incoherent Motion Maps from DW-MRI Using Conditional Auto-Regressive Bayesian Model

The Intra-Voxel Incoherent Motion (IVIM) model allows to estimate water diffusion and perfusion-related coefficients in biological tissues using diffusion weighted MR images. Among the available approaches to fit the IVIM bi-exponential decay, a segmented Bayesian algorithm with a Conditional Auto-Regressive (CAR) prior spatial regularization has been recently proposed to produce more reliable coefficient estimation. However, the CAR spatial regularization can generate inaccurate coefficient estimation, especially at the interfaces between different tissues. To overcome this problem, the segmented CAR model was coupled in this work with a k-means clustering approach, to separate different tissues and exclude voxels from other regions in the CAR prior specification. The proposed approach was compared with the original Bayesian CAR method without clustering and with a state-of-the-art Bayesian approach without CAR. The approaches were tested and compared on simulated images by calculating the estimation error and the coefficient of variation (CV). Furthermore, the proposed method was applied to some illustrative real images of oncologic patients. On simulated images, the proposed innovation reduced the average error of 47%, 21% and 58% for D, f and D*, respectively, compared to the state-of-the-art Bayesian approach, and of 48% and 34% for D and f, respectively, compared to the original CAR, while it achieved the same error for D*. The clustering approach was also able to consistently reduce the CV for each coefficient. On real images, the novel approach did not alter the IVIM maps obtained by the original CAR method, with the advantage of reducing their typical blotchy appearance at the boundaries. The proposed approach represents a valuable improvement over the state-of-the-art Bayesian CAR method and provides more reliable IVIM coefficient estimation, and is less sensitive to bias and inconsistency at tissue/tissue and tissue/background interfaces.

Placental functional assessment and its relationship to adverse pregnancy outcome: comparison of intravoxel incoherent motion (IVIM) MRI, T2-relaxation time, and umbilical artery Doppler ultrasound

BACKGROUND

Early identification of placental insufficiency can lead to appropriate treatment selections and can improve neonates' outcomes. Possible contributions of magnetic resonance imaging (MRI) have been suggested.

PURPOSE

To evaluate the prognostic capabilities of placental intravoxel incoherent motion (IVIM) parameters and T2-relaxation time, and their correlation with fetal growth and adverse outcomes, comparing umbilical artery (UmA) pulsatility index (PI).

MATERIAL AND METHODS

A total of 68 singleton pregnancies at 24-40 weeks of gestation underwent placental MRI and were reviewed retrospectively. UmA-PI was measured using Doppler ultrasound by obstetricians. IVIM parameters (Dfast, Dslow, and f) were calculated with a Bayesian model fitting. First, the associations between gestational age (GA) with placental IVIM parameters, T2-relaxation time, and placental thickness (PT) were evaluated. Second, IVIM parameters, T2 value (Z-score), PT (Z-score), and UmA-PI (Z-score) were compared between ( 1) those delivering small for gestational age (SGA) and appropriate for gestational age (AGA) neonates, ( 2) emergency cesarean section (ECS), and non-ECS, and ( 3) preterm birth and full-term birth.

RESULTS

Low birth weight was observed in 15/68 cases (22%). GA was significantly associated only with T2-relaxation time and PT. SGA was significantly associated with T2 value (Z-score), f, and UmA-PI (Z-score). In the ECS groups, T2 value (Z-score), f, and Dfast were significantly lower than those in non-ECS groups. All IVIM parameters and T2 values (Z-score) showed significantly lower scores in the preterm birth group.

CONCLUSION

Placental f and T2 value (Z-score) had significant associations with low birth weight and clinical adverse outcomes and could be potential imaging biomarkers of placental insufficiency.

Triexponential Diffusion Analysis of Diffusion-weighted Imaging for Breast Ductal Carcinoma in Situ and Invasive Ductal Carcinoma

Purpose

To obtain detailed information in breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) using triexponential diffusion analysis.

Methods

Diffusion-weighted images (DWI) of the breast were obtained using single-shot diffusion echo-planar imaging with 15 b-values. Mean signal intensities at each b-value were measured in the DCIS and IDC lesions and fitted with the triexponential function based on a two-step approach: slow-restricted diffusion coefficient (D_s) was initially determined using a monoexponential function with b-values > 800 s/mm². The diffusion coefficient of free water at 37°C was assigned to the fast-free diffusion coefficient (D_f). Finally, the perfusion-related diffusion coefficient (D_p) was derived using all the b-values. Furthermore, biexponential analysis was performed to obtain the perfusion-related diffusion coefficient (D^*) and the perfusion-independent diffusion coefficient (D). Monoexponential analysis was performed to obtain the apparent diffusion coefficient (ADC). The sensitivity and specificity of the aforementioned diffusion coefficients for distinguishing between DCIS and IDC were evaluated using the pathological results.

Results

The D_s, D, and ADC of DCIS were significantly higher than those of IDC (P < 0.01 for all). There was no significant correlation between D_p and D_s, but there was a weak correlation between D^* and D. The combination of D_p and D_s showed higher sensitivity and specificity (85.9% and 71.4%, respectively), compared to the combination of D^* and D (81.5% and 33.3%, respectively).

Conclusion

Triexponential analysis can provide detailed diffusion information for breast tumors that can be used to differentiate between DCIS and IDC.

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.

A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters

PURPOSE

To propose a multivariate multi-step framework for a systematic assessment of the estimation reliability and discriminability of Intravoxel Incoherent Motion (IVIM) model parameters.

METHODS

Monte-Carlo simulations were generated on a range of SNRs and in different IVIM combinations considering: i) a dense discretization with 24 b-values; ii) a discretization with 9 b-values. A state-of-the-art Bayesian fitting method was adopted. The framework assessed: i) the best model between mono- and bi-exponential, through the BIC index; ii) the fitting accuracy; iii) the power in discriminating two different IVIM parameters distributions of estimated coefficients, using a multivariate test. Exemplificative oncologic cases were also presented.

RESULTS

The bi-exponential fitting was reliable for perfusion fraction higher than 5%, with high accuracy in D estimation, acceptable error for f, but high uncertainty in D*. The discrimination of two distributions is generally feasible if differences in D values (at least 0.3 x10^-3 mm²/s) are present; in the case of similar D values, a minimal difference of 5% in f can be discriminated just in case of balanced sample size and dense b-values discretization, whereas the impact of D* is quite negligible. These results were also supported by clinical examples.

CONCLUSIONS

IVIM model is generally accurate in estimating diffusion, but uncertainties related to perfusion estimation are not negligible and compromise the discrimination power when different populations should be differentiated. The proposed framework should be adopted as interpretative guidelines to better understand when IVIM model applied on real data can provide reliable findings.

On the use of multicompartment models of diffusion and relaxation for placental imaging

Multi-compartment models of diffusion and relaxation are ubiquitous in magnetic resonance research especially applied to neuroimaging applications. These models are increasingly making their way into the world of placental imaging. This review provides a framework for their motivation and implementation and describes some of the outstanding questions that need to be answered before they can be routinely adopted.

Short-Term Repeatability of in Vivo Cardiac Intravoxel Incoherent Motion Tensor Imaging in Healthy Human Volunteers

BACKGROUND

Intravoxel incoherent motion (IVIM) tensor imaging is a promising technique for diagnosis and monitoring of cardiovascular diseases. Knowledge about measurement repeatability, however, remains limited.

PURPOSE

To evaluate short-term repeatability of IVIM tensor imaging in normal in vivo human hearts.

STUDY TYPE

Prospective.

POPULATION

Ten healthy subjects without history of heart diseases.

FIELD STRENGTH/SEQUENCE

Balanced steady-state free-precession cine sequence and single-shot spin-echo echo planar IVIM tensor imaging sequence (9 b-values, 0-400 seconds/mm² and six diffusion-encoding directions) at 3.0 T.

ASSESSMENT

Subjects were scanned twice with an interval of 15 minutes, leaving the scanner between studies. The signal-to-noise ratio (SNR) was evaluated in anterior, lateral, septal, and inferior segments of the left ventricle wall. Fractional anisotropy (FA), mean diffusivity (MD), mean fraction (MF), and helix angle (HA) in the four segments were independently measured by five radiologists.

STATISTICAL TESTS

IVIM tensor indexes were compared between observers using a one-way analysis of variance or between scans using a paired t-test (normal data) or a Wilcoxon rank-sum test (non-normal data). Interobserver agreement and test-retest repeatability were assessed using the intraclass correlation coefficient (ICC), within-subject coefficient of variation (WCV), and Bland-Altman limits of agreements.

RESULTS

SNR of inferior segment was significantly lower than the other three segments, and inferior segment was therefore excluded from repeatability analysis. Interobserver repeatability was excellent for all IVIM tensor indexes (ICC: 0.886-0.972; WCV: 0.62%-4.22%). Test-retest repeatability was excellent for MD of the self-diffusion tensor (D) and MF of the perfusion fraction tensor (f_p ) (ICC: 0.803-0.888; WCV: 1.42%-9.51%) and moderate for FA and MD of the pseudo-diffusion tensor (D^* ) (ICC: 0.487-0.532; WCV: 6.98%-10.89%). FA of D and f_p and HA of D presented good test-retest repeatability (ICC: 0.732-0.788; WCV: 3.28%-8.71%).

DATA CONCLUSION

The D and f_p indexes exhibited satisfactory repeatability, but further efforts were needed to improve repeatability of D^* indexes.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort

Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions. Twenty informed, consented patients with confirmed benign breast lesions underwent repeated DWI (3 T) using: sagittal single-shot spin-echo echo planar imaging, bipolar encoding, TR/TE: 11,600/86 ms, FOV: 180 x 180 mm, matrix: 90 x 90, slices: 60 x 2.5 mm, iPAT: GRAPPA 2, fat suppression, and 13 b-values: 0-700 s/mm² . A phase-reversed scan (b = 0 s/mm² ) was acquired for distortion correction. Voxel-wise repeat-measures coefficients of variation (CoVs) were derived for monoexponential (apparent diffusion coefficient [ADC]), biexponential (intravoxel incoherent motion: f, D, D*) and stretched exponential (α, DDC) across the parameter histograms for lesion regions of interest (ROIs). Goodness-of-fit for each representation was assessed by Bayesian information criterion. The volume of interest (VOI) definition was repeatable (CoV 13.9%). Within lesions, and across both visits and the cohort, there was no dominant best-fit model, with all representations giving the best fit for a fraction of the voxels. Diffusivity measures from the signal representations (ADC, D, DDC) all showed good repeatability (CoV < 10%), whereas parameters associated with pseudodiffusion (f, D*) performed poorly (CoV > 50%). The stretching exponent α was repeatable (CoV < 12%). This pattern of repeatability was consistent over the central part of the parameter percentiles. Assumptions often made in diffusion studies about analysis choices will influence the detectability of changes, potentially obscuring useful information. No single signal representation prevails within or across lesions, or across repeated visits; parameter robustness is therefore a critical consideration. Our results suggest that stretched exponential representation is more repeatable than biexponential, with pseudodiffusion parameters unlikely to provide clinically useful biomarkers.

Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain: Analysis of cancer and acute stroke

The intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion. In particular, a hierarchical and a spatial prior were combined. Performance was compared relative to conventional segmented least squares regression, hierarchical prior only (non-segmented and segmented data likelihoods) and a deep learning approach. Realistic numerical brain IVIM simulations were conducted to assess errors relative to ground truth. In-vivo, data of 11 central nervous system cancer patients and 9 patients with acute stroke were acquired. The proposed method yielded reduced error in simulations for both the cancer and acute stroke scenarios compared to other methods across the whole investigated SNR range. The contrast-to-noise ratio of the proposed method was better or on par compared to the other techniques in-vivo. The proposed Bayesian approach hence improves IVIM parameter estimation in brain cancer and acute stroke.

Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice

Purpose

To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer.

Methods

Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of 11 immunodeficient mice. Tumor growth was followed by weekly DWI examinations using a 7T MR scanner. Additional DWI examination was performed after repositioning following the fourth DWI examination to evaluate short term repeatability. DWI was performed using 15 and 12 b-values in the ranges of 0-500 and 0-2000 s/mm², respectively. Corrected Akaike information criteria and F-ratio were used to evaluate fitting quality of each model (mono-exponential, stretched exponential, kurtosis, and bi-exponential).

Results

Significant changes were observed in DWI data during the tumor growth, indicated by ADC_m, ADC_s, and ADC_k. Similar results were obtained using low as well as high b-values. No marked changes in model preference were present between the weeks 1−4. The parameters of the mono-exponential, stretched exponential, and kurtosis models had smaller confidence interval and coefficient of repeatability values than the parameters of the bi-exponential model.

Conclusion

Stretched exponential and kurtosis models showed better fit to DWI data than the mono-exponential model and presented with good repeatability.

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.

Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model

PURPOSE

To compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC).

METHODS

Fifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed.

RESULTS

There were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05).

CONCLUSIONS

The mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC.

Evaluating the clinical value of MRI multi-model diffusion-weighted imaging on liver fibrosis in chronic hepatitis B patients

PURPOSE

To explore the value of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential in assessing liver fibrosis in chronic hepatitis B (CHB).

METHODS

DWI and intravoxel incoherent motion (IVIM) MRI were performed prospectively on liver for 146 patients with CHB and 21 healthy volunteers. ADC values were obtained from monoexponential model imaging. Diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) obtained by biexponential model imaging, and stretched exponential model to obtain diffusion distribution coefficient (DDC) and diffusion heterogeneity index (α). Blood draw were performed on patients to obtain AST, ALT, and PLT, and then APRI and FIB-4 index were determined based on the serological diagnostic models. The fibrosis stage was staged (S0-S4) according to the pathology of liver puncture. Independent sample t test was used to compare the parameter values between liver fibrosis group and control group. One-way ANOVA was used to compare the parameters of different liver fibrosis grades. Bonferroni test was used for correcting multiple comparisons. Spearman correlation was used to analyze the correlation between each parameter and liver fibrosis grades. ROC was used to predict the diagnostic power of each parameter for liver fibrosis stages ≥ S2 and ≥ S3.

RESULTS

ADC, D, D*, f, and DDC values were significantly different between normal control group and hepatic fibrosis group (P < 0.05). There were significant differences in ADC, D*, f, and DDC value among liver fibrosis groups (P < 0.05). D* and DDC values were moderately negatively correlated with the grades of liver fibrosis (r =  - 0.483, P < 0.001; r =  - 0.622, P < 0.001). ADC and f values were slightly negatively correlated with the grades of liver fibrosis (r =  - 0.295, P < 0.001; r =  - 0.312, P < 0.001). DDC values have the highest diagnostic efficiency in liver fibrosis stages ≥ S2 and ≥ S3. The areas under ROC curve (AUC) were 0.813 and 0.832 for ≥ S2 and ≥ S3, respectively, the sensitivity is 83.72% and 73.53%, and the specificity of 83.33% and 66.04%, which were better than APRI and FIB-4.

CONCLUSION

D* obtained from biexponential and DDC obtained from stretched exponential DWI have better value in evaluating the degree of liver fibrosis in CHB.

A Bayesian approach to diffusional kurtosis imaging

PURPOSE

Diffusional kurtosis metrics show high performance for detecting pathological changes and are therefore expected to be disease biomarkers. Kurtosis maps, however, tend to be noisy. The maps' visual quality is crucial for disease diagnosis, even when kurtosis is being used quantitatively. A Bayesian method was proposed to curtail the large statistical error inherent in kurtosis estimation while maintaining potential application to biomarkers.

THEORY

Gaussian priors are determined from first-step estimations implemented using the least-square method (LSM). The likelihood-function variance is determined from the residuals of the estimation. Although the proposed approach is similar to a regularized LSM, regularization parameters do not have to be artificially adjusted. An appropriate balance between denoising and preventing false shrinkages of metric dispersions is automatically achieved.

METHODS

Map qualities achieved using the conventional and proposed methods were compared. The receiver-operating characteristic analysis was performed for glioma-grade differentiation using simulated low- and high-grade glioma DWI datasets. Noninferiority of the proposed method was tested for areas under the curves (AUCs).

RESULTS

The noisier the conventional maps, the better the proposed Bayesian method improved them. Noninferiority of the proposed method was confirmed by AUC tests for all kurtosis-related metrics. Superiority of the proposed method was also established for several metrics.

CONCLUSIONS

The proposed approach improved noisy kurtosis maps while maintaining their performances as biomarkers without increasing data acquisition requirements or arbitrarily choosing LSM regularization parameters. This approach may enable the use of higher-order terms in diffusional kurtosis imaging (DKI) fitting functions by suppressing overfitting, thereby improving the DKI-estimation accuracy.

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.

Quantification of intravoxel incoherent motion with optimized b-values using deep neural network

PURPOSE

To develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b-values for diffusion-weighted imaging are simultaneously optimized.

METHOD

A deep neural network (DNN) method is proposed for accurate quantification of IVIM parameters from multiple diffusion-weighted images. In addition, optimal b-values are selected to acquire the multiple diffusion-weighted images. The proposed framework consists of an MRI signal generation part and an IVIM parameter quantification part. Monte-Carlo (MC) simulations were performed to evaluate the accuracy of the IVIM parameter quantification and the efficacy of b-value optimization. In order to analyze the effect of noise on the optimized b-values, simulations were performed with five different noise levels. For in vivo data, diffusion images were acquired with the b-values from four b-values selection methods for five healthy volunteers at 3T MRI system.

RESULTS

Experiment results showed that both the optimization of b-values and the training of DNN were simultaneously performed to quantify IVIM parameters. We found that the accuracies of the perfusion coefficient (D_p ) and perfusion fraction (f) were more sensitive to b-values than the diffusion coefficient (D) was. Furthermore, when the noise level changed, the optimized b-values also changed. Therefore, noise level has to be considered when optimizing b-values for IVIM quantification.

CONCLUSION

The proposed scheme can simultaneously optimize b-values and train DNN to minimize quantification errors of IVIM parameters. The trained DNN can quantify IVIM parameters from the diffusion-weighted images obtained with the optimized b-values.

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.

Analysis of Renal Diffusion-Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intravoxel Incoherent Motion (IVIM) Models

Analysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.

Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation

PURPOSE

To develop a method that can reduce and estimate uncertainty in quantitative MR parameter maps without the need for hand-tuning of any hyperparameters.

METHODS

We present an estimation method where uncertainties are reduced by incorporating information on spatial correlations between neighbouring voxels. The method is based on a Bayesian hierarchical non-linear regression model, where the parameters of interest are sampled, using Markov chain Monte Carlo (MCMC), from a high-dimensional posterior distribution with a spatial prior. The degree to which the prior affects the model is determined by an automatic hyperparameter search using an information criterion and is, therefore, free from manual user-dependent tuning. The samples obtained further provide a convenient means to obtain uncertainties in both voxels and regions. The developed method was evaluated on T ₁ estimations based on the variable flip angle method.

RESULTS

The proposed method delivers noise-reduced T ₁ parameter maps with associated error estimates by combining MCMC sampling, the widely applicable information criterion, and total variation-based denoising. The proposed method results in an overall decrease in estimation error when compared to conventional voxel-wise maximum likelihood estimation. However, this comes with an increased bias in some regions, predominately at tissue interfaces, as well as an increase in computational time.

CONCLUSIONS

This study provides a method that generates more precise estimates compared to the conventional method, without incorporating user subjectivity, and with the added benefit of uncertainty estimation.

Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b-values

PURPOSE

Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion-related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b-values.

METHODS

Bayesian shrinkage prior (BSP) fitting method and different low b-value distributions were used to estimate IVIM parameters (diffusion D, pseudo-diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor.

RESULTS

In tumor regions, the value of D* tended to be decreased when the number of low b-values was insufficient, especially with LSQ. BSP required less low b-values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F.

CONCLUSIONS

Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b-values than LSQ for the parameter estimation.

Assessment of Hepatocellular Carcinoma Response to 90Y Radioembolization Using Dynamic Contrast Material-enhanced MRI and Intravoxel Incoherent Motion Diffusion-weighted Imaging

PURPOSE

To quantify diffusion and perfusion changes in hepatocellular carcinoma (HCC) induced by yttrium 90 (⁹⁰Y) radioembolization and to assess the value of dynamic contrast material-enhanced (DCE) MRI and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for predicting HCC response.

MATERIALS AND METHODS

Institutional review board approval was obtained for this prospective study (clinical trial registry NCT01871545). Twenty-four participants with HCC (mean age, 69 years ± 9 [standard deviation], 18 men) underwent multiparametric MRI, including IVIM DWI and gadoxetic acid DCE MRI before (n = 24) and 6 weeks (n = 21) after radioembolization. IVIM DWI and DCE MRI histogram parameters were quantified in HCCs and liver parenchyma. HCC response was assessed by using modified Response Evaluation Criteria in Solid Tumors at 6 weeks and 6-12 months after radioembolization. Logistic regression analysis was used to evaluate the diagnostic performance of baseline MRI and clinical parameters for prediction of response.

RESULTS

Twenty-five HCCs were analyzed (mean size, 3.6 cm ± 1.9). Radioembolization resulted in significantly decreased perfusion (DCE MRI arterial flow, P = .002; IVIM pseudodiffusion coefficient [D*], P = .014). Multivariate logistic regression selected combined serum α-fetoprotein and portal flow (F _p ) skewness (area under the curve [AUC] = 0.924) and combined D* standard deviation and F _p kurtosis (AUC = 0.916) for prediction of objective and complete response at 6 weeks, respectively. Standard deviation of DCE MRI parameter arterial fraction was selected as the optimal predictor for complete response at 6-12 months (AUC = 0.857).

CONCLUSION

Diffusion and perfusion MRI can be used to evaluate the response of HCC to radioembolization. Pretreatment DCE MRI histogram parameters may be useful for radioembolization treatment stratification. Supplemental material is available for this article. © RSNA, 2020.

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology.

Noise-Corrected, Exponentially Weighted, Diffusion-Weighted MRI (niceDWI) Improves Image Signal Uniformity in Whole-Body Imaging of Metastatic Prostate Cancer

Purpose: To characterize the voxel-wise uncertainties of Apparent Diffusion Coefficient (ADC) estimation from whole-body diffusion-weighted imaging (WBDWI). This enables the calculation of a new parametric map based on estimates of ADC and ADC uncertainty to improve WBDWI imaging standardization and interpretation: NoIse-Corrected Exponentially-weighted diffusion-weighted MRI (niceDWI).

Methods: Three approaches to the joint modeling of voxel-wise ADC and ADC uncertainty (σ_ADC) are evaluated: (i) direct weighted least squares (DWLS), (ii) iterative linear-weighted least-squares (IWLS), and (iii) smoothed IWLS (SIWLS). The statistical properties of these approaches in terms of ADC/σ_ADC accuracy and precision is compared using Monte Carlo simulations. Our proposed post-processing methodology (niceDWI) is evaluated using an ice-water phantom, by comparing the contrast-to-noise ratio (CNR) with conventional exponentially-weighted DWI. We present the clinical feasibility of niceDWI in a pilot cohort of 16 patients with metastatic prostate cancer.

Results: The statistical properties of ADC and σ_ADC conformed closely to the theoretical predictions for DWLS, IWLS, and SIWLS fitting routines (a minor bias in parameter estimation is observed with DWLS). Ice-water phantom experiments demonstrated that a range of CNR could be generated using the niceDWI approach, and could improve CNR compared to conventional methods. We successfully implemented the niceDWI technique in our patient cohort, which visually improved the in-plane bias field compared with conventional WBDWI.

Conclusions: Measurement of the statistical uncertainty in ADC estimation provides a practical way to standardize WBDWI across different scanners, by providing quantitative image signals that improve its reliability. Our proposed method can overcome inter-scanner and intra-scanner WBDWI signal variations that can confound image interpretation.

Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes

BACKGROUND

Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients.

METHODS

Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists.

RESULTS

Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model.

CONCLUSION

IVIM is useful in determining PLN involvement but the added value decreases with reader experience.

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

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^∗ .

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).

Placental MRI and its application to fetal intervention

OBJECTIVE

Magnetic resonance imaging (MRI) of placental invasion has been part of clinical practice for many years. The possibility of being better able to assess placental vascularization and function using MRI has multiple potential applications. This review summarises up-to-date research on placental function using different MRI modalities.

METHOD

We discuss how combinations of these MRI techniques have much to contribute to fetal conditions amenable for therapy such as singletons at high risk for fetal growth restriction (FGR) and monochorionic twin pregnancies for planning surgery and counselling for selective growth restriction and transfusion conditions.

RESULTS

The whole placenta can easily be visualized on MRI, with a clear boundary against the amniotic fluid, and a less clear placental-uterine boundary. Contrasts such as diffusion weighted imaging, relaxometry, blood oxygenation level dependent MRI and flow and metabolite measurement by dynamic contrast enhanced MRI, arterial spin labeling, or spectroscopic techniques are contributing to our wider understanding of placental function.

CONCLUSION

The future of placental MRI is exciting, with the increasing availability of multiple contrasts and new models that will boost the capability of MRI to measure oxygen saturation and placental exchange, enabling examination of placental function in complicated pregnancies.