Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer

Intravoxel incoherent motion and diffusion kurtosis imaging and their machine-learning-based texture analysis for detection and assessment of prostate cancer severity at 3 T

OBJECTIVES

To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) and their machine-learning-based texture analysis for the detection and assessment of severity in prostate cancer (PCa).

MATERIALS AND METHODS

Eighty-eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM-DKI data were acquired using 13 b values (0-2000 s/mm2) and analyzed using the IVIM-DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One-way analysis-of-variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM-DKI parameters. A chi-square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM-DKI parameters. These selected texture features were used in an artificial neural network for PCa detection.

RESULTS

ADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = -0.33; D, ρ = -0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM-DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ.

CONCLUSION

D, f, and k computed using the IVIM-DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM-DKI parameters showed high accuracy and AUC in PCa detection.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy

Background and purpose

Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma.

Materials and methods

Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT.

Results

Significant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors.

Conclusions

Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.

Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma

Background and purpose

Diffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response.

Materials and methods

Thirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease.

Results

ADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed.

Conclusion

The results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.

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.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.

Intravoxel incoherent motion to differentiate spinal metastasis: A pilot study

Background

To investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to discriminate spinal metastasis from tuberculous spondylitis.

Methods

This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 with tuberculous spondylitis. The IVIM parameters, including the single-index model (apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow, ADCfast, and f), and the stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α), were acquired. Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) analysis was used to evaluate the diagnostic performance. Each parameter was substituted into a logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.

Results

The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis (all p &lt; 0.05). The logistic regression model results showed that ADCfast and f were independent factors affecting the outcome (P &lt; 0.05). The AUC values of ADCfast and f were 0.823 (95% confidence interval (CI): 0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95% CI: 0.858 to 0.992).

Conclusions

IVIM MR imaging might be helpful to differentiate spinal metastasis from tuberculous spondylitis, and provide guidance for clinical treatment.

Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI

Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness

BACKGROUND

Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.

PURPOSE

To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.

STUDY TYPE

Prospective.

POPULATION

Thirty-three patients prospectively imaged prior to prostatectomy.

FIELD STRENGTH/SEQUENCE

3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence.

ASSESSMENT

Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).

STATISTICAL TEST

Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.

RESULTS

The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.

DATA CONCLUSION

We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 3.

The COMPLETE trial: HolistiC early respOnse assessMent for oroPharyngeaL cancEr paTiEnts; Protocol for an observational study

INTRODUCTION

The locoregional failure (LRF) rate in human papilloma virus (HPV)-negative oropharyngeal squamous cell carcinoma (OPSCC) remains disappointingly high and toxicity is substantial. Response prediction prior to or early during treatment would provide opportunities for personalised treatment. Currently, there are no accurate predictive models available for correct OPSCC patient selection. Apparently, the pivotal driving forces that determine how a OPSCC responds to treatment, have yet to be elucidated. Therefore, the holistiC early respOnse assessMent for oroPharyngeaL cancer paTiEnts study focuses on a holistic approach to gain insight in novel potential prognostic biomarkers, acquired before and early during treatment, to predict response to treatment in HPV-negative patients with OPSCC.

METHODS AND ANALYSIS

This single-centre prospective observational study investigates 60 HPV-negative patients with OPSCC scheduled for primary radiotherapy (RT) with cisplatin or cetuximab, according to current clinical practice. A holistic approach will be used that aims to map the macroscopic (with Intra Voxel Incoherent Motion Diffusion Kurtosis Imaging (IVIM-DKI); before, during, and 3 months after RT), microscopic (with biopsies of the primary tumour acquired before treatment and irradiated ex vivo to assess radiosensitivity), and molecular landscape (with circulating tumour DNA (ctDNA) analysed before, during and 3 months after treatment). The main end point is locoregional control (LRC) 2 years after treatment. The primary objective is to determine whether a relative change in the mean of the diffusion coefficient D (an IVIM-DKI parameter) in the primary tumour early during treatment, improves the performance of a predictive model consisting of tumour volume only, for 2 years LRC after treatment. The secondary objectives investigate the potential of other IVIM-DKI parameters, ex vivo sensitivity characteristics, ctDNA, and combinations thereof as potential novel prognostic markers.

ETHICS AND DISSEMINATION

The study was approved by the Medical Ethical Committee of Erasmus Medical Center. The main results of the trial will be presented in international meetings and medical journals.

TRIAL REGISTRATION NUMBER

NL8458.

The Histogram Analysis of Intravoxel Incoherent Motion-Kurtosis Model in the Diagnosis and Grading of Prostate Cancer-A Preliminary Study

Objectives

This study was conducted in order to explore the value of histogram analysis of the intravoxel incoherent motion-kurtosis (IVIM-kurtosis) model in the diagnosis and grading of prostate cancer (PCa), compared with monoexponential model (MEM).

Materials and Methods

Thirty patients were included in this study. Single-shot echo-planar imaging (SS-EPI) diffusion-weighted images (b-values of 0, 20, 50, 100, 200, 500, 1,000, 1,500, 2,000 s/mm²) were acquired. The pathologies were confirmed by in-bore MR-guided biopsy. The postprocessing and measurements were processed using the software tool Matlab R2015b for the IVIM-kurtosis model and MEM. Regions of interest (ROIs) were drawn manually. Mean values of D, D*, f, K, ADC, and their histogram parameters were acquired. The values of these parameters in PCa and benign prostatic hyperplasia (BPH)/prostatitis were compared. Receiver operating characteristic (ROC) curves were used to investigate the diagnostic efficiency. The Spearman test was used to evaluate the correlation of these parameters and Gleason scores (GS) of PCa.

Results

For the IVIM-kurtosis model, D (mean, 10th, 25th, 50th, 75th, 90th), D* (90th), and f (10th) were significantly lower in PCa than in BPH/prostatitis, while D (skewness), D* (kurtosis), and K (mean, 75th, 90th) were significantly higher in PCa than in BPH/prostatitis. For MEM, ADC (mean, 10th, 25th, 50th, 75th, 90th) was significantly lower in PCa than in BPH/prostatitis. The area under the ROC curve (AUC) of the IVIM-kurtosis model was higher than MEM, without significant differences (z = 1.761, P = 0.0783). D (mean, 50th, 75th, 90th), D* (mean, 10th, 25th, 50th, 75th), and f (skewness, kurtosis) correlated negatively with GS, while D (kurtosis), D* (skewness, kurtosis), f (mean, 75th, 90th), and K (mean, 75th, 90th) correlated positively with GS. The histogram parameters of ADC did not show correlations with GS.

Conclusion

The IVIM-kurtosis model has potential value in the differential diagnosis of PCa and BPH/prostatitis. IVIM-kurtosis histogram analysis may provide more information in the grading of PCa than MEM.

Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas

The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including 18F-FDG-PET/CT, 18F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) were investigated using the CDA based on a "spin-glass model" coupled with the Spearman's rank, ρ, analysis. Mean MRI T2 weighted tumor volumes and SULmean values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SULmean (ρ ≤ -0.42, p < 0.03 for both). K1 and Ktrans were positively correlated (ρ = 0.48, p = 0.01). In contrast, Ktrans and k3max were negatively correlated (ρ = -0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, Ktrans, and K1 had 8, 7, and 6 edges in the network, respectively. After validation in a larger population, the CDA approach may aid in identifying useful biomarkers for developing individual patient care in HNSCC.

IVIM-DKI for differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5 T vs. 3 T MRI

OBJECTIVE

To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)-diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5 T to the traditional IVIM-DKI model at 3 T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH).

MATERIALS AND METHODS

Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5 T, n = 16 scanned at 3 T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000 s/mm² up to 3 averages, 1.5 T: TR = 5.774 s, TE = 81 ms and 3 T: TR = 4.899 s, TE = 100 ms), T2-weighted, and T1-weighted imaging were used on the 1.5 T and 3 T MRI scanner, respectively. The IVIM-DKI signal was modeled using the traditional IVIM-DKI model and a novel model in which the total variation (TV) penalty function was combined with the traditional model to optimize non-physiological variations. Paired and unpaired t-tests were used to compare intra-scanner and scanner group differences in IVIM-DKI parameters obtained using the novel and the traditional models. Analysis of variance with post hoc test and receiver operating characteristic (ROC) curve analysis were used to assess the ability of parameters obtained using the novel model (at 1.5 T) and the traditional model (at 3 T) to characterize prostate lesions.

RESULTS

IVIM-DKI modeled using novel model with TV spatial penalty function at 1.5 T, produced parameter maps with 50-78% lower coefficient of variation (CV) than traditional model at 3 T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5 T estimated lower D* and f values as compared to traditional model at 3 T. At 1.5 T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5 T.

DISCUSSION

In comparison with the IVIM-DKI model at 3 T, IVIM-DKI signal modeled with the TV penalty function at 1.5 T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure

The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (D, D*, f, and K) models. The metrics Ktrans, ve, and τi were estimated using the FXR model. Cumulative incidence (CI) analysis and Fine-Gray (FG) modeling were performed considering death as a competing risk. Mean ve values were significantly different between patients with and without LRF (p = 0.03). Mean f values showed a trend towards the difference between the groups (p = 0.08). Histograms exhibited inter primary tumor heterogeneity. The CI curves showed significant differences for the dichotomized cutoff value of ADC ≤ 0.68 × 10-3 (mm2/s), D ≤ 0.74 × 10-3 (mm2/s), and f ≤ 0.18 (p < 0.05). τi ≤ 0.89 (s) cutoff value showed borderline significance (p = 0.098). FG's modeling showed a significant difference for the K cutoff value of ≤0.86 (p = 0.034). Results suggest that the role of pre-TX NGIVIM DW- and FXR DCE-MRI-derived metrics for predicting LRF in NPC than alone.

Optimal Model Mapping for Intravoxel Incoherent Motion MRI

In general, only one diffusion model would be applied to whole field-of-view voxels in the intravoxel incoherent motion-magnetic resonance imaging (IVIM-MRI) study. However, the choice of the applied diffusion model can significantly influence the estimated diffusion parameters. The quality of the diffusion analysis can influence the reliability of the perfusion analysis. This study proposed an optimal model mapping method to improve the reliability of the perfusion parameter estimation in the IVIM study. Six healthy volunteers (five males and one female; average age of 38.3 ± 7.5 years). Volunteers were examined using a 3.0 Tesla scanner. IVIM-MRI of the brain was applied at 17 b-values ranging from 0 to 2,500 s/mm². The Gaussian model, the Kurtosis model, and the Gamma model were found to be optimal for the CSF, white matter (WM), and gray matter (GM), respectively. In the mean perfusion fraction (f_p) analysis, the GM/WM ratios were 1.16 (Gaussian model), 1.80 (Kurtosis model), 1.94 (Gamma model), and 1.54 (Optimal model mapping); in the mean pseudo diffusion coefficient (D^*) analysis, the GM/WM ratios were 1.18 (Gaussian model), 1.19 (Kurtosis model), 1.56 (Gamma model), and 1.24 (Optimal model mapping). With the optimal model mapping method, the estimated f_p and D^* were reliable compared with the conventional methods. In addition, the optimal model maps, the associated products of this method, may provide additional information for clinical diagnosis.

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.

A comparative study of methods for determining Intravoxel incoherent motion parameters in cervix cancer

Background

To compare different fitting methods for determining IVIM (Intravoxel Incoherent Motion) parameters and to determine whether the use of different IVIM fitting methods would affect differentiation of cervix cancer from normal cervix tissue.

Methods

Diffusion-weighted echo-planar imaging of 30 subjects was performed on a 3.0 T scanner with b-values of 0, 30, 100, 200, 400, 1000 s/mm². IVIM parameters were estimated using the segmented (two-step) fitting method and by simultaneous fitting of a bi-exponential function. Segmented fitting was performed using two different cut-off b-values (100 and 200 s/mm²) to study possible variations due to the choice of cut-off. Friedman’s test and Student’s t-test were respectively used to compare IVIM parameters derived from different methods, and between cancer and normal tissues.

Results

No significant difference was found between IVIM parameters derived from the segmented method with b-value cutoff of 200 s/mm² and the simultaneous fitting method (P>0.05). Tissue diffusivity (D) and perfusion fraction (f) were significantly lower in cervix cancer than normal tissue (P< 0.05).

Conclusions

IVIM parameters derived using fitting methods with small cutoff b-values could be different, however, the segmented method with b-value cutoff of 200 s/mm² are consistent with the simultaneous fitting method and both can be used to differentiate between cervix cancer and normal tissue.

Diffusion and Perfusion MRI Predicts Response Preceding and Shortly After Radiosurgery to Brain Metastases: A Pilot Study

BACKGROUND AND PURPOSE

To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS).

METHODS

In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria.

RESULTS

We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, v_e , and volume transfer coefficient, K^trans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041).

CONCLUSIONS

Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.

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.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test–retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland–Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (D_app × 10⁻³ mm²/s) and kurtosis (K_app, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, D_app and K_app mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.

Quantitative Non-Gaussian Intravoxel Incoherent Motion Diffusion-Weighted Imaging Metrics and Surgical Pathology for Stratifying Tumor Aggressiveness in Papillary Thyroid Carcinomas

We assessed a priori aggressive features using quantitative diffusion-weighted imaging metrics to preclude an active surveillance management approach in patients with papillary thyroid cancer (PTC) with tumor size 1-2 cm. This prospective study enrolled 24 patients with PTC who underwent pretreatment multi-b-value diffusion-weighted imaging on a GE 3 T magnetic resonance imaging scanner. The apparent diffusion coefficient (ADC) metric was calculated from monoexponential model, and the perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), and diffusion kurtosis coefficient (K) metrics were estimated using the non-Gaussian intravoxel incoherent motion model. Neck ultrasonography examination data were used to calculate tumor size. The receiver operating characteristic curve assessed the discriminative specificity, sensitivity, and accuracy between PTCs with and without features of tumor aggressiveness. Multivariate logistic regression analysis was performed on metrics using a leave-1-out cross-validation method. Tumor aggressiveness was defined by surgical histopathology. Tumors with aggressive features had significantly lower ADC and D values than tumors without tumor-aggressive features (P < .05). The absolute relative change was 46% in K metric value between the 2 tumor types. In total, 14 patients were in the critical size range (1-2 cm) measured by ultrasonography, and the ADC and D were significantly different and able to differentiate between the 2 tumor types (P < .05). ADC and D can distinguish tumors with aggressive histological features to preclude an active surveillance management approach in patients with PTC with tumors measuring 1-2 cm.

Higher-order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

PURPOSE

Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher-order diffusion MRI models accounting for non-Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging.

METHODS

The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM-Kurtosis.

RESULTS

Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM-Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm² /ms) and malignant (1.02 ± 0.41 μm² /ms, P = .03) nodes. IVIM-Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T₂ -weighted imaging.

CONCLUSION

Higher-order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.

Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion in Differentiating Nasal Malignancies

OBJECTIVES/HYPOTHESIS

To evaluate the usefulness of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in the differentiation of sinonasal malignant tumors (SNMTs) with different histological types.

STUDY DESIGN

Retrospective observational and diagnostic study.

METHODS

Sixty-five patients with SNMTs who underwent DKI and IVIM were enrolled in this retrospective study, including 27 squamous cell carcinomas (SCCs), 13 olfactory neuroblastomas (ONBs), 14 malignant melanomas (MMs) and 11 lymphomas. The kurtosis (K) and diffusion coefficient (Dk) from DKI and the pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and the product of D* and f (f∙D*) from IVIM were measured. Kruskal-Wallis and Dunn multiple comparison tests with Bonferroni correction, receiver operating characteristic curve, and logistic regression analyses were used for statistical analysis.

RESULTS

Lymphomas demonstrated the highest K values but lowest Dk, D, D*, f, and f∙D* values among these four malignant tumors. ONBs exhibited high K values and MMs had highest D*, f, and f∙D* values. The cutoff value of ≤0.887 × 10^-3 mm² /sec for f∙D* provided a sensitivity, specificity, and an accuracy of 100%, 98.1%, and 98.5%, respectively, for differentiating lymphomas from the other three entities. The combination of f∙D* and D values showed a sensitivity of 92.9% and a specificity of 92.5% for the discrimination of MMs from ONBs and SCCs. The K value was useful for differentiating ONBs from SCCs, with a threshold value of 0.942 (sensitivity, 84.6%; specificity, 63.0%).

CONCLUSIONS

The combined use of DKI and IVIM is helpful for differentiating among four histological types of SNMTs.

LEVEL OF EVIDENCE

3 Laryngoscope, 2019.

Differentiating between malignant and benign renal tumors: do IVIM and diffusion kurtosis imaging perform better than DWI?

OBJECTIVE

To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) in differentiating between malignant and benign renal tumors.

METHODS

Multiple b value DWIs and DKIs were performed in 180 patients with renal tumors, which were divided into clear cell renal cell carcinoma (ccRCC), non-ccRCC, and benign renal tumor group. The apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) maps were calculated. The diagnostic efficacy of various diffusion parameters for predicting malignant renal tumors was compared.

RESULTS

The ADC, D, and MD values of ccRCCs were higher, while D*, f, and MK values were lower than those of benign renal tumors (all p < 0.025). The D* and f values of non-ccRCCs were lower than those of benign renal tumors (p = 0.002 and p < 0.001, respectively). The difference of ADC, D, MD, and MK values between non-ccRCCs and benign renal tumors was not statistically significant (p > 0.05). The ADC, D, MD, and f values of ccRCCs were higher, while MK values were lower than those of non-ccRCCs (all p < 0.001). The AUC values of ADC, D, D*, f, MK, and MD were 0.849, 0.891, 0.708, 0.656, 0.862, and 0.838 for differentiating ccRCCs from benign renal tumors, respectively. The AUC values of D* and f were 0.772 and 0.866 for discrimination between non-ccRCCs and benign renal tumors, respectively.

CONCLUSION

IVIM parameters are the best, while DWI and DKI parameters have similar performance in differentiating malignant and benign renal tumors.

KEY POINTS

• The D value is the best parameter for differentiating ccRCC from benign renal tumors. • The f value is the best parameter for differentiating non-ccRCC from benign renal tumors. • Conventional DWI and DKI have similar performance in differentiating malignant and benign renal tumors.

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

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.

Intravoxel incoherent motion magnetic resonance imaging of the normal-appearing parotid glands in patients with differentiated thyroid cancer after radioiodine therapy

Background Radiation damage to the salivary gland is a common complication of radioiodine therapy (RIT) in the patients with differentiated thyroid cancer (DTC). Purpose To investigate the feasibility of using intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to detect radiation-induced changes of normal-appearing parotid glands in the patients after RIT for DTC. Material and Methods We prospectively enrolled 20 patients with RIT-induced sialoadenitis and 20 healthy control (HC) participants. The patients were divided into intermediate and late groups, and a questionnaire was used to assess the related symptoms. IVIM MRI was scanned using nine b-values (0, 50, 100, 150, 200, 400, 600, 800, and 1000 s/mm²). Quantitative parameters (pseudodiffusion coefficient, D*; perfusion fraction, f; tissue diffusivity, D) were obtained using a biexponential model and compared among different groups using one-way analysis of variance (ANOVA) test. Correlations between significant parameters and symptom score were assessed using Spearman's correlation analysis. Results The f and D value differed significantly (f, P = 0.016; D, P = 0.006) among different groups. Post hoc analysis showed that f and D value of intermediate group were significantly higher than those of HC group (f, P = 0.012; D, P = 0.004), while no significant differences between late group and HC group (f, P = 0.852; D, P = 0.707). Significant positive correlation was found between f value and the total symptom score of the patients in intermediate group ( P = 0.028, r = 0.762). Conclusion The IVIM MRI might be feasible to detect the radiation-induced changes of parotid glands in the patients after RIT for DTC.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status

OBJECTIVES

To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status.

METHODS

Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%).

RESULTS

The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (K_max > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017].

CONCLUSION

ADC, Dk, K, D and f are correlated with Ki-67 LI. K_max is the strongest independent factor for predicting Ki-67 status.

KEY POINTS

• DWI-derived parameters from different models are capable of providing different pathophysiological information. • DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status. • K _max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.

Effects of diffusion time on non-Gaussian diffusion and intravoxel incoherent motion (IVIM) MRI parameters in breast cancer and hepatocellular carcinoma xenograft models

Background

Perfusion-related intravoxel incoherent motion (IVIM) and non-Gaussian diffusion magnetic resonance (MR) parameters are becoming important biomarkers for differentiating malignant from benign tumors without contrast agents. However, diffusion-time dependence has rarely been investigated in tumors.

Purpose

To investigate the relationship between diffusion time and diffusion parameters in breast cancer and hepatocellular carcinoma xenograft mouse models.

Material and Methods

Diffusion-weighted MR images (DWI) were obtained on a 7-T magnetic resonance imaging (MRI) scanner at two different diffusion times (9.6 ms and 27.6 ms) in human breast cancer (MDA-MB-231) and hepatocellular carcinoma (HepG2 and PLC/PRF/5) xenograft mouse models. Perfusion-related IVIM (fIVIM and D*) and non-Gaussian diffusion (ADC₀ and K) parameters were estimated. Parametric maps of diffusion changes with the diffusion times were generated using a synthetic apparent diffusion coefficient (sADC) obtained from b = 438 and 2584 s/mm².

Results

ADC₀ values significantly decreased when diffusion times were changed from 9.6 ms to 27.6 ms in MDA-MB-231, HepG2, and PLC/PRF/5 groups (P = 0.0163, 0.0351, and 0.0170, respectively). K values significantly increased in MDA-MB-231 and HepG2 groups (P < 0.0003 and = 0.0007, respectively); however, no significant difference was detected in the PLC/PRF/5 group. fIVIM values increased, although not significantly (P = 0.164–0.748). The maps of sADC changes showed that diffusion changes with the diffusion time were not homogeneous across tumor tissues.

Conclusion

Diffusion MR parameters in both breast cancer and HCC xenograft models were found to be diffusion time-dependent. Our results show that diffusion time is an important parameter to consider when interpreting DWI data.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

Purpose

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.

Material and methods

Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm²) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R² and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio.

Results

All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.

Conclusion

No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

To predict the radiosensitivity of nasopharyngeal carcinoma using intravoxel incoherent motion MRI at 3.0 T

PURPOSE

To investigate intravoxel incoherent motion (IVIM) MRI for evaluating the sensitivity of radiotherapy on nasopharyngeal carcinoma (NPC).

RESULTS

The reproducibility between intra-observer and inter-observer was relatively good. D (0.72×10^-3 mm²/s±0.14 vs. 0.54×10^-3 mm²/s±0.23; P < 0.001) and D* (157.92×10^-3 mm²/s±15.21 vs. 120.36×10^-3 mm²/s±10.22; P < 0.0001) were significantly higher in effective group than poor-effective group, whereas the difference of f (18.79%±2.51 vs. 16.47%±1.51) and ADC (1.21×10^-3 mm²/s±0.11 vs. 1.33×10^-3 mm²/s±0.23) could not reach statistical significant between the 2 groups (P > 0.05).

CONCLUSIONS

IVIM may be potentially useful in assessing the radiosensitivity of NPC. The higher D value combining with higher D* value might indicate the more radiosensitive of NPC, and increased D* might reflect increased blood vessel generation and parenchymal perfusion in NPC.

MATERIALS AND METHODS

Sixty consecutive patients (20 female, range, 27-83 years, mean age, 52 years) newly diagnosed NPC in the stage of T3 or T4 were enrolled. Forty-two of them were divided into effective group clinically after a standard radiotherapy according to the RECIST criteria. IVIM with 13 b-values (range, 0-800 s/mm²) and general MRI were performed at 3.0T MR scanner before and after radiotherapy. The parameters of IVIM including perfusion fraction (f), perfusion-related diffusion (D*), pure molecular diffusion (D) and apparent diffusion coefficient (ADC) were calculated. Two radiologists major in MRI diagnose analyzed all images independently and placed regions of interest (ROIs). Intra-class correlation coefficient (ICC) was used to evaluate intra-observer and inter-observer agreement. And Mann-Whitney test was used to assess the differences between the two groups.

Intravoxel incoherent motion MR imaging in nasopharyngeal carcinoma: comparison and correlation with dynamic contrast enhanced MR imaging

Objectives

To compare accuracy and assess agreement between intravoxel incoherent motion (IVIM) magnetic resonance (MR) perfusion-related parameters and quantitative dynamic contrast-enhanced (DCE) MR parameters in nasopharyngeal carcinoma (NPC).

Results

D, f, D*, K^trans, K_ep and V_p were significantly lower in the high stage group while V_e was significantly higher in the high stage group. Optimal cut-off values were: D=0.749 × 10⁻³ mm²/s; f=0.145; D*=100.401 × 10⁻³ mm²/s; K^trans=0.571/min; K_ep=0.8196/min; V_e=0.6556 %; V_p=0.0757 %. D* (p=0.001), K^trans (p<0.001), V_e (p=0.014) were all reliable independent predictors for AJCC staging. IVIM-MR perfusion-related (f, D*) and DCE-MR (K^trans, K_ep, V_e, V_p) parameters were significantly correlated (p<0.001).

Materials and Methods

75 patients with newly diagnosed NPC were prospectively recruited. Diffusion-weighted MR and DCE-MR imaging were performed with respective IVIM (D, f, D*) and DCE (K^trans, K_ep, V_e, V_p) MR parameters calculated. Patients were stratified into low and high tumor stage groups according to American Joint Committee on Cancer (AJCC) staging for determination of the predictive powers of IVIM-MR and DCE-MR parameters using t–test, ROC curve analyses and multiple logistic regression analysis. Correlation between IVIM-MR perfusion-related and DCE-MR parameters was assessed using Spearman's rank correlation.

Conclusion

IVIM-MR perfusion-related and quantitative DCE-MR parameters were significantly correlated in the assessment of NPC and were both reliable independent predictors in the prediction of AJCC staging. IVIM-MR perfusion imaging can be a potential useful non-invasive perfusion imaging tool for clinical use in the assessment of NPC.

State of the art MRI in head and neck cancer

Head and neck cancer affects more than 11,000 new patients per year in the UK¹ and imaging has an important role in the diagnosis, treatment planning, and assessment, and post-treatment surveillance of these patients. The anatomical detail produced by magnetic resonance imaging (MRI) is ideally suited to staging and follow-up of primary tumours and cervical nodal metastases in the head and neck; however, anatomical images have limitations in cancer imaging and so increasingly functional-based MRI techniques, which provide molecular, metabolic, and physiological information, are being incorporated into MRI protocols. This article reviews the state of the art of these functional MRI techniques with emphasis on those that are most relevant to the current management of patients with head and neck cancer.

Diffusional kurtosis imaging (DKI) incorporation into an intravoxel incoherent motion (IVIM) MR model to measure cerebral hypoperfusion induced by hyperventilation challenge in healthy subjects

OBJECTIVES

The objectives were to investigate the diffusional kurtosis imaging (DKI) incorporation into the intravoxel incoherent motion (IVIM) model for measurements of cerebral hypoperfusion in healthy subjects.

MATERIALS AND METHODS

Eight healthy subjects underwent a hyperventilation challenge with a 4-min diffusion weighted imaging protocol, using 8 b values chosen with the Cramer-Rao Lower Bound optimization approach. Four regions of interest in gray matter (GM) were analyzed with the DKI-IVIM model and the bi-exponential IVIM model, for normoventilation and hyperventilation conditions.

RESULTS

A significant reduction in the perfusion fraction (f) and in the product fD* of the perfusion fraction with the pseudodiffusion coefficient (D*) was found with the DKI-IVIM model, during the hyperventilation challenge. In the cerebellum GM, the percentage changes were f: -43.7 ± 40.1, p = 0.011 and fD*: -50.6 ± 32.1, p = 0.011; in thalamus GM, f: -47.7 ± 34.7, p = 0.012 and fD*: -47.2 ± 48.7, p = 0.040. In comparison, using the bi-exponential IVIM model, only a significant decrease in the parameter fD* was observed for the same regions of interest. In frontal-GM and posterior-GM, the reduction in f and fD* did not reach statistical significance, either with DKI-IVIM or the bi-exponential IVIM model.

CONCLUSION

When compared to the bi-exponential IVIM model, the DKI-IVIM model displays a higher sensitivity to detect changes in perfusion induced by the hyperventilation condition.

Intravoxel incoherent motion diffusion-weighted MRI during chemoradiation therapy to characterize and monitor treatment response in human papillomavirus head and neck squamous cell carcinoma

PURPOSE

Characterize and monitor treatment response in human papillomavirus (HPV) head and neck squamous cell carcinoma (HNSCC) using intra-treatment (intra-TX) imaging metrics derived from intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DW-MRI).

MATERIALS AND METHODS

Thirty-four (30 HPV positive [+] and 4 HPV negative [-]) HNSCC patients underwent a total of 136 MRI including multi-b value DW-MRI (pretreatment [pre-TX] and intra-TX weeks 1, 2, and 3) at 3.0 Tesla. All patients were treated with chemo-radiation therapy. Monoexponential (yielding apparent diffusion coefficient [ADC]) and bi-exponential (yielding perfusion fraction [f], diffusion [D], and pseudo-diffusion [D*] coefficients) fits were performed on a region of interest and voxel-by-voxel basis, on metastatic neck nodes. Response was assessed using RECISTv1.1. The relative percentage change in D, f, and D* between the pre- and intra-TX weeks were used for hierarchical clustering. A Wilcoxon rank-sum test was performed to assess the difference in metrics within and between the complete response (CR) and non-CR groups.

RESULTS

The delta (Δ) change in volume (V)_1wk-0wk for the CR group differed significantly (P = 0.016) from the non-CR group, while not for V_2wk-0wk and V_3wk-0wk (P > 0.05). The mean increase in ΔD_3wk-0wk for the CR group was significantly higher (P = 0.017) than the non-CR group. ADC and D showed an increasing trend at each intra-TX week when compared with pre-TX in CR group (P < 0.003). Hierarchical clustering demonstrated the existence of clusters in HPV + patients.

CONCLUSION

After appropriate validation in a larger population, these IVIM imaging metrics may be useful for individualized treatment in HNSCC patients.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:1013-1023.

A Standardized Parameter-Free Algorithm for Combined Intravoxel Incoherent Motion and Diffusion Kurtosis Analysis of Diffusion Imaging Data

OBJECTIVES

The aims of this study were to implement and systematically evaluate the performance of a new parameter-free segmented algorithm for analysis of diffusion imaging data using a combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) model of spin diffusion in comparison with the simpler intravoxel incoherent motion (IVIM) model.

MATERIALS AND METHODS

A multistep algorithm was implemented intended to separate diffusion kurtosis from IVIM effects in multi-b-value diffusion measurements using an adaptive b-value threshold technique. For each possible b-value threshold (separating diffusion and perfusion effects), diffusion kurtosis analysis of high b-values is followed by IVIM analysis keeping kurtosis parameters fixed. The b-value threshold with smallest Akaike information criterion is chosen as best model solution. The algorithm was tested in diffusion data sets of the upper abdomen from 8 healthy volunteers with 16 different b-values and compared with a standard multistep IVIM analysis.

RESULTS

The proposed algorithm could successfully be applied to all data sets and provided a significantly better fit of the observed signal decay in all assessed organs (all P < 0.03). Using the proposed IVIM-DKI model of diffusion instead of an IVIM model had a systematic impact on the resulting IVIM parameters: The pure diffusion coefficient and the pseudodiffusion coefficient were significantly increased (P < 0.03 in all assessed organs), accompanied by a decrease in the perfusion fraction in liver, pancreas, renal cortex, and skeletal muscle (all P < 0.02). Optimal b-value thresholds separating diffusion from perfusion effects had a tendency to lower values when the IVIM-DKI model was used.

CONCLUSIONS

The proposed algorithm provides a new approach for separation of IVIM and kurtosis effects of diffusion data without organ-specific adaptation.

Evaluation of Head and Neck Tumors with Functional MR Imaging

Head and neck cancer is one of the most common cancers worldwide. MR imaging-based diffusion and perfusion techniques enable the noninvasive assessment of tumor biology and physiology, which supplement information obtained from standard structural scans. Diffusion and perfusion MR imaging techniques provide novel biomarkers that can aid monitoring in pretreatment, during treatment, and posttreatment stages to improve patient selection for therapeutic strategies; provide evidence for change of therapy regime; and evaluate treatment response. This review discusses pertinent aspects of the role of diffusion and perfusion MR imaging and computational analysis methods in studying head and neck cancer.

Effects of perfusion on DTI and DKI estimates in the skeletal muscle

PURPOSE

In this study, we evaluated the effects of perfusion of the skeletal muscle on diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI) parameters and their reproducibility.

METHODS

Diffusion tensor imaging and DKI models, with and without intravoxel incoherent motion (IVIM) correction, were applied to simulated data at different physiological conditions and signal-to-noise ratio levels. Next, the same models were applied to data of the right calf of five healthy volunteers, acquired twice at 3 telsa. For six muscles, we evaluated the correlation of the perfusion signal fraction, with parameters derived from DTI and DKI, and performed repeatability analysis with and without IVIM correction. Additionally, the IVIM correction was compared to a multishell acquisition approach that minimizes perfusion effects on DTI estimates.

RESULTS

Simulations and acquired data showed that DTI and DKI estimates were biased proportionally to the perfusion signal fraction, and that IVIM correction was needed for accurate estimation of the DTI and DKI parameters. However, taking perfusion into account did not improve repeatability.

CONCLUSION

Blood perfusion has an effect on DTI and DKI estimations, but it can be minimized with IVIM correction or multishell acquisition strategies. Magn Reson Med 78:233-246, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Functional magnetic resonance imaging techniques and their development for radiation therapy planning and monitoring in the head and neck cancers

Radiation therapy (RT), in particular intensity-modulated radiation therapy (IMRT), is becoming a more important nonsurgical treatment strategy in head and neck cancer (HNC). The further development of IMRT imposes more critical requirements on clinical imaging, and these requirements cannot be fully fulfilled by the existing radiotherapeutic imaging workhorse of X-ray based imaging methods. Magnetic resonance imaging (MRI) has increasingly gained more interests from radiation oncology community and holds great potential for RT applications, mainly due to its non-ionizing radiation nature and superior soft tissue image contrast. Beyond anatomical imaging, MRI provides a variety of functional imaging techniques to investigate the functionality and metabolism of living tissue. The major purpose of this paper is to give a concise and timely review of some advanced functional MRI techniques that may potentially benefit conformal, tailored and adaptive RT in the HNC. The basic principle of each functional MRI technique is briefly introduced and their use in RT of HNC is described. Limitation and future development of these functional MRI techniques for HNC radiotherapeutic applications are discussed. More rigorous studies are warranted to translate the hypotheses into credible evidences in order to establish the role of functional MRI in the clinical practice of head and neck radiation oncology.

The Role of Intravoxel Incoherent Motion MRI in Predicting Early Treatment Response to Chemoradiation for Metastatic Lymph Nodes in Nasopharyngeal Carcinoma

INTRODUCTION

Pilot studies have suggested potential clinical applications for intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in head and neck cancers. This study aimed to characterize metastatic lymph nodes using IVIM MRI, and to evaluate the role of IVIM MRI in the prediction of the early treatment response of lymph node metastasis from nasopharyngeal carcinoma (NPC).

METHODS

A total of 122 patients with metastatic lymph nodes from NPC underwent two MRI examinations, pre-treatment and post-treatment (at 4 weeks and at ≥2 years from the end of chemoradiotherapy). Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors version 1.1. Differences in the initial IVIM parameters [pure molecular diffusion (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f)] between nodes with a partial response (PR) and a complete response (CR) were analyzed in 102 patients after the exclusion of 20.

RESULTS

The initial D*, D, and apparent diffusion coefficient (ADC) did not reveal a significant difference between nodes showing a PR or a CR. The mean initial f value was significantly higher in patients with a PR relative to patients with a CR (p = 0.003), and its sensitivity and specificity in predicting treatment response to chemoradiotherapy were 86.7% and 100%, respectively.

CONCLUSIONS

The present study indicated that the initial f value may be more accurate than the initial D*, D, and ADC in the early prediction of treatment response to chemoradiotherapy for metastatic lymph nodes in patients with NPC.

Simultaneous assessment of cerebral blood volume and diffusion heterogeneity using hybrid IVIM and DK MR imaging: initial experience with brain tumors

Objectives

To investigate the feasibility of simultaneously assessing cerebral blood volume and diffusion heterogeneity using hybrid diffusion-kurtosis (DK) and intravoxel-incoherent-motion (IVIM) MR imaging.

Methods

Fifteen healthy volunteers and 30 patients with histologically proven brain tumours (25 WHO grade II–IV gliomas and five metastases) were recruited. On a 3-T system, diffusion-weighted imaging was performed with six b-values ranging from 0 to 1,700 s/mm². Nonlinear least-squares fitting was employed to extract diffusion coefficient (D), diffusion kurtosis coefficient (K, a measure of the degree of non-Gaussian and heterogeneous diffusion) and intravascular volume fraction (f, a measure proportional to cerebral blood volume). Repeated-measures multivariate analysis of variance and receiver operating characteristic analysis were performed to assess the ability of D/K/f in differentiating contrast-enhanced tumour from peritumoral oedema and normal-appearing white matter.

Results

Based on our imaging setting (baseline signal-to-noise ratio = 32–128), coefficient of variation was 14–20 % for K, ~6 % for D and 26–44 % for f. The indexes were able to differentiate contrast-enhanced tumour (Wilks’ λ = 0.026, p < 10^-3), and performance was greatest with K, followed by f and D.

Conclusions

Hybrid DK IVIM imaging is capable of simultaneously measuring cerebral perfusion and diffusion indexes that together may improve brain tumour diagnosis.

Key Points

• Hybrid DK-IVIM imaging allows simultaneous measurement of K, D and f.

• Combined K/D/f better demarcates contrast-enhanced tumour than they do separately.

• f correlates better with contrast-leakage-corrected CBV_DSCthan with uncorrected CBV_DSC.