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

Relationship between diffusion parameters derived from intravoxel incoherent motion MRI and perfusion measured by dynamic contrast-enhanced MRI of soft tissue tumors

Our aim was to evaluate the link between diffusion parameters measured by intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and the perfusion metrics obtained with dynamic contrast-enhanced (DCE) MRI in soft tissue tumors (STTs). Twenty-eight patients affected by histopathologically confirmed STT were included in a prospective study. All patients underwent both DCE MRI and IVIM DWI. The perfusion fraction f, diffusion coefficient D and perfusion-related diffusion coefficient D* were estimated using a bi-exponential function to fit the DWI data. DCE MRI was acquired with a temporal resolution of 3-5 s. Maps of the initial area under the gadolinium concentration curve (IAUGC), time to peak (TTP) and maximum slope of increase (MSI) were derived using commercial software. The relationships between the DCE MRI and IVIM DWI measurements were assessed by Spearman's test. To exclude false positive results under multiple testing, the false discovery rate (FDR) procedure was applied. The Mann-Whitney test was used to evaluate the differences between all variables in patients with non-myxoid and myxoid STT. No significant relationship was found between IVIM parameters and any DCE MRI parameters. Higher f and D*f values were found in non-myxoid tumors compared with myxoid tumors (p = 0.004 and p = 0.003, respectively). MSI was significantly higher in non-myxoid tumors than in myxoid tumors (p = 0.029). From the visual assessments of single clinical cases, both f and D*f maps were in satisfactory agreement with DCE maps in the extreme cases of an avascular mass and a highly vascularized mass, whereas, for tumors with slight vascularity or with a highly heterogeneous perfusion pattern, this association was not straightforward. Although IVIM DWI was demonstrated to be feasible in STT, our data did not support evident relationships between perfusion-related IVIM parameters and perfusion measured by DCE MRI.

Parameter Estimation Error Dependency on the Acquisition Protocol in Diffusion Kurtosis Imaging

Mono-exponential kurtosis model is routinely fitted on diffusion weighted, magnetic resonance imaging data to describe non-Gaussian diffusion. Here, the purpose was to optimize acquisitions for this model to minimize the errors in estimating diffusion coefficient and kurtosis. Similar to a previous study, covariance matrix calculations were used, and coefficients of variation in estimating each parameter of this model were calculated. The acquisition parameter, b values, varied in discrete grids to find the optimum ones that minimize the coefficient of variation in estimating the two non-Gaussian parameters. Also, the effect of variation of the target values on the optimized values was investigated. Additionally, the results were benchmarked with Monte Carlo noise simulations. Simple correlations were found between the optimized b values and target values of diffusion and kurtosis. For small target values of the two parameters, there is higher chance of having significant errors; this is caused by maximum b value limits imposed by the scanner than the mathematical bounds. The results here, cover a wide range of parameters D and K so that they could be used in many directionally averaged diffusion weighted cases such as head and neck, prostate, etc.

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors

PURPOSE

To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis.

MATERIALS AND METHODS

This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44 ± 11.14 years, 50 malignant lesions and 12 benign) who underwent contrast-enhanced 3 T breast MRI and diffusion-weighted imaging. Apparent diffusion coefficient (ADC) and IVIM biomarkers of tissue diffusivity (Dt), perfusion fraction (fp), and pseudo-diffusivity (Dp) were calculated using voxel-based analysis for the whole lesion volume. Histogram analysis was performed to quantify tumour heterogeneity. Comparisons were made using Mann-Whitney tests between benign/malignant status, histological subtype, and molecular prognostic factor status while Spearman's rank correlation was used to characterize the association between imaging biomarkers and prognostic factor expression.

RESULTS

The average values of the ADC and IVIM biomarkers, Dt and fp, showed significant differences between benign and malignant lesions. Additional significant differences were found in the histogram parameters among tumour subtypes and molecular prognostic factor status. IVIM histogram metrics, particularly fp and Dp, showed significant correlation with hormonal factor expression.

CONCLUSION

Advanced diffusion imaging biomarkers show relationships with molecular prognostic factors and breast cancer malignancy. This analysis reveals novel diagnostic metrics that may explain some of the observed variability in treatment response among breast cancer patients.

KEY POINTS

• Novel IVIM biomarkers characterize heterogeneous breast cancer. • Histogram analysis enables quantification of tumour heterogeneity. • IVIM biomarkers show relationships with breast cancer malignancy and molecular prognostic factors.

Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice

Technologic advances enable performance of diffusion-weighted imaging (DWI) at ultrahigh b-values, where standard monoexponential model analysis may not apply. Rather, non-Gaussian water diffusion properties emerge, which in cellular tissues are, in part, influenced by the intracellular environment that is not well evaluated by conventional DWI. The novel technique, diffusion kurtosis imaging (DKI), enables characterization of non-Gaussian water diffusion behavior. More advanced mathematical curve fitting of the signal intensity decay curve using the DKI model provides an additional parameter Kapp that presumably reflects heterogeneity and irregularity of cellular microstructure, as well as the amount of interfaces within cellular tissues. Although largely applied for neural applications over the past decade, a small number of studies have recently explored DKI outside the brain. The most investigated organ is the prostate, with preliminary studies suggesting improved tumor detection and grading using DKI. Although still largely in the research phase, DKI is being explored in wider clinical settings. When assessing extracranial applications of DKI, careful attention to details with which body radiologists may currently be unfamiliar is important to ensure reliable results. Accordingly, a robust understanding of DKI is necessary for radiologists to better understand the meaning of DKI-derived metrics in the context of different tumors and how these metrics vary between tumor types and in response to treatment. In this review, we outline DKI principles, propose biostructural basis for observations, provide a comparison with standard monoexponential fitting and the apparent diffusion coefficient, report on extracranial clinical investigations to date, and recommend technical considerations for implementation in body imaging.

Repeatability Investigation of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging on Thyroid Glands

OBJECTIVE

To investigate the repeatability of the quantitative magnetic resonance imaging (MRI) metric (apparent diffusion coefficient [ADC]) derived from reduced field-of-view diffusion-weighted (rFOV DWI) on thyroid glands in a clinical setting.

MATERIALS AND METHODS

Ten healthy human volunteers were enrolled in MRI studies performed on a 3-T MRI scanner. Each volunteer was designed to undergo 3 longitudinal examinations (2 weeks apart) with 2 repetitive sessions within each examination, which included rFOV and conventional full field-of-view (fFOV) DWI scans. Diffusion-weighted images were assessed and scored based on image characteristics. Apparent diffusion coefficient values of thyroid glands from all participants were calculated based on regions of interest. Repeatability analysis was performed based on the framework proposed by the Quantitative Imaging Biomarker Alliance, generating 4 repeatability metrics: within-participant variance ((Equation is included in full-text article.)), repeatability coefficients, intraclass correlation coefficient, and within-participant coefficient of variation. Student t test was used to compare the performance difference between rFOV and fFOV DWI.

RESULTS

The overall image quality from rFOV DWI was significantly higher than that from fFOV DWI (P = 0.04). The ADC values calculated from rFOV DWI were significantly lower than corresponding values from fFOV DWI (P < 0.001). There was no significant difference in ADC values across sessions and examinations in either rFOV or fFOV DWI (P > 0.05). Reduced field-of-view DWI had lower values of (Equation is included in full-text article.), repeatability coefficient, and within-participant coefficient of variation and had a higher value of intraclass correlation coefficient compared with fFOV DWI across either sessions or examinations.

CONCLUSIONS

This study demonstrated that rFOV DWI produced more superior-quality DWI images and more repeatable ADC measurements compared with fFOV DWI, thus providing a feasible quantitative imaging tool for investigating thyroid glands in clinical settings.

A potential risk of overestimating apparent diffusion coefficient in parotid glands

Objectives

To investigate transient signal loss on diffusion weighted images (DWI) and overestimation of apparent diffusion coefficient (ADC) in parotid glands using single shot echoplanar DWI (EPDWI).

Materials and Methods

This study enrolled 6 healthy subjects and 7 patients receiving radiotherapy. All participants received dynamic EPDWI with a total of 8 repetitions. Imaging quality of DWI was evaluated. Probability of severe overestimation of ADC (soADC), defined by an ADC ratio more than 1.2, was calculated. Error on T2WI, DWI, and ADC was computed. Statistical analysis included paired Student t testing and Mann-Whitney U test. A P value less than 0.05 was considered statistically significant.

Results

Transient signal loss was visually detected on some excitations of DWI but not on T2WI or mean DWI. soADC occurred randomly among 8 excitations and 3 directions of diffusion encoding gradients. Probability of soADC was significantly higher in radiotherapy group (42.86%) than in healthy group (24.39%). The mean error percentage decreased as the number of excitations increased on all images, and, it was smallest on T2WI, followed by DWI and ADC in an increasing order.

Conclusions

Transient signal loss on DWI was successfully detected by dynamic EPDWI. The signal loss on DWI and overestimation of ADC could be partially remedied by increasing the number of excitations.

A simple noise correction scheme for diffusional kurtosis imaging

PURPOSE

Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis.

METHODS

Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates.

RESULTS

As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR.

CONCLUSION

The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms.

Intravoxel incoherent motion MRI: emerging applications for nasopharyngeal carcinoma at the primary site

Objectives

We compared pure molecular diffusion (D), perfusion-related diffusion (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) based on intravoxel incoherent motion (IVIM) theory in patients with nasopharyngeal carcinoma (NPC).

Methods

Sixty-five consecutive patients (48 men) with suspected NPC were examined using a 3.0-T MR system. Diffusion-weighted imaging (DWI) was performed with 13 b values (range, 0–800 s/mm²). We regarded the result of endoscopy and biopsy as the gold standard for detection. D, D* and f were compared between patients with primary NPC and enlarged adenoids.

Results

IVIM DWI was successful in 37 of 40 NPC and 23 of 25 enlarged adenoids cases. D (P = 0.001) and f (P < 0.0001) were significantly lower in patients with NPC than in patients with enlarged adenoids, whereas D* was significantly higher (P < 0.0001). However, the ADC was not significantly different between the two groups (P > 0.05). The area under the ROC curve (AUC) for D was 0.849 and was significantly larger than that for ADC (P < 0.05).

Conclusions

IVIM DWI is a feasible technique for investigating primary NPC. D was significantly decreased in primary NPC, and increased D* reflected increased blood vessel generation and parenchymal perfusion in primary NPC.

Key Points

• Intravoxel incoherent motion (IVIM) analysis permits separate quantification of diffusion and perfusion.

• IVIM DWI is a feasible technique for investigating primary NPC.

• IVIM suggests that primary NPC tissue voxels exhibit both perfusion and diffusion.

Comparison of accuracy of intravoxel incoherent motion and apparent diffusion coefficient techniques for predicting malignancy of head and neck tumors using half-Fourier single-shot turbo spin-echo diffusion-weighted imaging

PURPOSE

To evaluate the use of the intravoxel incoherent motion (IVIM) technique in half-Fourier single-shot turbo spin-echo (HASTE) diffusion-weighted imaging (DWI), and to compare its accuracy to that of apparent diffusion coefficient (ADC) to predict malignancy in head and neck tumors.

PATIENTS AND METHODS

HASTE DW images of 33 patients with head and neck tumors (10 benign and 23 malignant) were evaluated. Using the IVIM technique, parameters (D, true diffusion coefficient; f, perfusion fraction; D*, pseudodiffusion coefficient) were calculated for each tumor. ADC values were measured over a range of b values from 0 to 1000s/mm(2). IVIM parameters and ADC values in benign and malignant tumors were compared using Student's t test, receiver operating characteristics (ROC) analysis, and multivariate logistic regression modeling.

RESULTS

Mean ADC and D values of malignant tumors were significantly lower than those of benign tumors (P<0.05). Mean D* values of malignant tumors were significantly higher than those of benign tumors (P<0.05). There was no significant difference in mean f values between malignant and benign tumors (P>0.05). The technique of combining D and D* was the best for predicting malignancy; accuracy for this model was higher than that for ADC.

CONCLUSIONS

The IVIM technique may be applied in HASTE DWI as a diagnostic tool to predict malignancy in head and neck masses. The use of D and D* in combination increases the diagnostic accuracy in comparison with ADC.

Non-Gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma

PURPOSE

To technically investigate the non-Gaussian diffusion of head and neck diffusion weighted imaging (DWI) at 3 Tesla and compare advanced non-Gaussian diffusion models, including diffusion kurtosis imaging (DKI), stretched-exponential model (SEM), intravoxel incoherent motion (IVIM) and statistical model in the patients with nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

After ethics approval was granted, 16 patients with NPC were examined using DWI performed at 3T employing an extended b-value range from 0 to 1500 s/mm(2). DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models on primary tumor, metastatic node, spinal cord and muscle. Non-Gaussian parameter maps were generated and compared to apparent diffusion coefficient (ADC) maps in NPC.

RESULTS

Diffusion in NPC exhibited non-Gaussian behavior at the extended b-value range. Non-Gaussian models achieved significantly better fitting of DWI signal than the mono-exponential model. Non-Gaussian diffusion coefficients were substantially different from mono-exponential ADC both in magnitude and histogram distribution.

CONCLUSION

Non-Gaussian diffusivity in head and neck tissues and NPC lesions could be assessed by using non-Gaussian diffusion models. Non-Gaussian DWI analysis may reveal additional tissue properties beyond ADC and holds potentials to be used as a complementary tool for NPC characterization.

Assessment of diffusion parameters by intravoxel incoherent motion MRI in head and neck squamous cell carcinoma

The objectives of this study were to assess the diffusion parameters derived from intravoxel incoherent motion (IVIM) MRI in head and neck squamous cell carcinoma (HNSCC) and to investigate the agreement between different methods of tumor delineation and two numerical methods to extract the perfusion fraction f. Thirty-seven untreated patients with histopathologically confirmed primary HNSCC were included retrospectively in the study. The entire volume of the primary tumor was outlined on diffusion-weighted images using co-registered morphological images as a guide to the tumor location. Apparent diffusion coefficient (ADC) and IVIM diffusion parameters were estimated considering the largest tumor section as well as the entire tumor volume. A bi-exponential fit was implemented to extract f, D (pure diffusion coefficient) and D* (pseudo-diffusion coefficient). A second simplified method, based on an asymptotic extrapolation, was used to determine f. The agreement between ADC and IVIM diffusion parameters derived from the delineation of single and multiple slices, and between the two f estimations, was assessed by Bland-Altman plots. The inter-slice variability of ADC and IVIM diffusion parameters was evaluated. The Kruskal-Wallis test was used to investigate whether the tumor location had a statistically significant influence on the values of the parameters. Comparing the tumor delineation methods, a better accordance was found for ADC and D, with a mean percentage difference of less than 2%. Larger discrepancies were found for f and D*, with mean differences of 4.5% and 5.5%, respectively. When comparing the two f estimation methods, small mean differences were found (<3.5%), suggesting that the two methods may be considered as equivalent for the assessment of f in our patient population. The observed ADC and IVIM diffusion parameters were dependent on the anatomic site of the lesion, carcinoma of the nasopharynx showing more homogeneous and dissimilar estimations than other HNSCCs.

Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma

PURPOSE

To study the differentiation of malignant breast lesions from benign lesions and fibroglandular tissue (FGT) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters.

MATERIALS AND METHODS

This retrospective study included 26 malignant and 14 benign breast lesions in 35 patients who underwent diffusion-weighted MRI at 3.0T and nine b-values (0-1000 s/mm(2) ). ADC and IVIM parameters (perfusion fraction fp , pseudodiffusion coefficient Dp , and true diffusion coefficient Dd ) were determined in lesions and FGT. For comparison, IVIM was also measured in 16 high-risk normal patients. A predictive model was constructed using linear discriminant analysis. Lesion discrimination based on ADC and IVIM parameters was assessed using receiver operating characteristic (ROC) and area under the ROC curve (AUC).

RESULTS

In FGT of normal subjects, fp was 1.1 ± 1.1%. In malignant lesions, fp (6.4 ± 3.1%) was significantly higher than in benign lesions (3.1 ± 3.3%, P = 0.0025) or FGT (1.5 ± 1.2%, P < 0.001), and Dd ((1.29 ± 0.28) × 10(-3) mm(2) /s) was lower than in benign lesions ((1.56 ± 0.28) × 10(-3) mm(2) /s, P = 0.011) or FGT ((1.86 ± 0.34) × 10(-3) mm(2) /s, P < 0.001). A combination of Dd and fp provided higher AUC for discrimination between malignant and benign lesions (0.84) or FGT (0.97) than ADC (0.72 and 0.86, respectively).

CONCLUSION

The IVIM parameters provide accurate identification of malignant lesions.

Utility of MRI diffusion techniques in the evaluation of tumors of the head and neck

The use of diffusion-weighted imaging in the head and neck is an increasingly used technique that requires adaptation of the acquisition parameters. Parallel imaging and emerging techniques such as IVIM are playing a new role. The main indications for performing DWI are tissue characterization, nodal staging and therapy monitoring. Lower apparent diffusion coefficients have been reported in this region for malignant lesions such as SCC, lymphoma and metastatic lymph node, as opposed to higher ADC in benign lesions and lymph nodes. Follow-up and early response to treatment are reflected in an ADC increase in both primary tumor and nodal metastasis.