Normal Brain and Brain Tumor: Multicomponent Apparent Diffusion Coefficient Line Scan Imaging

An open relaxation-diffusion MRI dataset in neurosurgical studies

Diffusion MRI (dMRI) is a safe and noninvasive technique that provides insight into the microarchitecture of brain tissue. Relaxation-diffusion MRI (rdMRI) is an extension of traditional dMRI that captures diffusion imaging data at multiple TEs to detect tissue heterogeneity between relaxation and diffusivity. rdMRI has great potential in neurosurgical research including brain tumor grading and treatment response evaluation. However, the lack of available data has limited the exploration of rdMRI in clinical settings. To address this, we are sharing a high-quality rdMRI dataset from 18 neurosurgical patients with different types of lesions, as well as two healthy individuals as controls. The rdMRI data was acquired using 7 TEs, where at each TE multi-shell dMRI with high spatial and angular resolutions is obtained at each TE. Each rdMRI scan underwent thorough artifact and distortion corrections using a specially designed processing pipeline. The dataset's quality was assessed using standard practices, including quality control and assurance. This resource is a valuable addition to neurosurgical studies, and all data are openly accessible.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Low-Field Magnetic Resonance Imaging: Its History and Renaissance

ABSTRACT

Low-field magnetic resonance imaging (MRI) systems have seen a renaissance recently due to improvements in technology (both hardware and software). Originally, the performance of low-field MRI systems was rated lower than their actual clinical usefulness, and they were viewed as low-cost but poorly performing systems. However, various applications similar to high-field MRI systems (1.5 T and 3 T) have gradually become possible, culminating with high-performance low-field MRI systems and their adaptations now being proposed that have unique advantages over high-field MRI systems in various aspects. This review article describes the physical characteristics of low-field MRI systems and presents both their advantages and disadvantages for clinical use (past to present), along with their cutting-edge clinical applications.

Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging

SIMPLE SUMMARY

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM.

PURPOSE

Non-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival.

METHODS AND MATERIALS

Thirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS.

RESULTS

The large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).

CONCLUSION

The DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.

The diagnostic value of intravoxel incoherent motion imaging in differentiating high-grade from low-grade gliomas: a systematic review and meta-analysis

OBJECTIVE

This meta-analysis was carried out for assessing the accuracy of intravoxel incoherent motion (IVIM) parameters true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) in differentiating low-grade gliomas (LGGs) from high-grade gliomas (HGGs).

METHODS

Literatures concerning IVIM in the grading of brain gliomas published prior to October 20, 2020, searched in the Embase, PubMed, and Cochrane library. Use the quality assessment of diagnostic accuracy studies 2 (QUADAS 2) to evaluate the quality of studies. We estimated the pooled sensitivity, specificity, and the area under the summary ROC (SROC) curve to identification the accuracy of IVIM parameters D, D*, and f evaluation in grading gliomas.

RESULTS

Totally, 6 articles including 252 brain gliomas conform to the inclusion criteria. The pooled sensitivity of parameters D, D*, and f derived from IVIM were 0.85 (95%Cl, 0.76-0.91), 0.78 (95%Cl, 0.71-0.85), and 0.89 (95%Cl, 0.76-0.96), respectively. The pooled specificity were 0.78 (95%Cl, 0.60-0.90), 0.68 (95%Cl, 0.56-0.79), and 0.88 (95%Cl, 0.76-0.94), respectively. Meanwhile, the AUC of SROC curve were 0.89 (95%Cl, 0.86-0.92) , 0.81 (95%Cl, 0.77-0.84), and 0.94 (95%Cl, 0.92-0.96), respectively.

CONCLUSION

This meta-analysis suggested that IVIM parameters D, D*, and f have moderate or high diagnosis value accuracy in differentiating HGGs from LGGs, and the parameter f has greater sensitivity and specificity. Standardized methodology is warranted to guide the use of this method for clinical decision-making. However, more clinical studies are needed to prove our view.

ADVANCES IN KNOWLEDGE

IVIM parameter f showed greater sensitivity and specificity, as well as excellent performance than parameter D* and D.

Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma

Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm² were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (V_in), and extracellular (D_ex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, D_ex, and V_in from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). V_in values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.

A Bayesian approach to diffusional kurtosis imaging

PURPOSE

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

THEORY

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

PURPOSE

To prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG).

MATERIALS AND METHODS

A total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34-75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5-T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis.

RESULTS

Mean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10^-3 mm²/s and 1.21×10^-3 mm²/s cut-off values, yielding 70% sensitivity each (95% CI: 59-82% and 61-80%, respectively), 80% (95% CI: 79-98%) and 90% (95% CI: 82-97%) specificity, and 73% (95% CI: 66-91%) and 76% (95% CI: 72-90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58-83%) and 70% (95% CI: 59-84%), specificities of 90% (95% CI: 80-95%) and 70% (95% CI: 64-83%) and accuracies of 79% (95% CI: 69-89%) and 70% (95% CI: 64-77%), respectively.

CONCLUSION

Measurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

Identification and Classification of Alzheimer's Disease Patients Using Novel Fractional Motion Model

Most diffusion magnetic resonance imaging (dMRI) techniques use the mono-exponential model to describe the diffusion process of water in the brain. However, the observed dMRI signal decay curve deviates from the mono-exponential form. To solve this problem, the fractional motion (FM) model has been developed, which is regarded as a more appropriate model for describing the complex diffusion process in brain tissue. It is still unclear in the identification and classification of Alzheimer's disease (AD) patients using the FM model. The purpose of this study was to investigate the potential feasibility of FM model for differentiating AD patients from healthy controls and grading patients with AD. Twenty-four patients with AD and 11 healthy controls were included. The left and right hippocampus were selected as regions of interest (ROIs). The apparent diffusion coefficient (ADC) values and FM-related parameters, including the Noah exponent (α), the Hurst exponent (H), and the memory parameter (μ=H-1/α), were calculated and compared between AD patients and healthy controls and between mild AD and moderate AD patients using a two-sample t-test. The correlations between FM-related parameters α, H, μ, and ADC values and the cognitive functions assessed by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scales were investigated using Pearson partial correlation analysis in patients with AD. The receiver-operating characteristic analysis was used to assess the differential performance. We found that the FM-related parameter α could be used to distinguish AD patients from healthy controls (P < 0.05) with greater sensitivity and specificity (left ROI, 0.917 and 0.636; right ROI, 0.917 and 0.727) and grade AD patients (P < 0.05) showed higher sensitivity and specificity (right ROI, 0.917, 0.75). The α was found to be positively correlated with MMSE (P < 0.05) and MoCA (P < 0.05) scores in patients with AD, indicating that the α values in the bilateral hippocampus were a potential MRI-based biomarker of disease severity in AD patients. This novel diffusion model may be useful for further understanding neuropathologic changes in patients with AD.

Three-dimensional simultaneous brain T1 , T2 , and ADC mapping with MR Multitasking

PURPOSE

To develop a simultaneous T₁ , T₂ , and ADC mapping method that provides co-registered, distortion-free images and enables multiparametric quantification of 3D brain coverage in a clinically feasible scan time with the MR Multitasking framework.

METHODS

The T₁ /T₂ /diffusion weighting was generated by a series of T₂ preparations and diffusion preparations. The underlying multidimensional image containing 3 spatial dimensions, 1 T₁ weighting dimension, 1 T₂ -preparation duration dimension, 1 b-value dimension, and 1 diffusion direction dimension was modeled as a 5-way low-rank tensor. A separate real-time low-rank model incorporating time-resolved phase correction was also used to compensate for both inter-shot and intra-shot phase inconsistency induced by physiological motion. The proposed method was validated on both phantom and 16 healthy subjects. The quantification of T₁ /T₂ /ADC was evaluated for each case. Three post-surgery brain tumor patients were scanned for demonstration of clinical feasibility.

RESULTS

Multitasking T₁ /T₂ /ADC maps were perfectly co-registered and free from image distortion. Phantom studies showed substantial quantitative agreement ( R 2 = 0.999 ) with reference protocols for T₁ /T₂ /ADC. In vivo studies showed nonsignificant T₁ (P = .248), T₂ (P = .97), ADC (P = .328) differences among the frontal, parietal, and occipital regions. Although Multitasking showed significant differences of T₁ (P = .03), T₂ (P < .001), and ADC (P = .001) biases against the references, the mean bias estimates were small (ΔT₁ % < 5%, ΔT₂ % < 7%, ΔADC% < 5%), with all intraclass correlation coefficients greater than 0.82 indicating "excellent" agreement. Patient studies showed that Multitasking T₁ /T₂ /ADC maps were consistent with the clinical qualitative images.

CONCLUSION

The Multitasking approach simultaneously quantifies T₁ /T₂ /ADC with substantial agreement with the references and is promising for clinical applications.

Evaluation of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging for Detection of Bowel Inflammation in Patients With Crohn Disease

OBJECTIVES

This study aimed to evaluate the feasibility of intravoxel incoherent motion diffusion-weighted magnetic resonance imaging (DW-MRI) in detecting bowel inflammation in patients with Crohn disease (CD).

METHODS

Sixteen patients who underwent intravoxel incoherent motion DW-MRI for CD and colonoscopy were recruited. Seventy-nine bowel segments were selected, and their mean D, D*, f, and apparent diffusion coefficient (ADC) values were measured. The receiver operating characteristic curve was performed to distinguish inflamed from normal bowel.

RESULTS

The mean D, D*, f, and ADC values of inflamed bowel were significantly lower than those of normal bowel (P < 0.05). The area under the receiver operating characteristic curve for f (0.906) and ADC values (0.924) was greater than that for D (0.709) or D* values (0.686) for differentiating inflamed bowel from normal bowel (P < 0.05).

CONCLUSIONS

Intravoxel incoherent motion DW-MRI is a feasible technique for detecting inflammation in CD patients. The ADC and f values have more potential than the D and D* values.

Anomalous water dynamics in brain: a combined diffusion magnetic resonance imaging and neutron scattering investigation

Water diffusion is an optimal tool for investigating the architecture of brain tissue on which modern medical diagnostic imaging techniques rely. However, intrinsic tissue heterogeneity causes systematic deviations from pure free-water diffusion behaviour. To date, numerous theoretical and empirical approaches have been proposed to explain the non-Gaussian profile of this process. The aim of this work is to shed light on the physics piloting water diffusion in brain tissue at the micrometre-to-atomic scale. Combined diffusion magnetic resonance imaging and first pioneering neutron scattering experiments on bovine brain tissue have been performed in order to probe diffusion distances up to macromolecular separation. The coexistence of free-like and confined water populations in brain tissue extracted from a bovine right hemisphere has been revealed at the micrometre and atomic scale. The results are relevant for improving the modelling of the physics driving intra- and extracellular water diffusion in brain, with evident benefit for the diffusion magnetic resonance imaging technique, nowadays widely used to diagnose, at the micrometre scale, brain diseases such as ischemia and tumours.

Detection of subthreshold atrophy in crossed cerebellar degeneration via two-compartment mathematical modeling of cell density in DWI: A proof of concept study

Crossed cerebellar diaschisis (CCD) refers to transneuronal degeneration of the corticopontocerebellar pathway, resulting in atrophy of cerebellum contralateral to supratentorial pathology. CCD is traditionally diagnosed on nuclear medicine studies. Our aim is to apply a biexponential diffusion model, composed of intracellular and extracellular compartments, to the detection of subthreshold CCD on DWI, with the calculated fraction of the intracellular compartment as a proposed measure of cell density. At a voxel-by-voxel basis, we solve for intracellular and extracellular coefficients in each side of the cerebellum and compare the distribution of coefficients between each hemisphere. We demonstrate, in all six CCD cases, a significantly lower contribution of the intracellular compartment to the cerebellar hemisphere contralateral to supratentorial pathology (p < 0.01). In a separate, proof-of-concept case of pontine stroke, we also demonstrate reduced intracellular coefficients in bilateral cerebellar hemispheres, excluding middle cerebellar peduncles (p < 0.01). Our findings are consistent with a decreased intracellular fraction, presumably a surrogate for reduced cellular density in corticopontocerebellar degeneration, despite normal-appearing scans. Our approach allows detection of subthreshold structural changes and offers the additional advantage of applicability to most clinical cases, where only three DWI beta values are available.

Initial experience of correlating diffusion spectral parameters with histopathologic indexes in murine colorectal tumor homografts

Purpose

To determine the correlation between continuously distributed diffusion-weighted image (DWI)-derived parameters and histopathologic indexes.

Methods

Fifty-four mice bearing HCT-116 colorectal tumors were included for analysis; 12 mice were used for continuous observation, and the other 42 mice were used for break-point observation. All mice were randomly divided into radiotherapy and non-radiotherapy groups. Optical imaging and MRI were performed at different time points according to radiotherapy regimen (baseline, 24 h, 48 h, 72 h, 7 d, 14 d, and 28 d). Continuous observation data were analyzed to show the difference of dynamic changing trends of optical and MR-DWI–derived parameters between radiotherapy and non-radiotherapy groups (photon numbers, D_max, full width half maximum [FWHM], and apparent diffusion coefficient [ADC] value). Break-point observation data were used to analyze the correlation between histopathologic indices and DWI-derived parameters.

Results

There was a significant difference in the changing trends of photon numbers, D_max, FWHM, and ADC value between radiotherapy and non-radiotherapy groups, especially at early time points. There was moderate negative correlation between Ki67 and percentage changes of D_max, FWHM, and ADC values (the correlation coefficients were 0.632, 0.449, and 0.586, P<0.001, P=0.008, and P<0.001, respectively). There was moderate negative correlation between survivin and percentage changes of D_max and ADC values (correlation coefficients were 0.496 and 0.473, P=0.004 and P=0.006, respectively).

Conclusion

The continuously distributed DWI-derived parameters could reflect histological behavior to some extent and, thus, are potential markers for early noninvasive monitoring of tumor cell apoptosis and proliferation.

Value of multi-b value DWI in the assessment of early cerebral changes in asymptomatic HIV-positive adolescents

Background Magnetic resonance imaging (MRI) and functional MRI techniques have been widely used in the diagnosis of human immunodeficiency virus (HIV) infection related diseases. Purpose To explore whether magnetic resonance diffusion-weighted imaging (DWI) can track water molecular diffusion changes in the brain of asymptomatic HIV-positive adolescents. Material and Methods Multi-b value DWI was performed in 23 adolescents, including 15 HIV-positive participants and eight HIV-negative healthy participants. Mean apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (ADCs) values, fast apparent diffusion coefficient (ADCf) values, distribution diffusion coefficient (DDC) values, and heterogeneity index (α) values were calculated within regions of interest (ROIs) in the frontal lobes, basal ganglia, and temporal lobe. Non-parametric tests were then performed. Results In the bilateral frontal lobes, the mean α values in HIV-positive participants were significantly increased compared with those in healthy participants (right side P = 0.001; left side P = 0.000). In the left frontal lobe, the mean DDC value in HIV-positive participants was significantly increased compared with that in healthy participants ( P = 0.008). In the bilateral frontal lobes, the mean ADCf values in HIV-positive participants were significantly lower than those in healthy participants (right side P = 0.011; left side P = 0.008). In the left basal ganglia, the mean α values in HIV-positive participants were significantly lower than that in healthy participants ( P = 0.013). Conclusion Multi-b value DWI could reflect the early characteristics of water molecule diffusion in HIV infections.

Preoperative grading of supratentorial nonenhancing gliomas by high b-value diffusion-weighted 3 T magnetic resonance imaging

The purpose of this study was to determine the difference in discrimination between high- and low-grade supratentorial nonenhancing gliomas (HGGs and LGGs, respectively) when using apparent diffusion coefficient (ADC) values with high or standard b-value. Thirty-nine patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging (DWI) with standard and high b-values (b = 1000 and 3000 s/mm², respectively). Minimum, maximum, and mean ADC values (ADC_MIN, ADC_MAX, and ADC_MEAN, respectively) were measured from ADC maps with both b-values. Receiver operating curve analysis was used to determine the cutoff ADC values for distinguishing between nonenhancing HGGs and LGGs. ADC_MIN, ADC_MAX, and ADC_MEAN values for the nonenhancing HGGs were lower than those for LGGs. These differences were much larger when a high b-value was used (all P < 0.0001) than when a standard b-value was used (P = 0.0001, <0.0001, and <0.0001, respectively). Discriminant analysis indicated that the greatest likelihood for discriminating HGGs and LGGs when ADC_MEAN was obtained with a high b-value, with cutoff value of 0.814 × 10^-3 mm²/s. ADC values obtained with a high b-value can be useful for grading and surgical management of nonenhancing HGGs and LGGs. The lowest degree of overlap was obtained when ADC_MEAN was determined with a b-value of 3000 s/mm².

Optimal experimental design for filter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors

PURPOSE

Filter exchange imaging (FEXI) is sensitive to the rate of diffusional water exchange, which depends, eg, on the cell membrane permeability. The aim was to optimize and analyze the ability of FEXI to infer differences in the apparent exchange rate (AXR) in the brain between two populations.

METHODS

A FEXI protocol was optimized for minimal measurement variance in the AXR. The AXR variance was investigated by test-retest acquisitions in six brain regions in 18 healthy volunteers. Preoperative FEXI data and postoperative microphotos were obtained in six meningiomas and five astrocytomas.

RESULTS

Protocol optimization reduced the coefficient of variation of AXR by approximately 40%. Test-retest AXR values were heterogeneous across normal brain regions, from 0.3 ± 0.2 s-1 in the corpus callosum to 1.8 ± 0.3 s-1 in the frontal white matter. According to analysis of statistical power, in all brain regions except one, group differences of 0.3-0.5 s-1 in the AXR can be inferred using 5 to 10 subjects per group. An AXR difference of this magnitude was observed between meningiomas (0.6 ± 0.1 s-1 ) and astrocytomas (1.0 ± 0.3 s-1 ).

CONCLUSIONS

With the optimized protocol, FEXI has the ability to infer relevant differences in the AXR between two populations for small group sizes. Magn Reson Med 77:1104-1114, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Anomalous diffusion in cerebral glioma assessed using a fractional motion model

PURPOSE

To demonstrate the capability of the fractional motion (FM) model for describing anomalous diffusion in cerebral gliomas and to assess the potential feasibility of FM for grading these tumors.

METHODS

Diffusion MRI images were acquired from brain tumor patients using a special Stejskal-Tanner diffusion sequence with variable diffusion gradient amplitudes and separation times. Patients with histopathologically confirmed gliomas, including astrocytic and oligoastrocytic tumors, were selected. The FM-related parameters, including the Noah exponent ( α), the Hurst exponent ( H), and the memory parameter ( μ=H-1/α), were calculated and compared between low- and high-grade gliomas using a two-sample t-test. The grading performance was evaluated using the receiver operating characteristic analysis.

RESULTS

Twenty-two patients were included in the present study. The calculated α, H, and μ permitted the separation of tumor lesions from surrounding normal tissues in parameter maps and helped differentiate glioma grades. Moreover, α showed greater sensitivity and specificity in distinguishing low- and high-grade gliomas compared with the apparent diffusion coefficient.

CONCLUSION

The FM model could improve the diagnostic accuracy in differentiating low- and high-grade gliomas. This improved diffusion model may facilitate future studies of neuro-pathological changes in clinical populations. Magn Reson Med 78:1944-1949, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Imaging of local recurrence in prostate cancer

Diagnosis of prostate cancer (PCa) recurrence after therapy with curative intent currently depends primarily on biochemical serum analyses. When recurrence is suspected, further treatment decisions rely heavily on the confirmation of disease presence and determination of its extent. This is complicated by the fact that benign conditions can mimic biochemical recurrence, and serum studies do not reliably discriminate between local and distant recurrence. This review discusses the contemporary imaging paradigm for the evaluation of local PCa recurrence. The multidisciplinary implications for urologists, radiation oncologists and radiologists are examined. Emerging techniques and future directions of PCa imaging research are discussed.

High b-Value Diffusion MRI to Differentiate Recurrent Tumors from Posttreatment Changes in Head and Neck Squamous Cell Carcinoma: A Single Center Prospective Study

Recently DW-MR Imaging has shown promising results in distinguishing between recurrent tumors and posttreatment changes in Head and Neck Squamous Cell Carcinoma (HNSSC). Aim of this study was to evaluate the diagnostic performances of DWI at high b-value (b = 2000 s/mm²) compared to standard b-value (b = 1000 s/mm²) and ADC_ratio values (ADC_ratio = ADC₂₀₀₀/ADC₁₀₀₀ × 100) to differentiate recurrent tumors from posttreatment changes after treatment of HSNCC. 20 patients (16 M, 4 F) underwent MR Imaging between 2 and 16 months (mean 7) after treatment. Besides morphological sequences, we performed single-shot echo-planar DWI at b = 1000 s/mm² and b = 2000 s/mm², and corresponding ADC maps were generated (ADC₁₀₀₀ and ADC₂₀₀₀, resp.). By considering contrast-enhanced T1-weighted images as references, ROIs were drawn in order to evaluate mean ADC₁₀₀₀, ADC₂₀₀₀, and ADC_ratio. The mean ADC₁₀₀₀ and ADC₂₀₀₀ in recurrent tumors were significantly lower than those in posttreatment changes (P = 0.001 and P = 0.016, resp.). Moreover, the mean ADC_ratio between the two groups showed a statistically significant difference (P = 0.002). Sensitivity, specificity, and accuracy of ADC_ratio were 82.0%, 100%, and 90%, respectively, by considering an optimal cutoff value of 65.5%. ADC_ratio is a promising value to differentiate between recurrent tumors and posttreatment changes in HNSCC and may be more useful than ADC₁₀₀₀ and ADC₂₀₀₀.

The diagnostic value of biexponential apparent diffusion coefficients in myopathy

To investigate the performance of a biexponential signal decay model using DWI in myopathies and to differentiate Polymyositis (PM)/Dermatomyositis (DM), Glycogen Storage Diseases (GSDs) and Muscular Dystrophies (MDs) utilizing diffusion-weighted imaging. 11 healthy volunteers (control group) and 46 patients with myopathy were enrolled in the retrospective study. 27 of 46 patients had PM/DM, 7 patients GSDs and 12 patients MDs. After conventional MR sequences, diffusion weighted imaging with a b-factor ranging from 0 to 1200 s/mm(2) was performed on both thighs. The intra-muscular signal-to-noise ratios (SNRs) on multiple-b DWI images were measured for 7 different muscles and compared among the different groups. The median T2 signal intensity and biexponential apparent diffusion coefficients (ADC), including standard ADC, fast ADC, and slow ADC values, were compared among the different groups. The intra-muscular SNRs were statistically significantly different depending on the b value, and also found among the 4 groups (p < 0.05). The median T2 signal intensity of the normal muscles in control group was statistically significantly lower than that of edematous muscles in the PM/DM, GSDs and MDs groups (p = 0.000), while there were no statistically significant differences among the PM/DM, GSDs, and MDs groups (p > 0.05). The median standard ADC value of the edematous muscles in GSDs was statistically significantly lower than that of normal muscles in the control group (p = 0.000) and the median ADC value of the edematous muscles in PM/DM patients was statistically significantly greater than that of the GSDs (p = 0.000) and MDs groups (p = 0.005). The median slow ADC value of the edematous muscles in MDs patients and PM/DM patients was statistically significantly greater than that of GSDs patients (p < 0.05). Intra-muscular SNR decay curves and biexponential ADC parameters are useful in distinguishing among PM/DM, GSDs, and MDs.

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

BACKGROUND AND PURPOSE

Multimodality magnetic resonance imaging (MRI) can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multiparametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semiautomated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method.

METHODS

Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7-T animal scanner preradiation, and at day 1, day 4, and day 8 postradiation. Two experts performed manual and semiautomated methods to extract tumor ROIs on APTw images. Multimodality MRI signals of the tumors, including structural (T₂ and T₁ ), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively.

RESULTS

The semiautomated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the postradiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy.

CONCLUSIONS

The semiautomated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.

Estimation of the Number of Compartments Associated With the Apparent Diffusion Coefficient in MRI: The Theoretical and Experimental Investigations

OBJECTIVE

The goal of the present study was to estimate the number of compartments and the mean apparent diffusion coefficient (ADC) value with the use of the DWI signal curve.

MATERIALS AND METHODS

A useful new mathematic model that includes internal correlation among subcompartments with a distinct number of compartments was proposed. The DWI signal was simulated to estimate the approximate association between the number of subcompartments and the molecular density, with density corresponding to the ratio of the ADC values of the compartments, as determined using the Monte Carlo method.

RESULTS

Various factors, such as energy depletion, temperature, intracellular water accumulation, changes in the tortuosity of the extracellular diffusion paths, and changes in cell membrane permeability, have all been implicated as factors contributing to changes in the ADC of water (ADCw); therefore, one may consider them as pseudocompartments in the new model proposed in this study. The lower the coefficient is, the lower the contribution of the compartment to the net signal will be. The results of the simulation indicate that when the number of compartments increases, the signal will become significantly lower, because the gradient factor (i.e., the b value) will increase. In other words, the signal curve is approximately linear at all b values when the number of compartments in which the tissues have been severely damaged is low; however, when the number of compartments is high, the curve will become constant at high b values, and the perfusion parameters will prevail on the diffusion parameters at low b values. Therefore, normal tissues will be investigated when the number of compartments and the ADC values are high and the b values are low, whereas damaged tissues will be evaluated when the number of compartments and the ADC values are low and the b values are high.

CONCLUSION

The present study investigates damaged tissues at high b values for which the effect of eddy currents will also be compensated. These b values will probably be used in functional MRI.

Biexponential diffusion alterations in the normal-appearing white matter of glioma patients might indicate the presence of global vasogenic edema

PURPOSE

To investigate normal-appearing white matter (NAWM) microstructure of glioma patients with biexponential diffusion analysis in order to reveal the nature of diffusion abnormalities and to assess whether they are region-specific or global.

MATERIALS AND METHODS

Twenty-four newly diagnosed glioma patients (grade II-IV) and 24 matched control subjects underwent diffusion-weighted imaging at 3T. Diffusion parameters were calculated using monoexponential and biexponential models. Apparent diffusion coefficient (ADC) values were measured in the entire NAWM of the hemisphere contralateral and ipsilateral to the tumor. In the contralateral NAWM, regional ADC values were assessed in the frontal, parietal, occipital, and temporal NAWM.

RESULTS

ADCmono and ADCfast were significantly higher than control values in all investigated regions except the temporal NAWM (P < 0.04). ADCslow was significantly increased in the total contralateral, frontal, and parietal NAWM (P < 0.03), while pslow was decreased in both total hemispheric NAWM and the parietal NAWM of glioma patients compared to controls (P < 0.04). ADCmono , ADCfast , ADCslow , and pslow were significantly different among the NAWM of the four lobes of the contralateral hemisphere in both groups (P < 0.0001), and these regional differences were similar in patients and controls (P > 0.05). Hemispheric ADCmono and pslow differences were different between groups (P < 0.05).

CONCLUSION

Globally altered diffusion parameters suggest the presence of global vasogenic edema in the NAWM of glioma patients, which is further supported by the finding that regional differences in patients follow those found in controls. Alternatively, some tumor infiltration might contribute to diffusion abnormalities in the NAWM, especially in the tumor-affected hemisphere. J. Magn. Reson. Imaging 2016;44:633-641.

Squamous Cell Carcinoma of the Head and Neck: Comparison of Diffusion-weighted MRI at b-values of 1,000 and 2,000 s/mm(2) to Predict Response to Induction Chemotherapy

PURPOSE

Recent publications have reported contradictory results of pretreatment diffusion-weighted magnetic resonance imaging (DWI) for the prediction of chemoradiotherapeutic response in primary squamous cell carcinomas of the head and neck (HNSCC). Therefore, we evaluated the diagnostic performance of DWI obtained with both standard (b = 0 and 1,000 s/mm²) and high (b = 0 and 2,000 s/mm²) b-values for predicting response to induction chemotherapy in HNSCCs.

METHODS

For 25 patients with primary HNSCC who underwent DWI with both standard and high b-values prior to treatment, we calculated corresponding apparent diffusion coefficient (ADC) maps. Regions of interest containing the tumor were drawn on every section of ADC maps and summated to make volume-based data of the entire tumor. Histogram parameters (mean ADC, kurtosis, and skewness) were correlated with treatment response using unpaired Student t test. Univariate and multivariate analysis of the ADC parameters, patient age, sex, whole tumor volume, and T stage were also performed to predict tumor response to induction chemotherapy.

RESULTS

Response to induction chemotherapy was good in 13 of the 25 patients and poor in 12. The mean ADC values of good responders at standard b-value (ADC1000), 1.23 ± 0.34 (× 10⁻³ mm²/s), and high b-value (ADC2000), 0.62 ± 0.14 (× 10⁻³ mm²/s), were lower than those of poor responders (ADC1000, 1.32 ± 0.28 [× 10⁻³ mm²/s]; ADC2000, 0.76 ± 0.15 [× 10⁻³ mm²/s]), but significant difference was achieved only at the ADC2000 map (P = 0.02). In addition, mean tumor volume prior to treatment of good responders was smaller than that of poor responders. However, at multiple logistic regression analysis, only the mean ADC2000 value remained as a significant predictor of response to induction chemotherapy.

CONCLUSION

DWI with high b-values (b = 0 and 2,000 s/mm²) as an assessment of ADC values may help predict tumor response to neoadjuvant chemotherapy for primary HNSCCs.

Comparison of Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging and Arterial Spin Labeling MR Imaging in Gliomas

Gliomas grading is important for treatment plan; we aimed to investigate the application of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in gliomas grading, by comparing with the three-dimensional pseudocontinuous arterial spin labeling (3D pCASL). 24 patients (13 high grade gliomas and 11 low grade gliomas) underwent IVIM DWI and 3D pCASL imaging before operation; maps of fast diffusion coefficient (D^∗), slow diffusion coefficient (D), fractional perfusion-related volume (f), and apparent diffusion coefficient (ADC) as well as cerebral blood flow (CBF) were calculated and then coregistered to generate the corresponding parameter values. We found CBF and D^∗ were higher in the high grade gliomas, whereas ADC, D, and f were lower (all P < 0.05). In differentiating the high from low grade gliomas, the maximum areas under the curves (AUC) of D^∗, CBF, and ADC were 0.857, 0.85, and 0.902, respectively. CBF was negatively correlated with f in tumor (r = −0.619, P = 0.001). ADC was positively correlated with D in both tumor and white matter (r = 0.887, P = 0.000 and r = 0.824, P = 0.000, resp.). There was no correlation between CBF and D^∗ in both tumor and white matter (P > 0.05). IVIM DWI showed more efficiency than 3D pCASL but less validity than conventional DWI in differentiating the high from low grade gliomas.

Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging

Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000s. Before its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neurooncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions about the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hindered and restricted diffusion, and elucidating how diffusion parameters in tissue are derived from the measurements via the diffusion model. We describe one advanced DWI modeling technique, called restriction spectrum imaging (RSI). This technique offers a more direct in vivo measure of tumor cells, due to its ability to distinguish separable pools of water within tissue based on their intrinsic diffusion characteristics. Using RSI as an example, we then highlight the ability of advanced DWI techniques to address key clinical challenges in neurooncology, including improved tumor conspicuity, distinguishing actual response to therapy from pseudoresponse, and delineation of white matter tracts in regions of peritumoral edema. We also discuss how RSI, combined with new methods for correction of spatial distortions inherent in diffusion MRI scans, may enable more precise spatial targeting of lesions, with implications for radiation oncology and surgical planning. See all articles in this Cancer Research section, "Physics in Cancer Research."

Analysis of normal-appearing white matter of multiple sclerosis by tensor-based two-compartment model of water diffusion

Objectives

To compare the significance of the two-compartment model, considering diffusional anisotropy with conventional diffusion analyzing methods regarding the detection of occult changes in normal-appearing white matter (NAWM) of multiple sclerosis (MS).

Methods

Diffusion-weighted images (nine b-values with six directions) were acquired from 12 healthy female volunteers (22–52 years old, median 33 years) and 13 female MS patients (24–48 years old, median 37 years). Diffusion parameters based on the two-compartment model of water diffusion considering diffusional anisotropy was calculated by a proposed method. Other parameters including diffusion tensor imaging and conventional apparent diffusion coefficient (ADC) were also obtained. They were compared statistically between the control and MS groups.

Results

Diffusion of the slow diffusion compartment in the radial direction of neuron fibers was elevated in MS patients (0.121 × 10⁻³ mm2/s) in comparison to control (0.100 × 10⁻³ mm²/s), the difference being significant (P = 0.001). The difference between the groups was not significant in other comparisons, including conventional ADC and fractional anisotropy (FA) of diffusion tensor imaging.

Conclusion

The proposed method was applicable to clinically acceptable small data. The parameters obtained by this method improved the detectability of occult changes in NAWM compared to the conventional methods.

Key Points

• Water diffusion was compared between the controls and multiple sclerosis patients.

• A two-compartment model, considering diffusional anisotropy was selected for water diffusion analysis.

• Axial and radial diffusion of fast and slow diffusion components were evaluated.

• A new method was developed to obtain the metrics stably.

• The metrics indicated high detectability of slight differences between the groups.

Numerical study of a macroscopic finite pulse model of the diffusion MRI signal

Diffusion magnetic resonance imaging (dMRI) is an imaging modality that probes the diffusion characteristics of a sample via the application of magnetic field gradient pulses. The dMRI signal from a heterogeneous sample includes the contribution of the water proton magnetization from all spatial positions in a voxel. If the voxel can be spatially divided into different Gaussian diffusion compartments with inter-compartment exchange governed by linear kinetics, then the dMRI signal can be approximated using the macroscopic Karger model, which is a system of coupled ordinary differential equations (ODEs), under the assumption that the duration of the diffusion-encoding gradient pulses is short compared to the diffusion time (the narrow pulse assumption). Recently, a new macroscopic model of the dMRI signal, without the narrow pulse restriction, was derived from the Bloch-Torrey partial differential equation (PDE) using periodic homogenization techniques. When restricted to narrow pulses, this new homogenized model has the same form as the Karger model. We conduct a numerical study of the new homogenized model for voxels that are made up of periodic copies of a representative volume that contains spherical and cylindrical cells of various sizes and orientations and show that the signal predicted by the new model approaches the reference signal obtained by solving the full Bloch-Torrey PDE in O(ε(2)), where ε is the ratio between the size of the representative volume and a measure of the diffusion length. When the narrow gradient pulse assumption is not satisfied, the new homogenized model offers a much better approximation of the full PDE signal than the Karger model. Finally, preliminary results of applying the new model to a voxel that is not made up of periodic copies of a representative volume are shown and discussed.

Use of a high b-value for diffusion weighted imaging of peritumoral regions to differentiate high-grade gliomas and solitary metastases

BACKGROUND

To determine whether apparent diffusion coefficient (ADC) values obtained using a b-value of 3000 s/mm(2) can be used to differentiate high-grade glioma (HGG) from solitary metastases (SM).

METHODS

Forty patients underwent conventional magnetic resonance imaging (MRI) and standard and high b-value diffusion-weighted imaging (DWI). Minimum, maximum, and mean ADC values (ADCMIN , ADCMAX , and ADCMEAN , respectively) were measured from ADC maps obtained for the two b-values for each subject. ADC ratios were also measured. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGG and SM.

RESULTS

All ADC values for the peritumoral region of the HGGs examined were lower than those for the SM. Furthermore, a larger statistical difference was observed for ADCMIN , ADCMAX , and ADCMEAN values at a b-value of 3000 s/mm(2) versus 1000 s/mm(2) (P < 0.0001, P = 0.0010, and P = 0.0001 versus P = 0.0001, 0.0030, and 0.0002, respectively). A discriminant analysis identified the greatest log likelihood for the ADCMIN values obtained at a b-value of 3000 s/mm(2) , and the cutoff value for differentiating HGG and SM was 0.890 × 10(-3) mm/s(2) .

CONCLUSION

ADC values from DWI using a high b-value were found to distinguish HGG and SM. The lowest degree of overlap was obtained when an ADCMIN value was obtained at a b-value of 3000 s/mm(2) .

Determination of malignancy and characterization of hepatic tumor type with diffusion-weighted magnetic resonance imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived measurements

OBJECTIVE

The objective of this study was to compare the value of the apparent diffusion coefficient (ADC) determined with 3 b values and the intravoxel incoherent motion (IVIM)-derived parameters in the determination of malignancy and characterization of hepatic tumor type.

MATERIALS AND METHODS

Seventy-six patients with 86 solid hepatic lesions, including 8 hemangiomas, 20 lesions of focal nodular hyperplasia, 9 adenomas, 30 hepatocellular carcinomas, 13 metastases, and 6 cholangiocarcinomas, were assessed in this prospective study. Diffusion-weighted images were acquired with 11 b values to measure the ADCs (with b = 0, 150, and 500 s/mm) and the IVIM-derived parameters, namely, the pure diffusion coefficient and the perfusion-related diffusion fraction and coefficient. The diffusion parameters were compared between benign and malignant tumors and between tumor types, and their diagnostic value in identifying tumor malignancy was assessed.

RESULTS

The apparent and pure diffusion coefficients were significantly higher in benign than in malignant tumors (benign: 2.32 [0.87] × 10 mm/s and 1.42 [0.37] × 10 mm/s vs malignant: 1.64 [0.51] × 10 mm/s and 1.14 [0.28] × 10 mm/s, respectively; P < 0.0001 and P = 0.0005), whereas the perfusion-related diffusion parameters did not differ significantly between the 2 groups. The apparent and pure diffusion coefficients provided similar accuracy in assessing tumor malignancy (areas under the receiver operating characteristic curve of 0.770 and 0.723, respectively). In the multigroup analysis, the ADC was found to be significantly higher in hemangiomas than in hepatocellular carcinomas, metastases, and cholangiocarcinomas. In the same manner, it was higher in lesions of focal nodular hyperplasia than in metastases and cholangiocarcinomas. However, the pure diffusion coefficient was significantly higher only in hemangiomas versus hepatocellular and cholangiocellular carcinomas.

CONCLUSIONS

Compared with the ADC, the diffusion parameters derived from the IVIM model did not improve the determination of malignancy and characterization of hepatic tumor type.

Differentiating white matter lesions in multiple sclerosis and migraine using monoexponential and biexponential diffusion measurements

PURPOSE

To compare the white matter lesions seen in multiple sclerosis and migraine using monoexponential and high b-value biexponential diffusion measurements.

MATERIALS AND METHODS

Diffusion-weighted images were acquired on a 3.0-Tesla magnetic resonance imaging system. Diffusion parameters were estimated using monoexponential (0-1000 s/mm(2) ) and biexponential (0-5000 s/mm(2) ) approaches from 15 multiple sclerosis patients, 15 patients with migraine and 15 healthy control subjects. The study was performed in accordance with the approval of the Regional Research Ethics Committee. The apparent diffusion coefficient (ADC) values were measured in the lesions and the normal-appearing white matter of patients and in the white matter of controls.

RESULTS

High lesional ADCmono values were detected in both patient groups without significant differences between the groups (10.72 and 9.86 × 10(-4) mm(2) /s for MS and migraine respectively, P = 0.2134). The biexponential measurements showed significantly higher ADCfast , ADCslow , and Pslow values in the migraine lesions than in the multiple sclerosis lesions (16.47 versus 14.29, 1.41 versus 0.76, and 20.34 versus 12.01 all values in 10(-4) mm(2) /s; P = 0.0344, P = 0.0019, P = 0.0021, respectively).

CONCLUSION

Biexponential diffusion analysis may help to differentiate multiple sclerosis-related white matter lesions from migraine-related ones.

Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling

PURPOSE

To employ the stretched exponential and diffusional kurtosis models to study the non-Gaussian behavior of diffusion-related signal decay of the liver in an animal model of hepatic fibrosis.

MATERIALS AND METHODS

High b-value diffusion imaging data (up to 3500 s/mm(2) ) of ex vivo murine liver specimens was acquired using a 9.4 T MRI scanner. A simple monoexponential model as well as the stretched exponential and diffusional kurtosis models were employed to analyze the diffusion data, the results of which were correlated with liver histopathology.

RESULTS

Strong correlations between histopathological assessments of hepatic fibrosis and parameters derived from the stretched exponential and diffusional kurtosis models were found. Using Akaike's Information Criterion (AIC) analyses, the kurtosis model was found to result in an improved fit of the high b-value diffusion data when compared to both the monoexponential and stretched exponential models.

CONCLUSION

The use of diffusional kurtosis or stretched exponential models, applied to the characterization of the non-Gaussian behavior of the molecular diffusion of liver exhibited over an extended b-factor range, affords the potential for an increased capability of magnetic resonance imaging (MRI) in the characterization of chronic liver disease.

Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging

PURPOSE

To explore the role of histogram analysis of apparent diffusion coefficient (ADC) maps obtained at standard- and high-b-value (1000 and 3000 sec/mm(2), respectively) diffusion-weighted (DW) imaging in the differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board of Seoul National University Hospital, and informed consent requirement was waived. Thirty patients with histopathologically proved glioblastoma who had undergone concurrent chemotherapy and radiation therapy (CCRT) with temozolomide underwent diffusion-weighted MR imaging with b values of 1000 and 3000 sec/mm(2), and corresponding ADC maps were calculated from entire newly developed or enlarged enhancing lesions after completion of CCRT. Histogram parameters of each ADC map between true progression (n = 15) and pseudoprogression (n = 15) groups were compared by using the unpaired Student t test. Receiver operating characteristic analysis was used to determine the best cutoff values for predictors in the differentiation of true progression from pseudoprogression. Results were validated in an independent test set of nine patients by using the best cutoff value to predict differentiation of true progression from pseudoprogression. The accuracy of the selected best cutoff value in the independent test set was then calculated.

RESULTS

In terms of cumulative histograms, the fifth percentile of both ADC at b value of 1000 sec/mm(2) (ADC1000) and the ADC at b value of 3000 sec/mm(2) (ADC3000) were significantly lower in the true progression group than in the pseudoprogression group (P = .049 and P < .001, respectively). In contrast, neither the mean ADC1000 nor the mean ADC3000 was significantly different between the two groups. The diagnostic values of the parameters derived from ADC1000 and ADC3000 were compared, and a significant difference (0.224, P = .016) was found between the area under the receiver operating characteristic curve of the fifth percentile for ADC1000 and that for ADC3000. The accuracies were 66.7% (six of nine patients) and 88.9% (eight of nine patients) based on the fifth percentile of both ADC1000 and ADC3000 in the independent test set, respectively.

CONCLUSION

The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter in the differentiation of true progression from pseudoprogression of the newly developed or enlarged enhancing lesions after CCRT with temozolomide for glioblastoma treatment. Online supplemental material is available for this article.

Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: feasibility of the method and initial results

INTRODUCTION

The purpose of this study was to evaluate the feasibility of intravoxel incoherent motion (IVIM) imaging and its value in differentiating the histologic grade among human gliomas.

METHODS

The IVIM model generated parametric images for apparent diffusion coefficient ADC, slow diffusion coefficient D (or D slow), fast diffusion coefficient D* (or D fast), and fractional perfusion-related volume f in 22 patients with gliomas (WHO grade II-IV) using monopolar Stejskal-Tanner diffusion-weighted imaging (DWI) scheme and 14 b values ranging from 0 s/mm2 to a maximum of 1,300 s/mm2. A region-of-interest analysis on the tumor as well as in the white matter was conducted. The parameter values were tested for significant differences. The repeatability of the measurements was tested by coefficient of variation and Bland-Altman plots.

RESULTS

D, D*, and f in the high-grade gliomas demonstrated significant differences compared to the healthy white matter. D* and f showed a significant difference between low- and high-grade gliomas. D tended to be slightly lower in the WHO grade II compared to WHO grade III-IV tumors. f and D* demonstrated higher coefficients of variation than the ADC and D in tumor. The Bland-Altman plots demonstrated satisfactory results without any outliers outside the mean ± 1.96 standard deviation.

CONCLUSION

The IVIM-fitted post-processing of DWI-signal decay in human gliomas could show significantly different values of fractional perfusion-related volume and fast diffusion coefficient between low- and high-grade tumors, which might enable a noninvasive WHO grading in vivo.

Differentiation of recurrent tumor and posttreatment changes in head and neck squamous cell carcinoma: application of high b-value diffusion-weighted imaging

BACKGROUND AND PURPOSE

High b-value DWI has been expected to have an additional diagnostic role and demonstrated some promising results in head and neck cancer. The aim of this study was to evaluate the diagnostic performance of DWI at a high b-value (b=2000 s/mm(2)) compared with a standard b-value (b=1000 s/mm(2)) and the ratio of ADC values of high and standard b-values for their ability to differentiate between recurrent tumor and posttreatment changes after the treatment of head and neck squamous cell carcinoma.

MATERIALS AND METHODS

A total of 33 patients diagnosed with head and neck squamous cell carcinoma were enrolled in the present study; all had contrast-enhancing lesions on follow-up MR imaging. All patients underwent single-shot echo-planar DWI at b=1000 s/mm(2) and b=2000 s/mm(2), and corresponding ADC maps were generated (ADC1000 and ADC2000, respectively). The mean ADC1000, ADC2000, and ADCratio (ADCratio = ADC2000/ADC1000 × 100) values were evaluated within a manually placed ROI with contrast-enhanced T1-weighted images as references. For the statistical analysis, we performed a Student t test and multivariate logistic regression.

RESULTS

The mean ADC1000 in recurrent tumor was significantly lower than that in posttreatment changes (P < .001), whereas the mean ADC2000 resulted in no significant difference (P = .365). The mean ADCratio was significantly higher in recurrent tumor than that in posttreatment changes (73.5 ± 7.2% vs 56.9 ± 8.8%, respectively; P < .001). Multivariate logistic regression analysis revealed that the ADCratio was the only independently differentiating variable (P = .024). The sensitivity, specificity, and accuracy of ADCratio were 95.0%, 69.2%, and 84.8%, respectively, by use of the optimal cutoff value of 62.6%.

CONCLUSIONS

We suggest that the ADCratio calculated from the ADC1000 and ADC2000 is a promising value for the differentiation of recurrent tumor and posttreatment changes in head and neck squamous cell carcinoma.

Analysis of multiple B-value diffusion-weighted imaging in pediatric acute encephalopathy

Acute encephalopathy is a disease group more commonly seen in children. It is often severe and has neurological sequelae. Imaging is important for early diagnosis and prompt treatment to ameliorate an unfavorable outcome, but insufficient sensitivity/specificity is a problem. To overcome this, a new value (fraction of high b-pair (F_H)) that could be processed from clinically acceptable MR diffusion-weighted imaging (DWI) with three different b-values was designed on the basis of a two-compartment model of water diffusion signal attenuation. The purpose of this study is to compare F_H with the apparent diffusion coefficient (ADC) regarding the detectability of pediatric acute encephalopathy. We retrospectively compared the clinical DWI of 15 children (1–10 years old, mean 2.34, 8 boys, 7 girls) of acute encephalopathy with another 16 children (1–11 years old, mean 4.89, 9 boys, 7 girls) as control. A comparison was first made visually by mapping F_H on the brain images, and then a second comparison was made on the basis of 10 regions of interest (ROIs) set on cortical and subcortical areas of each child. F_H map visually revealed diffusely elevated F_H in cortical and subcortical areas of the patients with acute encephalopathy; the changes seemed more diffuse in F_H compared to DWI. The comparison based on ROI revealed elevated mean F_H in the cortical and subcortical areas of the acute encephalopathy patients compared to control with significant difference (P<0.05). Similar findings were observed even in regions where the findings of DWI were slight. The reduction of mean ADC was significant in regions with severe findings in DWI, but it was not constant in the areas with slighter DWI findings. The detectability of slight changes of cortical and subcortical lesions in acute encephalopathy may be superior in F_H compared to ADC.

Biexponential apparent diffusion coefficients values in the prostate: comparison among normal tissue, prostate cancer, benign prostatic hyperplasia and prostatitis

Objective

To investigate the biexponential apparent diffusion parameters of diverse prostate tissues and compare them with monoexponential apparent diffusion coefficient (ADC) value in the efficacy to discriminate prostate cancer from benign lesions.

Materials and Methods

Eleven healthy volunteers and 61 patients underwent a conventional (b-factors 0, 1000 s/mm²) and a 10 b-factor (0 to 3000 s/mm²) diffusion-weighted imaging (DWI). The monoexponential ADC value and biexponential parameters of fast ADC (ADCf), fraction of ADCf (f), slow ADC (ADCs) value for 29 prostate cancer, 28 benign prostatic hyperplasia (BPH), 24 prostatitis lesions and normal tissue were calculated and compared. Receiver operating characteristic analysis was performed to determine the sensitivity, specificity and optimal cut-off points.

Results

Prostate cancer had lower ADC, ADCf, f, and ADCs than all other tissues (p < 0.01). Prostatitis exhibited a lower ADC, ADCf, ADCs and f than the peripheral zone tissue (p < 0.01), and BPH showed a lower ADC and ADCf than the central gland tissue (p < 0.01). The ADCf demonstrated a comparable accuracy with ADC in differentiating cancer from BPH [area under the curve (AUC) 0.93 vs. 0.92] and prostatitis AUC 0.98 vs. 0.99) (both p > 0.05), but the AUC of f and ADCs in differentiating cancer from BPH (0.73 and 0.81) and prostatitis (0.88 and 0.91) were significantly lower than ADC (all p < 0.05).

Conclusion

The biexponential DWI appears to provide additional parameters for tissue characterization in prostate, and ADCf helps to yield comparable accuracy with ADC in differentiating cancer from benign lesions.

Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI

PURPOSE

We assessed whether a high b-value DWI at b=4000s/mm(2) would discriminate the histopathological differentiation of the tumor grade of meningiomas, and also focused on the relationship between radiologic features and the tumor grade.

MATERIALS AND METHODS

We acquired DWI at 3T with b=1000 and b=4000s/mm(2) in 77 patients (42, 31 and 4 patients were WHO grades I (G1), II (G2), and III (G3), respectively). The apparent diffusion coefficient (ADC) was measured by placing multiple regions of interest (ROIs) on ADC maps. The ADC values of each tumor were determined preoperatively from several ROIs, and expressed as the minimum (ADCMIN), mean (ADCMEAN), and maximum absolute values (ADCMAX). We evaluated the relationship between ADCs and histological findings, and assessed the radiologic features such as tumor location, tumor size, presence/absence of peritumoral edema, shape of the tumor, presence/absence of bone destruction or hyperplasia, status of contrast enhancement, presence/absence of calcification and cyst.

RESULTS

ADCs of the meningiomas were inversely correlated with the histological grade of meningiomas. According to results of the discriminant analysis, the apparent log likelihood value was greatest for ADCMIN at b=4000. Furthermore, only the ADCMIN value at b=4000 was significantly correlated with the histological grade of meningiomas when we performed a multiple logistic regression analysis to identify the significant independent factors such as shape of tumor, presence/absence of bone destruction, status of contrast enhancement, presence/absence of cyst and ADCMIN at b=4000.

CONCLUSION

A meningioma with a low ADCMIN at a high b-value might imply a high-grade meningioma.

Improved conspicuity and delineation of high-grade primary and metastatic brain tumors using "restriction spectrum imaging": quantitative comparison with high B-value DWI and ADC

BACKGROUND AND PURPOSE

Restriction spectrum imaging is a sensitive DWI technique for probing separable water diffusion compartments in tissues. Here, we evaluate RSI-CMs derived from the spherically-restricted water compartment for improved tumor conspicuity and delineation from nontumor tissue and reduced sensitivity to edema compared with high-b-value DWI and ADC.

MATERIALS AND METHODS

RSI was performed in 10 presurgical patients: 4 with glioblastoma, 3 with primary CNS lymphoma, and 3 with metastatic brain tumors. Multidirectional DWI data were collected at b = 500, 1500, and 4000 s/mm(2). Quantification of tumor conspicuity, edema conspicuity, and relative sensitivity to edema for RSI-CMs; DWI at b = 4000 (DWI-4000); and ADC were compared in manually drawn VOIs. Receiver operating characteristic curves were used to evaluate the sensitivity and specificity of each method for delineating tumor from normal-appearing WM.

RESULTS

Significant TC was seen with both RSI-CMs and DWI-4000, but not ADC. Significant EC was seen with ADC, but not RSI-CMs or DWI-4000. Significantly greater TC was seen with RSI-CMs compared with DWI-4000. Significantly reduced RSE was seen with RSI-CMs compared with both DWI-4000 and ADC. Greater sensitivity and specificity for delineating tumor from normal-appearing WM were seen with RSI-CMs (AUC = 0.91) compared with both DWI-4000 (AUC = 0.77) and ADC (AUC = 0.66).

CONCLUSIONS

RSI-CMs offer improved conspicuity and delineation of high-grade primary and metastatic brain tumors and reduced sensitivity to edema compared with high-b-value DWI and ADC.

Methodology of diffusion-weighted, diffusion tensor and magnetisation transfer imaging

MRI offers a number of opportunities to examine characteristics of tissue well below the spatial resolution of the imaging technique. The best known of these is diffusion imaging, which allows the production of images whose contrast reflects the ability of water molecules to move through the extravascular extracellular space. Less well-known, but increasingly important, is magnetisation transfer imaging, which produces contrast based on the ability of protons to move between the free water pool and local macromolecules. Both of these techniques offer unique information about the microscopic and molecular structure of tumour tissue. This article will briefly review the underlying theory and technical aspects associated with these imaging techniques.

Quantifying hepatic fibrosis using a biexponential model of diffusion weighted imaging in ex vivo liver specimens

The purpose of this study was to evaluate the non-Gaussian behavior of diffusion related signal decay of the ex vivo murine liver tissues from a dietary model of hepatic fibrosis. To this end, a biexponential formalism was used to model high b-value diffusion imaging (up to 3500 s/mm(2)), the findings of which were correlated with liver histopathology and compared to a simple monoexponential model. The presence of a major, fast diffusing component and a minor, slow diffusing component was demonstrated. With increasing hepatic fibrosis, the fractional contribution of the fast diffusing component decreased, as did the diffusion coefficient of the fast diffusing component. Strong correlation between the degrees of liver fibrosis and a two-predictor regression model incorporating parameters of the biexponential model was found. Using Akaike's Information Criterion analyses, the biexponential model resulted in an improved fit of the high b-value diffusion data when compared to the monoexponential model.

Head and neck squamous cell carcinoma: differentiation of histologic grade with standard- and high-b-value diffusion-weighted MRI

BACKGROUND

The correlation between the histologic grades and the apparent diffusion coefficient (ADC) in head and neck squamous cell carcinomas (HNSCCs) remains unclear. This study aimed to evaluate the potential of diffusion-weighted MRI (DW-MRI) at both standard and high b values to differentiate the histologic grades of HNSCC.

METHODS

In all, 54 consecutive patients with HNSCCs (34 well-differentiated, 10 moderately differentiated, and 10 poorly differentiated) performed DW-MRIs at both b = 1000 and 2000 s/mm(2) prior to biopsy or surgery. The ADC values were compared among the different histologic grades.

RESULTS

The ADC values of well-differentiated and poorly differentiated HNSCC were significantly different at both b values (p < .001 in both). However, significant difference between moderately differentiated and poorly differentiated HNSCC was revealed at only b = 2000 s/mm(2) (p = .014).

CONCLUSIONS

DW-MRIs at standard and high b values are helpful for differentiating histologic grades in HNSCC with better differentiation at a high b-value.

High b-value diffusion-weighted imaging: a sensitive method to reveal white matter differences in schizophrenia

Over the last 10 years, diffusion-weighted imaging (DWI) has become an important tool to investigate white matter (WM) anomalies in schizophrenia. Despite technological improvement and the exponential use of this technique, discrepancies remain and little is known about optimal parameters to apply for diffusion weighting during image acquisition. Specifically, high b-value diffusion-weighted imaging known to be more sensitive to slow diffusion is not widely used, even though subtle myelin alterations as thought to happen in schizophrenia are likely to affect slow-diffusing protons. Schizophrenia patients and healthy controls were scanned with a high b-value (4000 s/mm(2)) protocol. Apparent diffusion coefficient (ADC) measures turned out to be very sensitive in detecting differences between schizophrenia patients and healthy volunteers even in a relatively small sample. We speculate that this is related to the sensitivity of high b-value imaging to the slow-diffusing compartment believed to reflect mainly the intra-axonal and myelin bound water pool. We also compared these results to a low b-value imaging experiment performed on the same population in the same scanning session. Even though the acquisition protocols are not strictly comparable, we noticed important differences in sensitivities in the favor of high b-value imaging, warranting further exploration.

Diffusion coefficient measurement using a temperature-controlled fluid for quality control in multicenter studies

PURPOSE

To present the use of a quality control ice-water phantom for diffusion-weighted magnetic resonance imaging (DW-MRI). DW-MRI has emerged as an important cancer imaging biomarker candidate for diagnosis and early treatment response assessment. Validating imaging biomarkers through multicenter trials requires calibration and performance testing across sites.

MATERIALS AND METHODS

The phantom consisted of a center tube filled with distilled water surrounded by ice water. Following preparation of the phantom, ≈30 minutes was allowed to reach thermal equilibrium. DW-MRI data were collected at seven institutions, 20 MRI scanners from three vendors, and two field strengths (1.5 and 3T). The phantom was also scanned on a single system on 16 different days over a 25-day period. All data were transferred to a central processing site at the University of Michigan for analysis.

RESULTS

Results revealed that the variation of measured apparent diffusion coefficient (ADC) values between all systems tested was ±5%, indicating excellent agreement between systems. Reproducibility of a single system over a 25-day period was also found to be within ±5% ADC values. Overall, the use of an ice-water phantom for assessment of ADC was found to be a reasonable candidate for use in multicenter trials.

CONCLUSION

The ice-water phantom described here is a practical and universal approach to validate the accuracy of ADC measurements with ever changing MRI sequence and hardware design and can be readily implemented in multicenter clinical trial designs.