Diffusion Kurtosis Imaging Model Research Articles

Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models

BackgroundTo compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs).MethodsPatients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances.ResultsNODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05).ConclusionNODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.

Diffusion kurtosis imaging: correlation analysis of quantitative model parameters with molecular features in advanced lung adenocarcinoma.

Background:Due to development of magnetic resonance-based functional imaging, it is easier to detect micro-structural alterations of tumor tissues. The aim of this study was to conduct a preliminary evaluation of the correlation of non-Gaussian diffusion kurtosis imaging (DKI) parameters with expression of molecular markers (epidermal growth factor receptor [EGFR]; anaplastic lymphoma kinase [ALK]; Ki-67 protein) in patients with advanced lung adenocarcinoma, using routine diffusion-weighted imaging as the reference standard.Methods:Data from patients with primary lung adenocarcinoma diagnosed at Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) from 2016 to 2019 were collected for retrospective analysis. The pathologic and magnetic resonance imaging data of 96 patients who met the inclusion criteria were included in this study. Specifically, the Kapp and Dapp parameters measured from the DKI model; apparent diffusion coefficient (ADC) value from the diffusion-weighted imaging model; and the EGFR, ALK, and Ki-67 biomarkers detected by immunohistochemistry and/or molecular biology techniques after biopsy or surgery were evaluated. The relations between quantitative parameters (ADC, Kapp, Dapp) and pathologic outcomes (EGFR, ALK, and Ki-67 expression) were analyzed by Spearman correlation test.Results:Of the 96 lung adenocarcinoma lesions (from 96 patients), the number of EGFR- and ALK-positive and high Ki-67 expressing lesions were 53, 12, and 83, respectively. The Kapp values were significantly higher among patients with EGFR-positive mutations (0.81 ± 0.12 vs. 0.66 ± 0.10, t = 6.41, P < 0.001), ALK rearrangement-negative (0.76 ± 0.12 vs. 0.60 ± 0.15, t = 4.09, P < 0.001), and high Ki-67 proliferative index (PI) (0.76 ± 0.12 vs. 0.58 ± 0.13, t = 4.88, P < 0.001). The Dapp values were significantly lower among patients with high Ki-67 PI (3.19 ± 0.69 μm2/ms vs. 4.20 ± 0.83 μm2/ms, t = 4.80, P < 0.001) and EGFR-positive mutations (3.11 ± 0.73 μm2/ms vs. 3.59 ± 0.77 μm2/ms, t = 3.12, P = 0.002). The differences in mean Dapp (3.73 ± 1.26 μm2/ms vs. 3.26 ± 0.68 μm2/ms, t = 1.96, P = 0.053) or ADC values ([1.34 ± 0.81] × 10−3 mm2/s vs. [1.33 ± 0.41] × 10−3 mm2/s, t = 0.07, P = 0.941) between the groups with or without ALK rearrangements were not statistically significant. The ADC values were significantly lower among patients with EGFR-positive mutation ([1.19 ± 0.37] × 10−3 mm2/s vs. [1.50 ± 0.53] × 10−3 mm2/s, t = 3.38, P = 0.001) and high Ki-67 PI ([1.28 ± 0.39] × 10−3 mm2/s vs. [1.67 ± 0.77] × 10−3 mm2/s, t = 2.88, P = 0.005). Kapp was strongly positively correlated with EGFR mutations (r = 0.844, P = 0.008), strongly positively correlated with Ki-67 PI (r = 0.882, P = 0.001), and strongly negatively correlated with ALK rearrangements (r = −0.772, P = 0.001). Dapp was moderately correlated with EGFR mutations (r = −0.650, P = 0.024) or Ki-67 PI (r = −0.734, P = 0.012). ADC was moderately correlated with Ki-67 PI (r = −0.679, P = 0.033).Conclusions:The Kapp value of DKI parameters was strongly correlated with different expression of EGFR, ALK, and Ki-67 in advanced lung adenocarcinoma. The results potentially indicate a surrogate measure of the status of different molecular markers assessed by non-invasive imaging tools.

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

PurposeTo optimize the diffusion‐weighting b values and postprocessing pipeline for hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region.MethodsOptimized diffusion‐weighting b value sets ranging between 5 and 30 b values were constructed by optimizing the Cramér‐Rao lower bound of the hybrid intravoxel incoherent motion diffusion kurtosis imaging model. With this model, the perfusion fraction, pseudodiffusion coefficient, diffusion coefficient, and kurtosis were estimated. Sixteen volunteers were scanned with a reference b value set and 3 repeats of the optimized sets, of which 1 with volunteers swallowing on purpose.The effects of (1) b value optimization and number of b values, (2) registration type (none vs. intervolume vs. intra‐ and intervolume registration), and (3) manual swallowing artifact rejection on the parameter precision were assessed.ResultsThe SD was higher in the reference set for perfusion fraction, diffusion coefficient, and kurtosis by a factor of 1.7, 1.5, and 2.3 compared to the optimized set, respectively. A smaller SD (factor 0.7) was seen in pseudodiffusion coefficient. The sets containing 15, 20, and 30 b values had comparable repeatability in all parameters, except pseudodiffusion coefficient, for which set size 30 was worse. Equal repeatability for the registration approaches was seen in all parameters of interest. Swallowing artifact rejection removed the bias when present.ConclusionTo achieve optimal hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region, b value optimization and swallowing artifact image rejection are beneficial. The optimized set of 15 b values yielded the optimal protocol efficiency, with a precision comparable to larger b value sets and a 50% reduction in scan time.

Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

In diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to gray matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR≥50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue.

Beyond the vascular profile: conventional DWI, IVIM and kurtosis in the assessment of hepatocellular carcinoma.

To describe the role of the Diffusion Weighted Imaging (DWI) in the assessment of hepatocellular carcinoma (HCC). Several electronic databases were evaluated in the present review. The search included articles published from January 2010 to May 2019. The references of all articles were also evaluated. All titles and abstracts were assessed, and only the studies of DWI in patients with HCC were retained. HCC is the most common primitive hepatic cancer. The non-invasive radiological criteria for HCC diagnosis are based on the presence of the specific vascular profile characterized by contrast uptake during arterial phase, defined as arterial hyperenhancement, followed by washout in the venous/portal phase. However, arterial hyperenhancement and wash out appearance have a sensitivity rate of 50-60% in lesion smaller than 2 cm. Therefore, other functional parameters have been introduced in the detection and characterization of HCC nodules. DWI has been applied to liver imaging as an excellent tool for detection and characterization of focal liver lesions, increasing clinical confidence and decreasing false positives. The assessment of DW images can be done qualitatively and quantitatively, through the apparent diffusion coefficient (ADC) map. Intravoxel incoherent motion (IVIM) is a more sophisticated analysis, a biexponential model, to better defining the relationship between signal attenuation and increasing b value that separately reproduces tissue diffusivity and tissue perfusion. Traditionally DWI approach to analyze data is founded on the hypothesis that water molecules diffuse within a voxel following a single direction with a Gaussian behavior without any restriction. However, according to the presence of microstructures, water molecules within biologic tissues exhibits a non-Gaussian phenomena proposed by Jensen in 2005 called Diffusion Kurtosis Imaging (DKI). This approach assesses the kurtosis coefficient (K) that shows the deviance of diffusion from a Gaussian approach, and the diffusion coefficient (D) with the correction of non-Gaussian bias. DKI is an advanced DWI model that quantifies non-Gaussian behavior of diffusion and provides both a corrected ADC, as well as the excess kurtosis of tissue, a measure of the extent to which tissue diffusion deviates from a Gaussian pattern. It is believed that the DKI model is more sensitive to tissue microstructural complexity than standard DW. DWI should be an integral part of study protocol for HCC patients, considering the great advantages due to DWI and DWI-based approaches in detection and characterization of HCC.

Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy

PurposeTo assess the efficacy of diffusion kurtosis imaging (DKI) in the prediction of the treatment response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer (LAGC). MethodsA total of 31 LAGC patients were enrolled in this prospective study. All patients underwent diffusion-weighted MRI examination (with b = 01, 2001, 5001, 8002, 10004, 15004, 20006 s/mm2, the subscript denotes the number of signal averages) before and after chemotherapy. DKI and mono-exponential (b = 0, 800 s/mm2) models were built. Apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) of the LAGC tumors were measured. The absolute change values (ΔX) and percentage change values (%ΔX) of the above parameters post neoadjuvant chemotherapy (NACT) were calculated. The response was evaluated according to the pathological tumor regression grade scores (effective response group: TRG 0–2, poor response group: TRG 3). Mann–Whitney U test and receiver operating characteristic (ROC) curves were applicated for statistical analysis. ResultsThere were 17 patients in the effective response group (ERG), and 14 patients in the poor response group (PRG). The MKpre and MKpost values in PRG were significantly higher than those in ERG [(0.671 ± 0.026) and (0.641 ± 0.019) vs. (0.584 ± 0.023) and (0.519 ± 0.018), p < 0.001]. ADCpost and MDpost in PRG were significantly lower than those in ERG (p = 0.005, p =0.001). Significant differences were also observed for % ΔMK, ΔMD and ΔMK between the two groups (p < 0.05). The area under the curve (AUC) for the prediction of PRG was highest for MKpost (AUC = 0.958, cutoff value = 0.614). The MKpre and MKpost had the highest sensitivity (91.70 %) and specificity (93.80 %) in the prediction of PRG, respectively. ConclusionBoth DKI and ADC values show potential for the prediction of the PRG in LAGC patients. The DKI parameters, especially MKpost displayed the best performance.

Utility of a diffusion kurtosis model in the differential diagnosis of orofacial tumours

To compare the goodness of fit and correlations between diffusion kurtosis imaging (DKI) and a mono-exponential (ME) model, to compare the corrected apparent diffusion coefficient (Dapp) and apparent kurtosis (Kapp) of the DKI model, and the apparent diffusion coefficient (ADC) of the ME model among the various orofacial lesions, and to evaluate the diagnostic performances between the two models. A total of 100 orofacial lesions underwent echo-planar diffusion magnetic resonance imaging (MRI) with four b-values. The goodness of fit was evaluated using Akaike information criterion. The correlations of the diffusion-derived parameters were evaluated. The diagnostic performance was analysed by receiver operating characteristics (ROC). The DKI model showed a significantly better goodness of fit than the ME model (p<0.0001). The Kapp had a strongly negative correlation with the Dapp (ρ=-0.749) and ADC (ρ=-0.938). A strongly positive correlation existed between the Dapp and ADC (ρ=0.906). All parameters differed significantly between benign tumours and malignant tumours (p<0.05). In differentiating benign tumours from the malignant tumours, the AUC of Dapp (0.871) was larger than that of ADC (0.805); however, a significant difference was not found (p=0.102). The DKI model had better goodness of fit than the ME model. Furthermore, the Dapp and Kapp were also characteristic for each pathological category; however, the DKI model did not yield a significantly higher diagnostic performance than the ME model, which might be related to the high correlation among the diffusion-derived parameters and wide variation among categories.

Use of monoexponential diffusion-weighted imaging and diffusion kurtosis imaging and dynamic contrast-enhanced-MRI for the differentiation of spinal tumors.

To explore the diagnostic value of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE)-MRI for differentiating between spinal malignant and non-malignant tumors lacking typical imaging signs and correlation between the parameters of the three models. DWI, DKI, and DCE-MRI examinations were performed in 39 and 27 cases of spinal malignant and non-malignant tumors, respectively. Two radiologists independently evaluated apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) of the DWI and DKI models, and volume transfer constant (Ktrans), rate constant (kep), and extracellular extravascular volume ratio (ve) of the DCE-MRI model for post-processing analyses. Statistical differences of parameters were compared using an independent sample t test. The sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis was used to evaluate the correlation between these parameters. ADC, MD, and ve were significantly lower, while MK and kep were significantly higher for spinal malignant tumors than for non-malignant tumors. The MK had the highest area under the ROC curve of 0.940 and sensitivity (96.3%). Ve was weakly positively correlated with ADC (r = 0.468) and MD (r = 0.363) and weakly negatively correlated with MK (r = -0.469). kep was weakly positively correlated with MK (r = 0.375). Ktrans was weakly positively correlated with ADC (r = 0.325). Monoexponential DWI, DKI, and DCE-MRI have potential value in the differentiation of spinal malignant from non-malignant tumors lacking typical imaging signs, and there is a certain correlation between the parameters of the three models. These slides can be retrieved under Electronic Supplementary Material.

The comparative study of in transition zone prostate cancer: diagnostic performance of new intravoxel incoherent motion and diffusion kurtosis imaging models

Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti-parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI-transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo-diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t-test was used to compare the differences in lesion parameters between prostate cancer and BPH, low-risk (BPH+Gleason score 6 points) and medium-high-risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis. Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low-risk or high-risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia (BPH) in PZ were included, meanwhile, the cases of GS ≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa-BHP group and high-low risk group in TZ (P 0.05). ADC and MD showed relatively higher negativity correlations (r were -0.585 and -0.489, P 0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS ≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid-higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD. Key words: Prostate neoplasms; Benign prostatic hyperplasia; Intravoxel incoherent motion; Diffusion kurtosis imaging

The clinical value of histogram analysis of diffusion kurtosis imaging in prostate cancer and the correlation between histogram parameters and Gleason score

Objective To explore the value of diffusion kurtosis imaging (DKI) histogram analysis for differential diagnosis of prostate cancer and noncancerous foci and the correlation between histogram parameters and Gleason score. Methods Twenty-one patients were retrospectively enrolled in Beijing Hospital from May 2017 to May 2018. All subjects underwent Philips 3.0 T MR scanning. The pathologies were confirmed by in-bore MR-guided biopsy. Finally, 31 lesions were collected (two lesions each from 10 patients), including 15 prostate cancer and 16 noncancerous foci (benign prostatic hyperplasia and prostatitis). ROI was drawn manually by two experienced radiologists. All the lesions were measured mean apparent diffusion coefficient (Dapp), mean apparent kurtosis coefficient (Kapp) and their histogram parameters, the averages of two measurements were used to be calculated. The values of these parameters in cancer and noncancerous foci were compared using independent-samples t test. The Spearman test was used to evaluate the correlation of these parameters and Gleason scores of prostate cancer. Results Mean Dapp, 10th Dapp, 25th Dapp, 50th Dapp, 75th Dapp, 90th Dapp, minimum Dapp, maximum Dapp, mean Kapp, 50th Kapp, 75th Kapp, 90th Kapp, maximum Kapp between prostate cancer and noncancerous foci were statistically significant (P<0.05). 90th Kapp (r=0.630, P<0.05) and maximum Kapp (r=0.565, P<0.05) increased with the Gleason scores increasing. Conclusion Histogram analysis of DKI model is valuable for diagnosing and assessing aggressiveness of prostate cancer. Key words: Prostate neoplasms; Diffusion kurtosis imaging; Histogram analysis

Histogram analysis of diffusion kurtosis imaging based on whole-volume images of breast lesions.

Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved. To investigate the performance of whole-volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions. Retrospective. In all, 120 patients with breast lesions (62 malignant, 58 benign). DKI sequence with seven b-values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm2 ) and DWI sequence with two b-values (0 and 1000 s/mm2 ) on 3.0T MRI. Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume. Student's t-test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. Kmax , Dmin , and ADCmin had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and Kmax was found to outperform Dmin and ADCmin . ADC histogram parameters (from ADCmin to ADCsd ) were significantly lower than D histogram parameters in all groups. Kmax , Dmin , and ADCmin were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627-634.

Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma

To evaluate the additional value of diffusion kurtosis imaging (DKI) in the characterization of cervical carcinoma. Seventy-five patients (56.9 ± 13.4 years) with histologic-confirmed cervical carcinoma were included. Diffusion-weighted imaging (DWI) was acquired on a 3T MRI with five b values (0, 500, 800, 1000, and 1500 s/mm2). Data were analyzed based on DKI model (5 b values) and conventional DWI (0 and 1000 s/mm2). Largest single-slice region of interest (ROI) and volume of interest (VOI) were drawn around the tumor. Mean diffusivity (MD), mean kurtosis (MK), and apparent diffusion coefficient (ADC) of cervical carcinoma and normal myometrium were measured and compared. MD, MK, and ADC of cervical carcinoma were compared among histologic subtypes, tumor grades, and FIGO stages. ROI- and VOI-derived DKI parameters and ADC were all in excellent consistency (intraclass correlation coefficient, ICC > 0.90, respectively). Cervical carcinoma had significantly lower MD, ADC, and higher MK than normal myometrium (p < 0.001). MD and ADC showed significant differences between histologic subtypes and FIGO stages, lower in squamous cell carcinoma than adenocarcinoma and higher in FIGO I-II than FIGO III-IV (p < 0.050), but not with tumor grade. No difference was observed in MK for different clinicopathologic features tested. ROI and VOI analyses were in excellent consistency. MD and ADC were able to distinguish histologic subtypes and separating FIGO stages, MK could not. DKI showed no clear added value over conventional DWI in the characterization of cervical carcinoma.

Unveiling early cortical and subcortical neuronal degeneration in ALS mice by ultra-high field diffusion MRI

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease primarily characterized by the progressive impairment of motor functions. However, a significant portion of affected patients develops severe cognitive dysfunction, developing a widespread white (WM) and gray matter (GM) microstructural impairment. The objective of this study is to determine if Gaussian and non-Gaussian diffusion models gathered by ultra-high field diffusion MRI (UHFD-MRI) are an appropriate tool to detect early structural changes in brain white and gray matter in a preclinical model of ALS. ALS brains (G93A-SOD1mice) were scanned in a 16.7 T magnet. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) have shown presymptomatic decrease in axonal organization by Fractional Anisotropy (FA) and neurite content by Intracellular Volume Fraction (ICVF) across deep WM (corpus callosum) as well as superficial (cortex) and deep (hippocampus) GM. Additional diffusion kurtosis imaging (DKI) analysis demonstrated broader and earlier GM reductions in mean kurtosis (MK), possibly related to the decrease in neuronal complexity. Histological validation was obtained by an ALS fluorescent mice reporter (YFP, G93A-SOD1 mice). The combination of DTI, NODDI, and DKI models have proved to provide a more complete assessment of the early microstructural changes in the ALS brain, particularly in areas associated with high cognitive functions. This comprehensive approach should be considered as a valuable tool for the early detection of neuroimaging markers.

Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) beyond ADC at 3.0 T scanner with gleason score at final pathology.

To explore the preponderant diagnostic performances of IVIM and DKI in predicting the Gleason score (GS) of prostate cancer. Diffusion-weighted imaging data were postprocessed using monoexponential, lVIM and DK models to quantitate the apparent diffusion coefficient (ADC), molecular diffusion coefficient (D), perfusion-related diffusion coefficient (Dstar), perfusion fraction (F), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Spearman's rank correlation coefficient was used to explore the relationship between those parameters and the GS, Kruskal-Wallis test, and Mann-Whitney U test were performed to compare the above parameters between the different groups, and a receiver-operating characteristic (ROC) curve was used to analyze the differential diagnosis ability. The interpretation of the results is in view of histopathologic tumor tissue composition. The area under the ROC curves (AUCs) of ADC, F, D, Dapp, and Kapp in differentiating GS ≤ 3 + 4 and GS > 3 + 4 PCa were 0.744 (95% CI 0.581-0.868), 0.726 (95% CI 0.563-0.855), 0.732 (95% CI 0.569-0.860), and 0.752 (95% CI 0.590-0.875), 0.766 (95% CI 0.606-0.885), respectively, and those in differentiating GS ≤ 7 and GS > 7 PCa were 0.755 (95% CI 0.594-0.877), 0.734 (95% CI 0.571-0.861), 0.724 (95% CI0.560-0.853), and 0.716 (95% CI 0.552-0.847), 0.828 (95% CI 0.676-0.929), respectively. All the P values were less than 0.05. There was no significant difference in the AUC for the detection of different GS groups by using those parameters. Both the IVIM and DKI models are beneficial to predict GS of PCa and indirectly predict its aggressiveness, and they have a comparable diagnostic performance with each other as well as ADC.

Comparative analysis of the value of diffusion kurtosis imaging and diffusion-weighted imaging in evaluating the histological features of endometrial cancer

PurposeThis study evaluated and compared the performances of diffusion kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) for diagnosing and histologically grading endometrial cancer.Materials and methodsIn this retrospective study, DKI and DWI data for 61 patients with endometrial cancer and 30 patients with a normal endometrium were analyzed, and the mean kurtosis (MK), mean diffusion coefficient (MD) and apparent diffusion coefficient (ADC) values for the endometrial cancer tissue and normal endometrial tissue were acquired. The parameters for the normal endometrium group (G0) and the endometrial cancer groups (G1, G2 and G3) were compared and analyzed. The receiver operating characteristic (ROC) curve was used to evaluate each parameter’s diagnostic accuracy and threshold. Spearman correlation analysis was used to analyze the correlations between all parameters and histological grades.ResultsThe MK values for the G0, G1, G2 and G3 groups increased gradually, while the MD and ADC values decreased gradually. Except for the differences in the ADC values between G0 and G1, the differences among the groups were statistically significant (P < 0.05). The MK values had the highest diagnostic accuracy in differentiating G0 and (G1 + G2 + G3), G0 and G1, G1 and G2, and G2 and G3 (AUC = 0.93, 0.76, 0.91, 0.91, P < 0.05). MK was maximally correlated with histological grade, followed by MD and ADC (MK > MD > ADC; r = − 0.85, + 0.82, + 0.76, P < 0.01).ConclusionBoth DKI and DWI can be used to evaluate the diagnosis and histological grading of endometrial cancer. Compared with DWI, the DKI model is a more complete mathematical model with more sensitive parameters, which can more effectively evaluate the pathological and physiological characteristics of endometrial cancer.

Intravoxel incoherent motion and diffusion kurtosis imaging for discriminating soft tissue sarcoma from vascular anomalies.

To investigate the feasibility of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in discriminating soft tissue sarcoma from vascular anomalies.Twenty-two patients with lower extremity soft tissue sarcoma and 15 patients with lower extremity vascular anomalies underwent IVIM-DWI and DKI. IVIM model generated true diffusion (D), perfusion fraction (f), and pseudo-diffusion coefficient (D∗). DKI model generated mean kurtosis (MK) and mean diffusion (MD). These parameters were measured by 2 radiologists separately through drawing region of interest. Intraclass correlation coefficient (ICC) was calculated to evaluate the inter-reader viability in measurement. The Mann–Whitney test was used to compare the parameters between vascular anomalies and soft tissue sarcoma. Receiver operating characteristic curves were constructed for assessing diagnostic accuracies.ICC was more than 0.8 for apparent diffusion coefficient (ADC), D, D∗, f, MK, and MD. Mean ADC, D, and MD were significantly lower in soft tissue sarcoma versus vascular anomalies (P < .05). Mean D∗ and f were not significantly different (P > .05). Soft tissue sarcoma had significantly higher MK than vascular anomalies (P < .05). Areas under curve for ADC, D, MK, and MD were 0.876, 0.885, 0.894, and 0.812, respectively.IVIM and DKI are feasible in discriminating soft tissue sarcoma from vascular anomalies.

Diagnostic value of diffusion kurtosis imaging combined with quantitative dynamic contrast-enhanced MRI in breast lesions

Objective To evaluate the diagnostic efficacy of MRI diffusion kurtosis imaging (DKI) and quantitative dynamic contrast enhancement MRI (DCE-MRI) in benign and malignant breast lesions, and to explore the differential diagnosis ability for different pathological types and molecular subtype lesions. Methods Sixty four females were retrospectively enrolled in the study of MRI diffusion kurtosis imaging and quantitative dynamic contrast enhancement between November 20 and May 2017. All of them were confirmed to have benign or malignant lesions after surgical resection or puncture. All patients underwent axial T1WI, DKI and DCE-MRI examinations. The mean kurtosis (MK) and mean diffusivity (MD) values were calculated by the DKI model, and the hemodynamic parameters were obtained by quantitative dynamic contrast enhancement, including volume transfer constant (Ktrans), rate constant (Kep), extravascular extracellular space distribute volume per unit tissue volume (Ve) and blood volume fraction (Vp). Pathological analysis was performed to monitor the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) and nuclear proliferation index Ki67. The breast cancer was divided into Luminal A type, Luminal B type, HER-2 positive and triple-negative 4 subtypes. The differences of DKI parameters and DCE-MRI parameters between benign and malignant breast lesions were compared using two independent samples t test (normal distribution and homogeneity of variance) or Mann-Whitney U test (skewed distribution or variance). ROC analysis was used to evaluate the value of DKI and DCE-MRI parameters in differential diagnosis of benign and malignant breast lesions with pathological results as the gold standard. The Mann-Whitney U test and Kruskal-Wallis H test were used to compare the differences of DKI and DCE-MRI parameters among different prognostic factors and molecular subtypes of breast cancer. Spearman rank correlation analysis was used to evaluate the correlation between DKI and DCE-MRI parameters and different prognostic factors. Results Sixty-four cases were single lesions, with breast cancer in 23 cases and 41 cases of benign lesions. In breast cancer, there were 9 cases of Luminal A type, 7 cases of Luminal B type, 3 cases of HER-2 positive type and 4 cases of triple negative type. The positive numbers of ER, PR and HER-2 were 14, 11 and 10 cases respectively. Nineteen cases showed high expression of Ki67, while 4 cases showed low expression. There were significant differences in MK, MD, Ktrans and Kep between benign and malignant lesions (P 0.05).The area under the ROC for the differential diagnosis of benign and malignant breast lesions were 0.897, 0.808, 0.844 and 0.842, respectively. The combined multi-parameter differential diagnosis improved the efficacy. Combined with the above four parameters, the area under the ROC was 0.950. The diagnosis Sensitivity, specificity and accuracy were 0.870, 0.951 and 0.922 respectively. The Ktrans and Vp values of patients with ER positive and ER negative, as well as Ve value of deferent lymph node status, were significantly different (P 0.05). There was a moderate positive correlation between ER and Ktrans and Vp values. There was a low positive correlation between lymph node status and Ve value (r= 0.6, 0.5 and 0.4, respectively, P 0.05). There were no significant differences in DKI and DCE-MRI parameters among different subtypes of breast cancer patients (all P>0.05). Conclusion DKI combined with DCE-MRI can improve the differential diagnosis of breast lesions and some DCE-MRI parameters are related to prognostic factors. Key words: Breast neoplasms; Magnetic resonance imaging; Diffusion kurtosis imaging; Quantitative dynamic contrast enhancement

Mono-exponential, diffusion kurtosis and stretched exponential diffusion MR imaging response to chemoradiation in newly diagnosed glioblastoma.

To quantify changes and prognostic value of diffusion MRI measurements obtained using mono-exponential, diffusion kurtosis imaging (DKI) and stretched exponential (SE) models prior and after chemoradiation in newly diagnosed glioblastoma (GBM). Diffusion-weighted images (DWIs) were acquired in twenty-three patients following surgery, prior chemoradiation and within 7days following completion of treatment, using b-values ranging from 0 to 5000s/mm2. Mono-exponential diffusion (apparent diffusion coefficient: ADC), isotropic (non-directional) DKI model with apparent diffusivity (Dapp) and kurtosis (Kapp) estimates as well as SE model with distributed-diffusion coefficient (DDC) and mean intra-voxel heterogeneity (α) were computed for all patients prior and after chemoradiation. Median values were calculated for normal appearing white matter (NAWM) and contrast-enhancing tumor (CET). The magnitudes of diffusion change prior and after chemoradiation were used to predict overall survival (OS). Diffusivity in NAWM was consistent for all diffusion measures during chemoradiation, while diffusivity measurements (ADC, Dapp and DDC) within CET changed significantly. A strong positive correlation existed between ADC, Dapp, and DDC measurements prior to chemoradiation; however, this association was weak following chemoradiation, suggesting a more complex microstructural environment after cytotoxic therapy. When combined with baseline tumor volume and MGMT status, age and ADC changes added significant prognostic values, whereas more complex diffusion models did not show significant value in predicting OS. Despite increased tissue complexity following chemoradiation, advanced diffusion models have longer acquisition times, provide largely comparable measures of diffusivity, and do not appear to provide additional prognostic value compared to mono-exponential ADC maps.

Diffusion kurtosis imaging in the differential diagnosis of parotid gland disease and parotid adenolymphoma: preliminary results.

To study the value of diffusion kurtosis imaging (DKI) in diagnosis of parotid gland disease (PGD) with different pathological patterns and parotid adenolymphoma (PAL). 57 patients with different kinds of PGD were enrolled and underwent DKI and conventional diffusion-weighted imaging (DWI). All patients were categorized into different groups according to their pathological patterns. The result of calculating the value of DKI-derived parameters (Kmean, Krad, Kax, Dmean, Drad, Dax, and FA) and apparent diffusion coefficient (ADC) of DWI were used to study their diagnostic accuracy in PGD with different pathological patterns. The binary logistic regression method was used to confirm clinical valuable diffusion parameters (obtained with DKI and DWI models) for diagnosing PAL. Using MedCalc 13.0, receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic value of confirmed parameters based on the logistic regression equation. Both DKI parameters and conventional ADC showed statistical significance in diagnosing PGD with different pathological patterns (p < .01). By using the DKI model, kurtosis coefficients showed higher diagnostic capability than diffusion coefficients ([Kmean+Krad + Kax] vs [Dmean+Drad + Dax]: 22vs15, p < .01) did in the differential diagnosis among different PGD groups. In the diagnosis of PAL among all PGD patterns, the ROC analysis demonstrated that the area under curve (AUC) FA+Kax [0.881 ± 0.057 (0.824 to 0.938)] is higher than that when using FA [0.629 ± 0.095 (0.534 to 0.724)] and Kax [0.800 ± 0.070 (0.730 to 0.870)] alone (p < .05), with sensitivity, specificity, accuracy, and both positive and negative predictive values of 71.43, 95.78, 91.77, 76.92, and 94.44%, respectively. DKI showed higher diagnostic capacity in the differential diagnosis of PGD with different pathological patterns. Combined parameters of DKI can differentiate PAL from other PGD pathological patterns with a high degree of accuracy. This technique shows great potential for DKI in the differential diagnosis of PGD within a certain pathological category.

Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma.

Since neoadjuvant chemotherapy (NAC) has proven a benefit for locally advanced nasopharyngeal carcinoma (NPC), early response evaluation after chemotherapy is important to implement individualized therapy for NPC in the era of precision medicine. To determine the combined and independent contribution between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) in the early monitoring of NAC response for NPC. Prospective. Fifty-three locally advanced NPC patients. Four examinations before and at 4, 20, and 40 days after NAC initiation were performed at 3T MRI including DCE-MRI and DKI (b values = 0, 500, 1000, 1500 s/mm2 ). DCE-MRI parameters (Ktrans [the volume transfer constant of Gd-DTPA], kep [rate constant], νe [the extracellular volume fraction of the imaged tissue], and νp [the blood volume fraction]) and DKI parameters (Dapp [apparent diffusion for non-Gaussian distribution] and Kapp [apparent kurtosis coefficient]) were analyzed using dedicated software. MRI parameters and their corresponding changes were compared between responders and nonresponders after one or two NAC cycles treatment using independent-samples Student's t-test or Mann-Whitney U-test depending on the normality contribution test and then followed by logistic regression and receiver operating characteristic curve (ROC) analyses. The responder group (RG) patients presented significantly higher mean Ktrans and Dapp values at baseline and larger , Δvp(0-4) , and ΔDapp(0-4) values after either one or two NAC cycles compared with the nonresponder group (NRG) patients (all P < 0.05). ROC analyses demonstrated the higher diagnostic accuracy of combined DCE-MRI and DKI model to distinguish nonresponders from responders after two NAC cycles than using DCE-MRI (0.987 vs. 0.872, P = 0.033) or DKI (0.987 vs. 0.898, P = 0.047) alone. Combined DCE-MRI and DKI models had higher diagnostic accuracy for NAC assessment compared with either model used independently. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1208-1216.