A Statistical Evaluation to Compare and Analyze Estimations of the Diffusion Coefficient of Pertechnetate (99TcO4−) in Compacted Bentonite

Various numerical methods have been being extensively used to estimate the diffusion parameters of pertechnetate (99TcO4−) in compacted MX80 bentonite clay using through-diffusion (TD) techniques. In this study, an improved TD column test was applied, and the diffusion fluxes of tritium (HTO) as a non-reactive radionuclide, and 99TcO4− with various dry densities, were compared under the same experimental conditions. Similar results were obtained for the apparent diffusion coefficients of HTO and 99TcO4− using three estimation methods: a graphical method applying the asymptote calculation, an analytical solution using Lsqcurvefit installed in MATLAB, and the Marquardt–Levenberg optimization algorithm in the HYDRUS-1D inverse method. The statistical analysis showed that the densities using a one-way analysis of variance (ANOVA) for the three estimation methods ranged from 1200 to 2000 kg/m3, which indicated that there were no obvious differences between HTO and 99TcO4−. In general, the diffusion parameters of 99TcO4− were lower than those for HTO due to anion exclusion effects and lower accessible porosity.

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

Objective: To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression. Methods: This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity. Results: 60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (−11.7% and −9.5%) compared to non-progressors (−6.1% and −1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67. Conclusion: AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression. Advances in knowledge: This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (−10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.

Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study

BackgroundApparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value.ObjectivesWe aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks.MethodsThis prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient.ResultsThe s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P &lt; 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC.ConclusionThe deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

Diffusion Tensor Imaging in Rat Models of Preclinical Diabetic Nephropathy: A Preliminary Study

PurposeThis study aimed to investigate the value of diffusion tensor imaging to assess renal injury in a rat model of preclinical diabetic nephropathy.MethodsTwenty-eight male Sprague Dawley rats were divided into two groups: the normal control (NC) group of 10 rats and the diabetic nephropathy (DN) group of 18 rats. Eight weeks after diabetes induction by streptozotocin, 3.0-T magnetic resonance (MR) imaging (b = 0 and 600 s/mm2, 15 diffusion directions) using a 32-channel knee coil was performed. After MR imaging, we measured serum creatinine, and collected double kidney tissues for pathology. The apparent diffusion coefficients(ADC) and fractional anisotropy(FA) values of the renal cortex and medulla were calculated for all kidneys. Physiological parameters, laboratory parameters, and imaging results were compared between the two groups.ResultsAll DN group animals developed hyperglycemia, polyuria, and emaciation. Serum creatinine was not significantly different between the groups (P &gt; 0.05). Urinary albumin at 2, 4, and 8 weeks was higher in the DN group than in the NC group but &lt;20 µg/min (P &lt; 0.05). Pathologically, renal damage in the DN rats was observed. The ADC value was significantly increased in DN animals in the cortex (1.75×10-3mm2/s),medulla(1.53×10-3mm2/s)compared with NC group(cortex, 1.52×10-3mm2/s; medulla,1.35×10-3mm2/s). The FA value was significantly reduced in DN animals in the cortex (0.21),medulla(0.25)compared with NC group(cortex,0.26;medulla,0.3).ConclusionsIncreased apparent diffusion coefficients and decreased fractional anisotropy values on diffusion tensor imaging were associated with preclinical DN. Diffusion tensor imaging may be useful in early, non-invasive, quantitative detection, and therapy monitoring of DN.

Molecule Diffusion Behavior of Tritium and Selenium in Mongolia Clay Rock by Numerical Analysis of the Spatial and Temporal Variation

Clay rock in the Tamusu (TMS) area in the Inner Mongolia province is one candidate as a geological disposal site for highly radioactive waste in China. The diffusion behavior of HTO and Se(IV) in Tamusu clay rock was studied by through-diffusion (TD) experiments and numerical analysis to determine the spatial and temporal variation. A minimum error analysis was conducted to determine the HTO and Se(IV) diffusion coefficients in compacted TMS clay. The TD experimental results and numerical analysis showed that the diffusion of HTO and Se reached a steady state within 7 and 50 days, respectively, and the apparent diffusion coefficients (Da) decreased with the increases in the compacted density. In fact, there was retardation of Se diffusion in intact TMS clay rock. A two-site sorption model for Se was applied to simulate fast and slow sorption behavior quantitatively.

Apparent diffusion coefficients (ADC) in response assessment of transarterial radioembolization (TARE) for liver metastases of neuroendocrine tumors (NET): a feasibility study

Background In patients with hepatic neuroendocrine tumors (NETs) locoregional therapies such as transarterial radioembolization (TARE) are increasingly applied. Response evaluation remains challenging and previous studies assessing response with diffusion-weighted imaging (DWI) have been inconclusive. Purpose To perform a feasibility study to evaluate if response assessment with quantitative apparent diffusion coefficient (ADC) in patients with liver metastases of NETs after TARE will be possible. Material and Methods Retrospectively, 43 patients with 120 target lesions who obtained abdominal magnetic resonance imaging (MRI) with DWI 39±28 days before and 74±46 days after TARE were included. Intralesional ADC (ADCmin, ADCmax, and ADCmean) were measured for a maximum number of three lesions per patient on baseline and post-interventional DWI. Tumor response was categorized according to RECIST 1.1 and mRECIST. Results TARE resulted in partial remission (PR) in 23% (63%), in stable disease (SD) in 73% (23%), in progressive disease (PD) in 5% (7%) and in complete response (CR) in 0% (1%) according to RECIST 1.1 (mRECIST, respectively). ADC values increased significantly ( P<0.005) after TARE in the PR group whereas there was no significant change in the PD group. Post-therapeutic ADC values of SD lesions increased significantly when evaluated by RECIST 1.1 but not if evaluated by mRECIST. Percentual changes of ADCmean values were slightly higher for responders compared to non-responders ( P<0.05). Conclusion ADC values seem to represent an additional marker for treatment response evaluation after TARE in patients with secondary hepatic NET. A conclusive study seems feasible though patient-based evaluation and overall survival and progression free survival as alternate primary endpoints should be considered.

Swelling and Diffusion in Polymerized Ionic Liquids-Based Hydrogels

Hydrogels are one of the emerging classes of materials in current research. Besides their numerous applications in the medical sector as a drug delivery system or in tissue replacement, they are also suitable as irrigation components or as immobilization matrices in catalysis. For optimal application of these compounds, knowledge of the swelling properties and the diffusion mechanisms occurring in the gels is mandatory. This study is focused on hydrogels synthesized by radical polymerization of imidazolium-based ionic liquids. Both the swelling and diffusion behavior of these hydrogels were investigated via gravimetric swelling as well as sorption experiments implemented in water, ethanol, n-heptane, and tetrahydrofuran. In water and ethanol, strong swelling was observed while the transport mechanism deviated from Fickian-type behavior. By varying the counterion and the chain length of the cation, their influences on the processes were observed. The calculation of the diffusion coefficients delivered values in the range of 10−10 to 10−12 m2 s−1. The gravimetric results were supported by apparent diffusion coefficients measured through diffusion-weighted magnetic resonance imaging. A visualization of the water diffusion front within the hydrogel should help to further elucidate the diffusion processes in the imidazolium-based hydrogels.

Deep Learning-Based Magnetic Resonance Imaging Image Feature Analysis for Pathological Classification of Brain Glioma

To explore the application value of MRI in the diagnosis of brain glioma (BG), in the study, a deep learning-based multimodal feature fusion model was established, which was then applied in BG classification. 60 BG patients who came to our hospital for treatment were selected as research subjects. They all accepted the MRI scan and the enhanced scan, and the MRI results were compared with the pathological results. The results showed that the sensitivity of the algorithm was above 90%, and the sensitivity to diagnose grade IV glioma was as high as 98.28%; the specificity was above 78%, and the specificity to diagnose grade IV glioma was as high as 95.85%; the detection accuracy was above 95%. The relative fractional anisotropy (rFA) values of the tumor body were smaller than those of peritumoral edema in both the high-grade group and low-grade group, and the difference was notable P < 0.05 ; the relative apparent diffusion coefficients (rADC) values of the peritumoral edema were greater than those of tumor bodies of the same grade in both the high-grade group and the low-grade group, and the difference was notable P < 0.05 ; notable differences were noted in the rADC values of tumor bodies between the high-grade group and the low-grade group P < 0.05 and in the rADC values of the glioma peritumoral edema between the high-grade group and the low-grade group P < 0.05 . In summary, MRI based on deep learning raises the sensitivity, specificity, and accuracy to diagnose BG and can more accurately classify BG pathologically, providing reference for clinical treatment of BG.