Diffusion-weighted Imaging Voxelwise-matched Analyses of Lung Cancer at 3.0-T PET/MRI: Reverse Phase Encoding Approach for Echo-planar Imaging Distortion Correction.

Background PET/MRI has drawn increasing interest in thoracic oncology due to the simultaneous acquisition of PET and MRI data. Geometric distortions related to diffusion-weighted imaging (DWI) limit the evaluation of voxelwise multimodal analyses. Purpose To assess the effectiveness of reverse phase encoding in correcting DWI geometric distortion for multimodal PET/MRI voxelwise lung tumor analyses. Materials and Methods In this prospective study, reverse phase encoding method was implemented with 3.0-T PET/MRI to correct geometric distortions related to DWI. The method was validated in dedicated phantom and then applied to 12 consecutive patients (mean age, 66 years ± 13 [standard deviation]; 10 men) suspected of having lung cancer who underwent fluorodeoxyglucose PET/MRI between October 2018 and April 2019. The effects on DWI-related image matching and apparent diffusion coefficient (ADC) regional map computation were assessed. Consequences on multimodal PET/MRI voxelwise lung tumor analyses were evaluated. Spearman correlation coefficients (rs) between the standardized uptake value (SUV) and ADC data corrected for distortion were computed from optimal realigned DWI PET data, along with bootstrap confidence intervals. Results Phantom results showed that in highly distorted areas, correcting the distortion significantly reduced the mean error against the ground truth (-25% ± 10.6 to -18.4% ± 12.6; P < .001) and the number of voxels with more than 20% error (from 85.3% to 31.4%). In the 12 patients, the coregistration of multimodal PET/MRI tumor data was improved by using the reverse phase encoding method (0.4%-44%). In all tumors, voxelwise correlations (rs) between ADC and SUV revealed null or weak monotonic relationships (mean rs of 0.016 ± 0.24 with none above 0.5). Conclusion Reverse phase encoding is a simple-to-implement method for improved diffusion-weighted multimodal PET/MRI voxelwise-matched analyses in lung cancer. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Colletti in this issue.

PET KinetiX-A Software Solution for PET Parametric Imaging at the Whole Field of View Level.

Kinetic modeling represents the ultimate foundations of PET quantitative imaging, a unique opportunity to better characterize the diseases or prevent the reduction of drugs development. Primarily designed for research, parametric imaging based on PET kinetic modeling may become a reality in future clinical practice, enhanced by the technical abilities of the latest generation of commercially available PET systems. In the era of precision medicine, such paradigm shift should be promoted, regardless of the PET system. In order to anticipate and stimulate this emerging clinical paradigm shift, we developed a constructor-independent software package, called PET KinetiX, allowing a faster and easier computation of parametric images from any 4D PET DICOM series, at the whole field of view level. The PET KinetiX package is currently a plug-in for Osirix DICOM viewer. The package provides a suite of five PET kinetic models: Patlak, Logan, 1-tissue compartment model, 2-tissue compartment model, and first pass blood flow. After uploading the 4D-PET DICOM series into Osirix, the image processing requires very few steps: the choice of the kinetic model and the definition of an input function. After a 2-min process, the PET parametric and error maps of the chosen model are automatically estimated voxel-wise and written in DICOM format. The software benefits from the graphical user interface of Osirix, making it user-friendly. Compared to PMOD-PKIN (version 4.4) on twelve 18F-FDG PET dynamic datasets, PET KinetiX provided an absolute bias of 0.1% (0.05-0.25) and 5.8% (3.3-12.3) for KiPatlak and Ki2TCM, respectively. Several clinical research illustrative cases acquired on different hybrid PET systems (standard or extended axial fields of view, PET/CT, and PET/MRI), with different acquisition schemes (single-bed single-pass or multi-bed multipass), are also provided. PET KinetiX is a very fast and efficient independent research software that helps molecular imaging users easily and quickly produce 3D PET parametric images from any reconstructed 4D-PET data acquired on standard or large PET systems.

Specific salt and pH effects on foam film of a pH sensitive surfactant.

Steady state foams made of a pH sensitive surfactant, nonaoxyethylene oleylether carboxylic acid, with ion complexing properties was studied using small angle neutron scattering (SANS). The effect of pH variation and salt addition on the foam film thickness was investigated and discussed in terms of the influent parameters stabilizing the foam such as surface properties and electrostatic effects determined by tensiometry and zeta potential measurements. The decrease in the film thickness by adding mono (Na(+)) and divalent (Ca(2+)) salts is classically explained by screening of the double layer in foam films (transverse interactions). On the contrary, addition of acid or complexing ion (Nd(3+)) results in an increase in the film thickness and can be analyzed in terms of cohesive forces between surfactants at the liquid/gas interface (lateral interactions). pH and specific salt effects revealed that foams produced by nonaoxyethylene oleylether carboxylic acid are of interest in the potential use of this surfactant in ion separation process.

Fully Integrated Quantitative Multiparametric Analysis of Non-Small Cell Lung Cancer at 3-T PET/MRI: Toward One-Stop-Shop Tumor Biological Characterization at the Supervoxel Level.

INTRODUCTION: The aim of this study was to study the feasibility of a fully integrated multiparametric imaging framework to characterize non-small cell lung cancer (NSCLC) at 3-T PET/MRI. PATIENTS AND METHODS: An 18F-FDG PET/MRI multiparametric imaging framework was developed and prospectively applied to 11 biopsy-proven NSCLC patients. For each tumor, 12 parametric maps were generated, including PET full kinetic modeling, apparent diffusion coefficient, T1/T2 relaxation times, and DCE full kinetic modeling. Gaussian mixture model-based clustering was applied at the whole data set level to define supervoxels of similar multidimensional PET/MRI behaviors. Taking the multidimensional voxel behaviors as input and the supervoxel class as output, machine learning procedure was finally trained and validated voxelwise to reveal the dominant PET/MRI characteristics of these supervoxels at the whole data set and individual tumor levels. RESULTS: The Gaussian mixture model-based clustering clustering applied at the whole data set level (17,316 voxels) found 3 main multidimensional behaviors underpinned by the 12 PET/MRI quantitative parameters. Four dominant PET/MRI parameters of clinical relevance (PET: k2, k3 and DCE: ve, vp) predicted the overall supervoxel behavior with 97% of accuracy (SD, 0.7; 10-fold cross-validation). At the individual tumor level, these dimensionality-reduced supervoxel maps showed mean discrepancy of 16.7% compared with the original ones. CONCLUSIONS: One-stop-shop PET/MRI multiparametric quantitative analysis of NSCLC is clinically feasible. Both PET and MRI parameters are useful to characterize the behavior of tumors at the supervoxel level. In the era of precision medicine, the full capabilities of PET/MRI would give further insight of the characterization of NSCLC behavior, opening new avenues toward image-based personalized medicine in this field.

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data.

OBJECTIVES: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS: Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION: A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

New Biocide Foam Containing Hydrogen Peroxide for the Decontamination of Vertical Surface Contaminated With Bacillus thuringiensis Spores.

Despite scientific advances, bacterial spores remain a major preoccupation in many different fields, such as the hospital, food, and CBRN-E Defense sector. Although many disinfectant technologies exist, there is a lack for the decontamination of difficult to access areas, outdoor sites, or large interior volumes. This study evaluates the decontamination efficiency of an aqueous foam containing hydrogen peroxide, with the efficiency of disinfectant in the liquid form on vertical surfaces contaminated by Bacillus thurengiensis spores. The decontamination efficiency impact of the surfactant and stabilizer agents in the foam and liquid forms was evaluated. No interferences were observed with these two chemical additives. Our results indicate that the decontamination kinetics of both foam and liquid forms are similar. In addition, while the foam form was as efficient as the liquid solution at 4°C, it was even more so at 30°C. The foam decontamination reaction follows the Arrhenius law, which enables the decontamination kinetic to be predicted with the temperature. Moreover, the foam process used via spraying or filling is more attractive due to the generation of lower quantity of liquid effluents. Our findings highlight the greater suitability of foam to decontaminate difficult to access and high volume facilities compared to liquid solutions.

Evaporation and Marangoni driven convection in small heated water droplets.

Evaporation dynamics of small sessile water droplets under microgravity conditions is investigated numerically. The water-air interface is free, and the surrounding air is assumed to be quasisteady. The droplet is described by Navier-Stokes and heat equations and its surrounding water/air gaseous phase with Laplace equation. In the thermodynamic conditions of the simulations presented herein, the evaporative mass flow is nonlinear. It shows a minimum that indicates the existence of qualitative changes in the evaporative regimes although the droplet is sessile. Due to temperature gradients on the free interface, Marangoni motion occurs and generates inside the droplet convection cells that furthermore exhibit small fluctuating motion as evaporation goes on.