An Optimized Method for Evaluating the Potential Gd-Nanoparticle Dose Enhancement Produced by Electronic Brachytherapy.

This work reports an optimized method to experimentally quantify the Gd-nanoparticle dose enhancement generated by electronic brachytherapy. The dose enhancement was evaluated considering energy beams of 50 kVp and 70 kVp, determining the Gd-nanoparticle concentration ranges that would optimize the process for each energy. The evaluation was performed using delaminated radiochromic films and a Poly(methyl methacrylate) (PMMA) phantom covered on one side by a thin 2.5 µm Mylar filter acting as an interface between the region with Gd suspension and the radiosensitive film substrate. The results for the 70 kVp beam quality showed dose increments of 6±6%, 22±7%, and 9±7% at different concentrations of 10, 20, and 30 mg/mL, respectively, verifying the competitive mechanisms of enhancement and attenuation. For the 50 kVp beam quality, no increase in dose was recorded for the concentrations studied, indicating that the major contribution to enhancement is from the K-edge interaction. In order to separate the contributions of attenuation and enhancement to the total dose, measurements were replicated with a 12 µm Mylar filter, obtaining a dose enhancement attributable to the K-edge of 29±7% and 34±7% at 20 and 30 mg/mL, respectively, evidencing a significant additional dose proportional to the Gd concentration.

Machine learning to investigate superficial white matter integrity in early multiple sclerosis.

BACKGROUND AND PURPOSE: This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC). METHODS: Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age- and sex-matched HC. Thickness of 68 cortical regions and resting-state functional-connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank-sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1-SWM, 2-DWM, 3-SWM and DWM, 4-cortical thickness, or 5-RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5-folds cross-validation. Area under the receiver operating characteristic curve (AUCroc ) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUCroc values. RESULTS: Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre- and post-central cortices (p < .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUCroc = 0.75). The SWM model provided higher AUCroc (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all-features (0.68) models (p < .001 for all). CONCLUSION: Our results reveal a non-random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.

Group-Wise Cortical Surface Parcellation Based on Inter-Subject Fiber Clustering.

We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points. The final parcellation was obtained using parcel probability maps to model and integrate the connectivity information of all subjects, and graphs to represent the overlap between parcels. Two inter-subject clustering schemes were tested, generating a total of 171 and 109 parcels, respectively. The resulting parcels were quantitatively compared with three state-of-the-art atlases. The best parcellation returned 69 parcels with a Dice similarity coefficient greater than 0.5. To the best of our knowledge, this is the first diffusion-based cortex parcellation method based on whole-brain inter-subject fiber clustering.

Development of Resistance-Based pH Sensor Using Zinc Oxide Nanorods.

The resistance-based pH sensing capability of ZnO nanorods was presented in this study. Interdigitated finger structures of nickel/gold (Ni/Au) electrodes were fabricated on the substrates prior to the sensing material. The effect of varying electrode widths was also considered. Zinc oxide (ZnO) film, as seed layer, was deposited via spray pyrolysis, and zinc oxide nanorods (ZnO-NRs) were grown via low temperature chemical bath deposition. Resistance measurements have shown plausible difference in varying pH of a test solution. The sensor was found reasonably more appreciable in sensing acidic solutions. The electrode widths were also found to relay substantial consequence in the resistance-based sensor. The least electrode-width design has shown a significant increase in the sensitivity of the sensor, with higher initial resistance and greater range of response.

Near-Infrared and Ultraviolet Photodetector Based on p-n Homojunction of Undoped and Phosphorus-Doped Zinc Oxide Nanorods.

The application of a p-n homojunction based on zinc oxide (ZnO) nanorods as photodetector is presented in this study. The homojunctions were grown via chemical bath deposition for 6, 9, and 12 hours per layer of the junction. X-ray diffraction and scanning electron micrographs confirmed the material composition, structure, and morphology of the grown device. Current-voltage (I-V) measurements were done to verify the diode-like behavior of the ZnO p-n homojunction. Upon illumination, it is observed through I-V curves and through photocurrent measurements that the fabricated device is sensitive to ultraviolet and near-infrared light, respectively. The peak sensitivities in the photocurrent spectrum were found tunable based on the observed red shift as the length of the nanorods is increased. In addition to this, upon applying a positive voltage bias, the response of the device was observed to enhance by 5 orders of magnitude. In general, the device was successfully proven to have a great potential for applications in photodetection.