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The usefulness of 3-dimensional (3D)-printed disease models has been recognized in various medical fields. This study aims to introduce a production platform for patient-specific 3D-printed brain tumor model in clinical practice and evaluate its effectiveness. A full-cycle platform was created for the clinical application of a 3D-printed brain tumor model (3D-printed model) production system. Essential elements included automated segmentation software, cloud-based interactive communication tools, customized brain models with exquisite expression of brain anatomy in transparent material, adjunctive devices for surgical simulation, and swift process cycles to meet practical needs. A simulated clinical usefulness validation was conducted in which neurosurgeons assessed the usefulness of the 3D-printed models in 10 cases. We successfully produced clinically applicable patient-specific models within 4 days using the established platform. The simulated clinical usefulness validation results revealed the significant superiority of the 3D-printed models in surgical planning regarding surgical posture (p = 0.0147) and craniotomy design (p = 0.0072) compared to conventional magnetic resonance images. The benefit was more noticeable for neurosurgeons with less experience. We established a 3D-printed brain tumor model production system that is ready to use in daily clinical practice for neurosurgery.

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Few studies have investigated the gas-sensing properties of graphene oxide/titanium dioxide (GO/TiO2) composite combined with photocatalytic effect. Room temperature gas-sensing properties of the GO/TiO2 composite were investigated towards various reducing gases. The composite sensor showed an enhanced gas response and a faster recovery time than a pure GO sensor due to the synergistic effect of the hybridization, such as creation of a hetero-junction at the interface and modulation of charge carrier density. However, the issue of long-term stability at room temperature still remains unsolved even after construction of a composite structure. To address this issue, the surface and hetero-junction of the GO/TiO2 composite were engineered via a UV process. A photocatalytic effect of TiO2 induced the reduction of the GO phase in the composite solution. The comparison of gas-sensing properties before and after the UV process clearly showed the transition from n-type to p-type gas-sensing behavior toward reducing gases. This transition revealed that the dominant sensing material is GO, and TiO2 enhanced the gas reaction by providing more reactive sites. With a UV-treated composite sensor, the function of identifying target gas was maintained over a one-month period, showing strong resistance to humidity.

Purpose:To demonstrate the application of artificial-neural-network (ANN) for real-time processing of myelin water imaging (MWI).Methods: Three neural networks, ANN-IMWF, ANN-IGMT2, and ANN-II, were developed to generate MWI. ANN-IMWF and ANN-IGMT2 were designed to output myelin water fraction (MWF) and geometric mean T2 (GMT2,IEW), respectively whereas ANN-II generates a T2 distribution.For the networks, gradient and spin echo data from 18 healthy controls (HC) and 26 multiple sclerosis patients (MS) were utilized. Among them, 10 HC and 12 MS had the same scan parameters and were used for training (6 HC and 6 MS), validation (1 HC and 1 MS), and test sets (3 HC and 5 MS). The remaining data had different scan parameters and were applied to exam the effects of the scan parameters. The network results were compared with those of conventional MWI in the white matter mask and regions of interest (ROI). Results:The networks produced highly accurate results, showing averaged normalized root-mean-squared error under 3% for MWF and 0.4% for GMT2,IEW in the white matter mask of the test set. In the ROI analysis, the differences between ANNs and conventional MWI were less than 0.1% in MWF and 0.1 ms in GMT2,IEW (no statistical difference and R 2 > 0.97). Datasets with different scan parameters showed increased errors. The average processing time was 0.68 sec in ANNs, gaining 11,702 times acceleration in the computational speed (conventional MWI: 7,958 sec). Conclusion:The proposed neural networks demonstrate the feasibility of real-time processing for MWI with high accuracy. K E Y W O R D SMyelin water imaging, artificial neural network, T2 distribution, multiecho spin echo, multiple sclerosis, deep learning NRF-2017M3C7A1047864).The Institute of Engineering Research at Seoul National University provided research facilities for this work.This work is an extension of a previously published ISMRM abstract of the same authors 44 .