Photon Upconversion with a Low Threshold Excitation Intensity in Plain Water.

A triplet-triplet annihilation-based photon upconversion (TTA-UC) system with a low threshold excitation intensity (Ith) in plain water was developed. Water-soluble anionic porphyrin (PdTPPS4-) and diphenylanthracene (DCDPA2-) derivatives were used as light absorbers and emitter molecules, respectively, and no additives such as surfactants were required. The phosphorescence emission from PdTPPS4- under an excitation wavelength of 528 nm was quenched by DCDPA2-, resulting in triplet energy transfer, whereas fluorescence from DCDPA2- was observed in a short wavelength region (400-500 nm). Three independent emission studies utilizing different excitation light sources validated the TTA-UC process in a simple aqueous solution. TTA occurred after the triplet energy transfer, according to the time profiles of phosphorescence and fluorescence detected following pulse laser excitation. The Ith for TTA-UC was estimated to be lower than 6 mW cm-2, although it could not be exactly determined due to the sensitivity limit of the experimental setup. The upper limit of Ith for the aqueous solution of DCDPA2- and PdTPPS4- is the smallest value obtained to date for aqueous systems and comparable to that of high-performance TTA-UC systems in organic solutions.

Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.

In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation is essential for the automatic evaluation of chronological changes in the brainstem and pituitary gland. Thus, the purpose of our study was to use deep learning to automatically segment internal organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) in midsagittal slices of 2D T1-weighted images. Deep learning for the automatic segmentation of seven regions in the images was accomplished using two different methods: patch-based segmentation and semantic segmentation. The networks used for patch-based segmentation were AlexNet, GoogLeNet, and ResNet50, whereas semantic segmentation was accomplished using SegNet, VGG16-weighted SegNet, and U-Net. The precision and Jaccard index were calculated, and the extraction accuracy of the six convolutional network (DCNN) systems was evaluated. The highest precision (0.974) was obtained with the VGG16-weighted SegNet, and the lowest precision (0.506) was obtained with ResNet50. Based on the data, calculation times, and Jaccard indices obtained in this study, segmentation on a 2D image may be considered a viable and effective approach. We found that the optimal automatic segmentation of organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) on brain sagittal T1-weighted images could be achieved using SegNet with VGG16.

[Creation of Brain Parenchymal Equivalent Phantom for Diffusion Weighted Image Using Microwave Oven: A Different Heating Method].

The acute micro cerebral infarction phantom was created to evaluate the detectability of acute micro cerebral infarction and optimize scan conditions in magnetic resonance imaging (MRI) examinations. The creation of the brain parenchyma phantom requires a hot stirrer that can be heated and stirred simultaneously. However, few hospitals and facilities have hot stirrers. The aim of our study was to use a microwave oven instead of a hot stirrer for the creation of a brain parenchyma phantom. Five phantoms using a hot stirrer and five phantoms using a microwave oven were created. The phantom creation time, T2 value, apparent diffusion coefficient (ADC) value, uniformity, and reproducibility of MR images were compared between the two creation methods. The phantom creation time, when using a microwave oven was 108±8 minutes, which was significantly shorter than that of 213± 48 minutes when using a hot stirrer. T2 values, ADC values, uniformity, and reproducibility were not significantly different. Therefore, it is easier to create an acute micro cerebral infarction phantom using a microwave oven compared to a hot stirrer.

[Preparation of a Small Acute-phase Cerebral Infarction Phantom for Diffusion-weighted Imaging].

The present study aimed to prepare a small acute-phase cerebral infarction phantom made of gelatin and sucrose to simulate brain parenchymal cells, and a phantom made of collagen peptides and sucrose to simulate cerebral infarction for diffusion-weighted imaging (DWI). During the preparation of gelatin and sucrose mixture (17.0 wt% gelatin, 20.0 wt% sucrose), a cylindrical wooden bar was placed in the center of the phantom and covered with a heat-shrinkable film to ensure space remained after gelling. A mixed solution composed of collagen peptide and sucrose (16.0 wt% collagen peptide, 27.5 wt% sucrose) was then enclosed within the space. The T2 relaxation time and apparent diffusion coefficient (ADC) of the phantom were set equal to those observed in actual patients with acute-phase cerebral infarction. The mixture was selected based on the signal intensity of both the healthy brain tissue and that subjected to acute cerebral infarction, such that no contrast was observed during T2-weighted imaging (T2WI). T2WI and DWI were performed using a 1.5 T scanner. Although contrast between the mixed gel and mixed solution was obscure on T2WI, cerebral infarction was clearly visible on DWI. However, the phantom exhibited mono-exponential changes in the ADC value at b values of 0 and 1,000 (s/mm2), and was affected by the proton density and T1 value depending on the imaging condition.