Analysis of Changes in Signal Intensity of Choroid Plexus in MRI Using FLAIR-DW-EPI Pulse Sequence

In this study, the signal intensity of choroid plexus, which is producing cerebrospinal fluid, is analyzed according to the FLAIR diffusion-weighted imaging technique. In the T2*-DW-EPI diffusion-weighted image, the FLAIR-DW-EPI technique, which suppressed the water signal, was additionally examined for subjects with high choroid plexus signals and compared and analyzed the signal intensity. As a result of the experiment, it was confirmed that the FLAIR-DW-EPI technique showed a signal strength equal to or lower than that of the brain parenchyma, and there was a difference in signal strength between the two techniques. As a result of this study, if the choroidal plexus signal is high in the T2 * -DW-EPI diffusionweighted image, additional examination of the FLAIR-DW-EPI technique is thought to be useful in distinguishing functional problems of the choroid plexus. In conclusion, if the choroidal plexus signal is high on the T2*-DW-EPI diffuse weighted image, it is thought that further examination of the FLAIR-DW-EPI technique will be useful in distinguishing functional problems of the choroidal plexus.

Peak Signal-to-Noise Ratio Evaluation of Server Display Monitors and Client Display Monitors in a Digital Subtraction Angiography Devices

This study evaluated PSNR of server display monitor and client display monitor of DSA system. The signal is acquired and imaged during the surgery and stored in the PACS server. After that, distortion of the original signal is an important problem in the process of observation on the client monitor. There are many problems such as noise generated during compression and image storage/transmission in PACS, information loss during image storage and transmission, and deterioration in image quality when outputting medical images from a monitor. The equipment used for the experiment in this study was P's DSA. We used two types of monitors in our experiment, one is P’s company resolution 1280×1024 pixel monitor, and the other is W’s company resolution 1536×2048 pixel monitor. The PACS Program used MARO-view, and for the experiment, a PSNR measurement program using Visual C++ was implemented and used for the experiment. As a result of the experiment, the PSNR value of the kidney angiography image was 26.958dB, the PSNR value of the lung angiography image was 28.9174 dB, the PSNR value of the heart angiography image was 22.8315dB, and the PSNR value of the neck angiography image was 37.0319 dB, and the knee blood vessels image showed a PSNR value of 43.2052 dB, respectively. In conclusion, it can be seen that there is almost no signal distortion in the process of acquiring, storing, and transmitting images in PACS. However, it suggests that the image signal may be distorted depending on the resolution and performance of each monitor. Therefore, it will be necessary to evaluate the performance of the monitor and to maintain the performance.

Analysis of Fitting Degree of MRI and PET Images in Simultaneous MRPET Images by Machine Learning Neural Networks

Simultaneous MR-PET imaging is a fusion of MRI using various parameters and PET images using various nuclides. In this paper, we performed analysis on the fitting degree between MRI and simultaneous MR-PET images and between PET and simultaneous MR-PET images. For the fitness analysis by neural network learning, feature parameters of experimental images were extracted by discrete wavelet transform (DWT), and the extracted parameters were used as input data to the neural network. In comparing the feature values extracted by DWT for each image, the horizontal and vertical low frequencies showed similar patterns, but the patterns were different in the horizontal and vertical high frequency and diagonal high frequency regions. In particular, the signal value was large in the T1 and T2 weighted images of MRI. Neural network learning results for fitting degree analysis were as follows. 1. T1-weighted MRI and simultaneous MR-PET image fitting degree: Regression (R) values were found to be Training 0.984, Validation 0.844, and Testing 0.886. 2. Dementia-PET image and Simultaneous MR-PET Image fitting degree: R values were found to be Training 0.970, Validation 0.803, and Testing 0.828. 3. T2-weighted MRI and concurrent MR-PET image fitting degree: R values were found to be Training 0.999, Validation 0.908, and Testing 0.766. 4. Brain tumor-PET image and Simultaneous MR-PET image fitting degree: R values were found to be Training 0.999, Validation 0.983, and Testing 0.876. An R value closer to 1 indicates more similarity. Therefore, each image fused in the simultaneous MR-PET images verified in this study was found to be similar. Ongoing study of images acquired with pulse sequences other than the weighted images in the MRI is needed. These studies may establish a useful protocol for the acquisition of simultaneous MR-PET images.

Magnetic Nanoclusters for T2 MR Imaging in Cancer using Xenograft Mice Model

In this study, we tried to develop nanoprobe for molecular magnetic resonance (MR) imaging using magnetic nanoclusters (MNC). MNCs for magnetic resonance imaging were synthesized by thermal decomposition. The size of the synthesized MNC was confirmed to be 73 ± 32.4 nm. Cytotoxicity test of the synthesized MNCs showed that the cell state of about 80% or more did not change in all the treatment ranges and cell survival rate was high even though the MNCs were injected. MNC was injected intravenously into the tail vein of nude mice. As a result, it was found that enhancement of the contrast was confirmed in xenograft mice model using MNC. These results will contribute to clinical application and related research through magnetic nanocluster in the future.

Determining the Degree of Malignancy on Digital Mammograms by Artificial Intelligence Deep Learning

In this paper, we propose a method for determining degree of malignancy on digital mammograms using artificial intelligence deep learning. Digital mammography is a technique that uses a low-energy X-ray of approximately 30 KVp to examine the breast. The goal of digital mammography is to detect breast cancer in an early stage by identifying characteristic lesions such as microcalcifications, masses, and architectural distortions. Frequently, microcalcifications appear in clusters that increase ease of detection. In general, larger, round, and oval-shaped calcifications with uniform size have a higher probability of being benign; smaller, irregular, polymorphic, and branching calcifications with heterogeneous size and morphology have a higher probability of being malignant. The experimental images for this study were selected by searching for "mammogram" in the NIH database. The images were converted into JPEG format of 256 X 256 pixels and saved. The stored images were segmented, and edge detection was performed. Most of the lesion area was low frequency, but the edge area was high frequency. DCT was performed to extract the features of the two parts. Similarity was determined based on DCT values entered into the neural network. These were the findings of the study: 1) There were 6 types of images representing malignant tumors. 2) There were 2 types of images showing benign tumors. 3) There were two types of images demonstrating tumors that could worsen into malignancy. Medical images like those used in this study are interpreted by a radiologist in consideration of pathological factors. Since discrimination of medical images by AI is limited to image information, interpretation by a radiologist is necessary. To improve the discrimination ability of medical images by AI, extracting accurate features of these images is necessary, as is inputting clinical information and accurately setting targets. Study of learning algorithms for neural networks should be continued. We believe that this study concerning recognition of cancer on digital breast images by AI deep learning will be useful to the radiomics (radiology and genomics) research field.