Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. Anatomically, the RGVP can be separated into four subdivisions, including two decussating and two non-decussating fiber pathways, which cannot be identified by conventional magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the anatomy of the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for tractographic reconstruction of the RGVP. In this study, four different tractography algorithms, including constrained spherical deconvolution (CSD) model based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) tractography methods, are compared for reconstruction of the RGVP. Experiments are performed using diffusion MRI data of 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Anatomical measurements are used to assess the advantages and limitations of the four tracking strategies, including the reconstruction rate of the four RGVP subdivisions, the percentage of decussating fibers, the correlation between volumes of the traced RGVPs and a T1w-based RGVP segmentation, and an expert judgment to rank the anatomical appearance of the reconstructed RGVPs. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy.

Post-Earthquake Night-Time Light Piecewise (PNLP) Pattern Based on NPP/VIIRS Night-Time Light Data: A Case Study of the 2015 Nepal Earthquake

Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (kp), recovery rate (kr1), reconstruction rate (kr2), development rate (kd), relative reconstruction rate (krp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction.

Current status of and trends in post-mastectomy breast reconstruction in Korea

Since April 2015, post-mastectomy breast reconstruction has been covered by the Korean National Health Insurance Service (NHIS). The frequency of these procedures has increased very rapidly. We analyzed data obtained from the Big Data Hub of the Health Insurance Review and Assessment Service (HIRA) and determined annual changes in the number of breast reconstruction procedures and related trends in Korea. We evaluated the numbers of mastectomy and breast reconstruction procedures performed between April 2015 and December 2018 using data from the HIRA Big Data Hub. We determined annual changes in the numbers of total, autologous, and implant breast reconstructions after NHIS coverage commenced. Data were analyzed using Microsoft Excel. The post-mastectomy breast reconstruction rate increased from 19.4% in 2015 to 53.4% in 2018. In 2015, implant reconstruction was performed in 1,366 cases and autologous reconstruction in 905 (60.1% and 39.8%, respectively); these figures increased to 3,703 and 1,570 (70.2% and 29.7%, respectively) in 2018. Free tissue transfer and deep inferior epigastric perforator flap creation were the most common autologous reconstruction procedures. For implant-based reconstructions, the rates of directto-implant and tissue-expander breast reconstructions (first stage) were similar in 2018. This study summarizes breast reconstruction trends in Korea after NHIS coverage was expanded in 2015. A significant increase over time in the post-mastectomy breast reconstruction rate was evident, with a trend toward implant-based reconstruction. Analysis of data from the HIRA Big Data Hub can be used to predict breast reconstruction trends and convey precise information to patients and physicians.

CNN-SINDy Based Reduced Order Modeling of Unsteady Flow Fields

We present a new framework of nonlinear reduced order model to extract low-dimensional modes and to predict their temporal evolutions. Autoencoder-type Convolutional Neural Network (CNN) which can learn nonlinearity of data is used to extract low-dimensional modes. For obtaining the temporal evolution of a mapped data by CNN, Sparse Identification of Nonlinear Dynamics (SINDy) is performed. The proposed method is applied to a circular cylinder wake at ReD = 100. The CNN trained using fluctuation components of velocity vector u, v shows better results than the snapshot Proper Orthogonal Decomposition in terms of the energy reconstruction rate. Although time-evolving flow fields reproduced by SINDy equation also show reasonable agreement with the reference direct numerical simulation, the errors of CNN and SINDy are accumulated through integral computation. The error of CNN can be reduced by devising a better network structure; however, the error of SINDy depends on the waveform of latent vector.

IMAGE HIDING ON HIGH FREQUENCY SPEECH COMPONENTS USING WAVELET PACKET TRANSFORM

This paper propose a method for security threw hiding the image inside the speech signal by replacing the high frequencycomponents of the speech signal with the data of the image where the high frequency speech components are separated and analyzed usingthe Wavelet Packet Transform (WPT) where the new signal will be remixed to create a new speech signal with an embedded image. The algorithm is implemented on MATLAB 15 and is designed to achieve best image hiding where the reconstruction rate was more than 94% while trying to maintain the same size of the speech signal to overcome the need for a powerful channel to handle the task. Best results were achieved with higher speech resolution (higher number of bits per sample) and longer periods (higher number of samples in the media file).