Model Augmented Deep Neural Networks for Medical Image Reconstruction Problems

Solving an ill-posed inverse problem is difficult because it doesn\u27t have a unique solution. In practice, for some important inverse problems, the conventional methods, e.g. ordinary least squares and iterative methods, cannot provide a good estimate. For example, for single image super-resolution and CT reconstruction, the results of these conventional methods cannot satisfy the requirements of these applications. While having more computational resources and high-quality data, researchers try to use machine-learning-based methods, especially deep learning to solve these ill-posed problems. In this dissertation, a model augmented recursive neural network is proposed as a general inverse problem method to solve these difficult problems. In the dissertation, experiments show the satisfactory performance of the proposed method for single image super-resolution, CT reconstruction, and metal artifact reduction

Image processing for correlative light and electron microscopy

People have never stopped exploring the microscopic world. Studying the microstructure of cells helps people better understand the people themselves and has the potential to overcome specific diseases at a fundamental level. Correlative light and electron microscopy (CLEM) can let people intuitively understand the sample information through imaging. In CLEM measurements, samples are measured in both fluorescence microscopy and electron microscopy. Due to technical differences between LM and EM, images obtained from LM and EM contain different information. With the fluorescent labels, one can easily observe the structures of interest. However, due to the diffraction limit, LM image resolution is limited to a few hundred nanometers. EM images can capture the detailed structure of a sample down to the atomic level. However, grayscale images obtained from EM often contain very complex structures. Identifying structures of interest from these complex structures using only EM images is usually a challenge. The CLEM technology provides an opportunity to specify the structures of interest by comparing the CLEM images. However, due to the resolution difference between LM and EM, these structures are usually still not directly distinguishable by simply overlaying the fluorescence microscopy images on high-resolution grayscale electron microscopy images. This thesis aims to investigate a new deconvolution algorithm, EM-guided deconvolution, to automate fusing the LM information on the correlative EM image. We discuss the algorithm with simulated CLEM images and further apply it to experimental data sets. The algorithm can enhance the image resolution to nanometers from correlative wide-field (or confocal) fluorescence microscopy images. The algorithm can effectively recognise, e.g., membrane structures or identify the structures with a suitable point spread function and precise image registration

Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)