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- All Subjects: Medical Imaging
- Creators: Wang, Yalin
- Creators: Dela Rosa, Trixia
- Creators: Li, Jing
In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer's Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features.
This thesis develops a classification method to investigate the performance of FDG-PET as an effective biomarker for Alzheimer's clinical group classification. This involves dimensionality reduction using Probabilistic Principal Component Analysis on max-pooled data and mean-pooled data, followed by a Multilayer Feed Forward Neural Network which performs binary classification. Max pooled features result into better classification performance compared to results on mean pooled features. Additionally, experiments are done to investigate if the addition of important demographic features such as Functional Activities Questionnaire(FAQ), gene information helps improve performance. Classification results indicate that our designed classifiers achieve competitive results, and better with the additional of demographic features.
X-ray phase contrast imaging (XPCI) is a novel imaging method that utilizes phase information of X-rays in order to produce images. XPCI allows for highly resolved features that traditional X-ray imaging modalities cannot discern. The objective of this experiment was to model initial simulations predicting the output signal of the future compact x-ray free electron laser (CXFEL) XPCI source. The signal was reported in tonal values (“counts”), where MATLAB and MATLAB App Designer were the computing environments used to develop the simulations. The experimental setup’s components included a yttrium aluminum garnet (YAG) scintillating screen, mirror, and Mako G-507C camera with a Sony IMX264 sensor. The main function of the setup was to aim the X-rays at the YAG screen, then measure its scintillation through the photons emitted that hit the camera sensor. The resulting quantity used to assess the signal strength was tonal values (“counts”) per pixel on the sensor. Data for X-ray transmission through water, air, and polyimide was sourced from The Center for X-ray Optics’s simulations website, after which the data was interpolated and referenced in MATLAB. Matrices were an integral part of the saturation calculations; field-of-view (FOV), magnification and photon energies were also necessary. All the calculations were compiled into a graphical user interface (GUI) using App Designer. The code used to build this GUI can be used as a template for later, more complex GUIs and is a great starting point for future work in XPCI research at CXFEL.
X-ray phase contrast imaging (XPCI) is a novel imaging method that utilizes phase information of X-rays in order to produce images. XPCI allows for highly resolved features that traditional X-ray imaging modalities cannot discern. The objective of this experiment was to model initial simulations predicting the output signal of the future compact x-ray free electron laser (CXFEL) XPCI source. The signal was reported in tonal values (“counts”), where MATLAB and MATLAB App Designer were the computing environments used to develop the simulations. The experimental setup’s components included a yttrium aluminum garnet (YAG) scintillating screen, mirror, and Mako G-507C camera with a Sony IMX264 sensor. The main function of the setup was to aim the X-rays at the YAG screen, then measure its scintillation through the photons emitted that hit the camera sensor. The resulting quantity used to assess the signal strength was tonal values (“counts”) per pixel on the sensor. Data for X-ray transmission through water, air, and polyimide was sourced from The Center for X-ray Optics’s simulations website, after which the data was interpolated and referenced in MATLAB. Matrices were an integral part of the saturation calculations; field-of-view (FOV), magnification and photon energies were also necessary. All the calculations were compiled into a graphical user interface (GUI) using App Designer. The code used to build this GUI can be used as a template for later, more complex GUIs and is a great starting point for future work in XPCI research at CXFEL.