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TCI Volume 6 | 2020

Segmentation-Driven Optimization For Iterative Reconstruction in Optical Projection Tomography: An Exploration

Three-dimensional reconstruction of tomograms from optical projection microscopy is confronted with several drawbacks. In this paper we employ iterative reconstruction algorithms to avoid streak artefacts in the reconstruction and explore possible ways to optimize two parameters of the algorithms, i.e., iteration number and initialization, in order to improve the reconstruction performance. As benchmarks for direct reconstruction evaluation in optical projection tomography are absent, we consider the assessment through the performance of the segmentation on the 3D reconstruction. In our explorative experiments we use the zebrafish model system which is a typical specimen for use in optical projection tomography system; and as such frequently used.

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An End-to-End Deep Network for Reconstructing CT Images Directly From Sparse Sinograms

Recently, deep-learning based methods have been widely used for computed tomography (CT) reconstruction. However, most of these methods need extra steps to convert the sinogrmas into CT images and so their networks are not end-to-end. In this paper, we propose an end-to-end deep network for CT image reconstruction, which directly maps sparse sinogramss to CT images. Our network has three cascaded blocks, where the first block is used to denoise and interpolate the sinograms, the second to map the sinograms to CT images and the last to denoise the CT images.

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Estimation of Moisture Content Distribution in Porous Foam Using Microwave Tomography With Neural Networks

The use of microwave tomography (MWT) in an industrial drying process is demonstrated in this feasibility study with synthetic measurement data. The studied imaging modality is applied to estimate the moisture content distribution in a polymer foam during the microwave drying process. Such moisture information is crucial in developing control strategies for controlling the microwave power for selective heating.

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Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction With Low-Dose Scans

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with lowdose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise, and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object.

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Unbalanced Optimal Transport Regularization for Imaging Problems

The modeling of phenomenological structure is a crucial aspect in inverse imaging problems. One emerging modeling tool in computer vision is the optimal transport framework. Its ability to model geometric displacements across an image's support gives it attractive qualities similar to optical flow methods that are effective at capturing visual motion, but are restricted to operate in significantly smaller state-spaces. 

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Deep Recursive Network for Hyperspectral Image Super-Resolution

Fusion based hyperspectral image (HSI) super-resolution method, which obtains a spatially high-resolution (HR) HSI by fusing a low-resolution (LR) HSI and an HR conventional image, has been a prevalent method for HSI super-resolution. One effective fusion based method is to cast HSI super-resolution into a unified optimization problem, where handcrafted priors such as sparse prior or low rank prior are always adopted to regularize the latent HR HSI to be optimized. 

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Compressive Spectral Imaging Based on Hexagonal Blue Noise Coded Apertures

The coded aperture snapshot spectral imager (CASSI) is a computational imaging system that acquires a three dimensional (3D) spectral data cube by a single or a few two dimensional (2D) measurements. The 3D data cube is reconstructed computationally. Binary on-off random coded apertures with square pixels are primarily implemented in CASSI systems to modulate the spectral images in the image plane.

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