Abstract: The present invention relates to the field of chip packaging technology, and proposes a package structure and method of sensing chip. The package structure comprising: a substrate; a silicon interposer; and a plurality of chips, the plurality of chips comprising sensing chips as well as non-sensing chips, wherein the non-sensing chips are molded with molding compound and the sensing chips are exposed. By arranging a protective cover above the sensing chip before the front side of the chip is molded, and grinding off the top of the protective cover after molding, the present invention can well solve the problem that the sensing signal of the sensing chip cannot penetrate the molding compound in the prior art, and can be effectively used for the packaging of the sensing chip.

Abstract: Metal artifacts are reduced in x-ray computed tomography (“CT”) images using a suitably trained neural network, such as a convolutional neural network (“CNN”). Virtual metal DATA objects are inserted to either the raw projection data or CT image data (e.g., from pre-procedural CT scans) to generate sets of matching artifact-corrupted and artifact-uncorrupted images, and a CNN, or other neural network, is trained to separate the contribution to each image pixel due to patient anatomy, metal object, or metal object-induced artifact. The contributions from metal object-induced artifacts can then be removed to generate a final, artifact-reduced image.

Abstract: Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (p?1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.