Abstract: Various systems are provided for non-uniform thickness and/or sampling of slabs of the breast to present DBT acquisitions. A method for generating a patient image as a set of slabs representing an imaged object, the method comprising acquiring a tomosynthesis projection, reconstructing a series of slab images, each slab representing a portion of a breast, and a plurality of slabs of non-uniform thickness and/or non-uniform sampling in a 3D reconstructed domain defined by x-, y-, and z-axes.

Abstract: A convolutional neural network (CNN) is employed in an automated feature and/or anomaly detection system for analyzing images provided by X-ray imaging system. The automated detection system operates in a manner that reduces the number of full-resolution CNN convolution layers required in order to speed up the network inference and learning processes for the detection system. To do so, the detection system utilizes as an input a more compact representation of the tomographic data to alleviate the CNN memory footprint and computation time issues in prior art X-ray systems.

Abstract: A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

Abstract: Various methods and systems are provided for enhancing the generation of a synthetic 2D image from tomosynthesis projection images, such as a synthetic 2D image. To enhance the image, the image processing system utilizes a selected height interval to scan for objects of interest within a volume reconstructed from the tomosynthesis projection images. The height interval is larger than normal slices formed from the reconstructed volume, such that pixel information on larger masses can be obtained from adjacent slices within the volume. Further, the illustration of the object of interest in the synthetic 2D image can be modified by contributing pixel information from all tomosynthesis projections for the presentation of the object or interest. The use of pixel information from all tomosynthesis projections enhances the illustration of the high frequency components and the low frequency components of the object of interest within the enhanced image.

Abstract: This disclosure proposes to speed up computation time of a convolutional neural network (CNN) by leveraging information specific to a pre-defined region, such as a breast in mammography and tomosynthesis data. In an exemplary embodiment, a method for an image processing system is provided, comprising, generating an output of a trained convolutional neural network (CNN) of the image processing system based on an input image, including a pre-defined region of the input image as an additional input into at least one of a convolutional layer and a fully connected layer of the CNN to limit computations to input image data inside the pre-defined region; and storing the output and/or displaying the output on a display device.

Abstract: An image processing system and method is provided for correcting artefacts within a three-dimensional (3D) volume reconstructed from a plurality of two-dimensional (2D) projection images of an object. The system and method is implemented on an imaging system having a processing unit operable to control the operation of a radiation source and a detector to generate a plurality of 2D projection images. The system also includes a memory connected to the processing unit and storing processor-executable code that when executed by the processing unit operates to reconstruct the 3D volume from the plurality of 2D projection images, the 3D volume defined in a pseudo parallel geometry based on a zero angle from the plurality of 2D projection images, wherein reconstructing the 3D volume comprises reconstructing a 3D virtual object defined in a pseudo parallel geometry based on the zero angle from the plurality of 2D projection images, and correcting the 3D virtual object to form the 3D volume.

Abstract: A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

Abstract: A convolutional neural network (CNN) is employed in an automated feature and/or anomaly detection system for analyzing images provided by X-ray imaging system. The automated detection system operates in a manner that reduces the number of full-resolution CNN convolution layers required in order to speed up the network inference and learning processes for the detection system. To do so, the detection system utilizes as an input a more compact representation of the tomographic data to alleviate the CNN memory footprint and computation time issues in prior art X-ray systems.

Abstract: Various systems are provided for non-uniform thickness and/or sampling of slabs of the breast to present DBT acquisitions. A method for generating a patient image as a set of slabs representing an imaged object, the method comprising acquiring a tomosynthesis projection, reconstructing a series of slab images, each slab representing a portion of a breast, and a plurality of slabs of non-uniform thickness and/or non-uniform sampling in a 3D reconstructed domain defined by x-, y-, and z-axes.

Abstract: This disclosure proposes to speed up computation time of a convolutional neural network (CNN) by leveraging information specific to a pre-defined region, such as a breast in mammography and tomosynthesis data. In an exemplary embodiment, a method for an image processing system is provided, comprising, generating an output of a trained convolutional neural network (CNN) of the image processing system based on an input image, including a pre-defined region of the input image as an additional input into at least one of a convolutional layer and a fully connected layer of the CNN to limit computations to input image data inside the pre-defined region; and storing the output and/or displaying the output on a display device.

Abstract: There is described herein imidopiperidine compounds as inhibitors of human polynucleotide kinase phosphatase.

Abstract: Systems and methods for synthesizing a two-dimensional (2D) image of an organ of a patient by obtaining an pre-exposure x-ray image of the organ in order to ascertain parameters needed for acquisition of 2D (tomosynthesis) projection images of the organ, imaging the organ to obtain a plurality of 2D (tomosynthesis) projection images of the organ, and generating a synthetic 2D image of the organ from a combination of both the plurality of tomosynthesis projection images and the pre-exposure x-ray image.

Abstract: Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.

Abstract: Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.

Abstract: Systems and methods for synthesizing a two-dimensional (2D) image of an organ of a patient by obtaining an pre-exposure x-ray image of the organ in order to ascertain parameters needed for acquisition of 2D (tomosynthesis) projection images of the organ, imaging the organ to obtain a plurality of 2D (tomosynthesis) projection images of the organ, and generating a synthetic 2D image of the organ from a combination of both the plurality of tomosynthesis projection images and the pre-exposure x-ray image.

Abstract: Described herein are compounds, methods of making such compounds, pharmaceutical compositions, and medicaments comprising such compounds, and methods of using such compounds to treat cancer.

Abstract: Described herein are compounds, methods of making such compounds, pharmaceutical compositions, and medicaments comprising such compounds, and methods of using such compounds to treat cancer.

Abstract: Described herein are compounds, methods of making such compounds, pharmaceutical compositions, and medicaments comprising such compounds, and methods of using such compounds to treat cancer.

Abstract: A system and method for the detection of ROIs in images obtained of a breast or other tissue of a patient significantly improves the speed and precision/accuracy of navigation between multimodality 2D and 3D images. In the system and method, images of the tissue are obtained in a DBT acquisition to generate a synthetic 2D image of the imaged tissue and in a 3D, e.g., ultrasound, image acquisition. The 2D image generation process creates a synthetic 2D image that embed a navigation map correlating pixels in the 2D images to sections of the 3D ultrasound volume, such as via a registration between the 3D ultrasound volume and a 3D volume created using the DBT image data. When a synthetic 2D image is reviewed, an ROI on the 2D image is selected and the system will additionally present the user with the section of the 3D volume containing that ROI.

Abstract: Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.