Abstract: Provided herein are methods of treating cancer comprising a KRAS G12C mutation in a patient comprising administering to the patient sotorasib and an anti-epidermal growth factor receptor (EGFR) antibody in amounts effective to treat the cancer. Further provided herein are methods further comprising administering FOLFIRI (irinotecan, 5-FU and leucovorin) to the patient.

Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

Abstract: A method includes obtaining behavioral source code defining logic to be performed using at least one logic device and constraints identifying data movements associated with execution of the logic. The at least one logic device contains multiple components that support at least one of: internal data movements within the at least one logic device and external data movements external to the logic device as defined by the behavioral source code and the constraints. The constraints identify characteristics of at least one of: the internal data movements and the external data movements. The method also includes automatically designing one or more data movers for use within the at least one logic device, where the one or more data movers are configured to perform at least one of the internal and external data movements in accordance with the characteristics.

Abstract: The current disclosure provides systems and methods for automatic image alignment of three-dimensional (3D) medical image volumes. The method includes pre-processing the 3D medical image volume by selecting a sub-volume of interest, detecting anatomical landmarks in the sub-volume using a deep neural network, estimating transformation parameters based on the anatomical landmarks to adjust rotation angles and translation of the sub-volume, adjusting the rotation angles and translation to produce a first aligned sub-volume, determining confidence in the transformation parameters based on the first aligned sub-volume, and iteratively refining the transformation parameters if the confidence is below a predetermined threshold. The disclosed approach for automated image alignment reduces the need for manual alignment and, increases a probability of the 3D image volume converging to a desired orientation compared to conventional approaches.

Abstract: The present invention features interferon-free therapies for the treatment of HCV. Preferably, the treatment is over a shorter duration of treatment, such as no more than 12 weeks. In one aspect, the treatment comprises administering at least two direct acting antiviral agents to a subject with HCV infection, wherein the treatment lasts for 12 weeks and does not include administration of either interferon or ribavirin, and said at least two direct acting antiviral agents comprise (a) Compound 1 or a pharmaceutically acceptable salt thereof and (b) Compound 2 or a pharmaceutically acceptable salt thereof.

Abstract: Systems or techniques that facilitate hybrid 3D-to-2D slice-wise object localization ensembles are provided. In various embodiments, a system can access at least one three-dimensional voxel array. In various aspects, the system can localize, via execution of a deep learning ensemble, an object depicted in the at least one three-dimensional voxel array. In various instances, the deep learning ensemble can receive as input the at least one three-dimensional voxel array. In various cases, the deep learning ensemble can produce as output a set of two-dimensional object location indicators respectively corresponding to a set of two-dimensional slices of the at least one three-dimensional voxel array.

Abstract: Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

Abstract: Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. In particular, color maps (e.g., heat maps and/or probability maps) and/or color overlays based on material decomposition information may be superimposed on contrast-optimized images, where the color maps accentuate a contrast between diseased tissues and healthy tissues. An automated report may be generated including a first visualization based on a first scan, and a second visualization based on a second, earlier scan, to show a progression of a disease of the patient.

Abstract: Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. The visualizations may increase a contrast between diseased tissues and non-diseased tissues of the different anatomical regions, and may include color maps (e.g., heat maps and/or probability maps) and/or color overlays based on spectral decomposition information. The different visualizations may be combined into a single 2D image, where each anatomical region (e.g., organ, bone, etc.) is displayed in high contrast, and where aspects of various anatomical regions may be highlighted or colorized to visualize specific information.

Abstract: A computer-implemented method for automatically performing a longitudinal review of medical imaging data via one or more processors includes obtaining, at a computing device, a first image volume acquired of a subject with a medical imaging system of an imaging modality. The method also includes obtaining, at the computing device, a second image volume acquired of the subject with the medical imaging system or another medical imaging system of the imaging modality or a different imaging modality, wherein the first image volume was acquired at an earlier time point than the second image volume. The method further includes automatically aligning, via the computing device, the second image volume to the first image volume to generate aligned image volumes.

Abstract: Provided herein are methods of treating a cancer comprising a KRAS G12C mutation in a patient with active brain metastases, comprising administering sotorasib to the patient in amount effective to treat the cancer.

Abstract: The present invention features interferon-free therapies for the treatment of HCV. Preferably, the treatment is over a shorter duration of treatment, such as no more than 12 weeks. In one aspect, the treatment comprises administering at least two direct acting antiviral agents to a subject with HCV infection, wherein the treatment lasts for 12 weeks and does not include administration of either interferon or ribavirin, and said at least two direct acting antiviral agents comprise (a) Compound 1 or a pharmaceutically acceptable salt thereof and (b) Compound 2 or a pharmaceutically acceptable salt thereof.

Abstract: The present invention features interferon-free therapies for the treatment of HCV. Preferably, the treatment is over a shorter duration of treatment, such as no more than 12 weeks. In one aspect, the treatment comprises administering at least two direct acting antiviral agents to a subject with HCV infection, wherein the treatment lasts for 12 weeks and does not include administration of either interferon or ribavirin, and said at least two direct acting antiviral agents comprise (a) Compound 1 or a pharmaceutically acceptable salt thereof and (b) Compound 2 or a pharmaceutically acceptable salt thereof.

Abstract: A method includes obtaining behavioral source code defining logic to be performed using at least one logic device and constraints identifying data movements associated with execution of the logic. The at least one logic device contains multiple components that support at least one of: internal data movements within the at least one logic device and external data movements external to the logic device as defined by the behavioral source code and the constraints. The constraints identify characteristics of at least one of: the internal data movements and the external data movements. The method also includes automatically designing one or more data movers for use within the at least one logic device, where the one or more data movers are configured to perform at least one of the internal and external data movements in accordance with the characteristics.

Abstract: The present invention features interferon-free therapies for the treatment of HCV. Preferably, the treatment is over a shorter duration of treatment, such as no more than 12 weeks. In one aspect, the treatment comprises administering at least two direct acting antiviral agents to a subject with HCV infection, wherein the treatment lasts for 12 weeks and does not include administration of either interferon or ribavirin, and said at least two direct acting antiviral agents comprise (a) Compound 1 or a pharmaceutically acceptable salt thereof and (b) Compound 2 or a pharmaceutically acceptable salt thereof.

Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

Abstract: Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.

Abstract: Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

Abstract: Techniques are described for automatically detecting scan characteristics of a medical image series. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise an image generation component that generates a representative image of a medical image series comprising a plurality of scan images, and a series characterization component that processes the representative image using one or more characteristic detection algorithms to determine one or more characteristics of the medical image series. The system can further tailor the visualization layout for viewing the medical image series based on the one or more characteristics and/or automatically perform various workflow tasks based on the one or more characteristics.

Abstract: A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.