Abstract: Systems and methods are provided for contrast-enhanced diagnostic imaging. In one aspect, a system includes an x-ray source; a detector; a data acquisition system (DAS) operably connected to the detector; and a computing device operably connected to the DAS and configured with instructions that when executed cause the computing device generate a first estimated time to perform a diagnostic scan; determine a first confidence level of the first estimated time; control the x-ray source and the detector to perform the diagnostic scan of the subject at the first estimated time in response to a first confidence level being above a threshold; and generate a second estimated time to perform the diagnostic scan and a second confidence level of the second estimated time in response to the first confidence level of the first estimated time being below a threshold.

Abstract: Systems and methods are provided for contrast-enhanced diagnostic imaging. In one aspect, a system comprises an x-ray source that emits a beam of x-rays towards a subject to be imaged; a detector that receives the x-rays attenuated by the subject; a data acquisition system (DAS) operably connected to the detector; and a computing device operably connected to the DAS and configured with executable instructions in non-transitory memory that when executed cause the computing device to generate a first estimated time to perform a diagnostic scan of the subject based on demographic information and clinical information of the patient; and control the x-ray source and the detector to perform the diagnostic scan of the subject at the first estimated time responsive to a first confidence level of the first estimated time above a threshold.

Abstract: Systems and methods for improved image denoising using a deep learning network model are disclosed. An example system includes an input data processor to process a first patient image of a first patient to add a first noise to the first patient image to form a noisy image input. The example system includes an image data denoiser to process the noisy image input using a first deep learning network to identify the first noise. The image data denoiser is to train the first deep learning network using the noisy image input. When the first deep learning network is trained to identify the first noise, the image data denoiser is to deploy the first deep learning network as a second deep learning network model to be applied to a second patient image of the first patient to identify a second noise in the second patient image.

Abstract: Modifying a motion plan for an autonomously-operated inspection platform (AIP) includes obtaining sensor data for an industrial asset area of interest, analyzing the obtained sensor data during execution of an initial motion plan to determine if modification of the initial motion plan is required. If modification is required then performing a pose estimation on a first group of potential targets and a second group of potential targets, optimizing the results of the pose estimation to determine a modification to the initial motion plan, performing reactive planning to the initial motion plan to include the modification, the reactive planning providing a modified motion plan that includes a series of waypoints defining a modified path, and autonomously controlling motion of the AIP along the modified path. The analysis, pose estimation, optimization, and reactive planning occurring during movement of the AIP along a motion plan. A system and computer-readable medium are disclosed.

Abstract: Systems and methods for improved image denoising using a deep learning network model are disclosed. An example system includes an input data processor to process a first patient image of a first patient to add a first noise to the first patient image to form a noisy image input. The example system includes an image data denoiser to process the noisy image input using a first deep learning network to identify the first noise. The image data denoiser is to train the first deep learning network using the noisy image input. When the first deep learning network is trained to identify the first noise, the image data denoiser is to deploy the first deep learning network as a second deep learning network model to be applied to a second patient image of the first patient to identify a second noise in the second patient image.

Abstract: An imaging system determines a heart rate of a patient, a cardiac disease state, and/or a target cardiac anatomy of the patient. The system calculates an image acquisition time range from a patient population model using the heart rate, the cardiac disease state, and/or the target anatomy. The model represents relationships between cardiac motion of other patients and time or cardiac phases of the other patients. The system also determines imaging settings to acquire image data of the target anatomy during the image acquisition time range that is calculated. Imaging the target anatomy of the patient according to the imaging settings generates image data of the target anatomy having less cardiac motion and/or a reduced image acquisition time range relative to determining the imaging settings without using the patient population model.

Abstract: An imaging system determines a heart rate of a patient, a cardiac disease state, and/or a target cardiac anatomy of the patient. The system calculates an image acquisition time range from a patient population model using the heart rate, the cardiac disease state, and/or the target anatomy. The model represents relationships between cardiac motion of other patients and time or cardiac phases of the other patients. The system also determines imaging settings to acquire image data of the target anatomy during the image acquisition time range that is calculated. Imaging the target anatomy of the patient according to the imaging settings generates image data of the target anatomy having less cardiac motion and/or a reduced image acquisition time range relative to determining the imaging settings without using the patient population model.

Abstract: Methods and systems are provided for contrast-enhanced diagnostic imaging. In one embodiment, a method comprises estimating a time to perform a diagnostic scan of a patient based on demographics of the patient, and performing the diagnostic scan of the patient at the estimated time responsive to a confidence level of the estimated time above a threshold. In this way, a contrast-enhanced diagnostic scan may be performed without directly monitoring the contrast flow, thereby reducing radiation dose and contrast load while maintaining or improving image quality.

Abstract: Modifying a motion plan for an autonomously-operated inspection platform (AIP) includes obtaining sensor data for an industrial asset area of interest, analyzing the obtained sensor data during execution of an initial motion plan to determine if modification of the initial motion plan is required. If modification is required then performing a pose estimation on a first group of potential targets and a second group of potential targets, optimizing the results of the pose estimation to determine a modification to the initial motion plan, performing reactive planning to the initial motion plan to include the modification, the reactive planning providing a modified motion plan that includes a series of waypoints defining a modified path, and autonomously controlling motion of the AIP along the modified path. The analysis, pose estimation, optimization, and reactive planning occurring during movement of the AIP along a motion plan. A system and computer-readable medium are disclosed.