Abstract: Systems and methods are provided for increasing a quality of computed tomography (CT) images. In one embodiment, a computed tomography (CT) detector system comprises a layer of energy integrating detectors (EID) arranged below a layer of photon counting (PC) sensors with respect to an incoming x-ray, where a number of the PC sensors exceeds a number of the EID detectors; and an image processing unit configured to correct PC data using EID data, and denoise and increase a resolution of an image reconstructed from EID data and PC data using a deep learning convolutional neural network (CNN) trained on pairs of images, each pair of images including a target image reconstructed from a first signal from the layer of PC sensors, and an input image reconstructed from a second signal from the layer of EID detectors, the EID data and PC data acquired concurrently from a same patient ray path.

Abstract: The present invention relates to a method for the evaluation of tissue gadolinium deposition that offers advantages compared with known methods. Comparison of different gadolinium-based contrast agents (GBCAs) based on retention, organ distribution, washout and safety is facilitated using the methods of the present invention.

Abstract: The present invention relates to a method for the evaluation of tissue gadolinium deposition that offers advantages compared with known methods. Comparison of different gadolinium-based contrast agents (GBCAs) based on retention, organ distribution, washout and safety is facilitated using the methods of the present invention.

Abstract: Systems and methods are provided that receive a clinical identifier, an acquisition condition target, and one or more patient characteristics. The systems and methods further generate a scan attribute based on the clinical identifier. The scan attribute corresponding to characteristics of a resultant medical image. The systems and methods determine select scan settings from a plurality of scan settings based on the scan attribute and the one or more patient characteristics, and calculate candidate acquisition conditions associated with the select scan settings. Further, the systems and methods identify one of the select scan settings as a scan prescription based on a relation between the candidate acquisition conditions and the acquisition condition target.

Abstract: Methods and systems are provided that obtain a scan prescription and receive physiologic feature or interest (FOI) measurements of an object of interest during a scan time. The methods and systems further compare at least one of the received physiologic FOI measurements with a predetermined threshold range, and determine a scan setting by evaluating an acquisition rule set based on the physiologic FOI measurement.

Abstract: Systems and methods are provided that receive a clinical identifier, an acquisition condition target, and one or more patient characteristics. The systems and methods further generate a scan attribute based on the clinical identifier. The scan attribute corresponding to characteristics of a resultant medical image. The systems and methods determine select scan settings from a plurality of scan settings based on the scan attribute and the one or more patient characteristics, and calculate candidate acquisition conditions associated with the select scan settings. Further, the systems and methods identify one of the select scan settings as a scan prescription based on a relation between the candidate acquisition conditions and the acquisition condition target.

Abstract: Methods and systems are provided that obtain a scan prescription and receive physiologic feature or interest (FOI) measurements of an object of interest during a scan time. The methods and systems further compare at least one of the received physiologic FOI measurements with a predetermined threshold range, and determine a scan setting by evaluating an acquisition rule set based on the physiologic FOI measurement.