Abstract: Medical scanner application platforms and associated components (e.g., using a computerized tool) is enabled. For example, a system, can comprise: a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, based on package schema data representative of a package schema of an application received via a network, one or more attributes of the application, based on the one or more attributes of the application, determining one or more constraints of the application applicable to a medical scanner communicatively coupled to the system via an agent of the medical scanner, and storing the application in an application database accessible to the agent of the medical scanner.

Abstract: Methods and systems are provided for generating deep learning training data with an imaging system. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, training a deep neural network on the imaging data to obtain updates to the deep neural network, and transmitting the updates to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: Methods and systems are provided for capturing deep learning training data from imaging systems. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, inputting the imaging data to a deep neural network, displaying an output of the deep neural network and an image reconstructed from the imaging data, and transmitting an intermediate representation of the imaging data generated by the deep neural network to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: Methods and systems are provided for adaptive scan control. In one embodiment, a method comprises: while performing a scan of a scan subject, processing acquired projection data to measure a contrast level; responsive to the contrast level increasing above a first threshold, automatically switching the scan from a first scan protocol to a second scan protocol; responsive to the contrast level decreasing below a second threshold, automatically switching the scan from the second scan protocol to the first scan protocol; and responsive to the contrast level decreasing below a third threshold, automatically ending the scan. In this way, multiple scan protocols, such as angiography and perfusion scan protocols, can be interleaved within a single scan without the use of a separate timing bolus scan.

Abstract: Methods and systems are provided for generating deep learning training data with an imaging system. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, training a deep neural network on the imaging data to obtain updates to the deep neural network, and transmitting the updates to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: Methods and systems are provided for capturing deep learning training data from imaging systems. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, inputting the imaging data to a deep neural network, displaying an output of the deep neural network and an image reconstructed from the imaging data, and transmitting an intermediate representation of the imaging data generated by the deep neural network to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: Methods and systems are provided for adaptive scan control. In one embodiment, a method comprises: while performing a scan of a scan subject, processing acquired projection data to measure a contrast level; responsive to the contrast level increasing above a first threshold, automatically switching the scan from a first scan protocol to a second scan protocol; responsive to the contrast level decreasing below a second threshold, automatically switching the scan from the second scan protocol to the first scan protocol; and responsive to the contrast level decreasing below a third threshold, automatically ending the scan. In this way, multiple scan protocols, such as angiography and perfusion scan protocols, can be interleaved within a single scan without the use of a separate timing bolus scan.

Abstract: Embodiments of methods, systems and non-transitory computer readable media for tomographic imaging are presented. 3D TOF projection data is processed to generate corresponding data in a designated format that allows for computationally cheaper image reconstruction than the 3D TOF projection data. Further, one or more preliminary images are reconstructed from the processed data using a particular image reconstruction technique for one or more iterations. To that end, one or more imaging parameters are iteratively varied every one or more iterations. The imaging parameters, for example, include the designated format, the image reconstruction technique and one or more image quality characteristics. One or more intermediate images are reconstructed from the one or more preliminary images using the iteratively varying imaging parameters.

Abstract: Embodiments of methods, systems and non-transitory computer readable media for tomographic imaging are presented. 3D TOF projection data is processed to generate corresponding data in a designated format that allows for computationally cheaper image reconstruction than the 3D TOF projection data. Further, one or more preliminary images are reconstructed from the processed data using a particular image reconstruction technique for one or more iterations. To that end, one or more imaging parameters are iteratively varied every one or more iterations. The imaging parameters, for example, include the designated format, the image reconstruction technique and one or more image quality characteristics. One or more intermediate images are reconstructed from the one or more preliminary images using the iteratively varying imaging parameters.

Abstract: A voice recognition system (204, 206, 207, 208) generates a variable rejection strictness as a function of at least one background noise level measured during training and noise signal measurements made during an input utterance made during recognition mode of operation. A word entrance penalty is assigned as a function of the variable rejection strictness.