Abstract: Provided are techniques for detecting a short circuit fault at an output of a regulator and protecting the regulator from the short circuit fault. An error amplifier receives a reference voltage and a feedback voltage and compares comparing the reference voltage with the feedback voltage for driving a power transistor of the regulator. A modification stage compares an output voltage of the voltage regulator with a fault reference voltage and in response to determining that the output voltage of the voltage regulator is less than the fault reference voltage, drives the power transistor using an internal node of the error amplifier by changing states of a first switch and a second switch and supplies the reference voltage to both the first and second inputs of the error amplifier by changing states of a third switch and a fourth switch.

Abstract: A Low Voltage Differential Signaling (LVDS) transmitter includes driver circuit with a first transistor, a second transistor, a third transistor, a fourth transistor, a first resistor, and a second resistor. The first transistor is coupled between a first node and first output. The second transistor is coupled between the first node and a second output. The third transistor is coupled between the first output and a second node. The fourth transistor is coupled between the second output and the second node. The first resistor is coupled between the first output and a common mode node. The second resistor is coupled between the second output and the common mode node. A pre-driver circuit generates gate control signals controlling the first, second, third, and fourth transistors in response to a data signal. A controlled timing delay is applied to the timing of logic state transistors for the control signals.

Abstract: A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: Various methods and systems are provided for translating magnetic resonance (MR) images to pseudo computed tomography (CT) images. In one embodiment, a method comprises acquiring an MR image, generating, with a multi-task neural network, a pseudo CT image corresponding to the MR image, and outputting the MR image and the pseudo CT image. In this way, the benefits of CT imaging with respect to accurate density information, especially in sparse regions of bone which exhibit with high dynamic range, may be obtained in an MR-only workflow, thereby achieving the benefits of enhanced soft-tissue contrast in MR images while eliminating CT dose exposure for a patient.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: Low-voltage differential signaling (LVDS) receiver circuits, electronic devices, and methods are provided. A LVDS receiver includes an input differential pair of transistors that receive a differential input signal. The input differential pair includes a first NMOS transistor that receives a first input signal and a second NMOS transistor that receives a second input signal. A third NMOS transistor has source and drain terminals respectively coupled to source and drain terminals of the first NMOS transistor, and a fourth NMOS transistor has source and drain terminals respectively coupled to source and drain terminals of the second NMOS transistor. A first level shifter is coupled to a gate of the third NMOS transistor, and a second level shifter is coupled to a gate of the fourth NMOS transistor.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: In various embodiments, the present disclosure provides low-voltage differential signaling (LVDS) receiver circuits, electronic devices, and methods. In one embodiment, a LVDS receiver includes an input differential pair of transistors that receive a differential input signal. The input differential pair includes a first NMOS transistor that receives a first input signal and a second NMOS transistor that receives a second input signal. A third NMOS transistor has source and drain terminals respectively coupled to source and drain terminals of the first NMOS transistor, and a fourth NMOS transistor has source and drain terminals respectively coupled to source and drain terminals of the second NMOS transistor. A first level shifter is coupled to a gate of the third NMOS transistor, and a second level shifter is coupled to a gate of the fourth NMOS transistor.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.

Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.