The present invention is a Miniature Vein Enhancer, for use in imaging the subcutaneous veins of a target area of a patient by a practitioner. The miniature vein enhancer includes a Miniature Projection Head that is secured to a tourniquet, where the tourniquet may be mounted to the bicep of a patient. The Miniature Projection Head includes a housing, and apparatus that images subcutaneous veins of the target area, and projects the image(s) of the veins onto the target area to overlie the subcutaneous veins, which aids the practitioner in pinpointing a vein location for a venipuncture procedure such as an intravenous drip, blood test, and the like.

Provided in the present invention are a magnetic resonance imaging system and method, and a computer-readable storage medium. The method comprises: performing a medical scan of a subject and acquiring a first medical image having a first noise interference artifact, and performing an additional scan of the subject to acquire a second medical image, wherein the second medical image has a second noise interference artifact, and the location mapping of the second noise interference artifact in the first medical image is symmetrical to the location of the first noise interference artifact relative to a pixel center of the first medical image; and performing synthesis-related processing on the first medical image and the second medical image to acquire a post-processed image with reduced noise interference artifacts.

Methods and systems are provided for automated graphical prescriptions with deep learning systems. In one embodiment, a method for a medical imaging system comprises acquiring, by the medical imaging system, localizer images of a subject, generating, by a trained neural network system, a graphical prescription using the localizer images, and performing, by the medical imaging system, a scan of the subject according to the graphical prescription. In this way, a desired region of interest of the subject may be accurately scanned with minimal input from an operator of the medical imaging system.

CT scan parameters for performing a CT scan of an anatomical target region of a patient are determined and/or adjusted. An initial set of the CT scan parameters for starting to perform the CT scan is determined based on an initial set of attenuation curves associated with the anatomical target region of the patient. The initial set of attenuation curves are determined based on optical imaging data depicting the patient.

One or more techniques and/or systems are disclosed for providing for improved monitoring of an individual, wherein imaging data is received from a plurality of imaging sensors. The received imaging data is aggregated and patterns at targeted data segments of the aggregated imaging data are analyzed using one or more algorithms to determine one or more values. The one or more values determined from the analyzed patterns are classified to represent at least one of a physiological state determination or a positioning of the individual. Data corresponding to the classified one or more values is then output and displayed.

A magnetic resonance tomography unit and a method is provided in which a patient couch may be moved in relation to the longitudinal direction into the patient tunnel in the transversal direction into a left-hand side extreme position and an opposite-lying right-hand side extreme position. Using an image acquisition facility in the left-hand side extreme position a right-hand side part is acquired and in the right-hand side extreme position a left-hand side part of the outer contour of the predetermined object is acquired. Using the image acquisition facility, the outer contour of the object is subsequently created from the left-hand side part of the outer contour and also from the right-hand side part of the outer contour.

To provide a technology that can easily acquire body weight information of a patient. A processing part that deduces the body weight of a patient based on a camera image of the patient lying on a table of a CT device, including a generating part that generates an input image based on the camera image, and a deducing part that deduces the body weight of the patient when the input image is input into a learned model. The learned model is generated by a neural network executing learning using a plurality of learning images C1 to Cn generated based on a plurality of camera images, and a plurality of correct answer data G1 to Gn corresponding to the plurality of learning images C1 to Cn, where each of the plurality of correct answer data G1 to Gn represents a body weight of a human included in a corresponding learning image.

The invention provides for a magnetic resonance imaging system (100, 200) comprising a memory (148) for storing machine executable instructions (150) and pulse sequence commands (152). The pulse sequence commands are configured for acquiring a four dimensional magnetic resonance data set (162) from an imaging region of interest (109). The four dimensional magnetic resonance data set is at least divided into three dimensional data magnetic resonance data sets (400, 402, 404, 406, 408) indexed by a repetitive motion phase of the subject. The three dimensional data magnetic resonance data sets are further at least divided into and indexed by k-space portions (410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436). The magnetic resonance imaging system further comprises a processor (144) for controlling the magnetic resonance imaging system. Execution of the machine executable instructions causes the processor during a first operational portion (310) to iteratively: receive (300) a motion signal (156) descriptive of the repetitive motion phase; acquire (302) an initial k-space portion using the pulse sequence commands, wherein the initial k-space portion is selected from the k-space portions; store (304) the motion signal and the initial k-space portion in a buffer (158) for each iteration of the first operational portion; at least partially construct (306) a motion phase mapping (160) between the motion signal and the repetitive motion phase; and continue (308) the first operational portion until the motion phase mapping is complete. Execution of the machine executable instructions causes the processor to assign (312) the initial k-space portion for each iteration of the first operational portion in the temporary buffer to the four dimensional magnetic resonance data set using the motion phase mapping. Execution of the machine executable instructions causes the processor during a second operational portion (332) to iteratively: receive (314) the motion signal; determine (316) a predicted next motion phase using the motion signal and the motion phase mapping; select (318) a subsequent k-space portion (154) from the k-space portions of the four dimensional magnetic resonance data set using the predicted next motion phase; acquire (320) the subsequent k-space portion using the pulse sequence commands; rereceive (322) the motion signal; determine (324) a current motion phase using the re-received motion signal and the motion phase mapping; assign (326) the

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A method for modeling soft tissue includes receiving one or more images showing an anatomical geometry of a first subject. The anatomical geometry includes a soft tissue. The method also includes measuring a plurality of parameters of the anatomical geometry of the first subject using one or more sensors attached to the first subject. The method also includes receiving a first set of material properties for the soft tissue of the first subject, a second subject, or both. The method also includes identifying a second set of material properties that characterizes the soft tissue while the first subject performs a task. The method also includes determining a strain on the soft tissue, a stress on the soft tissue, or both based at least partially upon the one or more images, the parameters, the first set of material properties, and the second set of material properties.