Intravascular devices, systems, and methods are disclosed. In some embodiments, a medical imaging system for imaging vasculature of a patient is provided. The imaging system includes a console that has one or more processors with a medical imaging system interface running thereon, an acquisition card in communication with the one or more processors and in communication with a patient interface module (PIM), and an intravascular imaging component in communication with the PIM and disposed on a distal end of a flexible elongate member. The medical imaging system interface provides a plurality of settings groups for selection by a user, each of the settings groups having pre-acquisition parameters and post-acquisition parameters that are optimal for imaging a desired viewing target within the vasculature. Associated methods and computer-readable media are provided.

A medical imaging system is provided. Imaging detector columns are installed in a gantry to receive imaging information about a subject. Imaging detector columns can extend and retract radially as well as be rotated orbitally around the gantry. The system can automatically adjust setup configuration and an imaging operation based on subject shape estimation information.

For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Disclosed herein is a medical system (100, 300, 500). The execution of machine executable instructions (112) causes a computational system (104) to: receive (200) a baseline anatomical measurement (114) descriptive of a clinical magnetic resonance image of a subject (318); receive (202) scan metadata (116) descriptive of the clinical magnetic resonance image of the subject; send (204) scan parameters via a network connection (350) to a low-field magnetic resonance imaging system (301); receive (206) subsequent k-space data (122) from the low-field magnetic resonance imaging system via the network connection in response to sending the scan parameters; reconstruct (208) a subsequent magnetic resonance image (124) from the subsequent k-space data; determine (210) a subsequent anatomical measurement (128) in response to inputting the subsequent magnetic resonance image into the segmentation module; and provide (212) a warning signal (132) if the subsequent anatomical measurement varies from the baseline anatomical measurement by more than a predetermined amount.

A photoplethysmogram (PPG) sensor includes a pixel array that collects light, a pixel sampler that converts the light collected through the pixel array into a plurality of pixel data, an effective area determiner that determines an effective area and a non-effective area of the pixel array based on the pixel data, a power controller that is operable to cut off power to the non-effective area of the pixel array, and a PPG data generator that generates PPG data from pixel data corresponding to the effective area among the pixel data.

Embodiments of the present invention provide a method for identifying a body position of a detection object in medical imaging, a medical imaging system, and a computer-readable storage medium. The method comprises: receiving an image group by a trained deep learning network, the image group comprising a plurality of pre-scan images in a plurality of directions obtained by pre-scanning a detection object; and outputting body position information of the detection object by the deep learning network.

A system and method for positioning a scanning table are provided. The method may include obtaining a body length of an object; and determining the number of table positions for scanning the object based on a length of each scanning region of the scanning table, an initial length of an overlapping region of the scanning table at two adjacent table positions, and the body length of the object.

A method for determining an ROI in medical imaging may include receiving first position information related to a body contour of a subject with respect to a support from a flexible device configured with a plurality of position sensors. The flexible device may be configured to conform to the body contour of the subject, and the support may be configured to support the subject. The method may also include generating a 3D model of the subject based on the first position information. The method may further include determining an ROI of the subject based on the 3D model of the subject.

A diagnostic method for determining if a patient is viable candidate for a surgical procedure to permanently eliminate migraine headache pain. The method includes determining if a patient's pain is a migraine pain condition or pain from another medical condition and determining an anatomical location of the determined migraine pain condition. Once the anatomical location is determined, the method correlates the determined anatomical location of the migraine pain condition to a root cause nasal/sinus location and determines a root cause nasal/sinus condition that is causing the patient's migraine pain. Once these diagnostic procedures are complete, the patient may be scheduled for surgery to eliminate the root cause nasal/sinus condition, and thus, permanently eliminate the migraine headache pain.

A method for temperature quantification using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a region of interest in a subject using an MRF pulse sequence with smoothly varying RF phase for MR resonant frequencies that is played out continuously. For each of a plurality of time intervals during acquisition of the MRF data the method further includes comparing a set of the MRF data associated with the time interval to an MRF dictionary to determine at least one quantitative parameter of the acquired MRF data, determining a temperature change based on the at least one quantitative parameter and generating a quantitative map of the temperature change in the region of interest. The region of interest can include aqueous and adipose tissue.