The present disclosure relates to a positioning system suitable for use in an imaging system. The positioning system may include one or more cameras configured to capture images or videos of an imaging object and surrounding environment thereof for ROI targeting or patient position recognition. The positioning system may also include one or more position probes and sources configured to determine an instant location of an imaging object or an ROI thereof in a non-contact manner.

A method of using long wave infrared thermography (LWIT) to quantify the characteristic temperature changes associated with infected wounds to accurately confirm the existence of or absence of infection.

An apparatus and method for creating a three dimensional imaging system is disclosed. There is a first source of laser light and a second source of laser light having a wavelength different from the wavelength of the laser light of the first source. The laser light from the first and second sources are combined, and the combined laser light is transmitted to a scanner. The scanner further transmits the combined light to a surface to be imaged.

For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.

A method and system for imaging a body using a magnetic resonance imaging (MRI) apparatus, including motion tracking of a target object of the body using MRI by generating an MRI image of a region of interest of the body by performing a weighted combination of a signal received by each coil of an MRI apparatus during an MRI scan.

An example system includes a thermal camera, a memory, and processing circuitry coupled to the thermal camera and the memory. The processing circuitry is configured to acquire a core temperature of a patient and acquire a first thermal image associated with the patient. The processing circuitry is configured to determine, based on the first thermal image, a first sensed temperature of a location associated with the patient. The processing circuitry is configured to determine a core temperature delta between the core temperature and the first sensed temperature. The processing circuitry is configured to acquire a second thermal image associated with the patient. The processing circuitry is configured to determine, based on the second thermal image, a second sensed temperature. The processing circuitry is configured to determine, based on the second sensed temperature and the core temperature delta, a measure of the core temperature.

Aspects of the disclosure relate generally to aligning a patient with a target position of a magnetic resonance imaging system. For example, a computer may receive an input identifying a set of coordinates to be aligned with a target position of the magnetic resonance imaging system. The coordinates may correspond to a position of an anatomical feature of a patient within the magnetic resonance imaging system. The computer may also calculate a first travel distance and a second travel distance. Each of the first and second travel distances may be a distance along first and second axes, respectively, along which a patient handling system of the magnetic resonance imaging system is capable of moving. The computer may further reposition the patient handling system according to both the first travel distance and the second travel distance such that the anatomical feature is aligned with the target position.

In order to eliminate a global phase change caused by static magnetic field inhomogeneity included in a nuclear magnetic resonance signal, focusing on that phase components generated in a nuclear magnetic resonance signal caused by the static magnetic field inhomogeneity is in a predetermined frequency band (low-frequency band), phase components in the frequency band caused by the static magnetic field inhomogeneity is eliminated from an image generated from the nuclear magnetic resonance signal in main imaging. The predetermined frequency band of the phase components caused by the static magnetic field inhomogeneity is calculated from the nuclear magnetic resonance signal obtained in preliminary imaging.

In a method and magnetic resonance apparatus for acquiring a high-resolution magnetic resonance image dataset of at least one limited body region having at least one anatomical structure of a patient, an overview image dataset is first acquired, using which an item of position information of the at least one anatomical structure is ascertained, the item of position information designating an exact position of the at least one anatomical structure and/or a relative position of the at least one anatomical structure relative to the reference body region. A high-resolution magnetic resonance image dataset of the anatomical structure is then created using the position information and the high-resolution magnetic resonance image dataset is evaluated. The evaluated high-resolution image data is then made available in electronic form.

Embodiments discussed herein facilitate predicting response to therapy in Crohn's disease. A first set of embodiments discussed herein relates to accessing a radiological image of a region of tissue demonstrating Crohn's disease associated with a patient; defining a mesenteric fat region by segmenting mesenteric fat represented in the radiological image; extracting a set of radiomic features from the mesenteric fat region; providing the set of radiomic features to a machine learning classifier configured to compute a probability of response to therapy in Crohn's disease based, at least in part, on the set of radiomic features; receiving, from the machine learning classifier, a probability that the region of tissue will respond to therapy; generating a classification of the patient as a responder or non-responder based, at least in part, on the probability; and displaying the classification.