A method of identifying tumor treatment resistance is provided. In some embodiments, the method may include: detecting tumor oxygenated blood perfusion region inside tumor by having a patient breathe air to acquire Mill baseline data; inhalation of hyperoxia gas to generate higher than baseline HbO.sub.2 blood circulating in body to acquire MRI enhanced data; the region-of-interest (ROI), which in this case is a tumor volume (V), and which may be performed by volume contour tracing/region-of-interest (ROI) analysis and 3D tumor volumetry methods; calculating voxel's enhanced signal intensity (ΔSI); calculating tumor oxygenated perfusion percentage (OPP %); selecting different threshold and calculating maps such as a reconstruction OPP % pseudo color map; calculating tumor volume change ratio (Vt %); overlaying reconstruction OPP % pseudo color map to original images for visualizing tumor response data; drawing or plotting the OPP % and Vt % on a cancer treatment response information diagram, and identifying the type of drug resistance, classifying the drug resistance being caused by poor drug distribution factor or cells-specific factor based on pooled collected data.

Disclosed herein is a method of treating a subject having arterial stenosis. The method comprises: (a) providing a plurality of image frames of an artery of the subject taken in sequence; (b) in a plurality of cross-sections of the artery, determining a maximum diameter and a minimum diameter of each of the plurality of cross-sections of the artery among the plurality of image frames of the step (a); (c) calculating an average vasodilation ratio of the artery base on the maximum diameter and the minimum diameter determined in the step (b); and (d) treating the subject based on the average vasodilation ratio calculated in the step (c), by implanting a stent to the subject when the average vasodilation ratio is equal to or greater than 0.2; or administering to the subject an effective amount of a vasodilator when the average vasodilation ratio is less than 0.2.

Herein are described data acquisition systems and methods applying such systems to determine three-dimensional (3D) diffusion tensors, and simultaneously, to perform 3D structure imaging. Example data acquisition systems can include computing systems in communication with modified light sheet microscopes that are configured for high-speed volumetric imaging to record 3D diffusion processes and high-resolution 3D structural imaging.

A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Systems and methods include determination of a first relationship between change in photopeak energy and event time skew based on a first detection event signal acquired from a detector at a first temperature and a subsequent detection event signal acquired from the detector at a next temperature, acquisition of a subsequent detection event signal from the detector, determination of an event time associated with this detection event signal, determination of an event time skew based on an energy of this detection event signal and the first relationship, determination of a corrected event time based on the event time and the event time skew, and identification of a coincidence based on the corrected event time.

The disclosure provides a system for EGRT. The system may include a radiotherapy device for treating a subject. The radiotherapy device may include a scintillation camera that is directed at an ROI of the subject. The subject may be injected with a radioactive tracer or implanted with a radioactive marker before treatment. The ROI may undergo a physiological motion during the treatment. The system may deliver a treatment session to the subject by the radiotherapy device. During the treatment session, the system may acquire a target image of the ROI indicative of a distribution of the radioactive tracer or the radioactive maker in the ROI by the scintillation camera, and adapt a radiation beam to be delivered to the subject with respect to the physiological motion of the ROI by adjusting the radiation beam based on the target image.

Systems and methods for automated physiological parameter estimation from ultrasound image sequences are provided. An ultrasound system includes an ultrasound imaging device configured to acquire a sequence of ultrasound images of a patient. An anatomical structure recognition module includes processing circuitry configured to receive the acquired sequence of ultrasound images from the ultrasound imaging device, and automatically recognize an anatomical structure in the received sequence of ultrasound images. A physiological parameters estimation module includes processing circuitry configured to automatically estimate one or more physiological parameters associated with the recognized anatomical structure.

A method including: automatically detecting, using at least one machine learning algorithm, one or more abnormalities depicted in a medical image of a patient; automatically determining whether the one or more abnormalities have remained temporally and unchanged, based on an older medical image of the patient; and upon determining that the one or more abnormalities have remained temporally and spatially unchanged: automatically inpainting the one or more abnormalities in the medical image, and automatically enrich a new training set with the inpainted medical image.

The present disclosure provides an image processing method, apparatus and device, and an image display method. The image processing method includes: acquiring multiple images continuously photographed by a photographing device; performing eye contour extraction on each of the images, thereby acquiring eye contour image data; comparing multiple groups of eye contour image data of an identical eye contour, thereby acquiring optimal eye contour image data of each eye contour; and fusing multiple pieces of acquired optimal eye contour image data with a main image of the images, thereby obtaining a target image.

A deep machine-learning approach is used for medical image fusion by a medical imaging system. This one approach may be used for different applications. For a given application, the same deep learning is used but with different application-specific training data. The resulting deep-learnt classifier provides a reduced feature vector in response to input of intensities of one image and displacement vectors for patches of the one image relative to another image. The output feature vector is used to determine the deformation for medical image fusion.