Methods and systems are provided for adaptive scan control. In one embodiment, a method includes, upon an injection of a contrast agent, performing a plurality of perfusion acquisitions of a first anatomical region of interest (ROI) of a subject with the imaging system, processing projection data of the first anatomical ROI obtained from the plurality of perfusion acquisitions to measure a contrast signal of the contrast agent, performing a plurality of angiography acquisitions, each angiography acquisition performed at a respective time determined based on the contrast signal, and performing one or more additional perfusion acquisitions between each angiography acquisition.

Embodiments include systems and methods for determining cardiovascular information for a patient. A method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

Optical coherence tomography (OCT) angiography (OCTA) data is generated by one or more machine learning systems to which OCT data is input. The OCTA data is capable of visualization in three dimensions (3D) and can be generated from a single OCT scan. Further, motion artifact can be removed or attenuated in the OCTA data by performing the OCT scans according to special scan patterns and/or capturing redundant data, and by the one or more machine learning systems.

A system performs a method for characterizing passage of a patient fluid through a conduit. The method includes quantifying flow of fluidic content through a conduit, where the fluidic content includes a patient fluid, estimating a concentration of a fluid component of the patient fluid in the fluidic content, and characterizing passage of the patient fluid loss through the conduit based on the quantified flow and the concentration of the fluid component. At least one of the quantified flow or the concentration of the fluid component is based on sensor data from a sensor arrangement coupled to the conduit. Other apparatus and methods are also described.

Systems and methods for detecting, counting and analyzing the blood content of a surgical textile are provided, utilizing an infrared or depth camera in conjunction with a color image.

The present invention discloses means and methods for detecting irregularities in the cells throughout a healthy tissue. The method generally relates to cancer detection, diagnosis and treatment, and more specifically pertains to detection, diagnosis and treatment guidance of cancerous or precancerous conditions through the use of thermal imaging technology and analysis.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

An image processing method includes: obtaining DCE magnetic resonance images corresponding to a plurality of time points for a same detection target; determining average pixel grayscale values of images of a same lesion region in the DCE magnetic resonance images of the plurality of time points respectively; determining a time to peak according to the average pixel grayscale values corresponding to the plurality of time points; and generating a first-stage time-intensity image before the time to peak and a second-stage time-intensity image after the time to peak respectively according to the DCE magnetic resonance images and the time to peak. The first-stage time-intensity image and the second-stage time-intensity image are 3D images. A pixel grayscale value of each pixel in the first-stage time-intensity image and the second-stage time-intensity image represents a change rate of blood supply intensity and reflects a severity level of a lesion corresponding to the lesion region.

Devices, systems, and methods for evaluating blood flow with vascular perfusion imaging are disclosed. In an embodiment, a medical system is disclosed. One embodiment of the medical system comprises a perfusion imaging system configured to obtain perfusion imaging data associated with movement of contrast through a vessel of a patient, a graphical user interface, and a medical processing unit in communication with the perfusion imaging system and the graphical user interface. The medical processing unit is configured to receive a first set of perfusion imaging data from the perfusion imaging system, determine at least one parameter representative of the movement of the contrast through the vessel of the patient, generate a first graphical representation of the first set of perfusion imaging data and the at least one parameter determined based on the first set of perfusion imaging data, and output the first graphical representation to the graphical user interface.

Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.