Biomedical imaging

US 11490961 A method for reconfiguring a biomedical laser device, comprising collecting information indicative of light output properties of the device measured during a given operational mode; detecting deviation in measured light output properties with respect to predefined light output properties for a given operational mode; determining a new set of operational parameters that are to be employed for at least one of a new medical procedure, new medical treatment, activation of a new drug or illumination of a new dye; and sending a new set of operational parameters to the laser for reconfiguring it to be operable in a new operational mode. Modulight Corp. (Tampere, Finland) Vilokkinen V, Uusimaa P, Orsila S 11/8/2022 US 11490797 An apparatus, device and method for capsule microscopy. The apparatus can include a first optical arrangement configured to transceive at least one type of electromagnetic radiation to and from the portion(s) and can also include a wavelength-dispersive second arrangement, which can be configured to disperse the electromagnetic radiation. A pill-shaped housing can be provided, enclosing the first and second arrangements. The General Hospital Corp. (Boston, MA, USA) Gora M, Kang D, Nishioka NS, Bouma BE, Tearney GJ, Carruth RA, Gallagher KW, Sauk J, Gu Lee M, Tabatabaei N, Shishkov M 11/8/2022 US 11490814 Detectors and their method of use for sensing electromagnetic fields, electromagnetic signals, biochemical analytes and/or other conditions in subjects. The device may include an inductively coupled implantable coil-based transducer that converts electrical, photonic, biochemical and/or other appropriate signals and/or conditions originating in tissues and/or transplanted tissue grafts into changes in a property of the transducer, such as a resonant frequency, that may be detected using an alternating magnetic field that may be provided by a magnetic resonance imaging (MRI) signal and/or other appropriate source. Massachusetts Institute of Technology (Cambridge, MA, USA) Jasanoff AP, Spanoudaki V, Hai A 11/8/2022 US 11494902 Methods, devices and systems for quantifying an extent of various pathologic patterns in scanned subject images. The methods, devices and systems generate unique dictionaries of elements based on actual image data scans to automatically identify pathology in new image data scans of subjects. The automatic detection and quantification system can detect a number of pathologies, including a usual interstitial pneumonia pattern on computed tomography images, which is subject to high inter-observer variation, in the diagnosis of idiopathic pulmonary fibrosis. National Jewish Health (Denver, CO, USA) Lynch DA, Humphries SM 11/8/2022 US 11488313 An imaging device and a method for generating a motion-compensated image or video. The device is configured to acquire a posture of an inertial measurement unit and, on the basis thereof, to carry out a registration between coordinate systems of the inertial measurement unit and the image data; to acquire motion data from the inertial measurement unit arranged on the target object; and, by processing the motion data, to generate the motion-compensated image or video. Siemens Healthcare (Erlangen, Germany) Alzaga A, Lauritsch G, Regensburger A 11/1/2022 US 11484365 A system and method for improved medical device navigation, which can include a processor configured to determine an emplacement of a 2D medical image in a 3D virtual space; to determine an emplacement of a virtual medical device in the 3D space; to determine an intersection based on the emplacement of the 2D medical image and the emplacement of the virtual medical device; and/or to determine a dynamic point-of-projection location for the virtual medical device based, at least in part, on the determined intersection. InnerOptic Technology (Hillsborough, NC, USA) Kohli L, Heaney B, Keller K 11/1/2022 US 11488401 A method of classifying the nuclei in prostate tissue images with a trained deep learning network and using said nuclear classification to classify regions, such as glandular regions, according to their malignancy grade. The method also trains a deep learning network to identify the category of each nucleus in prostate tissue image data, said category representing the malignancy grade of the tissue surrounding the nuclei. The method automatically segments the glands and identifies the nuclei in a prostate tissue dataset. CADESS.AI (Uppsala, Sweden) Avenel C, Carlbom I 11/1/2022

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