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13000results about "Computerised tomographs" patented technology

Systems and methods for imaging large field-of-view objects

InactiveUS7108421B2Quantity minimizationAvoiding corrupted and resulting artifacts in image reconstructionMaterial analysis using wave/particle radiationRadiation/particle handlingBeam sourceX-ray
An imaging apparatus and related method comprising a source that projects a beam of radiation in a first trajectory; a detector located a distance from the source and positioned to receive the beam of radiation in the first trajectory; an imaging area between the source and the detector, the radiation beam from the source passing through a portion of the imaging area before it is received at the detector; a detector positioner that translates the detector to a second position in a first direction that is substantially normal to the first trajectory; and a beam positioner that alters the trajectory of the radiation beam to direct the beam onto the detector located at the second position. The radiation source can be an x-ray cone-beam source, and the detector can be a two-dimensional flat-panel detector array. The invention can be used to image objects larger than the field-of-view of the detector by translating the detector array to multiple positions, and obtaining images at each position, resulting in an effectively large field-of-view using only a single detector array having a relatively small size. A beam positioner permits the trajectory of the beam to follow the path of the translating detector, which permits safer and more efficient dose utilization, as generally only the region of the target object that is within the field-of-view of the detector at any given time will be exposed to potentially harmful radiation.
Owner:MEDTRONIC NAVIGATION

Lung nodule detection and classification

A computer assisted method of detecting and classifying lung nodules within a set of CT images includes performing body contour, airway, lung and esophagus segmentation to identify the regions of the CT images in which to search for potential lung nodules. The lungs are processed to identify the left and right sides of the lungs and each side of the lung is divided into subregions including upper, middle and lower subregions and central, intermediate and peripheral subregions. The computer analyzes each of the lung regions to detect and identify a three-dimensional vessel tree representing the blood vessels at or near the mediastinum. The computer then detects objects that are attached to the lung wall or to the vessel tree to assure that these objects are not eliminated from consideration as potential nodules. Thereafter, the computer performs a pixel similarity analysis on the appropriate regions within the CT images to detect potential nodules and performs one or more expert analysis techniques using the features of the potential nodules to determine whether each of the potential nodules is or is not a lung nodule. Thereafter, the computer uses further features, such as speculation features, growth features, etc. in one or more expert analysis techniques to classify each detected nodule as being either benign or malignant. The computer then displays the detection and classification results to the radiologist to assist the radiologist in interpreting the CT exam for the patient.
Owner:RGT UNIV OF MICHIGAN

Surgical navigation systems including reference and localization frames

A system for use during a medical or surgical procedure on a body. The system generates an image representing the position of one or more body elements during the procedure using scans generated by a scanner prior or during the procedure. The image data set has reference points for each of the body elements, the reference points of a particular body element having a fixed spatial relation to the particular body element. The system includes an apparatus for identifying, during the procedure, the relative position of each of the reference points of each of the body elements to be displayed. The system also includes a processor for modifying the image data set according to the identified relative position of each of the reference points during the procedure, as identified by the identifying apparatus, said processor generating a displaced image data set representing the position of the body elements during the procedure. The system also includes a display utilizing the displaced image data set generated by the processor, illustrating the relative position of the body elements during the procedure. Methods relating to the system are also disclosed. Also disclosed are devices for use with a surgical navigation system having a sensor array which is in communication with the device to identify its position. The device may be a reference frame for attachment of a body part of the patient, such as a cranial reference arc frame for attachment to the head or a spine reference arc frame for attachment to the spine. The device may also be a localization frame for positioning an instrument relative to a body part, such as a localization biopsy guide frame for positioning a biopsy needle, a localization drill guide assembly for positioning a drill bit, a localization drill yoke assembly for positioning a drill, or a ventriculostomy probe for positioning a catheter.
Owner:SURGICAL NAVIGATION TECH +1

System and method of measuring disease severity of a patient before, during and after treatment

A system and method of obtaining serial biochemical, anatomical or physiological in vivo measurements of disease from one or more medical images of a patient before, during and after treatment, and measuring extent and severity of the disease is provided. First anatomical and functional image data sets are acquired, and form a first co-registered composite image data set. At least a volume of interest (ROI) within the first co-registered composite image data set is identified. The first co-registered composite image data set including the ROI is qualitatively and quantitatively analyzed to determine extent and severity of the disease. Second anatomical and functional image data sets are acquired, and form a second co-registered composite image data set. A global, rigid registration is performed on the first and second anatomical image data sets, such that the first and second functional image data sets are also globally registered. At least a ROI within the globally registered image data set using the identified ROI within the first co-registered composite image data set is identified. A local, non-rigid registration is performed on the ROI within the first co-registered composite image data set and the ROI within the globally registered image data set, thereby producing a first co-registered serial image data set. The first co-registered serial image data set including the ROIs is qualitatively and quantitatively analyzed to determine severity of the disease and/or response to treatment of the patient.
Owner:SIEMENS MEDICAL SOLUTIONS USA INC
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