Abstract:
The invention relates to a CT imaging apparatus and a method for generating sectional images of an object such as a patient on a patient table. According to one embodiment, first projections (P) are generated along a first helical scanning path (Tr1) of a first X-ray source according to a sparse angular sampling scheme. Additional projections (Q1, Q2, R1) may dynamically be introduced along said first helical scanning path (Tr1) and/or along a second helical scanning path (Tr2) of an additional X-ray source based on the evaluation of previous projections (P1).
Abstract:
The present invention relates to a sensor device for detecting dose of radiation received at the sensor device, the sensor device comprising a flexible body having a cross-section being comparatively small relative to the length of the device, a cladding at the flexible body, the cladding converting incoming radiation into visible light, and an optical shape sensing device disposed within the flexible body and configured to determine a shape of the flexible instrument relative to a reference, the shape sensing device configured to collect information based on its configuration to map an intraluminal structure during a procedure. The present invention further relates to a radiation therapy system including such a sensor device and a method of operating a radiation therapy system including such a sensor device.
Abstract:
An image reconstruction apparatus and related method. The amount of out-field-of view material for a CT scanner (IMA) with a given field of view (FoV) in a bore (B) is established. Based on the measurement, a hybrid-image reconstructor (RECONX) is configured to switch between different reconstruction algorithms.
Abstract:
An apparatus and a method for correcting a CT slice image for an image artifact (330) caused by the motion of a high attenuation part (140) in an object (135) of interest. The CT slice image is based on projection images (310a,b). The apparatus and method uses a footprint (315a,b) of the part in each of the projection images (310a,b).
Abstract:
A method and system for generating imaging data during a medical intervention are provided. An external optical sensor is attached on the patient's body, and includes at least one external orientation fiber core configured to measure an orientation of the external optical sensor relative to a point of reference. The external optical sensor is interrogated to generate external sensor orientation information during the medical intervention. That information is used to estimate an orientation of the external optical sensor, which is then displayed during the medical intervention.
Abstract:
An apparatus and a method for correcting a CT slice image for an image artifact (330) caused by the motion of a high attenuation part (140) in an object (135) of interest. The CT slice image is based on projection images (310a,b). The apparatus and method uses a footprint (315a,b) of the part in each of the projection images (310a,b).
Abstract:
An apparatus for assessing a coronary vasculature and a corresponding method are provided which allow to globally assess a coronary artery disease directly from the contrast agent dynamics as derived from diagnostic images acquired using an invasive medical imaging modality by following the time course of the area occupied by the vessels in the diagnostic images.
Abstract:
A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to simulate coronary or carotid flow and pressure effects induced by a cardiac valve device implantation, using cardiac image data and a device model (212). The computing system further includes a processor (120) configured to execute the biophysical simulator to simulate the coronary or carotid flow and the pressure effects induced by the device implantation with the cardiac image data and the device model. The computing system further includes a display configured to display results of the simulation.
Abstract:
An apparatus for image reconstruction that includes input circuitry configured to acquire, from an image sequence, first and second 2D projection data of a region of interest of an object at respective acquisition times; and provide first 3D object data of a first object in a region of interest and second 3D object data of a second object in a region of interest. The apparatus also includes a processor configured to generate a first registration of the first and/or second 3D object data to the first 2D projection data and a second registration of the first and/or second 3D object data to the second 2D projection data; provide a vector field defining motion of the second object relative to the first object between the acquisition times based on the registrations; generate corrected projection data using the vector field; and generate a motion-compensated image sequence based on the corrected projection data.
Abstract:
Disclosed herein is a medical system (100, 300, 400) comprising a memory (110) storing a trainable machine learning module (122) trained using training data descriptive of a training data distribution (600) to output a reconstructed medical image (136) in response to receiving measured medical image data (128) as input. The medical system comprises a computational system (104). The execution of machine executable instructions (120) causes the computational system to: receive (200) the measured medical image data and determine (202) the out-of-distribution score and the in-distribution accuracy score consecutively in an order determined a sequence, detect (204) a rejection of the measured medical image data using the out-of-distribution score and/or the in-distribution accuracy score during execution of the sequence, provide (206) a warning signal (134) if the rejection of the measured medical image data is detected. The out-of-distribution score is determined by inputting the measured medical image data into the out-of-distribution estimation module. The in-distribution accuracy score is determined by inputting the measured medical image data into the in-distribution accuracy estimation module.