Abstract:
According to an exemplary embodiment a targeting method for targeting a first object from an entry point to a target point in an object (110) under examination is provided, wherein the method comprises selecting a two-dimensional image (301) of the object under examination depicting the entry point (305) and the target point (303) and determining a planned path (304) from the entry point to the target point, wherein the planned path has a first direction. Furthermore, the method comprises recording data representing a fluoroscopic image of the object under examination, wherein the fluoroscopic image is recorded under a second direction so that a normal of the image coincide with the first direction and determining whether the first object is on the determined planned path based on shape and/or position of the projection of the first object in the fluoroscopic image.
Abstract:
It is described a gain calibration for a two-dimensional X-ray detector (315), in which the gain coefficients for scattered radiation (307b) and direct radiation (307a) are measured or estimated separately. A weighed average may be applied on the appropriate scatter fraction. The scatter fraction depending gain calibration method produces less ring artifacts in X-ray images as compared to known gain calibration methods, which do not take into account the fraction of scattered radiation reaching the X-ray detector (315).
Abstract:
Prior to an intervention, a 3D rotational scan is acquired (at block 10) in respect of a body volume and reconstructed. In addition, three-dimensional image data in respect of the body volume is acquired (at block 12) using another modality, such as computerised tomography (CT) or magnetic resonance (MR), reconstructed, and prepared for visualisation. During the actual intervention, live two-dimensional fluoroscopic images are acquired (at block 14), using the imaging system employed to acquire the 3D rotational scan, and processed for visualisation. The 2D image data is registered (at block 16) to the 3D rotational image data acquired and reconstructed in respect of the body volume of interest, and then a 3D-3D registration process is employed (at block 18) to register the 3D image data acquired in respect of the same body volume using, for example, CT or MR imaging systems to the 3D rotational image data, and a display module (20) is used to align the 2D fluoroscopic image and the 3D MR/CT image as a fused or composite image and display the image.
Abstract:
A computed-tomography system comprises a data-processing system arranged to receive attenuation profiles for respective orientations. A lowest representative noise level of the individual attenuation profiles is determined. The attenuation profiles are filtered in dependence of said lowest representative noise level. In particular it is achieved that the filtered attenuation profiles have the lowest maximum noise level among the received attenuation profiles.
Abstract:
Prior to an intervention, a 3D rotational scan is acquired (at block 10) in respect of a body volume and reconstructed. In addition, three-dimensional image data in respect of the body volume is acquired (at block 12) using another modality, such as computerised tomography (CT) or magnetic resonance (MR), reconstructed, and prepared for visualisation. During the actual intervention, live two-dimensional fluoroscopic images are acquired (at block 14), using the imaging system employed to acquire the 3D rotational scan, and processed for visualisation. The 2D image data is registered (at block 16) to the 3D rotational image data acquired and reconstructed in respect of the body volume of interest, and then a 3D-3D registration process is employed (at block 18) to register the 3D image data acquired in respect of the same body volume using, for example, CT or MR imaging systems to the 3D rotational image data, and a display module (20) is used to align the 2D fluoroscopic image and the 3D MR/CT image as a fused or composite image and display the image.
Abstract:
An artifact correcting image reconstruction apparatus includes a reconstruction processor (70) that reconstructs acquired projection data (60) into an uncorrected reconstructed image (74). A classifying processor (78) classifies pixels of the uncorrected reconstructed image (74) at least into high, medium, and low density pixel classes. A pixel replacement processor (88) replaces pixels of the uncorrected reconstructed image (74) that are of the high density and low density classes with pixel values of the low density pixel class to generate a synthetic image (90). A forward projecting processor (94) forward projects the synthetic image (90) to generate synthetic projection data (96). A projection replacement processor (100, 110) replaces acquired projection data (60) contributing to the pixels of the high density class with corresponding synthetic projection data (96) to generate corrected projection data (112). The reconstruction processor (70) reconstructs the corrected projection data (112) into a corrected reconstructed image (120).
Abstract:
The size of a detail of an object is derived from a data set of data values relating to the object. The data set assigns the data values to positions in a multidimensional space. A direction is selected in the multidimensional space. The spatial resolution of the data set is higher in the selected direction as compared to the spatial resolution in other directions. The size of the detail is derived from data values in the selected direction. The selected direction can extend along the line of intersection which intersects a scanning plane in which the data values are acquired and a transverse plane extending at right angles to the longitudinal axis of the detail. The data values can be acquired an X-ray computed tomography imaging system, a magnetic resonsance imaging system, or a 3D ultrasound imaging system.
Abstract:
A cross-sectional distribution along a cutting plane is derived from an object data set of data values. The cross-sectional distribution comprises density values. The density values are calculated from data values of the object data set in positions outside the cutting plane. The object data set represents an object to be examined and is, for example, acquired by way of volumetric computed tomography or magnetic resonance imaging. For example, the density values of the cross-sectional distribution are calculated by local (slab) MIP or mIP or by interpolation.
Abstract:
A computer tomography device includes an X-ray source (1) and an X-ray detection system (3) for forming a number of density profiles of an object to be radiologically examined. A reconstruction unit (4) derives an image signal from the density profiles. A control system (20) adjusts the X-ray source (1) on the basis of a density value of the object and the control system is arranged to adjust the X-ray source on the basis of a part of the object to be examined. The control system is also arranged to adjust the X-ray source on the basis of a reference adjustment of the X-ray source. The reference adjustment is dependent on the part of the object to be examined.
Abstract:
A computer tomography system includes an X-ray tube having an elongate anode across which a beam spot can be displaced, a scanogram being formed by correctly shifting the profiles measured in the various source positions with respect to one another, followed by superposition. Parts of the object which are situated in a selected layer are thus emphasized in an image, parts of the object which are situated outside the selected layer being blurred. When a point of interest in the object is determined by observation of the scanogram, the same apparatus is used to produce a tomography slice image transverse the scanogram image. An important additional advantage consists in that the permissible power to be applied to the X-ray source may be higher. Furthermore, a scanogram thus obtained is not necessarily disturbed by the failure of one or more detectors.