A computer-assisted imaging and localization system assists the physician in positioning surgical effecters, such as implants, tools and instruments, within a surgical site in a patient’s body. The system displays overlapping images - one image of the surgical site with the patient’s anatomy and another image showing the surgical effecter(s). The overlapping image of the surgical effecter(s) is moved over the static image of the anatomy as the implant/instrument is moved. The movement of the surgical effecter(s) is determined in a three-dimensional coordinate system at a home base location in the patient’s anatomy, which home base can be moved during the procedure without interrupting the displays of the overlapping images.

An X-ray detector includes a detection unit to convert X-rays into a signal value and an evaluation unit. The detection unit and the evaluation unit are configured in a common component, the extent of the component along a first direction being not greater than the extent of the detection unit. The evaluation unit includes at least one correction unit to correct the signal values, a computation unit to control the correction, and a memory unit to store at least one correction parameter. The evaluation unit is designed such that the signal values are corrected as a function of the at least one correction parameter. A method and detector group are also disclosed.

Apparatus for identifying a patient, said apparatus comprising: a medical device for implantation into the patient; an optical identifier affixed to the device; wherein at least a portion of the optical identifier is radiopaque, whereby to generate a scannable X-ray image of the optical identifier when the medical device is imaged using X-ray.

A method, system and program product are provided for planning an intervention procedure in a body lumen. A CT scan of the body lumen is performed. A virtual rendering is created of the inside of the body lumen corresponding to an interventional camera image. Then a virtual tape corresponding to a planned path for the intervention procedure is projected onto a wall of the body lumen. The virtual tape is projected onto the lumen wall, which is relatively distant from the camera point on the virtual rendering, so the tape does not appear to oscillate like a central thread. Also, since the virtual tape is located on the lumen wall, it does not occlude the center of the lumen, allowing a user to better visualize the lumen during planning, during fly through, and even during an actual intervention.

The present disclosure is related to imaging systems and methods. The method includes obtaining image data of a subject to be scanned by a medical device. The method includes determining a scan range of the subject based on the image data. The scan range includes at least one scan area of the subject. The method includes determining at least one parameter value of at least one scan parameter based on the at least one scan area of the subject.

In a computer tomography system having an x-ray detector with a detector surface at which sensor pixels, for detection of x-ray radiation, are distributed non-uniformly, and a method for operating such a system, either a pitch factor is selected, and a value range for an extent of a reconstruction field for image data is determined dependent on the distribution of the sensor pixels and dependent on the selected pitch factor, or a value for the extent of the reconstruction field is selected, and a value range for the pitch factor is determined dependent on the distribution of the sensor pixels and dependent on the selected value for the extent of the reconstruction field.

A non-spectral computed tomography scanner includes a radiation source configured to emit x-ray radiation, a detector array configured to detect x-ray radiation and generate non-spectral data, and a memory configured to store a spectral image module that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

A hand-grip type oral X-ray device having an alignment function according to an embodiment includes an X-ray tube configured to generate and emit X-rays, an X-ray receiver including a first gyro-sensor and a first communication module, having, at a corner thereof, a magnet configured to generate a magnetic force, and configured to receive the X-rays generated from the X-ray tube to acquire X-ray data about an object in the oral cavity, and a handle including a rod-shaped grip operable by the hand of a user and an extension arm connecting the X-ray receiver and the grip to each other, and coupled to the X-ray receiver.

An artificial intelligence (AI) measurement system for an X-ray image is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect and locate various landmarks of the anatomy presented within the X-ray image. A set of key image features approximating the locations of the landmarks having known distance relationships to one another is overlaid onto the X-ray image. The positions of the key image features are then adjusted to correspond to the landmarks within the X-ray image. These adjustments are made relative to the prior known distance relationships between the key features, which enables the measurement system to readily calculate desired angular and length measurements between landmarks as a result.

An artificial intelligence (AI) lead marker detection system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to scan post-exposure X-ray images to detect and insert various lead markers, to digitize information provided by the type and location of the lead marker, and to employ the marker information in different X-ray system workflow automations. The marker information obtained by the AI lead marker detection system can also provide useful data for use in downstream clinical and quality applications apart from the X-ray system, such as either AI or non-AI analytical applications.