The most appropriate image for a diagnostic target among a plurality of images is selected and accurate diagnosis support information is presented regardless of the type of a selected image, a modality, or the like. An image diagnosis support apparatus includes: a diagnostic information generation unit that generates diagnostic information based on a plurality of medical images; a reliability calculation unit that evaluates an image quality and calculates an image reliability for each of the plurality of medical images; and a degree-of-contribution calculation unit that calculates a degree of contribution of each of the plurality of medical images to the diagnostic information using an internal parameter indicating a degree of appropriateness of each medical image for a diagnostic target and the reliability calculated by the reliability calculation unit. An image for detection used by the diagnostic information generation unit is generated based on the degree of contribution.

Healthcare services can be automated utilizing a system that recognizes at least one characteristic of a patient based on images of the patient acquired by an image capturing device. Relying on information extracted from these images, the system may automate multiple aspects of a medical procedure such as patient identification and verification, positioning, diagnosis and/or treatment planning using artificial intelligence or machine learning techniques. By automating these operations, healthcare services can be provided remotely and/or with minimum physical contact between the patient and a medical professional.

A method of orthodontic treatment planning for a patient includes receiving three-dimensional intraoral surface scan data of a dentition of the patient, receiving three-dimensional volumetric scan data of a dentition of the patient, and determining, for use in planning an orthodontic treatment, a mandibular rotation axis and a glenoid fossae of the temporal bone based on a mandibular condyle of the patient using the scan data.

The present invention refers to the field of cardiac electrophysiology (EP) and, more specifically, to image-guided radio frequency ablation and pacemaker placement procedures. For those procedures, it is proposed to display the overlaid 2D navigation motions of an interventional tool intraoperatively obtained from the same projection angle for tracking navigation motions of an interventional tool during an image-guided intervention procedure while being navigated through a patient's bifurcated coronary vessel or cardiac chambers anatomy in order to guide e.g. a cardiovascular catheter to a target structure or lesion in a cardiac vessel segment of the patient's coronary venous tree or to a region of interest within the myocard. In such a way, a dynamically enriched 2D reconstruction of the patient's anatomy is obtained while moving the interventional instrument. By applying a cardiac and/or respiratory gating technique, it can be provided that the 2D live images are acquired during the same phases of the patient's cardiac and/or respiratory cycles. Compared to prior-art solutions which are based on a registration and fusion of image data independently acquired by two distinct imaging modalities, the accuracy of the two-dimensionally reconstructed anatomy is significantly enhanced.

The present invention provides novel compounds that are useful as radiopharmaceuticals, imaging agents and for treatment of cancer.

Disclosed is surface guided cropping in volume rendering of 3D volumetric data from intervening anatomical structures in the patient's body. A digital 3D representation expressing the topography of a first anatomical structure is used to define a clipping surface or a bounding volume which then is used in the volume rendering to exclude data from an intervening structure when generating a 2D projection of the first anatomical structure.

Systems and methods for image-guided medical procedures use fluoroscopic 3D reconstructions to plan and navigate a percutaneously-inserted device such as a biopsy tool from an entry point to a target.

Noise is reduced for a medical image for which noise cannot be quantified by a general-purpose image quality evaluation index. An image processor has a preprocessor that generates input images including an original image and one or more images with reduced noise compared with the original image; and a noise reduction processor outputs an image, which is obtained by reducing noise from the original image based on the input images, by applying a learned network. The learned network used in the noise reduction processor is constructed by performing deep learning using a plurality of learning sets in which one or more of a medical image including noise, a noise-reduced image obtained by performing noise reduction processing on the medical image, and an intermediate image obtained during the noise reduction processing are input images and a correct image is obtained based on the input images an output image.

In a method for recording a PET image data set, an overall recording area is moved continuously through the FOV at a constant movement speed, an attenuation map of the overall recording area being used to reconstruct the PET image data record from the PET raw data. The magnetic resonance data of a slice of the patient currently located within the FOV and movement status information relating to a cyclical movement of the patient are recorded simultaneously with recording the PET raw data. A movement status class is assigned to the PET raw data and the magnetic resonance data in each case. Using the magnetic resonance data assigned to the different movement status classes, attenuation maps of the patient are determined for the different movement status classes and applied to the PET raw data assigned to the corresponding movement status class to reconstruct the PET image data set.

Various methods and systems are provided for a medical imaging system. In one embodiment, a method includes acquiring a series of medical images of a lung, identifying a pleural line in each medical image of the series, evaluating the pleural line for irregularities in each medical image of the series, and outputting an annotated version of each medical image of the series, the annotated version including visual markers for healthy pleura and irregular pleura. In this way, an operator of the medical imaging system may be alerted to pleural irregularities during a scan.