Devices, systems, and methods for localized delivery of a chemotherapy, hormonal therapy, or targeted drug/biologic therapy to a target tissue area of an internal body organ of a patient. Computer systems may be used for planning and navigation in super selective drug delivery via a tracheobronchial airway. A catheter may be used to form a sealed treatment chamber in a natural lumen extending through the target tissue area. Air is purged from the chamber, which is then filled with a liquid drug solution for an adequate treatment session time, solution volume and drug concentration to saturate the target tissue area, thereby providing the treatment. The liquid drug solution may be circulated or recirculated through the chamber or maintained stationary therewithin to saturate the target tissue area. The chamber is evacuated at the end of the treatment session.

Systems and methods for destroying or inhibiting cancer cells and other rapidly-dividing cells include coupling an alternating polarity (AP) magnetic field generator to a target body area and applying an AP magnetic field having a frequency of 0.5-500 kHz and a field strength of 0.5-5 mT to the target body area to achieve a desired inhibiting effect on cancer cells or other rapidly-dividing cells. Treatments provided by the system may be co-administered with an anti-cancer drug such as a chemotherapy drug, a hormone therapy drug, targeted therapy drugs, immunotherapy drugs, or an angiogenesis inhibitor drug.

Methods and systems for navigating to a target through a patient's bronchial tree are disclosed including a bronchoscope, a probe insertable into a working channel of the bronchoscope and including a location sensor, and a workstation in operative communication with the probe and the bronchoscope, the workstation including a user interface that guides a user through a navigation plan and is configured to present a central navigation view including a plurality of views configured for assisting the user in navigating the bronchoscope through central airways of the patient's bronchial tree toward the target, a peripheral navigation view including a plurality of views configured for assisting the user in navigating the probe through peripheral airways of the patient's bronchial tree to the target, and a target alignment view including a plurality of views configured for assisting the user in aligning a distal tip of the probe with the target.

Methods and systems for navigating to a target through a patient's bronchial tree are disclosed including a bronchoscope, a probe insertable into a working channel of the bronchoscope and including a location sensor, and a workstation in operative communication with the probe and the bronchoscope, the workstation including a user interface that guides a user through a navigation plan and is configured to present a central navigation view including a plurality of views configured for assisting the user in navigating the bronchoscope through central airways of the patient's bronchial tree toward the target, a peripheral navigation view including a plurality of views configured for assisting the user in navigating the probe through peripheral airways of the patient's bronchial tree to the target, and a target alignment view including a plurality of views configured for assisting the user in aligning a distal tip of the probe with the target.

Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.

A system includes a processor circuit configured to receive a set of intravascular data from an intravascular sensor at a first location within a blood vessel. The processor circuit simultaneously receives a set of cardiovascular data from a heart monitor. After the intravascular sensor is moved from the first location to a second location, the processor circuit receives an additional set of intravascular data from the intravascular sensor and an additional set of cardiovascular data from the heart monitor. The processor circuit then determines a distance between the first location and the second location and determines a pulse wave velocity associated with the blood flow within the blood vessel based on the sets of intravascular data, the sets of cardiovascular data, and the distance. The processor circuit then outputs the pulse wave velocity to a display.

Metal complexes including a radionuclide and a compound of Formula I and Formula II are potent inhibitors of PSMA. ##STR00001##

A method for determining the dry weight of a patient after dialysis therapy, wherein the patient's blood volume is monitored and blood volume values are output. The blood volume values are recorded and evaluated for a predetermined period of time after reaching an ultrafiltration volume appropriately predetermined for the patient, wherein the dry weight of the patient then is determined on the basis of the rate of change of the blood volume during the predetermined period of time.

This invention relates to an image processing device (1) comprising an input (2) for receiving image data representative of a region of interest in the body of a patient from a medical X-ray imaging apparatus (100). The image data comprises a first dark-field image obtained for a first X-ray spectrum and a second dark-field image obtained for a second, different, X-ray spectrum. A combination unit (3) provides a combination image that is representative of a medical condition map, e.g. a lung condition map, by combining the first dark-field image and the second dark-field image.

There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).