The present disclosure provides methods of imaging cancerous cells in a subject, wherein the cancerous cells are localized to the skeletal system or central nervous system of the subject, the method comprising administering to the subject an effective amount of 18F-fluoroacetate, detecting a first signal emitted by 18F-fluoroacetate, and generating an image representative of the location and/or amount of the first signal to image the cancerous cells. In some embodiments, the methods further comprising diagnosing, prognosing, staging, and/or monitoring the progression of a disease or disorder, such as acute lymphoblastic leukemia and/or leptomeningeal disease.

Disclosed herein is an apparatus comprising an insertion tube; an image sensor inside the insertion tube; wherein the image sensor comprises an array of pixels; wherein the image sensor is configured to count numbers of particles of radiation incident on the pixels, within a period of time. Also disclosed herein is a method of using this apparatus.

Disclosed is a system and a method for monitoring a CT scan image. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A region of interest may be identified on each slice using an image processing technique. The region of interest may be masked on each slice using deep learning. Subsequently, a nodule may be detected as the region of interest using the deep learning. Further, a plurality of characteristics associated with the nodule may be identified. Furthermore, an emphysema may be detected in the region of interest on each slice. A malignancy risk score for the patient may be computed. A progress of the nodule may be monitored across subsequent CT scan images. Finally, a report of the patient may be generated.

The present disclosure provides systems and methods for training and/or employing machine-learned models (e.g., artificial neural networks) to diagnose chest conditions such as, as examples, pneumothorax, opacity, nodules or masses, and/or fractures based on chest radiographs. For example, one or more machine-learned models can receive and process a chest radiograph to generate an output. The output can indicate, for each of one or more chest conditions, whether the chest radiograph depicts the chest conditions (e.g., with some measure of confidence). The output of the machine-learned models can be provided to a medical professional and/or patient for use in providing treatment to the patient (e.g., to treat a detected condition).

The present invention relates to an apparatus (10) for determining an orientation of a patients chest. The apparatus comprises: an input unit (20); and a processing unit (30). The input unit is configured to receive an image of a patient, the image comprising image data of the patients chest. The input unit is configured to receive an X-ray radiograph of the patient's chest acquired by an X-ray imaging unit with an X-ray imaging axis extending from an X-ray source to an X-ray detector. The input unit is configured to provide the image and the X-ray radiograph to the processing unit. The processing unit is configured to determine an orientation of the patients chest in the X-ray radiograph with respect to the X-ray imaging axis, the determination comprising utilization of the image and the X-ray radiograph.

Various embodiments of a device for in-vivo measurements radiopharmaceuticals used for diagnosis and monitoring of radiotherapy are presented. In some embodiments, the present disclosure relates to a device having a cannula that may include a measurement chamber, a radiation detector and a delivery lumen, wherein the device may be used to both deliver material to the patient (e.g., radiotracers used in radiopharmaceuticals) and measure levels and concentrations of radioactive material in, for example, the patient's blood both during and after administration of the radioactive material. In some embodiments, particles emitted by the radioactive material interact with a scintillation material, resulting in the release of light that may be transmitted, via the scintillation material and/or fiber optic material, to an optical detectors or processor for processing. In some embodiments, particle absorbing materials may be used to limit measurements to materials within the measurement chamber or other area of interest.

Systems and method for destroying or inhibiting cancer cells and other rapidly-dividing cells include applying AP magnetic fields having a defined frequency of 5 Hz-500 kHz and a field strength of 0.1-5000 μT to a target body area that includes the cancer or other rapidly-dividing cells, and modifying the cancer or tumor microenvironment to increase the presence of cancer-suppressive cells or decrease the presence of cancer-promoting cells. In various embodiments, the systems and methods may include adjusting the therapy based on the dosage of an immunotherapy drug administered to the patient before, during, or after the application of the AP magnetic fields.

A mechanism is provided in a data processing system for refining lesion contours with combined active contour and inpainting. The mechanism receives an initial segmented medical image having organ tissue including a set of object contours and a contour to be refined. The mechanism inpaints object voxels inside all contours of the set. The mechanism calculates an updated contour around the contour to be refined based on the in-painted object voxels to form an updated segmented medical image. The mechanism determines whether the updated segmented medical image is improved compared to the initial segmented medical image. The mechanism keeps the updated segmented medical image responsive to the updated segmented medical image being improved.

Methods and systems are provided for detecting focal lesions in multi-phase or multi-sequence medical imaging studies of medical images. One system includes memory for storing medical images and an electronic processor. The electronic processor is configured to: detect native candidate lesions for first phase computer tomography (CT) scans, detect candidate lesions for second phase CT scans, and project the candidate lesions detected for the first phase CT scans and/or the second phase CT scans on a same common domain. Once the candidate lesions are registered together, the electronic processor performs a matching algorithm for each of the native candidate lesions of the first phase CT scans with the registered candidate lesions from the second phase CT scans to determine presence and contours of valid lesions for the medical images, and to discard candidate lesions that are not acceptable.

A computer that determines at least an anatomic feature of a left atrial appendage (LAA) is described. During operation, the computer generates a 3D image associated with an individual's heart. This 3D image may present a view along a perpendicular direction to an opening of the LAA. Then, the computer may receive information specifying a set of reference locations. For example, the set of reference locations may include: a location on a circumflex artery, a location between a superior portion of the LAA and a left pulmonary vein, and/or a location on a superior wall of the LAA and distal to trabeculae carneae. Next, the computer automatically determines, based, at least in part, on the set of reference locations, at least the anatomical feature of the LAA, which is associated with the opening of the LAA and a size of a device used in an LAA closure (LAAC) procedure.