A mobile X-ray imaging apparatus is an apparatus that enables the performance of a fluoroscopic procedure in any setting. The apparatus may include an elongated frame, a wheeled base, a U-shaped support, a height-adjusting track, a support carriage, an X-ray generator, and an image-capturing device. The elongated frame supports the U-shaped support and maintains the U-shaped support at a desired height. The wheeled base maintains the elongated frame upright and facilitates the relocation of the apparatus. The U-shaped support maintains the X-ray generator and the image-capturing device at the desired arrangement to perform the fluoroscopic procedure. The height-adjusting track enables the repositioning of the U-shaped support along the elongated frame to accommodate the patient. The support carriage connects the U-shaped support to the height-adjusting track and keeps the U-shaped support in place during the procedure. The X-ray generator and the image-capturing device enable the fluoroscopic procedure to be performed.

Systems and method for destroying or inhibiting cancer cells and other rapidly-dividing cells include applying AP magnetic fields having a frequency of 0.5-500 kHz and a field strength of 0.5-5 mT to a target body area that includes the cancer or other rapidly-dividing cells.

A system and method of using machine learning to predict the pharmacokinetics of a therapeutic radiopharmaceutical on a subject patient using the biodistribution data of the patient in order to dynamically treat the patient using the radiopharmaceutical.

An x-ray imaging apparatus and associated methods are provided to receive measured projection data in a primary region and measured scatter data in asymmetrical shadow regions and determine an estimated scatter in the primary region based on the measured scatter data in the shadow region(s). The asymmetric shadow regions can be controlled by adjusting the position of the beam aperture center on the readout area of the detector. Penumbra data may also be used to estimate scatter in the primary region.

A system and method for providing dynamic 3-D anatomical visualizations generates a 3D visualization of a patient's airway that may be dynamically transformed to characterize changes to the airway (e.g., as the patient breathes, when patient performs a Mueller's manoeuvre, and/or in response to mandibular repositioning). The 3D visualization is generated based on an integration of static 3D image data and acoustic reflection measurements of the airway determined and provided by an acoustic reflection measurement device. The movements or changes to the region of interest may be determined based on real-time dynamic readings taken by the acoustic reflection measurement device, and the 3D visualization may be transformed in real-time or near real-time stream based on the dynamic readings. The transformation of the 3D visualization provides an animated visualization of the airway.

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.

A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

Disclosed herein is a method, comprising: introducing a tracer into a body region of an organism at an introduction site of the organism; causing emission of characteristic X-rays of the tracer in the body region; capturing images of the tracer in the body region with the characteristic X-rays; determining a first three-dimensional (3D) distribution of the tracer in the body region based on the images; and examining the first 3D distribution of the tracer in the body region to identify a sentinel lymph node for the introduction site. If the sentinel lymph node is not identified in the first 3D distribution, the method further comprises repeating said causing, said capturing, and said determining thereby resulting in a second 3D distribution of the tracer in the body region; and examining the second 3D distribution to identify the sentinel lymph node.

A system for measuring muscle mass of a patient has a dual-energy radiation emission source. A radiation detector is configured to detect radiation emitted from the dual-energy radiation emission source passed through the patient. A processor has a memory with storing instructions, that when executed by the processor, perform a set of operations. The operations include receiving radiation detection data; generating a scan representation; identifying a primary fat target; determining an amount of fat in the primary fat target; comparing the amount of fat in the primary fat target to a reference; and based on the comparison, correcting an estimated amount of lean tissue to generate a corrected muscle mass value.