A method of detecting a protrusion in an intestinal tract in a computer according to an embodiment of the present disclosure includes acquiring a three-dimensional model of the intestinal tract scanned by a scanning device, the three-dimensional model comprising three-dimensional data of the intestinal tract; mapping, in the computer, the three-dimensional model to a two-dimensional plane in an area-preserving manner; and detecting an area of the protrusion in the two-dimensional plane. The method can replace traditional modes such as enteroscopy, and the protrusion in the intestinal tract is detected in a painless and low-cost mode.

A method for detecting or ruling out non-small cell lung cancer (NS-CLC) in a patient comprises: (a) administering to a patient a detectable amount of a compound of formula (I): Formula (I) wherein the compound is targeted to any NSCLC tumor in the patient; and (b) acquiring an image to detect the presence or absence of any NSCLC tumor in the patient, wherein at least one of the atoms in formula (I) is replaced with .sup.11C, .sup.13N, .sup.15O, .sup.18F, .sup.34mCI, .sup.38K, .sup.45Ti, .sup.51Mn, .sup.52Mn, .sup.52Fe, .sup.55Co, .sup.60CU, .sup.61Cu, .sup.62Cu, .sup.64Cu, .sup.66Ga, .sup.68Ga, .sup.71As, .sup.72As, .sup.74As, .sup.75Br, .sup.75Br, .sup.76Br, .sup.82Rb, .sup.86Y, .sup.89Zr, .sup.90Nb, .sup.94mTc, .sup.110mIn, .sup.118Sb, .sup.120I, .sup.121I, .sup.122I, and .sup.124I.

##STR00001##

The present disclosure provides methods and systems directed to management and visualization of radiological data. A method for processing at least one medical image of a location of a body of a subject may comprise (a) retrieving, from a remote server via a network connection, the medical image; (b) identifying one or more regions of interest (ROIs) in the medical image, wherein the ROIs correspond to an anatomical structure of the location of the body of the subject; (c) annotating the ROIs with label information corresponding to the anatomical structure, thereby producing an annotated medical image; (d) generating educational information based at least in part on the annotated medical image; and (e) generating a visualization of the anatomical structure, based at least in part on the educational information.

An object detection device that detects a specific object included in an input image includes a first candidate region specifying unit that specifies a first candidate region in which an object candidate is included from a first input image obtained by imaging a subject in a first posture, a second candidate region specifying unit that specifies a second candidate region in which an object candidate is included from a second input image obtained by imaging the subject in a second posture different from the first posture, a deformation displacement field generation unit that generates a deformation displacement field between the first input image and the second input image, a coordinate transformation unit that transforms a coordinate of the second candidate region to a coordinate of the first posture based on the deformation displacement field, an association unit that associates the first candidate region with the transformed second candidate region that is close to the first candidate region, and a same object determination unit that determines that the object candidates included in the candidate regions associated with each other by the association unit are the same object and are the specific object.

An X-ray sleeve is configured to be releasably attached a mattress cover. The sleeve is configured to receive an X-ray cassette, protected from foreign substances, such as against contaminants, such as bodily fluids, and can be removeable for sterilization and sanitation.

An effect of a treatment on an organ, e.g., a lung, is assessed by acquiring a first measurement for each of a plurality of regions of the organ, and then acquiring a second measurement for each of the plurality of regions of the organ, after acquisition of the first measurements. A regional change measurement is obtained for each of the plurality of regions of the organ based on the first measurement and the second measurement of the region. A treatment effect is then determined based the plurality of regional change measurements and treatment information of the treatment delivered to the organ.

Methods for quantitatively separating x-ray images of a subject having three or more component materials into component images using spectral imaging or multiple-energy imaging with 2D radiographic hardware implemented with scatter removal methods. The multiple-energy system may be extended by implementing DRC multiple energy decomposition and K-edge subtraction imaging methods.

Disclosed is a diagnostic apparatus for a chronic obstructive pulmonary disease (COPD) based on prior knowledge CT subregion radiomics, belonging to the field of medical imaging. The diagnostic apparatus comprises: a subregion partitioning module based on prior knowledge configured for partitioning a CT lung image of a patient into three subregions based on the CT values of the interior of the lung, wherein the CT value of the interior of the lung of a subregion 1 is in the range of (−1024, −950), the CT value of the interior of the lung of a subregion 2 is in the range of (−190, 110), and the CT value of the interior of the lung of a subregion 3 is in the range of (−950, −190); a feature extraction module configured for extracting the radiomics features of the three subregions, respectively, and obtaining the LAA-950I features.

Described herein are means for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism, in the context of processing medical imaging. According to a particular embodiment, there is a system specially configured for diagnosing a Pulmonary Embolism (PE) within new medical images which form no part of the dataset upon which the AI model was trained. Such a system executes operations for receiving a plurality of medical images and processing the plurality of medical images by executing an image-level classification algorithm to determine the presence or absence of a Pulmonary Embolism (PE) within each image via operations including: pre-training an AI model through supervised learning to identify ground truth; fine-tuning the pre-trained AI model specifically for PE diagnosis to generate a pre-trained PE diagnosis and detection AI model; wherein the pre-trained AI model is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture to extract informative features from the plurality of medical images by fusing spatial and channel-wise information; applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient.

A system can have an x-ray source that generates a series of individual x-ray pulses for multi-energy imaging. A first x-ray pulse can have a first energy level and a subsequent second x-ray pulse in the series can have a second energy level different from the first energy level. An x-ray imager can receive the x-rays from the x-ray source and can detect the received x-rays for image generation. A generator interface box (GIB) controls the x-ray source to provide the series of individual x-ray pulses and synchronizes detection by the x-ray imager with generation of the individual x-ray pulses. The GIB can control x-ray pulse generation and synchronization to optimize image generation while minimizing unnecessary x-ray irradiation.