An image alignment apparatus includes at least one processor, and the processor derives, for each of first and second three-dimensional images each including a plurality of tomographic images and a common structure, first and second three-dimensional coordinate information that define an end part of the structure in a direction intersecting the tomographic image. The processor aligns the first three-dimensional image and the second three-dimensional image by using the first and second three-dimensional coordinate information to align the common structure included in each of the first three-dimensional image and the second three-dimensional image at least in the direction intersecting the tomographic image.

There is provided an analyzer apparatus capable of generating crisp scanned images. In the analyzer apparatus, a sample is scanned with a probe such that a first signal and a second signal are emitted from the sample. The analyzer apparatus comprises: a first detector for detecting the first signal and producing a first detector signal; a second detector for detecting the second signal and producing a second detector signal; and an image processing unit operating (i) to produce a first scanned image and a second scanned image from the first detector signal and the second detector signal, respectively, (ii) to create a filter based on the second scanned image having a higher signal-to-noise ratio than that of the first scanned image, and (iii) to apply the filter to the first scanned image.

Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting risk of lung cancer (e.g., current or future risk of lung cancer) for one or more subjects. Individual risk prediction models are trained on nodule-specific and non-nodule specific features, including longitudinal nodule specific and longitudinal non-nodule specific features, such that each risk prediction model can predict risk of lung cancer across different time horizons. Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.

The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

Systems and methods for image space control of a medical instrument are provided. In one example, a system is configured to display a two-dimensional medical image including a view of at least a distal end of an instrument. The system can determine, based on one or more fiducials on the instrument, a roll estimate of the instrument. The system further can receive a user input comprising a heading command to change a heading of the instrument within a plane of the medical image, or an incline command to change an incline of the instrument into or out of the plane of the medical image. Based on the roll estimate and the user input, the system can generate one or more motor commands configured to cause a robotic system coupled to the medical instrument to move the robotic medical instrument.

A processing method for enhancing the image quality of an image, more particularly a digital medical grey scale image, that comprises the steps of a) decomposing an original image into multiple detail images at different resolution levels and/or orientations, b) processing the detail images to obtain processed detail images, c) computing a result image by applying a reconstruction algorithm to the processed detail ages, said reconstruction algorithm being such that if it were applied to the detail images without processing, then said original image or a close approximation thereof would be obtained, the processing of the detail images comprises the steps of: d) calculating at least one conjugate detail image, and e) computing at least one value of the processed detail images as a function of said conjugate detail image and said detail images.

Systems and methods for generating patient-specific surgical guides comprising: capturing a first and second images of an orthopedic element in different reference frames using a radiographic imaging technique, detecting spatial data defining anatomical landmarks on or in the orthopedic element using a neural network, applying a mask to the orthopedic element defined by an anatomical landmark, projecting the spatial data from the first image and the second image to define volume data, applying the neural network to the volume data to generate a reconstructed three-dimensional (“3D”) model of the orthopedic element; and calculating dimensions for a patient-specific surgical guide configured to abut the orthopedic element.

Various methods and systems are provided for a medical imaging system. In one embodiment, a method for a projection imaging system includes acquiring a first image of a region of interest (ROI) with the projection imaging system in a first position, determining a three-dimensional (3D) location of an annotation on the first image via a geometric transformation using planes, acquiring a second image of the ROI with the projection imaging system in a second position, determining a location of the annotation on the second image based on the 3D location of the annotation in the first position and a geometry of the second position, and displaying the annotation on the second image in response to an accuracy check being satisfied.

The present disclosure provides a method and system for a dynamic fluoroscopy of a C-shaped arm device. The method comprises: photographing a subject during a photography cycle, obtaining, during the photography cycle, first fluoroscopic data of a radiation source irradiating the subject at a first energy, and obtaining second fluoroscopic data of the radiation source irradiating the subject at a second energy different from the first energy (210); photographing the subject in multiple successive photography cycles (220); and displaying a dynamic image of the subject based on the first fluoroscopic data and the second fluoroscopic data obtained in each of the multiple successive photography cycles (230).

An image processing apparatus includes at least one processor. The processor is configured to execute region-of-interest image generation processing of generating a region-of-interest image from a projection image, which is obtained at an irradiation position closest to a position facing a detection surface of a radiation detector, among a series of projection images obtained by irradiating a breast with radiations and imaging the breast, and shape type determination processing of determining a type of a shape of a calcification image included in the region-of-interest image generated by the region-of-interest image generation processing.