A method of training a neural network is described that includes receiving an image of an anatomical portion of a subject, receiving a CAD model of a surgical implant, generating a first simulated image based on the image and the CAD model, the first simulated image depicting the surgical implant and the anatomical portion of the subject, modifying the simulated image to include simulated artifacts from metal, beam hardening, and scatter, to yield a second simulated image corresponding to the first simulated image, and providing the second simulated image to a neural network as an example input and the first simulated image to the neural network as an example output.

Methods and systems are provided for adjusting medical imaging parameters based on imaging parameters used during a previous imaging session. In one embodiment, a method for a computed tomography (CT) system includes reducing a radiation level of at least one CT scan of one or more successive CT scans performed on a patient based on CT scan information obtained from a previous CT scan performed on the patient, the radiation level reduced relative to a radiation level of the previous CT scan.

In one embodiment, there is provided an apparatus for ultra-low-dose (ULD) computed tomography (CT) reconstruction. The apparatus includes a low dimensional estimation neural network, and a high dimensional refinement neural network. The low dimensional estimation neural network is configured to receive sparse sinogram data, and to reconstruct a low dimensional estimated image based, at least in part, on the sparse sinogram data. The high dimensional refinement neural network is configured to receive the sparse sinogram data and intermediate image data, and to reconstruct a relatively high resolution CT image data. The intermediate image data is related to the low dimensional estimated image.

A nuclear imaging device that solves continuing problems with existing nuclear imaging systems that are often rendered inoperable because of a detector component failure or a mechanical component failure. The present nuclear imaging device includes a plurality of detectors functioning harmoniously but independently from one another and positionable about a scanning arc. Each detector generally includes a gamma radiation camera, a radiation shield, and a diverging pinhole collimator applicable to the gamma camera. Each pinhole collimator may be positioned a variable distance from the detector to provide zoom in and zoom out optical capabilities that yield higher-quality results and allow for rapid imaging, cutting standard scanning times by more than half.

A system and method for producing a computed tomography (CT) medical image includes receiving x-rays passing through an object with a photon-counting detector system, which includes a plurality of detector pixels configured to generate a photon-counting signal in response to receiving each photon of the x-rays having passed through the object. The method also includes summing a charge associated with each photon received at a given detector pixel of the plurality of pixels to generate a charge integration signal, utilizing the charge integration signal to correct a count of the photon-counting signal for pileup-induced count losses to create a corrected photon-counting signal, and reconstructing an image of the object using the corrected photon-counting signal.

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition parameter for imaging a subject using a medical imaging apparatus; acquiring, using the medical imaging apparatus, a medical image of the subject according to the accelerated image acquisition parameter; applying a deep network model to the medical image to generate a corresponding transformed medical image with improved quality; and combining the medical image and the corresponding transformed medial image using an adaptive mixing algorithm to generate output image.

A medical image processing apparatus comprises processing apparatus configured to: receive CT (computed tomography) image data acquired using a first scan setting; input the CT image data to an adapter to obtain corrected CT image data, wherein the adapter is configured to transform a first image style of the CT image data acquired using the first scan setting to a second, different image style that is characteristic of CT image data acquired using a second scan setting that is different from the first scan setting; and output the corrected CT image data.

A method is provided for processing images comprising retrieving measured data for a first image. The method then generates partially filtered data by applying a first filter to the measured data. The first filter is a generic filter. The method then reconstructs the partially filtered data to generate a partially filtered image. The method then generates a partially processed image by applying a first processing routine to the partially filtered image. The method then generates a filtered image by applying a second filter to the partially processed image, where the second filter is a filter selected from a plurality of potential secondary filters. The method then outputs the filtered image. Systems are provided for implementing the claimed method and training methods for neural networks used in the method are provided as well.

Real-time monitored computed tomography (CT) reconstruction for reducing a radiation dose. During helical CT scanning of a target object, projections may be acquired in either a full mode which subjects the target object to a full radiation dose, or a reduced mode which subjects the target object to a reduced radiation dose (e.g., by reducing the number of projections acquired, reducing the exposure time, etc.). After a sector is acquired in the full mode, a slice of the target object that is influenced by that sector is identified, and a CT image of that slice is reconstructed using projections that have been previously acquired for that slice. When a stopping rule is satisfied based on this partial reconstruction, the full mode is switched to the reduced mode, and at least one subsequent sector is acquired in the reduced mode.

A method for generating the multi-modality image, including obtaining a first modality image of an object to be radiotherapy, where the first modality image is an image generated for the object to be radiotherapy injected with a tracer; and performing a modality conversion on the first modality image to obtain the multi-modality image of the object to be radiotherapy.