An X-ray image diagnostic apparatus or a medical information processing apparatus according to an embodiment includes an X-ray irradiator that outputs an X-ray and an X-ray detector that detects the X-ray output by the X-ray irradiator and transmitted through a subject. The X-ray image diagnostic apparatus or the medical information processing apparatus that generates an X-ray image based on detection data obtained by detection by the X-ray detector includes processing circuitry. The processing circuitry receives a first instruction, receive an instruction to add the X-ray image, acquires the degree of conformity between the first instruction and a second instruction based on a database in which a plurality of the second instructions and a plurality of prompts are associated with each other, acquires an evaluation value obtained by evaluating the plurality of prompts identified by the acquired degree of conformity, and displays one or a plurality of prompts to which the X-ray image is added selected from the plurality of prompts based on the evaluation value.

Disclosed are a three-dimensional image generation method and an electronic device for performing same, according to various embodiments. The electronic device according to one embodiment of the present invention comprises: an image capture device for acquiring a plurality of radiological images for a sample moving on a transport device; and a processor, wherein the processor can: determine feature points of the plurality of radiological images, for reconstructing a three-dimensional image of the sample; use the location of the feature points to calculate the location information of the feature points; generate a feature point image on the basis of the location information; and generate the three-dimensional image by using the feature point image and the location information.

Noise-reduced images of a subject are generated from x-ray projection data acquired from the subject using a computed tomography (CT) system. A neural network or other machine learning algorithm is trained to receive images reconstructed from the projection data as an input and to generate an output as noise-reduced images. The neural network or other machine learning algorithm is trained using a self-training procedure, in which the training data used to train the neural network or other machine learning algorithm are generated directly from the projection data acquired from the subject using data augmentation (e.g., random rotations of the projection data and/or noise insertion).

Medical computed tomography (CT) data is processed as follows. Projection data obtained by scanning a subject is retrieved. The retrieved projection data is processed to reconstruct a three-dimensional image. A two-dimensional image is generated based on the reconstructed three-dimensional image. A disease or an uncertainty in an identification of the disease is identified in the two-dimensional image. The projection data or the three-dimensional image is reprocessed into an updated three-dimensional image such that at least one para-meter of the reprocessing is based on the disease or the uncertainty in the identification of the disease. An updated two-dimensional image is then generated based on the updated three-dimensional image.

A system for image reconstruction includes an input for receiving image data, a processor, and a memory. The memory stores instructions that cause the processor to reconstruct an image from the image data using a self-supervised deep learning model.

Images are reconstructed from k-space acquired with a magnetic resonance imaging (MRI) system using a multi-shot pulse sequence. In each iteration, a phase-aware image reconstruction, a data-consistency update across all shots or subsets of data, and a relative phase estimation across the reconstructed images for each shot are performed. In this way, the reconstruction framework recasts the problem as an iterative relative phase estimation problem, which allows for the use relative phase estimation techniques. Through an iterative search, an artifact-free combined image and the smooth relative phase between each shot in the multi-shot k-space data can be jointly estimated.

An image processing device includes at least one processor. The processor detects a specific structural pattern indicating a lesion candidate structure for a breast in a series of a plurality of projection images obtained by performing tomosynthesis imaging on the breast or in a plurality of tomographic images obtained from the plurality of projection images, synthesizes the plurality of tomographic images to generate a synthesized two-dimensional image, specifies a priority target region, in which the specific structural pattern is present, in the synthesized two-dimensional image, and performs determination regarding a diagnosis of a lesion on the basis of the synthesized two-dimensional image and the priority target region.

Disclosed herein are systems, methods, and instrumentalities associated with MRI image reconstruction. According to embodiments of the disclosure, an apparatus configured to perform the MRI image reconstruction task may be configured to obtain an under-sampled MRI image and generate a reconstructed MRI image based on the under-sampled MRI image and a machine-learned (ML) model. The ML model may be trained via contrastive learning, during which randomly selected locations of the reconstructed MRI data generated by the ML model may be replaced with values that are different than the under-sampled MRI data, and the MRI data thus derived may be used as a negative example for the training.

A system includes a spatial mismatch correction module configured to receive functional emission data, anatomical image data, and functional image data reconstructed based on the functional emission data and attenuation corrected based on the anatomical image data. The system further includes a data set provider configured to provide a first data set and a second data set, which are spatially mismatched. The system further includes a voxel of interest identifier configured to identify voxels or regions of reconstruction inconsistency due to a spatial mismatch between true attenuation values and attenuation values derived from the anatomical image data based on relations between the first and second data sets. The system further includes an image data generator configured to morph the functional image data and generate corrected functional image data based on the identified voxels or regions, independent of functional-anatomical structural correlation, while maintaining an image quality of the functional image data.

According to one embodiment, a learning apparatus includes processing circuitry. The processing circuitry is configured to obtain a first training data set, the first training data set is based on target data items and having a first resolution. The processing circuitry is configured to train a first machine learning model using the first training data set to generate a first trained model. The processing circuitry is configured to train a second machine learning model using a second training data set and the first trained model to generate a second trained model, the second training data set is based on the target data items and having a second resolution higher than the first resolution.