The image processing apparatus acquires a first three-dimensional image, generates a second three-dimensional image by applying image processing to the first three-dimensional image having been acquired, and generates a two-dimensional projected image by applying projection processing to the second three-dimensional image having been generated. The image processing apparatus determines a parameter to be used for the image processing of the three-dimensional image on the basis of a parameter for the projection processing.

A radiation diagnostic apparatus according to an embodiment includes plural radiation detection elements and a processing circuitry. The radiation detection elements are arranged in a two-dimensional direction. The processing circuitry determines, based on a first output relating to a first detection element included in the radiation elements and a second output relating to a second detection element, an ideal output relating to the first detection element when it is assumed that a surface at which a radiation first arrives on the first detection element is an incident position of the radiation.

A system for imaging reconstruction is provided. The system may obtain a first set of image data of a subject acquired by a scanner and a second set of image data of the subject acquired by the scanner. The first set of image data may correspond to a first angle range of the scanner. The second set of image data may correspond to a second angle range of the scanner. The first angle range may be different from the second angle range. The system may also generate a first image based on the first set of image data and generate a second image based on the second set of image data. The system may further generate a target image based on the first image and the second image.

A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

A method of imaging nervous tissue, comprising acquiring functional imaging modality data from a functional imaging modality which images an intrabody volume of a patient having a body part, the patient having been injected with an imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS); and locating the nervous tissue in the intrabody volume based on the functional imaging modality data.

A system for medical imaging is provided. The system includes a scanning device configured with a scanning cavity, a control device, and an output device configured within the scanning cavity. The control device is configured to obtain one or more scan protocols and acquire at least one guide instruction corresponding to the one or more scan protocols. The output device is configured to obtain guide information corresponding to the at least one guide instruction and present the guide information. The scanning device is configured to scan a subject with the presentation of the guide information according to the one or more scan protocols.

Various methods and systems are provided for stationary CT imaging. In one embodiment, an imaging system comprises a chamber shaped to enclose a subject to be imaged, a support surface disposed within the chamber and shaped to maintain the subject in an upright position, and an annular imaging unit encircling the chamber and having a fixed angular orientation to the chamber, the annular imaging unit including a distributed x-ray unit and a detector array arranged opposite to each other across the chamber. The imaging system may image the subject without rotation of the annular imaging unit.

According to an embodiment of the present invention, there is provided a magnetic resonance image processing method that is performed by a magnetic resonance image processing apparatus, the magnetic resonance image processing method including: acquiring first k-space data calculated based on a sub-sampled magnetic resonance signal; acquiring second k-space data from the first k-space data by using a first artificial neural network model; and acquiring a first magnetic resonance image from the second k-space data by using an inverse Fourier operation.

A magnetic resonance (MR) acquisition acceleration method is provided. The method includes, for each acquisition of a plurality of acquisitions, generating an excitation radio frequency (RF) pulse that excites a plurality of slices, applying, to a subject by an MR system, the excitation RF pulse, and acquiring MR signals of the plurality of slices. A number of slices in the plurality of slices is not a power of two, wherein generating an excitation RF pulse further includes modulating a base excitation RF pulse with a different modulation function during each acquisition. A number of acquisitions is equal to the number of slices, and MR signals from each acquisition are algebraic combinations of MR signals of individual slices. The method also includes reconstructing MR images of individual slices based on the MR signals from the plurality of acquisitions, and outputting the reconstructed images of the individual slices.

A method for generating an image representation of slices through a body based on tomographic imaging data for the body. The method comprises processing reconstructed tomographic image slices to selectively embed in each slice image information from at least one 3D volume rendering of the slice plane within the 3D tomographic image dataset. This is done through a selection process wherein, based on a set of pre-defined criteria, a decision is made for each pixel in each reconstructed tomographic slice as to whether the pixel value should be replaced with a new, modified pixel value determined based on the at least one volume rendering. This may comprise simply swapping the pixel value for the value of the corresponding pixel value in the volume rendering, or it may comprise a more complex process, for instance blending the two values, or adjusting a transparency of the pixel value based on the at least one volume rendering.