The present invention provides method and apparatus for medical imaging by applying image segmentation to an estimation of a medical image to obtain an anatomical and/or functional sparse constraint and reconstructing the medical image with respect to the anatomical and/or functional sparse constraint. This invention considers the anatomical and/or function sparsity inherent to medical images and allows conceptually to represent the medical image in an even more compact manner in the image domain, reducing the need of coherent samplings or information for the reconstruction. In this way, it is possible to reduce the number of sampled data and simplify the image reconstruction process, thereby increasing the efficiency and speed of medical imaging while ensuring the quality of the reconstructed image.

The disclosure relates to a computer-implemented method for processing a magnetic resonance data set of an acquisition area, which is based on an acquisition in an examination procedure in a magnetic resonance facility with a magnetic resonance sequence in which multiple echoes are acquired in an echo train, in particular after a common radio-frequency excitation pulse. For the magnetic resonance data set, a path data set describing the signal path of the measured magnetic resonance signal over an acquisition period, in particular the echo train, in the magnetic resonance sequence is determined in the k-space. A trained image processing function for determining a result data set is transferred together with the magnetic resonance data set as input data.

Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

The disclosure relates to a system and method for correcting PET image data. PET image data of a first part of a subject may be obtained. CT image data of a second part of the subject may be obtained. The first part may include the second part. PET image data of the first part may be obtained based on the PET image data of the first part. A relationship between the CT image data and PET image data of the second part may be determined. CT image data of a third part of the subject may be determined based on the relationship and PET image data of the third part. The first part may include the third part. An attenuation map may be determined based on the CT image data of the second part and the third part. The PET image data of the first part may be corrected based on the attenuation map.

The disclosure relates to a system and method for correcting PET image data. PET image data of a first part of a subject may be obtained. CT image data of a second part of the subject may be obtained. The first part may include the second part. PET image data of the first part may be obtained based on the PET image data of the first part. A relationship between the CT image data and PET image data of the second part may be determined. CT image data of a third part of the subject may be determined based on the relationship and PET image data of the third part. The first part may include the third part. An attenuation map may be determined based on the CT image data of the second part and the third part. The PET image data of the first part may be corrected based on the attenuation map.

The present disclosure provides systems and methods for material decomposition. The systems may obtain scan projection data of a target object. The systems may determine corrected projection data by correcting, based on one or more pixel parameters, the scan projection data. The systems may also determine a reconstructed image by performing, based on the corrected projection data, image reconstruction. The systems may further determine density distribution images of at least two target materials of the target object by decomposing the reconstructed image.

A system of generating data from diffusion-weighted images for pre-processing and a method thereof are disclosed. In the system, a processing parameter set including diffusion information is acquired; after a raw diffusion-weighted image including data images and image information is acquired, the image information is interpreted to set image processing data of the raw diffusion-weighted image, and non-deformation correction and deformation correction are performed on the raw diffusion-weighted image to generate a pre-processed diffusion-weighted image based on the processing parameter set and the image processing data. Therefore, the image processing data can be automatically set based on the raw diffusion-weighted image, to achieve the effect of lowering difficulty for analyzing DWI and saving setup time of image processing data.

A system of generating data from diffusion-weighted images for pre-processing and a method thereof are disclosed. In the system, a processing parameter set including diffusion information is acquired; after a raw diffusion-weighted image including data images and image information is acquired, the image information is interpreted to set image processing data of the raw diffusion-weighted image, and non-deformation correction and deformation correction are performed on the raw diffusion-weighted image to generate a pre-processed diffusion-weighted image based on the processing parameter set and the image processing data. Therefore, the image processing data can be automatically set based on the raw diffusion-weighted image, to achieve the effect of lowering difficulty for analyzing DWI and saving setup time of image processing data.