Abstract: The present invention discloses an analysis method for dynamic contrast-enhanced magnetic resonance image. Firstly, the time-series signal of vascular contrast agent concentration, AIF, of biological individual is obtained from DCE-MRI time-series data. Secondly, perform the nonlinear least sum of square fitting by using the full Shutter-Speed model (SSMfull) and the simplified vascular Shutter-Speed model (SSMvas) on the DCE-MRI time-series signal of each pixel, and the fitting results of DCE-MRI time-series signal are obtained. Thirdly, the corrected Akaike Information Criterion (AICC) score is used to comparing the DCE-MRI time-series signal fitting results to select the optimal model. If the optimal model is SSMfull, distribution maps of five physiological parameters. Ktrans, pb po, kbo, and kio, are produced after fitting; if the optimal model is SSMvas, distribution maps of three physiological parameters, Ktrans, pb, and kbo, are produced after fitting.

Abstract: The invention discloses a method for measuring the intracellular water transmembrane efflux rate (kio): setting magnetic resonance imaging parameters and measuring the noise level during scanning. Optimizing the flip angle and resetting it through Monte Carlo simulation. Scanning quantitative T1 imaging. Scanning dynamic-contrast-enhanced magnetic resonance imaging and injecting contrast agent. The full shutter speed model (SSMfull) is used to analyze every voxel in the tumor area and obtain the kio of them. This method significantly improves the accuracy of kio. The invention discloses the measuring method, system and application of kio as a magnetic resonance imaging biomarker of glioma, which is not for disease diagnosis, to evaluate the expression level of AQP4. The invention discloses the application of kio as a magnetic resonance imaging marker of glioma in the preparation of a product for predicting the sensitivity of glioma radiotherapy and chemotherapy.

Abstract: The present invention discloses the cross-domain network method for magnetic resonance imaging undersampling trajectory optimization in Fourier domain and reconstruction network in image domain.

Abstract: The present invention discloses an analysis method for dynamic contrast-enhanced magnetic resonance image. Firstly, the time-series signal of vascular contrast agent concentration, AIF, of biological individual is obtained from DCE-MRI time-series data. Secondly, perform the nonlinear least sum of square fitting by using the full Shutter-Speed model (SSMfull) and the simplified vascular Shutter-Speed model (SSMvas) on the DCE-MRI time-series signal of each pixel, and the fitting results of DCE-MRI time-series signal are obtained. Thirdly, the corrected Akaike Information Criterion (AICC) score is used to comparing the DCE-MRI time-series signal fitting results to select the optimal model. If the optimal model is SSMfull, distribution maps of five physiological parameters. Ktrans, pb po, kbo, and kio, are produced after fitting; if the optimal model is SSMvas, distribution maps of three physiological parameters, Ktrans, pb, and kbo, are produced after fitting.

Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.

Abstract: An approach is presented to reconstruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.

Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.

Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.