The invention relates to a method of MR imaging of an object (10). It is an object of the invention to enable MR imaging using a 3D radial or spiral acquisition scheme providing an enhanced image quality in the presence of motion. The method comprises the steps of: —generating MR signals by subjecting the object (10) to an imaging sequence comprising RF pulses and switched magnetic field gradients; —acquiring the MR signals using a 3D radial or spiral acquisition scheme with oversampling of a central portion (26) of k-space; —detecting motion-induced displacements (d) and/or deformations of the object (10) during the acquisition of the MR signals and assigning each of the acquired MR signals to a motion state; —reconstructing an MR image from the MR signals weighted in the central portion (26) of k-space, wherein a stronger weighting (W, 30) is applied to MR signals acquired in more frequent motion states, while a weaker weighting (W, 31, 32) is applied to MR signals acquired in less frequent motion states. Moreover, the invention relates to a MR device (1) and to a computer program for a MR device (1).

A system and method includes receiving time-resolved images of a urinary tract of a subject as a bladder of the urinary tract begins, continues through, and completes a dynamic process involving a bladder and segmenting the time-resolved images of the urinary tract to identify boundaries of anatomical structures of the urinary tract. The method further includes performing a surface mapping of the boundaries of the anatomical structures to produce a consistent set of mapped anatomical structures across the time-resolved images, using a flow model and the consistent set of mapped anatomical structures, calculating metrics describing function of the urinary tract during the dynamic process, and generating a report using the metrics describing function of the urinary tract during the dynamic process.

In one embodiment, an MRI apparatus includes: a scanner that includes a static magnetic field magnet, a gradient coil, and a WB coil; and processing circuitry. The processing circuitry is configured to: cause the scanner to image, under a first imaging method, a tissue including a perfusion route of body fluid that removes waste products of the object the body fluid including neurofluid; generate an anatomical image of the tissue from first data acquired by imaging under the first imaging method; cause the scanner to image perfusion behavior of the body fluid in real time under a second imaging method using non-contrast perfusion imaging; generate a perfusion image indicating the perfusion behavior of the body fluid from second data acquired by imaging under the second imaging method; and generate a fused image by combining the anatomical image and the perfusion image.

Systems and methods for ascertaining an item of movement information concerning movement of an object under examination during a magnetic resonance scan. A pilot tone signal generator of a magnetic resonance apparatus transmits a pilot tone signal. At least one first coil element of the magnetic resonance apparatus receives the pilot tone signal. The pilot tone signal received by the at least one first coil element is in each case a first pilot tone received signal. At least one second coil element of the magnetic resonance apparatus receives the pilot tone signal. The pilot tone signal received by the at least one second coil element is in each case a second pilot tone received signal. The at least one first pilot tone received signal is corrected with the aid of the at least one second pilot tone received signal. The item of movement information for the object under examination is ascertained using the corrected at least one first pilot tone received signal.

A method for processing images of lungs, the method comprising defining an inhale region of interest of the lungs at an inhale position and an exhale region of interest of the lungs at an exhale position, determining a spatial transformation of each voxel within the lungs between the lungs at the inhale position and the lungs at the exhale position to provide displacement vector estimates for each voxel within the lungs, and performing volume change inference operations to determine a volume change between the lungs at the inhale position and the lungs at the exhale position based on the inhale region of interest, the exhale region of interest, and the displacement vector estimates for each voxel within the lungs.

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 (SSM.sub.full) and the simplified vascular Shutter-Speed model (SSM.sub.vas) 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 (AIC.sub.C) score is used to comparing the DCE-MRI time-series signal fitting results to select the optimal model. If the optimal model is SSM.sub.full, distribution maps of five physiological parameters. K.sup.trans, p.sub.b p.sub.o, k.sub.bo, and k.sub.io, are produced after fitting; if the optimal model is SSM.sub.vas, distribution maps of three physiological parameters, K.sup.trans, p.sub.b, and k.sub.bo, are produced after fitting. Finally, perform error analysis on the k.sub.io and k.sub.bo, resulting the final distribution maps of k.sub.io and k.sub.bo along with distribution maps of parameters K.sup.trans, p.sub.b, p.sub.o. This method can improve the estimation accuracy of K.sup.trans, p.sub.b, p.sub.o, k.sub.bo and k.sub.io.

Systems and methods are provided for performing automated reconstruction of a dynamic MRI dataset that is acquired without a fixed temporal resolution. On one or more image quality metrics (IQMs) are obtained by processing a subset of the acquired dataset. In one example implementation, at each stage of an iterative process, one or more IQMs of the image subset is computed, and the parameters controlling the reconstruction and/or the strategy for data combination are adjusted to provide an improved or optimal image reconstruction. Once the IQM of the image subset satisfies acceptance criteria based on an estimate of the overall temporal fidelity of the reconstruction, the full reconstruction can be performed, and the estimate of the overall temporal fidelity can be reported based on the IQM at the final iteration.

The invention provides for a method of training a neural network (322) configured for providing a further processing location (326). The method comprises providing (200) a labeled medical image (100), wherein the labeled medical image comprises multiple labels each indicating a truth processing location (102, 104, 106). The method further comprises inputting (202) the labeled medical image into the neural network to obtain one trial processing location. The one trial processing location comprises a most likely trial processing location (108). The method further comprises determine (204) the closest truth processing location (106) for the most likely trial processing location. The method further comprises calculating (206) an error vector (110) using the closest truth processing location and the most likely trial processing location. The method further comprises training (208) the neural network using the error vector.

An image processing method includes: obtaining DCE magnetic resonance images corresponding to a plurality of time points for a same detection target; determining average pixel grayscale values of images of a same lesion region in the DCE magnetic resonance images of the plurality of time points respectively; determining a time to peak according to the average pixel grayscale values corresponding to the plurality of time points; and generating a first-stage time-intensity image before the time to peak and a second-stage time-intensity image after the time to peak respectively according to the DCE magnetic resonance images and the time to peak. The first-stage time-intensity image and the second-stage time-intensity image are 3D images. A pixel grayscale value of each pixel in the first-stage time-intensity image and the second-stage time-intensity image represents a change rate of blood supply intensity and reflects a severity level of a lesion corresponding to the lesion region.

A magnetic resonance imaging method is includes collecting spatially encoded data from a subject using an MRI system, and directly extracting a number of temporal basis functions using at least a first portion of the spatially encoded data. The method further includes, after directly extracting the number of temporal basis functions, iteratively calculating a number of coefficient images using the number of temporal basis functions and at least a second portion of the spatially encoded data. Finally, the method includes generating a 4D image based on the number of temporal basis functions and the number of coefficient images.