Imaging failure of a positioning image due to the difference in the position or the size of a subject placed in the examination space is prevented, and accordingly, the extension of the examination time is prevented. A pre-scan for appropriately setting the imaging position for positioning imaging is automatically performed prior to the positioning imaging and the main imaging of an MRI apparatus, and a region where an examination part of a subject is present (the extent of the examination part) is detected using the measurement data. By using the detected extent of the examination part, it is possible to subsequently determine the imaging position or calculate the scan parameters used for imaging.

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition scheme for imaging a subject using a medical imaging apparatus; acquiring a medical image of the subject according to the accelerated image acquisition scheme using the medical imaging apparatus; applying a deep network model to the medical image to improve the quality of the medical image; and outputting an improved quality image of the subject, for analysis by a physician.

Systems and methods include conversion of a first frame of k-space data acquired using a first initial readout polarity to first hybrid (k.sub.x, y)-space data, conversion of a second frame of k-space data acquired using a second initial readout polarity to second hybrid (k.sub.x, y)-space data, determination of a relationship between phase difference and y-position based on phase differences between a plurality of pixels located at k.sub.x=a of first hybrid (k.sub.x, y)-space data and a plurality of pixels at k.sub.x=b of second hybrid (k.sub.x, y)-space data, where a and b are constants, modification of the second hybrid (k.sub.x, y)-space data based on the relationship, conversion of the modified second hybrid (k.sub.x, y)-space data to a modified second frame of k-space data, generation of two single-polarity readout k-space frames based on the first frame of k-space data and the modified second frame of k-space data, and correction of a third frame of EPI image data based on the two single-readout polarity k-space frames.

A system and method of mapping and correcting the inhomogeneity of a magnetic field within an object using an Magnetic Resonance Imaging (MRI) system where there is a single dominant resonance. The method includes acquiring at least three MRI images, each at different echo times (TE). At least two ΔTE images (ΔTE.sub.i=1 . . . N) are generated based on the at least three MRI images, wherein the subscripts I=1 N refer to images with sequentially increasing ΔTE times. Aliasing in the ΔTE.sub.1 image is permitted. The ΔTE times of ΔTE.sub.1 and ΔTE.sub.2 are set such that the alias points at which wrapping occurs in ΔTE.sub.1 does not overlap with the alias points of ΔTE.sub.2. Each ΔTE image is unwrapped. A final B0 map is set to the unwrapped ΔTE.sub.N image.

A method for reordering a segmented MRI pulse sequence includes synchronizing to a physiologic signal of a heart or vessel, to a respiratory signal, or to an external trigger source, and acquiring a plurality of data collecting segments as a contiguous block in a phase encoding direction such that lines of the plurality of data collecting segments are alternately acquired in a forward direction and a reverse direction for each consecutive data collecting segment.

A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

In a system and method for performing MR image reconstruction based on acquired MR measurement data of an organ structure of a patient, the MR measurement data of the organ structure is received, a-priori information about the organ structure from which the MR measurement data have been acquired is received, MR image reconstruction is performed based on the MR measurement data and taking into account the a-priori information, and the reconstructed MR image data is provided.

In a method for avoiding artifacts during acquisition of MR data, a first measurement data set (MDS) of a target region of the examination object and at least one second MDS of the target region are acquired, and a combined MDS is created based on the acquired data sets. The first MDS does not sample a first region of k-space to be sampled according to Nyquist and corresponding to a first partial factor, and a second MDS does not sample a second region of k-space to be sampled according to Nyquist and corresponding to a second partial factor. The first and second regions of the k-space are different from each other. Advantageously, a k-space region acquired in none of the acquisitions made can be minimized by the inventive variation in the respective sampling pattern of the acquired MDS, so artifacts are reduced/avoided in MR images reconstructed from the MDS.

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition scheme for imaging a subject using a medical imaging apparatus; acquiring a medical image of the subject according to the accelerated image acquisition scheme using the medical imaging apparatus; applying a deep network model to the medical image to improve the quality of the medical image; and outputting an improved quality image of the subject, for analysis by a physician.

Images are reconstructed from undersampled k-space data using a residual machine learning algorithm (e.g., a ResNet architecture) to estimate missing k-space lines from acquired k-space data with improved noise resilience. Using a residual machine learning algorithm provides for combining the advantages of both linear and nonlinear k-space reconstructions. The linear residual connection can implement a convolution that estimates most of the energy in k-space, and the multi-layer machine learning algorithm can be implemented with nonlinear activation functions to estimate imperfections, such as noise amplification due to coil geometry, that arise from the linear component.