Provided are high-speed magnetic resonance imaging methods and apparatuses that enable simultaneously obtaining magnetic resonance images with different resolutions. The present embodiments may produce magnetic resonance images with different resolutions more quickly by decreasing time taken to complete scan operations that are performed for producing the magnetic resonance images.

The disclosed embodiments provide a method for acquiring MR data at resolutions down to tens of microns for application in in vivo diagnosis and monitoring of pathology for which changes in fine tissue textures can be used as markers of disease onset and progression. Bone diseases, tumors, neurologic diseases, and diseases involving fibrotic growth and/or destruction are all target pathologies. Further the technique can be used in any biologic or physical system for which very high-resolution characterization of fine scale morphology is needed. The method provides rapid acquisition of signal at selected values in k-space, with multiple successive acquisitions at individual k-values taken on a time scale on the order of microseconds, within a defined tissue volume, and subsequent combination of the multiple measurements in such a way as to maximize SNR. The reduced acquisition volume, and acquisition of only signal values at select places in k-space, along selected directions, enables much higher in vivo resolution than is obtainable with current MRI techniques.

First k-space data are received for a MRI examination of a subject in a first field of view (FOV), and second k-space data are received for a second field of view that is adjacent to or overlaps the first field of view. Alternatively, second k-space data, comprising phase and/or slice oversampling k-space data of the first field of view are retrieved from a non-transitory data storage medium. The first k-space data are reconstructed by using at least a portion of the second k-space data as phase and/or slice oversampling to generate a first extended image of a first extended field of view that encompasses the first field of view and extends into the second field of view. The first extended image is cropped to the first field of view to generate an image of the first field of view for the first MRI examination.

The disclosed embodiments provide a method for acquiring MR data at resolutions down to tens of microns for application in in vivo diagnosis and monitoring of pathology for which changes in fine tissue textures can be used as markers of disease onset and progression. Bone diseases, tumors, neurologic diseases, and diseases involving fibrotic growth and/or destruction are all target pathologies. Further the technique can be used in any biologic or physical system for which very high-resolution characterization of fine scale morphology is needed. The method provides rapid acquisition of signal at selected values in k-space, with multiple successive acquisitions at individual k-values taken on a time scale on the order of microseconds, within a defined tissue volume, and subsequent combination of the multiple measurements in such a way as to maximize SNR. The reduced acquisition volume, and acquisition of only signal values at select places in k-space, along selected directions, enables much higher in vivo resolution than is obtainable with current MRI techniques.

According to one embodiment, a magnetic resonance imaging apparatus includes sequence control circuitry and processing circuitry. The sequence control circuitry performs under-sampled data acquisition whose sample points are located at an equal interval in k-space and acquires k-space frames. The processing circuitry generates a plurality of k-space frames related to a plurality of time resolutions based on the k-space frames. In each of the plurality of k-space frames, the sample points are located at an equal interval, and the interval differs for each of the plurality of k-space frames. The processing circuitry generates a time-series image based on the plurality of k-space frames.

A method and a system automatically perform an image reconstruction of a biological object. The method includes acquiring at different time points t_i signal data for imaging the biological object and clustering a set of data in connection with the acquired signal data. The clustering includes constructing a matrix C, wherein an element C.sub.i,j of the matrix C is the value n_j of one of the data of the dataset acquired at the time point t_i, and then performing a similarity clustering based on the matrix C. At least one of the clusters is selected and determining for each of the time points t_i that are part of the cluster all acquired signal data that have been acquired within a predefined temporal threshold with respect to the considered time point t_i. The image reconstruction of the biological object is performed with the previously determined acquired signal data.

An method according to an embodiment divides k-space data into a k-space central segment and a k-space peripheral segment by segment. The method acquires the k-space central segment in a first time interval and acquires the k-space peripheral segment in a second time interval different from the first time interval. The method reconstructs an MR (Magnetic Resonance) image from k-space data obtained by combining data on the acquired k-space central segment and data on the acquired k-space peripheral segment. Furthermore, the first time interval includes a plurality of cardiac cycles. The k-space central segment is repeatedly acquired over the cardiac cycles. As a central segment of k-space data used to reconstruct the MR image, data in a cardiac cycle less affected by an arrhythmia among the cardiac cycles is selected.

The present disclosure is directed to a computational procedure for accelerated, calibrationless magnetic resonance image (CI-MRI) reconstruction that is fast, memory efficient, and scales to high dimensional imaging. The computational procedure, High-dimensional fast ConvolUtional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points.

Among the various aspects of the present disclosure is the provision of methods and systems for real-time 3D MRI that combines dynamic keyhole data sharing with super-resolution imaging methods to improve real-time 3D MR images in the presence of motion.

A variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution, providing added flexibility for real-time applications where optimal temporal resolution may not be known in advance. The methods provide for a computationally efficient sampling methods where a first step includes producing a uniformly random sampling pattern using a golden ratio on a grid, and the second step is applying a nonlinear stretching operation to create a variable density sampling pattern. Diagnostic quality images may be recovered at different temporal resolutions.