In one embodiment, a magnetic resonance imaging apparatus includes: a scanner that includes a static magnetic field magnet configured to generate a static magnetic field, a gradient coil configured to generate a gradient magnetic field, and a WB (Whole Body) coil configured to apply an RF pulse to an object; and processing circuitry. The processing circuitry is configured to: set (i) a pulse sequence in which a sequence element is repeated, the sequence element including at least an inversion pulse and (ii) a data acquisition sequence executed after a delay time from the inversion pulse; and cause the scanner to execute the pulse sequence by using virtual gating.

A method and apparatus for generating a T1 or T2 map for a three-dimensional (3D) image volume of a subject. The method includes acquiring first, second, and third 3D images of the image volume of the subject. Signal evolutions of voxels through the first to third 3D images by comparing voxel intensity levels of corresponding voxel locations in the first, second, and third 3D images. A simulation dictionary representing the signal evolutions for a number of different tissue parameter combinations is obtained. The T1 or T2 map is generated by comparing the determined signal evolutions to entries in the dictionary and by finding, for each of the determined signal evolutions, the entry in the dictionary that best matches the determined signal evolution.

A device for locating target points on a magnetic resonance image of the brain of a subject includes a trained neural network configured to receive as input a 3D MR image of the brain of a subject, and to output the location, on the image, of at least one determined brain target point. The neural network includes a plurality of processing stages. Each processing stage processes an image at a respective resolution, and the processing stage of lowest resolution outputs an estimate of the location of each target point. Each other processing stage is configured to receive, from a lower resolution processing stage, an estimate of the locations of the target points, crop the input image to a smaller region surrounding each estimated target point, determine an updated estimate of the location of each target point, and provide the updated estimation to the processing stage of the next higher resolution.

A computer-implemented method for generating an artifact corrected reconstructed contrast image from magnetic resonance imaging (MRI) data is provided. The method includes inputting into a trained deep neural network both a synthesized contrast image derived from multi-delay multi-echo (MDME) scan data or the MDME scan data acquired during a first scan of an object of interest utilizing a MDME sequence and a composite image, wherein the composite image is derived from both the MDME scan data and contrast scan data acquired during a second scan of the object of interest utilizing a contrast MRI sequence. The method also includes utilizing the trained deep neural network to generate the artifact corrected reconstructed contrast image based on both the synthesized contrast image or the MDME scan data and the composite image. The method further includes outputting from the trained deep neural network the artifact corrected reconstructed contrast image.

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

A diagnostic system to analyze imaging data includes a memory configured to store hybrid imaging data of a tissue sample. The system also includes a processor operatively coupled to the memory and configured to generate a four quadrant plot based on the hybrid imaging data. Each point in the four quadrant plot corresponds to an image voxel of the tissue sample. The processor is also configured to determine one or more angle values and one or more distance values for image voxels in the four quadrant plot. The processor is further configured to identify one or more characteristics of the tissue sample based at least in part on the one or more angle values and the one or more distance values. The processor is further configured to perform a matrix analysis of the data, which can be used to identify the one or more characteristics of the tissue sample.

A delta-relaxation magnetic resonance imaging (DREMR) system is provided. The system includes a main field magnet and field shifting coils. A main magnetic field with a strength B0 can be generated using the main filed magnet and the strength B0 of the main magnetic field can be varied through the use of the field-shifting coils. The DREMR system can be used to perform signal acquisition based on a pulse sequence for acquiring at least one of T2*-weighted signals imaging; MR spectroscopy signals; saturation imaging signals and MR signals for fingerprinting. The MR signal acquisition can be augmented by varying the strength B0 of the main magnetic field for at least a portion of the pulse sequence used to acquire the MR signal.

A magnetic resonance imaging apparatus according to an embodiment includes processing circuitry. The processing circuitry sets a pulse sequence to collect plural echo signals by application of a refocusing pulse more than once after application of an excitation pulse once, and collects data on plural slices that are parallel to each other by executing the pulse sequence more than once. The processing circuitry sets the pulse sequence such that a slice thickness for the refocusing pulse becomes larger than a slice thickness for the excitation pulse, and collects the data on the plural slices by executing the pulse sequence without consecutively collecting data on adjacent ones of the plural slices.

According to one embodiment, MRI apparatus includes processing circuitry and an imaging device. The processing circuitry is configured to acquire at least one of body size information relating to a size of an object and breath-hold information relating to a breath-holdable time of the object. The processing circuitry is further configured to determine an imaging condition to be performed on the object based on the at least one of the body size information and the breath-hold information. The imaging device performs imaging of the object in accordance with the determined imaging condition.

Systems and methods for late gadolinium enhancement (“LGE”) tissue viability imaging in a dynamic (e.g., temporally-resolved) manner using magnetic resonance imaging (“MRI”) are provided. Dynamic LGE images can be generated throughout the entire cardiac cycle at high temporal resolution in a single breath-hold. Dynamic, semi-quantitative longitudinal relaxation maps are acquired and retrospective synthetization of dynamic LGE images is implemented using those semi-quantitative longitudinal relaxation maps.