According to one embodiment, a medical information processing apparatus has processing circuitry. The processing circuitry acquires medical data on a subject, acquires numerical data obtained by digitizing an acquisition condition of the medical data, and applies a machine learning model to input data including the numerical data and the medical data, thereby generating output data based on the medical data.

Automated processing and radiomic analysis of magnetic resonance imaging (“MRI”), such as multi-contrast MR images, and magnetic resonance fingerprinting (“MRF”) data, such as quantitative parameter maps, are integrated into a single workflow.

Radiomic analysis of multiparametric magnetic resonance imaging (“MRI”) and magnetic resonance fingerprinting (“MRF”) data enhances delineation and mapping of tumor regions. Radiomic features are extracted from MRI and MRF tumor images. Distinct tumor regions, including but not limited to necrotic core, enhancing tumor, and peritumoral white matter, are segmented and mapped. Whole tumor as well as tumor region characteristics are evaluated. Tumors can also be differentiated and classified by pathology, grading, staging, and so on. Tumor infiltration into peritumoral white matter regions can also be mapped for recurrence prediction

The present disclosure provides a system and method for image data acquisition. The method may include obtaining image data of a subject including a first type of tissue and a second type of tissue. The method may include determining, based on the image data of the subject, a target portion including at least a portion of at least one of the first type of tissue or the second type of tissue. The method may include determining, based at least in part on the target portion represented in the image data, a scan mode corresponding to the target portion. The method may include causing an imaging device to acquire, based on the scan mode, image data of the target portion.

The present invention relates to a method of calculating a proton density fat fraction, PDFF, from a water and fat separated magnetic resonance imaging, MRI, based on fat-referenced lipid quantification in a region of interest (ROI) and using determination of a reference tissue. The method comprises the step of determining: F.Math.β.sub.f/R, wherein F is the fat signal in the ROI provided from the MRI, β.sub.f is a function providing a ratio between T1 saturation values of the fat signals in the reference tissue and in the ROI; and R is a representation of the sum of fat and water signals on an intensity scale where the saturation of each of the fat and water signals equals the saturation of fat in the reference tissue.

Systems and methods are provided for susceptibility contrast imaging of nanoparticles at low magnetic fields. A susceptibility-based MRI technique, such as imaging with a balanced steady-state free precession (bSSFP) pulse sequence, may be used for imaging a contrast agent such as biocompatible superparamagnetic nanoparticles at ultra-low fields. The contrast agent and imaging technique may be used to improving the visibility of anatomical structures and detecting diseases, such as cancer, with low-field MRI.

A magnetic resonance imaging apparatus according to an embodiment includes a processing circuitry configured to generate a pulse sequence including a plurality of repetition times (TRs) each of which includes an echo train and a driven equilibrium pulse applied following the echo train, vary a flip angle of the driven equilibrium pulse, obtain magnetic resonance image data collected by executing the pulse sequence, and reconstruct a magnetic resonance image by using the magnetic resonance image data.

A computer-implemented method of reconstructing a motion-compensated magnetic resonance image uses raw k-space data acquired at a first resolution over successive respiratory and/or cardiac cycles of a patient. After binning data based on corresponding motion states derived from these cycles, the resolution of the binned K-space data in each bin is reduced. This is done by selecting a sub-group of binned k-space data. Bin images are reconstructed from the reduced-resolution data, and histogram-equalised versions of the reconstructed reduced-resolution bin image generated for each bin. Motion fields are estimated and interpolated to the first resolution such that motion data can be incorporated into a final reconstruction of a motion compensated 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 T1-weighted turbo-spin-echo magnetic resonance imaging system configured to capture data associated with a subject's heart during a time period and produce MR images has a dark-blood preparation module, a data capture module, and an image reconstruction module. The dark-blood preparation module performs dark-blood preparation through double inversion during some, but not all of the heartbeats within the time period. The data capture module configured performs data readouts to capture imaging data of an imaging slice during every heartbeat in which dark-blood preparation is performed. The data capture module also performs a steady state maintenance step during every heartbeat in which dark-blood preparation is not performed in order to maintain maximum T1-weighting. The image reconstruction module configured to reconstruct a T1-weighted image based on the imaging data.