The present disclosure provides a method and system for processing multi-modality images. The method may include obtaining multi-modality images; registering the multi-modality images; fusing the multi-modality images; generating a reconstructed image based on a fusion result of the multi-modality images; and determining a removal range with respect to a focus based on the reconstructed image. The multi-modality images may include at least three modalities. The multi-modality images may include a focus.

A diffusion-weighted magnetic resonance imaging (MRI) method acquires MRI scan data from a multi-direction, multi-shot, diffusion-weighted MRI scan, and jointly reconstructs from the MRI scan data 1) magnitude images for multiple diffusion-encoding directions and 2) phase images for multiple shots and multiple diffusion-encoding directions using an iterative reconstruction method. Each iteration of the iterative reconstruction method comprises a gradient calculation, a phase update to update the phase images, and a magnitude update to update the magnitude images. Each iteration minimizes a cost function comprising a locally low-rank (LLR) regularization constraint on the magnitude images from the multiple diffusion-encoding directions.

Systems and methods are contemplated for monitoring and analyzing the glymphatic system and brain to predict, prognose, diagnose, treat, modify or improve treatment, and track progression of neurological diseases. A first and second MRI image are taken of an extracellular space in a region of interest in a patient's brain, with one image taken while the patient is awake and the other image taken while the patient is asleep. The first and second images are compared to detect changes in the extracellular space, and the comparison is used to predict, prognose, diagnose, treat, modify or improve treatment, and track progression of neurological diseases associated with the extracellular space.

A system and method for estimating a physiological parameter from data acquired with a medical imaging system includes acquiring data with the medical imaging system. A physiological parameter is estimated from the acquired data using an iterative estimation in which a model of the medical imaging system is decoupled from a physics-based model of the acquired data.

The present solution can segment tracts by performing two-pass tractography. The system can first perform deterministic tractography and then probabilistic tractography. The system can use the result from the deterministic tractography to update and refine initial identified regions of interest. The refined regions of interest can be used to filter and select streamlines identified through the probabilistic tractography.

The present disclosure discusses systems and methods for identifying biomarkers that can help with the diagnosis, prognosis, and treatment choices of patients with neurodegenerative diseases. Diffusion based magnetic resonance imaging can often fail for patients with a neurodegenerative disease because parameters fractional anisotropy, mean diffusivity, and radial diffusivity are based on simple models that can fail in the presence of neurodegeneration, such as demyelination. The present disclosure discusses systems and methods that enhance dMRI images and enable tractography to be performed on images of a damaged nervous system. The damaged tracks identified by the present system can be used as a biomarker for the assessment of patients. In some implementations, the biomarkers are converted into clinical scales that can be used to compare patients to one another or over time.

Methods and systems for medical diagnosis by machine learning are disclosed. Imaging data obtained from different medical techniques can be used as a training set for a machine learning method, to allow diagnosis of medical conditions in a faster a more efficient manner. A three-dimensional convolutional neural network can be employed to interpret volumetric data available from multiple scans of a patient. The imaging data can be analyzed according to a forced taxonomy and any discrepancy in the labels of the taxonomy during data analysis by machine learning and human experts can be resolved based on the forced taxonomy.

A system and method for optimized diffusion-weighted imaging is provided. In one aspect, the method includes providing a plurality of constraints for imaging a target at a selected diffusion weighting, and applying an optimization framework to generate an optimized diffusion encoding gradient waveform satisfying the plurality of constraints. The method also includes performing, using the MRI system, a pulse sequence comprising the optimized diffusion encoding gradient waveform to generate diffusion-weighted data, and generating at least one image of the target using the diffusion-weighted data.

An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires a magnetic resonance (MR) image in which objects of interest scattered in a brain of a subject are rendered. The processing circuitry acquires connection information indicating connectivity among a plurality of regions of the brain. The processing circuitry performs an analysis with use of the MR image and the connection information, and calculates analytical values related to the objects of interest and allocated to the regions.

A myelin image is generated with a stable contrast regardless of an imaging condition with an MRI apparatus that measures an echo signal generated from a subject by applying a high-frequency magnetic field and a gradient magnetic field to the subject placed in a static magnetic field according to a predetermined imaging sequence. A reconstructed image is obtained from the echo signal. A distribution of a quantitative value of the subject is estimated using a plurality of the reconstructed images, each of which is obtained by a plurality of types of imaging having different imaging conditions of the imaging sequence, and a signal function defining a relationship between the quantitative value of the subject and a signal value of the reconstructed image. An image generation unit generates an arbitrary image from the distribution of the quantitative value.