In one embodiment, an MRI apparatus includes: a scanner that includes a static magnetic field magnet, a gradient coil, and a WB coil; and processing circuitry. The processing circuitry is configured to: cause the scanner to image, under a first imaging method, a tissue including a perfusion route of body fluid that removes waste products of the object the body fluid including neurofluid; generate an anatomical image of the tissue from first data acquired by imaging under the first imaging method; cause the scanner to image perfusion behavior of the body fluid in real time under a second imaging method using non-contrast perfusion imaging; generate a perfusion image indicating the perfusion behavior of the body fluid from second data acquired by imaging under the second imaging method; and generate a fused image by combining the anatomical image and the perfusion image.

A computerized system and method of modeling myocardial tissue perfusion can include acquiring a plurality of original frames of magnetic resonance imaging (MRI) data representing images of a heart of a subject and developing a manually segmented set of ground truth frames from the original frames. Applying training augmentation techniques to a training set of the originals frame of MRI data can prepare the data for training at least one convolutional neural network (CNN). The CNN can segment the training set of frames according to the ground truth frames. Applying the respective input test frames to a trained CNN can allow for segmenting an endocardium layer and an epicardium layer within the respective images of the input test frames. The segmented images can be used in calculating myocardial blood flow into the myocardium from segmented images of the input test frames.

A quality control phantom and an evaluation method for magnetic resonance arterial spin labeling perfusion imaging includes: a phantom main body; a container, a circulating liquid being provided in the container; a tube, comprising a first tube and a second tube, one end of the first tube being in communication with the container, the other end being in communication with a liquid inlet of the phantom main body, one end of the second tube being in communication with the container, the other end being in communication with a liquid outlet of the phantom main body, and the first tube, the phantom main body, the second tube and the container jointly forming a closed loop; a pump, provided on the first tube and used to drive the circulating liquid to circulate along the closed loop to generate a perfusion signal in the phantom main body.

Described herein are methods for cardiovascular imaging for diagnosing and/or detecting various cardiovascular diseases. Various embodiments of the invention provide using magnetic resonance imaging of the cardiovascular system of a subject at rest or a normocapnic condition, as well as at a stressed or hypercapnic condition, in a repeated manner enhancing the statistical power, such that fast, motion-corrected, free-breathing, whole-heart imaging of the cardiovascular system is utilized to identify impaired cardiovascular function in a manner with improved specificity and accuracy.

The present invention discloses an analysis method for dynamic contrast-enhanced magnetic resonance image. Firstly, the time-series signal of vascular contrast agent concentration, AIF, of biological individual is obtained from DCE-MRI time-series data. Secondly, perform the nonlinear least sum of square fitting by using the full Shutter-Speed model (SSM.sub.full) and the simplified vascular Shutter-Speed model (SSM.sub.vas) on the DCE-MRI time-series signal of each pixel, and the fitting results of DCE-MRI time-series signal are obtained. Thirdly, the corrected Akaike Information Criterion (AIC.sub.C) score is used to comparing the DCE-MRI time-series signal fitting results to select the optimal model. If the optimal model is SSM.sub.full, distribution maps of five physiological parameters. K.sup.trans, p.sub.b p.sub.o, k.sub.bo, and k.sub.io, are produced after fitting; if the optimal model is SSM.sub.vas, distribution maps of three physiological parameters, K.sup.trans, p.sub.b, and k.sub.bo, are produced after fitting. Finally, perform error analysis on the k.sub.io and k.sub.bo, resulting the final distribution maps of k.sub.io and k.sub.bo along with distribution maps of parameters K.sup.trans, p.sub.b, p.sub.o. This method can improve the estimation accuracy of K.sup.trans, p.sub.b, p.sub.o, k.sub.bo and k.sub.io.

Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

The invention provides for a method of operating a magnetic resonance imaging system for imaging a subject. The method comprises acquiring (700) tagged magnetic resonance data (642) and a first portion (644) of fingerprinting magnetic resonance data by controlling the magnetic resonance imaging system with tagging pulse sequence commands (100). The tagging pulse sequence commands comprise a tagging inversion pulse portion (102) for spin labeling a tagging location within the subject. The tagging pulse sequence commands comprise a background suppression portion (104). The background suppression portion comprises MRF pulse sequence commands for acquiring fingerprinting magnetic resonance data according to a magnetic resonance fingerprinting protocol. The tagging pulse sequence commands comprise an image acquisition portion (106). The method comprises acquiring (702) control magnetic resonance data (646) and a second portion (648) of the fingerprinting magnetic resonance data by controlling the magnetic resonance imaging system with control pulse sequence commands. The control pulse sequence commands comprise a control inversion pulse portion (202). The control pulse sequence commands comprise the background suppression portion (104′). The control pulse sequence commands comprise the image acquisition portion (106). The method comprises reconstructing (704) tagged magnitude images (650) using the tagged magnetic resonance data. The method comprises reconstructing (706) a control magnitude images (652) using the control magnetic resonance data. The method comprises constructing (708) an ASL image by subtracting the control magnitude images and the tagged magnitude images from each other. The method comprises reconstructing (710) a series of magnetic resonance fingerprinting images (656) using the first portion of the fingerprinting magnetic resonance data and/or the second portion of the fingerprinting magnetic resonance data. The method comprises generating (712) at least one magnetic resonance parametric map (658) by comparing the series of magnetic resonance fingerprinting images with a magnetic resonance fingerprinting dictionary.

The invention provides for a method of training a neural network (322) configured for providing a further processing location (326). The method comprises providing (200) a labeled medical image (100), wherein the labeled medical image comprises multiple labels each indicating a truth processing location (102, 104, 106). The method further comprises inputting (202) the labeled medical image into the neural network to obtain one trial processing location. The one trial processing location comprises a most likely trial processing location (108). The method further comprises determine (204) the closest truth processing location (106) for the most likely trial processing location. The method further comprises calculating (206) an error vector (110) using the closest truth processing location and the most likely trial processing location. The method further comprises training (208) the neural network using the error vector.

A method of processing magnetic resonance (MR) data of a sample under investigation, includes the steps of providing the MR data being collected with an MRI scanner apparatus, and subjecting the MR data to a multi-parameter nonlinear regression procedure being based on a non-linear MR model and employing a set of input parameters, wherein the regression procedure results in creating a parameter map of model parameters of the sample, wherein the input parameters (initial values and possibly boundaries) of the regression procedure are estimated by a machine learning based estimation procedure applied to the MR data. The machine learning based estimation procedure preferably includes at least one of at least one neural network and a support vector machine. Furthermore, an MRI scanner apparatus is described.

The present disclosure includes methods, apparatuses, and systems for three-dimensional phenotyping and physiologic characterization of brain lesions and tissue encompassing one or more enlarged boundaries surrounding the brain lesion to study the metabolic and physiologic profiles from tissue within and around lesions and their impacts on lesion shape and surface texture. The non-invasive biomarker blood-oxygen their impacts on lesion shape and surface texture. The non-invasive biomarker blood-oxygen-level-dependent (BOLD) slope was used to metabolically characterize lesions. Metabolically active lesions with more intact tissue and myelin architecture have more symmetrical shapes and more complex surface textures compared to metabolically inactive lesions with less intact tissue and myelin architecture. The association of lesions' shapes and surface features with their metabolic signatures aid in the translation of MRI data to clinical management by providing information related to metabolic activity, lesion age, and risk for disease reactivation and self-repair.