Abstract: A magnetic resonance imaging system and method are provided for improved determination of noise bias effects in calculating fitted parameters for quantitative MRI procedures. The system and method includes selecting a range for the SNR and fitted parameter values, and for each of a plurality of base pairs of these values and for a plurality of b values, adding a random noise term to the real and imaginary components of a plurality of corresponding signal terms, fitting magnitudes of the resulting “noisy” signals to determine a “noisy” fitted parameter value, and compare the “noisy” and base fitted parameter values to determine a noise-based error for each pair of base values. The noise-based errors can be used to generate an error map, modify imaging parameters to reduce such errors, or correct fitted parameters directly.

Abstract: In a magnetic resonance (MR) apparatus and a method for operating such an apparatus, a T1 parameter map is generated with fat fraction correction, by using a model in which the fat fraction of acquired MR data is used as a known parameter. The T1 values from the acquired MR data are fat fraction-corrected in such a manner, so as to generate fat fraction-corrected entries for the T1 parameter map according to the model.

Abstract: In some aspects, the disclosed technology relates to magnetic field monitoring of spiral echo train imaging. In one embodiment, a method for spiral echo train imaging of an area of interest of a subject includes measuring k-space values and field dynamics corresponding to each echo of a spiral echo pulse train, using a dynamic field camera and a magnetic resonance imaging (MRI) system. The dynamic field camera is configured to measure characteristics of fields generated by the MRI system; the characteristics include at least one imperfection associated with the MRI system. The spiral echo pulse train corresponds to a spiral trajectory scan from the MRI system that obtains magnetic resonance imaging data using a pulse sequence which applies spiral gradients in-plane with through-plane phase encoding.

Abstract: A method and apparatus for automated prostate tumor detection and classification in multi-parametric magnetic resonance imaging (MRI) is disclosed. A multi-parametric MRI image set of a patient, including a plurality of different types of MRI images, is received. Simultaneous detection and classification of prostate tumors in the multi-parametric MRI image set of the patient are performed using a trained multi-channel image-to-image convolutional encoder-decoder that inputs multiple MRI images of the multi-parametric MRI image set of the patient and includes a plurality of output channels corresponding to a plurality of different tumor classes. For each output channel, the trained image-to image convolutional encoder-decoder generates a respective response map that provides detected locations of prostate tumors of the corresponding tumor class in the multi-parametric MRI image set of the patient.

Abstract: A framework for facilitating visualization, including localizing at least one anatomical structure of interest in image data. The structure of interest is then highlighted by reformatting the image data by mapping landmarks associated with the structure of interest to corresponding points along a contour of a geometric shape and warping the image data based on the mapped landmarks. The resulting reformatted image data is rendered for display to a user.

Abstract: In a computer and a magnetic resonance method and apparatus for automatic characterization (classification) of liver tissue in a region of interest of a liver, at least one value tuple of the region of interest of the liver is acquired, the value tuple including at least one T1 value determined from magnetic resonance images of the region of interest, or a reciprocal value thereof, and a T2 or T2* value or a reciprocal value thereof. The value tuple is transferred into a multidimensional parameter space and the characterization of the liver tissue is then performed on the basis of the position of the value tuple in the parameter space.

Abstract: Disclosed herein is a framework for identifying signal components in image data. In accordance with one aspect, the framework receives multiple measured signal values corresponding to respective quantified signal components in image data. The framework determines at least one first measure of fit map of a signal model based on the measured signal values. The measured signal values may be swapped to generate swapped signal values. At least one second measure of fit map of the signal model may be determined based on the swapped signal values. The multiple signal components may then be identified by comparing the first and second measure of fit maps.

Abstract: A method is disclosed for creating a motion correction for PET data acquired by a PET system from a volume segment of an examination object. The method includes acquisition of MR data within the volume segment by the magnetic resonance system; and determination of a motion model of a motion within the volume segment as a function of the MR data. The motion model, as a function of a respective motion state of the motion, provides a correction specification for PET data which is acquired during this motion state. During acquisition of the MR data, specific MR data is acquired in the center of the k-space or of a straight-line segment which passes through the center of the k-space. The MR data determined is converted by a mathematical function into one value, as a function of which the respective motion state is determined.

Abstract: In a method to correct noise effects in magnetic resonance (MR) images, which is executed in a processor (computer), the processor executes a fitting algorithm in order to calculate initial values for each of selected variables in signal model that models noise effects in a modeled, noise-containing MR image. The processor then iteratively executes the same or a different fitting algorithm, in order to generate final values for each of the selected variables. The processor is provided with an actual, acquired MR image that contains noise, and the processor uses the final values of the selected variables to calculate synthetic signal intensities in the MR image, thereby producing a synthetic MR image with no noise bias effects of errors. This synthetic image is made available in electronic form at an output of the processor, as a data file.

Abstract: In some aspects, the disclosed technology relates to magnetic field monitoring of spiral echo train imaging. In one embodiment, a method for spiral echo train imaging of an area of interest of a subject includes measuring k-space values and field dynamics corresponding to each echo of a spiral echo pulse train, using a dynamic field camera and a magnetic resonance imaging (MRI) system. The dynamic field camera is configured to measure characteristics of fields generated by the MRI system; the characteristics include at least one imperfection associated with the MRI system. The spiral echo pulse train corresponds to a spiral trajectory scan from the MRI system that obtains magnetic resonance imaging data using a pulse sequence which applies spiral gradients in-plane with through-plane phase encoding.

Abstract: In a magnetic resonance (MR) apparatus and a method for operating such an apparatus, a T1 parameter map is generated with fat fraction correction, by using a model in which the fat fraction of acquired MR data is used as a known parameter. The T1 values from the acquired MR data are fat fraction-corrected in such a manner, so as to generate fat fraction-corrected entries for the T1 parameter map according to the model.

Abstract: In a magnetic resonance method and apparatus for determination of a measurement variable that is relevant to a function of an organ of a patient, a first longitudinal relaxation rate R11 is determined before a contrast medium is administered to the patient. A second longitudinal relaxation rate R12 is determined after a contrast medium is administered to the patient. A property of the contrast medium in the organ is determined based on R11 and R12. The measurement variable is determined based on the property of the contrast medium in the organ.

Abstract: A method and apparatus for automated prostate tumor detection and classification in multi-parametric magnetic resonance imaging (MRI) is disclosed. A multi-parametric MRI image set of a patient, including a plurality of different types of MRI images, is received. Simultaneous detection and classification of prostate tumors in the multi-parametric MRI image set of the patient are performed using a trained multi-channel image-to-image convolutional encoder-decoder that inputs multiple MRI images of the multi-parametric MRI image set of the patient and includes a plurality of output channels corresponding to a plurality of different tumor classes. For each output channel, the trained image-to image convolutional encoder-decoder generates a respective response map that provides detected locations of prostate tumors of the corresponding tumor class in the multi-parametric MRI image set of the patient.

Abstract: Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.

Abstract: In a computer and a magnetic resonance method and apparatus for automatic characterization (classification) of liver tissue in a region of interest of a liver, at least one value tuple of the region of interest of the liver is acquired, the value tuple including at least one T1 value determined from magnetic resonance images of the region of interest, or a reciprocal value thereof, and a T2 or T2* value or a reciprocal value thereof. The value tuple is transferred into a multidimensional parameter space and the characterization of the liver tissue is then performed on the basis of the position of the value tuple in the parameter space.

Abstract: In a magnetic resonance method and apparatus for determination of a measurement variable that is relevant to a function of an organ of a patient, a first longitudinal relaxation rate R11 is determined before a contrast medium is administered to the patient. A second longitudinal relaxation rate R12 is determined after a contrast medium is administered to the patient. A property of the contrast medium in the organ is determined based on R11 and R12. The measurement variable is determined based on the property of the contrast medium in the organ.

Abstract: In a method for displaying quantitative magnetic resonance image data, and a processor, and a magnetic resonance (MR) apparatus that implement such a method, first quantitative MR image data of an examination object are provided to the processor, the first quantitative MR image having been obtained using an MR scanner with a first basic magnetic field strength. The first quantitative magnetic resonance image data are converted in the processor from the first basic magnetic field strength to a second basic magnetic field strength, thereby generating second quantitative MR image data, which are then displayed.

Abstract: Some aspects of the present disclosure relate to ultrashort-echo-time (UTE) imaging. In one embodiment, a method includes acquiring UTE imaging data associated with an area of interest of a subject. The acquiring comprises applying an imaging pulse sequence with a three-dimensional (3D) spiral acquisition and a nonselective excitation pulse. The method also includes reconstructing at least one image of the area of interest from the acquired UTE imaging data.

Abstract: In a method and magnetic resonance (MR) apparatus for performing an adjustment of the MR system, an examination object under is divided into at least one excitation volume. First adjustment parameters for the at least one excitation volume of the object, and second adjustment parameters for the at least one excitation volume of the object, which differ from the first adjustment parameters are determined. First MR signals are acquired from the at least one excitation volume using the first adjustment parameters. Second MR signals are acquired from an excitation volume using the second adjustment parameters. A first MR image of the at least one excitation volume is reconstructed using the first MR signal. A second MR image of the at least one excitation volume is reconstructed using the second MR signal.

Abstract: Disclosed herein is a framework for facilitating image processing. In accordance with one aspect, the framework receives first image data acquired by a first modality and one or more articulated models. The one or more articulated models may include at least one section image acquired by the first modality and aligned with a local image acquired by a second modality. The framework may align an anatomical region of the first image data with the section image and non-rigidly register a first region of interest extracted from the section image with a second region of interest extracted from the aligned anatomical region. To generate a segmentation mask of the anatomical region, the registered first region of interest may be inversely mapped to a subject space of the first image data.