摘要:
In a method for automatically detecting contrast enhancement at predetermined phases as a contrast agent bolus perfuses a target tissue volume in a patient, a continuous acquisition MRI imaging system is provided for obtaining dynamic contrast enhanced MRI data for use in creating images. The contrast agent bolus is injected into a blood stream of the patient which passes through the target volume. With the imaging system, a center of a k-space of the target volume is repeatedly sampled to obtain k-space data. A bolus time curve signal is automatically extracted from the k-space data which indicates a course of bolus contrast enhancement which is used to automatically pick time frames at the predetermined phases of the perfusion which are then used to identify corresponding key images to be obtained at the time frames.
摘要:
In a method and magnetic resonance (MR) apparatus to determine sample points of a random undersampling scheme of k-space to acquire reduced MR data with multiple coils, a set of sample points of the random undersampling scheme to acquire the reduced MR data is determined, and an indicator of a signal noise in reconstructed MR data is calculated. Furthermore, an additional sample point, which is not included in the set of sample points is determined, and a change of the indicator that results by an addition of the additional sample point to the set of sample points is calculated. The additional sample point is selectively added to the set of sample points dependent on the calculated change.
摘要:
In a method to select an undersampling scheme of k-space and an associated set of reconstruction kernels to acquire reduced magnetic resonance (MR) data sets with multiple coils, a calibration data set is acquired for each of the respective coils, a noise covariance is determined from autocorrelations and correlations of the noise of the various coils. At least one set of reconstruction kernels is calculated for each of the multiple undersampling schemes from the calibration data sets of the various coils. For each set of reconstruction kernels, a characteristic value is calculated from the noise covariance and the respective reconstruction kernels of the coils, with the characteristic value being proportional to a spatial mean value of a signal noise of an MR image. A selected undersampling scheme and a selected set of reconstruction kernels are selected based on the calculated characteristic values.
摘要:
A computer-implemented method for quantifying fat and iron in anatomical tissue includes acquiring a plurality of multi-echo signal datasets representative of the anatomical tissue using a magnetic resonance (MR) pulse sequence. A plurality of multi-echo signal datasets are selected from the plurality of multi-echo signal datasets and used to determine a first water magnitude value and a first fat magnitude value. In response to determining that the multi-echo signal datasets include at least three multi-echo datasets, a first stage analysis is performed. This first stage analysis comprises selecting a first effective transverse relaxation rate value. Next, first algorithm inputs comprising the first water magnitude value, the first fat magnitude value, and the first effective transverse relation rate value are created. Then, a non-linear fitting algorithm is performed based on the first algorithm inputs to calculate a second water magnitude value, a second fat magnitude value, and a second effective transverse relaxation rate value. A first proton density fat fraction value is then determined based on the second water magnitude value and the second fat magnitude value.
摘要:
Embodiments relate to evaluating properties of tissues with magnetic resonance imaging (MRI). A MR image is used to measure a characteristic that influences a particular chemical property of a tissue. In an exemplary embodiment, tissue transverse relaxation values or relaxation rates, which can readily be measured from MR images, are used to evaluate iron deposition in tissue. Iron deposition influences the tissue transverse relaxation values (T2 or T2*) or relaxation rates (R2=1/T2 or R2*=1/T2*). A clinically relevant R2CR* map is calculated based on the known values of the effective R2eff*, the water R2w*, and the fat R2f* by incorporating the most relevant value for each individual image element of a plurality of image elements of an MR image of the tissue. The clinically relevant R2CR* map provides an accurate evaluation of iron deposition in any region of the tissue with the use of one map.
摘要:
Embodiments relate to evaluating properties of tissues with magnetic resonance imaging (MRI). A MR image is used to measure a characteristic that influences a particular chemical property of a tissue. In an exemplary embodiment, tissue transverse relaxation values or relaxation rates, which can readily be measured from MR images, are used to evaluate iron deposition in tissue. Iron deposition influences the tissue transverse relaxation values (T2 or T2*) or relaxation rates (R2=1/T2 or R2*=1/T2*). A clinically relevant R2CR* map is calculated based on the known values of the effective R2eff*, the water R2w*, and the fat R2f* by incorporating the most relevant value for each individual image element of a plurality of image elements of an MR image of the tissue. The clinically relevant R2CR* map provides an accurate evaluation of iron deposition in any region of the tissue with the use of one map.
摘要:
A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating P=V∥V∥H, calculating a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) … ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .
摘要:
In a method to select an undersampling scheme of k-space and an associated set of reconstruction kernels to acquire reduced magnetic resonance (MR) data sets with multiple coils, a calibration data set is acquired for each of the respective coils, a noise covariance is determined from autocorrelations and correlations of the noise of the various coils. At least one set of reconstruction kernels is calculated for each of the multiple undersampling schemes from the calibration data sets of the various coils. For each set of reconstruction kernels, a characteristic value is calculated from the noise covariance and the respective reconstruction kernels of the coils, with the characteristic value being proportional to a spatial mean value of a signal noise of an MR image. A selected undersampling scheme and a selected set of reconstruction kernels are selected based on the calculated characteristic values.
摘要:
In a method and magnetic resonance (MR) apparatus to determine sample points of a random undersampling scheme of k-space to acquire reduced MR data with multiple coils, a set of sample points of the random undersampling scheme to acquire the reduced MR data is determined, and an indicator of a signal noise in reconstructed MR data is calculated. Furthermore, an additional sample point, which is not included in the set of sample points is determined, and a change of the indicator that results by an addition of the additional sample point to the set of sample points is calculated. The additional sample point is selectively added to the set of sample points dependent on the calculated change.
摘要:
A computer-implemented method for quantifying fat and iron in anatomical tissue includes acquiring a plurality of multi-echo signal datasets representative of the anatomical tissue using a magnetic resonance (MR) pulse sequence. A plurality of multi-echo signal datasets are selected from the plurality of multi-echo signal datasets and used to determine a first water magnitude value and a first fat magnitude value. In response to determining that the multi-echo signal datasets include at least three multi-echo datasets, a first stage analysis is performed. This first stage analysis comprises selecting a first effective transverse relaxation rate value. Next, first algorithm inputs comprising the first water magnitude value, the first fat magnitude value, and the first effective transverse relation rate value are created. Then, a non-linear fitting algorithm is performed based on the first algorithm inputs to calculate a second water magnitude value, a second fat magnitude value, and a second effective transverse relaxation rate value. A first proton density fat fraction value is then determined based on the second water magnitude value and the second fat magnitude value.