Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: The present disclosure relates to a method and apparatus correcting a metal artifact in magnetic resonance imaging (MRI) data. The method and apparatus acquire plural slices along a slice direction of a scanned region associated with a body part, estimate a spatial extent of a signal dispersion of the acquired plural slices along the slice direction, and combine the signal of the acquired plural slices along the slice direction based on the estimated spatial extent of the signal dispersion to generate a reconstructed image of the scanned region.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes a processing circuit. The processing circuit acquires a plurality of echo signals not to be encoded corresponding to a plurality of respective shots about acquisition of a plurality of echo signals having been encoded by magnetic resonance imaging for a subject, compares the echo signals not to be encoded with each other about the shots, and identifies a shot to be removed about generation of a magnetic resonance image about the subject out of the shots based on a comparison result of the echo signals not to be encoded.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: A method and apparatuses are provided to perform chemical species separation in magnetic resonance (MR) imaging (MRI). At least three MR images corresponding respectively to different echo times are obtained and represent signals from multiple chemical species including a first species and a second species in a tissue. A plurality of dual-echo pairs is selected from the at least three MR images. For each pair, a set of dual-echo separated images including a B0 field map, a first image for the first species, and a second image for the second species is estimated. An initial set of combined images including at least one of: an initial combined B0 field map, first, and second image is generated by combining at least one of: two or more of the B0 field maps, two or more of the first images, and two or more of the second images.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: A method and apparatuses are provided to perform chemical species separation in magnetic resonance (MR) imaging (MRI). At least three MR images corresponding respectively to different echo times are obtained and represent signals from multiple chemical species including a first species and a second species in a tissue. A plurality of dual-echo pairs is selected from the at least three MR images. For each pair, a set of dual-echo separated images including a B0 field map, a first image for the first species, and a second image for the second species is estimated. An initial set of combined images including at least one of: an initial combined B0 field map, first, and second image is generated by combining at least one of: two or more of the B0 field maps, two or more of the first images, and two or more of the second images.

Abstract: An object-based approach is used to initialize the magnetic field inhomogeneity estimation for chemical species separation, such as water-fat separation, and other imaging applications. For example, a susceptibility distribution in the subject being imaged is estimated from images reconstructed from single-echo or multi-echo k-space data and used to initialize the magnetic field inhomogeneity estimation. This approach can be applied to any complex-based chemical shift encoded chemical species separation technique and to other imaging applications, such as susceptibility-weighted imaging and quantitative susceptibility mapping. The field map can also be used to correct for image distortions and to generate magnetic field shimming values.

Abstract: An object-based approach is used to initialize the magnetic field inhomogeneity estimation for chemical species separation, such as water-fat separation, and other imaging applications. For example, a susceptibility distribution in the subject being imaged is estimated from images reconstructed from single-echo or multi-echo k-space data and used to initialize the magnetic field inhomogeneity estimation. This approach can be applied to any complex-based chemical shift encoded chemical species separation technique and to other imaging applications, such as susceptibility-weighted imaging and quantitative susceptibility mapping. The field map can also be used to correct for image distortions and to generate magnetic field shimming values.