scispace - formally typeset
Search or ask a question
Author

Daniel K. Sodickson

Bio: Daniel K. Sodickson is an academic researcher from New York University. The author has contributed to research in topics: Iterative reconstruction & Electromagnetic coil. The author has an hindex of 61, co-authored 258 publications receiving 15371 citations. Previous affiliations of Daniel K. Sodickson include Harvard University & Beth Israel Deaconess Medical Center.


Papers
More filters
PatentDOI
TL;DR: SiMultaneous Acquisition of Spatial Harmonics (SMASH) as mentioned in this paper is a partially parallel imaging strategy, which is readily integrated with many existing fast imaging sequences, yielding multiplicative time savings without a significant sacrifice in spatial resolution or signal-to-noise ratio.
Abstract: A magnetic resonance (MR) imaging apparatus and technique exploits spatial information inherent in a surface coil array to increase MR image acquisition speed, resolution and/or field of view. Partial signals are acquired simultaneously in the component coils of the array and formed into two or more signals corresponding to orthogonal spatial representations. In a Fourier embodiment, lines of the k-space matrix required for image production are formed using a set of separate, preferably linear combinations of the component coil signals to substitute for spatial modulations normally produced by phase encoding gradients. The signal combining may proceed in a parallel or flow-through fashion, or as post-processing, which in either case reduces the need for time-consuming gradient switching and expensive fast magnet arrangements. In the post-processing approach, stored signals are combined after the fact to yield the full data matrix. In the flow-through approach, a plug-in unit consisting of a coil array with an on board processor outputs two or more sets of combined spatial signals for each spin conditioning cycle, each directly corresponding to a distinct line in k-space. This partially parallel imaging strategy, dubbed SiMultaneous Acquisition of Spatial Harmonics (SMASH), is readily integrated with many existing fast imaging sequences, yielding multiplicative time savings without a significant sacrifice in spatial resolution or signal-to-noise ratio. An experimental system achieved two-fold improvement in image acquisition time with a prototype three-coil array, and larger factors are achievable with ther coil arrangements.

2,256 citations

Journal ArticleDOI
TL;DR: In this paper, a variational network approach is proposed to reconstruct the clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data.
Abstract: PURPOSE To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. THEORY AND METHODS Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. RESULTS The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. CONCLUSION Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

1,111 citations

Journal ArticleDOI
TL;DR: The low‐rank plus sparse (L+S) matrix decomposition model is applied to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest.
Abstract: Purpose To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. Theory and Methods The L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear norm is used to enforce low rank in L and the l1 norm is used to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dynamic MRI experiments with true acceleration, including cardiac perfusion, cardiac cine, time-resolved angiography, and abdominal and breast perfusion using Cartesian and radial sampling. Results The L+S model increased compressibility of dynamic MRI data and thus enabled high-acceleration factors. The inherent background separation improved background suppression performance compared to conventional data subtraction, which is sensitive to motion. Conclusion The high acceleration and background separation enabled by L+S promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling. Magn Reson Med 73:1125–1136, 2015. © 2014 Wiley Periodicals, Inc.

619 citations

Journal ArticleDOI
TL;DR: To develop a fast and flexible free‐breathing dynamic volumetric MRI technique, iterative Golden‐angle RAdial Sparse Parallel MRI (iGRASP), that combines compressed sensing, parallel imaging, and golden‐angle radial sampling.
Abstract: Purpose To develop a fast and flexible free-breathing dynamic volumetric MRI technique, iterative Golden-angle RAdial Sparse Parallel MRI (iGRASP), that combines compressed sensing, parallel imaging, and golden-angle radial sampling. Methods Radial k-space data are acquired continuously using the golden-angle scheme and sorted into time series by grouping an arbitrary number of consecutive spokes into temporal frames. An iterative reconstruction procedure is then performed on the undersampled time series where joint multicoil sparsity is enforced by applying a total-variation constraint along the temporal dimension. Required coil-sensitivity profiles are obtained from the time-averaged data. Results iGRASP achieved higher acceleration capability than either parallel imaging or coil-by-coil compressed sensing alone. It enabled dynamic volumetric imaging with high spatial and temporal resolution for various clinical applications, including free-breathing dynamic contrast-enhanced imaging in the abdomen of both adult and pediatric patients, and in the breast and neck of adult patients. Conclusion The high performance and flexibility provided by iGRASP can improve clinical studies that require robustness to motion and simultaneous high spatial and temporal resolution. Magn Reson Med 72:707–717, 2014. © 2013 Wiley Periodicals, Inc.

567 citations

Journal ArticleDOI
TL;DR: Comp compressed sensing and parallel imaging are combined by merging the k‐t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging and a new theoretical framework is presented for understanding the combination of k-t SParSE with SENSE based on distributed compressed sensing theory.
Abstract: First-pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k-t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k-t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k-t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8-fold acceleration in vivo with whole-heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to Generalized autocalibrating partially parallel acquisitions (GRAPPA) with 2-fold acceleration.

547 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
Abstract: The sparsity which is implicit in MR images is exploited to significantly undersample k -space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressedsensing, images with a sparse representation can be recovered from randomly undersampled k -space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the 1 norm of a transformed image, subject to data

6,653 citations

PatentDOI
TL;DR: The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k‐space sampling patterns and special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density.
Abstract: The invention relates to a method of parallel imaging for obtaining images by means of magnetic resonance (MR). The method includes the simultaneous measurement of sets of MR singals by an array of receiver coils, and the reconstruction of individual receiver coil images from the sets of MR signals. In order to reduce the acquisition time, the distance between adjacent phase encoding lines in k-space is increased, compared to standard Fourier imaging, by a non-integer factor smaller than the number of receiver coils. This undersampling gives rise to aliasing artifacts in the individual receiver coil images. An unaliased final image with the same field of view as in standard Fourier imaging is formed from a combination of the individual receiver coil images whereby account is taken of the mutually different spatial sensitivities of the receiver coils at the positions of voxels which in the receiver coil images become superimposed by aliasing. This requires the solution of a linear equation by means of the generalised inverse of a sensitivity matrix. The reduction of the number of phase encoding lines by a non-integer factor compared to standard Fourier imaging provides that different numbers of voxels become superimposed (by aliasing) in different regions of the receiver coil images. This effect can be exploited to shift residual aliasing artifacts outside the area of interest.

6,562 citations

Journal ArticleDOI
TL;DR: This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD‐AUTO‐SMASH reconstruction techniques and provides unaliased images from each component coil prior to image combination.
Abstract: In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.

5,022 citations

Journal ArticleDOI
12 Jun 2008-Nature
TL;DR: An overview of the current state of fMRI is given, and the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation are presented.
Abstract: Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and analysis methods promise to push forward fMRI from mere cartography to the true study of brain organization. However, fundamental questions concerning the interpretation of fMRI data abound, as the conclusions drawn often ignore the actual limitations of the methodology. Here I give an overview of the current state of fMRI, and draw on neuroimaging and physiological data to present the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation.

3,075 citations