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193
Multicontrast large deformation diffeomorphic metric mapping for diffusion tensor imaging
 NEUROIMAGE 47 (2009) 618–627
, 2009
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Realistic analytical phantoms for parallel Magnetic Resonance Imaging
 IEEE Trans. Med. Imaging
"... Abstract—The quantitative validation of reconstruction algorithms requires reliable data. Rasterized simulations are popular but they are tainted by an aliasing component that impacts the assessment of the performance of reconstruction. We introduce analytical simulation tools that are suited to par ..."
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Cited by 16 (2 self)
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Abstract—The quantitative validation of reconstruction algorithms requires reliable data. Rasterized simulations are popular but they are tainted by an aliasing component that impacts the assessment of the performance of reconstruction. We introduce analytical simulation tools that are suited to parallel magnetic resonance imaging and allow one to build realistic phantoms. The proposed phantoms are composed of ellipses and regions with piecewisepolynomial boundaries, including spline contours, Bézier contours, and polygons. In addition, they take the channel sensitivity into account, for which we investigate two possible models. Our analytical formulations provide welldefined data in both the spatial and kspace domains. Our main contribution is the closedform determination of the Fourier transforms that are involved. Experiments validate the proposed implementation. In a typical parallel magnetic resonance imaging reconstruction experiment, we quantify the bias in the overly optimistic results obtained with rasterized simulations—the inversecrime situation. We provide a package that implements the different simulations and provide tools to guide the design of realistic phantoms. Index Terms—Fourier analytical simulation, inverse crime, magnetic resonance imaging (MRI), Shepp–Logan. I.
Iterative image reconstruction in MRI with separate magnitude and phase regularization
 In Proc. IEEE Intl. Symp. Biomed. Imag
, 2004
"... Iterative methods for image reconstruction in MRI are useful in several applications, including reconstruction from nonCartesian kspace samples, compensation for magnetic field inhomogeneities, and imaging with multiple receive coils. Existing iterative MR image reconstruction methods are either u ..."
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Cited by 15 (6 self)
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Iterative methods for image reconstruction in MRI are useful in several applications, including reconstruction from nonCartesian kspace samples, compensation for magnetic field inhomogeneities, and imaging with multiple receive coils. Existing iterative MR image reconstruction methods are either unregularized, and therefore sensitive to noise, or have used regularization methods that smooth the complex valued image. These existing methods regularize the real and imaginary components of the image equally. In many MRI applications, including T ∗ 2weighted imaging as used in fMRI BOLD imaging, one expects most of the signal information of interest to be contained in the magnitude of the voxel value, whereas the phase values are expected to vary smoothly spatially. This paper proposes separate regularization of the magnitude and phase components, preserving the spatial resolution of the magnitude component while strongly regularizing the phase component. This leads to a nonconvex regularized leastsquares cost function. We describe a new iterative algorithm that monotonically decreases this cost function. The resulting images have reduced noise relative to conventional regularization methods. 1.
Improved kt BLAST and kt SENSE using FOCUSS
 Phys Med Biol
, 2007
"... Abstract. The dynamic MR imaging of timevarying objects, such as beating hearts or brain hemodynamics, requires a significant reduction of the data acquisition time without sacrificing spatial resolution. The classical approaches for this goal include parallel imaging, temporal filtering, and their ..."
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Cited by 14 (2 self)
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Abstract. The dynamic MR imaging of timevarying objects, such as beating hearts or brain hemodynamics, requires a significant reduction of the data acquisition time without sacrificing spatial resolution. The classical approaches for this goal include parallel imaging, temporal filtering, and their combinations. Recently, modelbased reconstruction methods called kt BLAST and kt SENSE have been proposed which largely overcome the drawbacks of the conventional dynamic imaging methods without a priori knowledge of the spectral support. Another recent approach called kt SPARSE also does not require exact knowledge of the spectral support. However, unlike the kt BLAST/SENSE, kt SPARSE employs the socalled compressed sensing theory rather than using training. The main contribution of this paper is a new theory and algorithm that unifies the abovementioned approaches while overcoming their drawbacks. Specifically, we show that the celebrated kt BLAST/SENSE are the special cases of our algorithm, which is asymptotically optimal from the compressed sensing theory perspective. Experimental results show that the new algorithm can successfully reconstruct a high resolution cardiac sequence and functional MRI data even from severely limited kt samples, without incurring aliasing artifacts often observed in conventional methods.
MotionAdaptive SpatioTemporal Regularization (MASTeR) for accelerated dynamic
, 2012
"... Accelerated MRI techniques reduce signal acquisition time by undersampling kspace. A fundamental problem in accelerated MRI is the recovery of quality images from undersampled kspace data. Current stateoftheart recovery algorithms exploit the spatial and temporal structures in underlying image ..."
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Cited by 13 (2 self)
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Accelerated MRI techniques reduce signal acquisition time by undersampling kspace. A fundamental problem in accelerated MRI is the recovery of quality images from undersampled kspace data. Current stateoftheart recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this paper, a new recovery algorithm, motionadaptive spatiotemporal regularization (MASTeR), is presented. MASTeR, which uses compressed sensing principles to recover dynamic MR images from highly undersampled kspace data, takes advantage of spatial and temporal structured sparsity in MR images. In contrast to existing algorithms, MASTeR models temporal sparsity using motionadaptive linear transformations between neighboring images. The efficiency of MASTeR is demonstrated with experiments on cardiac MRI for a range of reduction factors. Results are also compared with kt FOCUSS with motion estimation and compensation—another recently proposed recovery algorithm for dynamic MRI.
A unified variational approach to denoising and bias correction
 in MR,” Inf. Proc. Med. Imag
, 2003
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SNRoptimality of sumofsquares reconstruction for phasedarray magnetic resonance imaging
 J. Magnetic Resonance
, 2003
"... Abstract We consider the commonly used ''SumofSquares'' (SoS) reconstruction method for phasedarray magnetic resonance imaging with unknown coil sensitivities. We show that the signaltonoise ratio (SNR) in the image produced by SoS is asymptotically (as the input SNR ! 1) e ..."
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Cited by 10 (1 self)
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Abstract We consider the commonly used ''SumofSquares'' (SoS) reconstruction method for phasedarray magnetic resonance imaging with unknown coil sensitivities. We show that the signaltonoise ratio (SNR) in the image produced by SoS is asymptotically (as the input SNR ! 1) equal to that of maximumratio combining, which is the best unbiased reconstruction method when the coil sensitivities are known. Finally, we discuss the implications of this result.
How GPUs can improve the quality of magnetic resonance imaging
 In The First Workshop on General Purpose Processing on Graphics Processing Units
, 2007
"... Abstract — In magnetic resonance imaging (MRI), nonCartesian scan trajectories are advantageous in a wide variety of emerging applications. Advanced reconstruction algorithms that operate directly on nonCartesian scan data using optimality criteria such as leastsquares (LS) can produce significan ..."
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Cited by 9 (3 self)
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Abstract — In magnetic resonance imaging (MRI), nonCartesian scan trajectories are advantageous in a wide variety of emerging applications. Advanced reconstruction algorithms that operate directly on nonCartesian scan data using optimality criteria such as leastsquares (LS) can produce significantly better images than conventional algorithms that apply a fast Fourier transform (FFT) after interpolating the scan data onto a Cartesian grid. However, advanced LS reconstructions require significantly more computation than conventional reconstructions based on the FFT. For example, one LS algorithm requires nearly six hours to reconstruct a single threedimensional image on a modern CPU. Our work demonstrates that this advanced reconstruction can be performed quickly and efficiently on a modern GPU, with the reconstruction of a 64 3 3D image requiring just three minutes, an acceptable latency for key applications. This paper describes how the reconstruction algorithm leverages the resources of the GeForce 8800 GTX (G80) to achieve over 150 GFLOPS in performance. We find that the combination of tiling the data and storing the data in the G80’s constant memory dramatically reduces the algorithm’s required bandwidth to offchip memory. The G80’s special functional units provide substantial acceleration for the trigonometric computations in the algorithm’s inner loops. Finally, experimentdriven code transformations increase the reconstruction’s performance by as much as 60 % to 80%. I.
Sparsityenforced sliceselective MRI RF excitation pulse design
 IEEE Trans. Med. Imag
, 2008
"... Abstract—We introduce a novel algorithm for the design of fast sliceselective spatiallytailored magnetic resonance imaging (MRI) excitation pulses. This method, based on sparse approximation theory, uses a secondorder cone optimization to place and modulate a small number of sliceselective sinc ..."
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Cited by 9 (1 self)
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Abstract—We introduce a novel algorithm for the design of fast sliceselective spatiallytailored magnetic resonance imaging (MRI) excitation pulses. This method, based on sparse approximation theory, uses a secondorder cone optimization to place and modulate a small number of sliceselective sinclike radiofrequency (RF) pulse segments (“spokes”) in excitationspace, enforcing sparsity on the number of spokes allowed while simultaneously encouraging those that remain to be placed and modulated in a way that best forms a userdefined inplane target magnetization. Pulses are designed to mitigate 1 inhomogeneity in a water phantom at 7 T and to produce highlystructured excitations in an oil phantom on an eightchannel parallel excitation system at 3 T. In each experiment, pulses generated by the sparsityenforced method outperform those created via conventional Fourierbased techniques, e.g., when attempting to produce a uniform magnetization in the presence of severe 1 inhomogeneity, a 5.7ms 15spoke pulse generated by the sparsityenforced method produces an excitation with 1.28 times lower root mean square error than conventionallydesigned 15spoke pulses. To achieve this same level of uniformity, the conventional methods need to use 29spoke pulses that are 7.8 ms long. Index Terms — 1 inhomogeneity mitigation, high field strength, magnetic resonance imaging (MRI) radiofrequency (RF) pulse sequence design, parallel transmission, sparse approximation, threedimensional (3D) RF excitation. I.
Punishment i n o r g a n i z a t i o n s : A rev iew, p r o p o s i t i o n s and r e s e a r c h s u g g e s t i o n s
 Academy of Management Review
, 1980
"... Dissociation, as the editor of this important volume reminds us, "challenges many comfortable assumptions." From a theoretical vantage, it demands great conceptual clarity and a knowledge ofmany areas of import in psychology; from a clinical vantage, it has brought about one ofthe most co ..."
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Cited by 6 (0 self)
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Dissociation, as the editor of this important volume reminds us, "challenges many comfortable assumptions." From a theoretical vantage, it demands great conceptual clarity and a knowledge ofmany areas of import in psychology; from a clinical vantage, it has brought about one ofthe most contentious debates in recent history, whether or notimportant memories can be "forgotten " only to appear as habits, behaviors, and dreams, or even later as fullfledged remembrances. But above all, dissociative phenomena challenge the cherished notion that our conscious self is an allknowing, integrated entity. This volume is the result of a 1991 conference at the Center for Advanced Study in the Behavioral Sciences, sponsored by the MacArthur Foundation. I was fortunate to be present at this truly multidisciplinary meeting.