Error decomposition for parallel imaging reconstruction using modulation-domain representation of undersampled data.

This paper presents a quantitative approach to evaluating and optimizing parallel imaging reconstruction for a clinical requirement. By introducing a "modulation domain representation" for undersampled data, the presented approach decomposes parallel imaging reconstruction error into multiple error components that can be grouped into three categories: image fidelity error, residue aliasing artifacts, and amplified noise. It is experimentally found that these error components have different image-space patterns that compromise imaging quality in different fashions. An error function may be defined as the weighted summation of these error components. By choosing a set of weighting coefficients that can quantify desirable image quality, parallel imaging may be optimized for a clinical requirement. It is found that error decomposition model may improve clinical utility of parallel imaging, providing an application-oriented approach to clinical parallel imaging.

[1]  Leslie Ying,et al.  Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) , 2007, Magnetic resonance in medicine.

[2]  J. Polimeni,et al.  96‐Channel receive‐only head coil for 3 Tesla: Design optimization and evaluation , 2009, Magnetic resonance in medicine.

[3]  F. Huang,et al.  High‐pass GRAPPA: An image support reduction technique for improved partially parallel imaging , 2008, Magnetic resonance in medicine.

[4]  M. Law.  MR Spectroscopy of Brain Tumors , 2004, Topics in magnetic resonance imaging : TMRI.

[5]  Yu Li,et al.  Regionally Optimized Reconstruction for Partially Parallel Imaging in MRI Applications , 2009, IEEE Transactions on Medical Imaging.

[6]  M. McDougall,et al.  64‐channel array coil for single echo acquisition magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[7]  Feng Huang,et al.  k‐t GRAPPA: A k‐space implementation for dynamic MRI with high reduction factor , 2005, Magnetic resonance in medicine.

[8]  Martin Blaimer,et al.  General formulation for quantitative G‐factor calculation in GRAPPA reconstructions , 2009, Magnetic resonance in medicine.

[9]  Xiaoping P. Hu,et al.  Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction , 2008, Magnetic resonance in medicine.

[10]  J. Bissler,et al.  Tuberous sclerosis complex: renal imaging findings. , 2002, Radiology.

[11]  C. Chefd'Hotel,et al.  High spatial and temporal resolution cardiac cine MRI from retrospective reconstruction of data acquired in real time using motion correction and resorting , 2009, Magnetic resonance in medicine.

[12]  I. Smith,et al.  Magnetic resonance spectroscopy in medicine: clinical impact , 2002 .

[13]  L. Wald,et al.  Accelerated proton echo planar spectroscopic imaging (PEPSI) using GRAPPA with a 32‐channel phased‐array coil , 2008, Magnetic resonance in medicine.

[14]  M. Nittka,et al.  Partially parallel imaging with localized sensitivities (PILS) , 2000, Magnetic resonance in medicine.

[15]  Yi Wang,et al.  Description of parallel imaging in MRI using multiple coils , 2000, Magnetic resonance in medicine.

[16]  A. Samsonov.  On optimality of parallel MRI reconstruction in k‐space , 2008, Magnetic resonance in medicine.

[17]  N J Pelc,et al.  Unaliasing by Fourier‐encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI , 1999, Magnetic resonance in medicine.

[18]  René M. Botnar,et al.  Characterizing radial undersampling artifacts for cardiac applications , 2006, Magnetic resonance in medicine.

[19]  J. Hennig,et al.  Fast functional brain imaging using constrained reconstruction based on regularization using arbitrary projections , 2009, Magnetic resonance in medicine.

[20]  Thoralf Niendorf,et al.  Highly parallel volumetric imaging with a 32‐element RF coil array , 2004, Magnetic resonance in medicine.

[21]  P. Kellman,et al.  Virtual coil concept for improved parallel MRI employing conjugate symmetric signals , 2009, Magnetic resonance in medicine.

[22]  D. Sodickson,et al.  A generalized approach to parallel magnetic resonance imaging. , 2001, Medical physics.

[23]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[24]  L. Wald,et al.  32‐channel 3 Tesla receive‐only phased‐array head coil with soccer‐ball element geometry , 2006, Magnetic resonance in medicine.

[25]  Feng Huang,et al.  Reconstruction in image space using basis functions for partially parallel imaging. , 2008, Magnetic resonance imaging.

[26]  E. de Vita,et al.  Pediatric and adolescent lymphoma: comparison of whole-body STIR half-Fourier RARE MR imaging with an enhanced PET/CT reference for initial staging. , 2010, Radiology.

[27]  Yu-Chung N. Cheng,et al.  Susceptibility weighted imaging (SWI) , 2004, Zeitschrift fur medizinische Physik.

[28]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[29]  Matti S Hämäläinen,et al.  Dynamic magnetic resonance inverse imaging of human brain function , 2006, Magnetic resonance in medicine.

[30]  F. Lin,et al.  Parallel MRI reconstruction using variance partitioning regularization , 2007, Magnetic resonance in medicine.

[31]  Christina Triantafyllou,et al.  A 128‐channel receive‐only cardiac coil for highly accelerated cardiac MRI at 3 Tesla , 2008, Magnetic resonance in medicine.

[32]  Walter F Block,et al.  Self‐calibrated GRAPPA method for 2D and 3D radial data , 2007, Magnetic resonance in medicine.

[33]  José Millet-Roig,et al.  Noquist: Reduced field‐of‐view imaging by direct Fourier inversion , 2004, Magnetic resonance in medicine.

[34]  P. Roemer,et al.  The NMR phased array , 1990, Magnetic resonance in medicine.

[35]  The use of T2*-weighted multi-echo GRE imaging as a novel method to diagnose hepatocellular carcinoma compared with gadolinium-enhanced MRI: a feasibility study. , 2010, Magnetic resonance imaging.

[36]  Chris R. Johnson,et al.  POCSENSE: POCS‐based reconstruction for sensitivity encoded magnetic resonance imaging , 2004, Magnetic resonance in medicine.