Eric Peterson¹, Murat Aksoy¹, Julian Maclaren¹, and Roland Bammer^1
1Department of Radiology, Stanford University, Stanford, California, United States

When using parallel imaging accelerated echo planar imaging (EPI), Nyquist ghost correction typically necessitates an additional pre-scan or calibration lines. This work presents a method to perform Nyquist ghost correction in k-space on individual parallel imaging shots using an approached based on the singular value decomposition (SVD) that does not require a complete image or calibration lines. This obviates the standard Nyquist ghost correction pre-scan. A further advantage is that ghost correction can be performed on a shot-by-shot basis, which could be beneficial in the case of calibration drift or prospective motion correction.

Curtis N Wiens¹, Nathan S Artz^1,2, Hyungseok Jang¹, Alan B McMillan¹, and Scott B Reeder^1,3
1Department of Radiology, University of Wisconsin, Madison, Wisconsin, United States, ²Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States, ³Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, United States

3D multi-spectral imaging (3D-MSI) acquires multiple acquisitions at different frequency offsets, in order to excite signal over a wide range of off-resonance induced by metallic implants. Self-calibrated parallel imaging approaches can accelerate 3D-MSI techniques but a substantial amount of time is required to acquire calibration data for each offset. In this work we developed a method for exter-nally calibrated parallel imaging near metallic implants, and demonstrated feasibility using a hip prosthesis phantom and a volunteer with a cobalt/chromium/molybdenum alloy hip head placed posterior to the knee. Comparisons to self-calibrated parallel imaging showed significant reductions in acquisition time, particularly for SR-FPE with ~25% reductions in scan time.

Mehmet Akcakaya¹, Vahid Tarokh², and Reza Nezafat^1
1Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States, ²Harvard University, Cambridge, MA, United States

In this study, we introduce a new sparsity-regularized reconstruction paradigm based on using conventional complex k-space measurements, along with magnitude-only k-space measurements available as side information.

Tae Hyung Kim¹ and Justin P. Haldar^1
1Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States

This work proposes a novel approach to simultaneous multi-slice (SMS) parallel MRI reconstruction, based on the low-rank modeling of local k-space neighborhoods (LORAKS) framework. Compared to existing SMS reconstruction methods, the proposed SMS-LORAKS approach is flexible enough to reconstruct highly-undersampled SMS data in the absence of prior coil information or autocalibration data. SMS-LORAKS can also be applied to single-channel MRI data. Reconstruction results are shown with real retrospectively-undersampled MRI data to demonstrate the potential of the approach.

Aiqi Sun¹, Bo Zhao², Rui Li¹, and Chun Yuan^1,3
1Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, China, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³Department of radiology, University of Washington, WA, United States

Phase-contrast (PC) cine MRI has been demonstrated as a promising technique for studying hemodynamics. However, it remains limited in clinical practice because of its long scan time. To date, a number of fast imaging methods have been applied to PC cine MRI, including kinds of k-t reconstruction algorithms, but most of them could cause temporal blurring and large deviation in flow measurements under higher acceleration rate. In this work, we propose a new complex-difference constrained reconstruction technique based on low-rank and sparsity model, and we further integrate it with ESPIRiT-based parallel imaging reconstruction to achieve even higher acceleration.

Adrian Martin^1,2, Itthi Chatnuntawech¹, Berkin Bilgic³, Kawin Setsompop^3,4, Elfar Adalsteinsson^1,5, and Emanuele Schiavi^6
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ²Applied Mathematics, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain, ³A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General hospital, Charlestown, MA, United States, ⁴Harvard Medical School, Boston, MA, United States, ⁵Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁶Universidad Rey Juan Carlos, Mostoles, Madrid, Spain

Typical clinical MRI routines include multiple imaging of the same region of interest under different contrast settings. In this work we extend the Total Generalized Variation (TGV) operator to jointly reconstruct multiple MRI contrasts from undersampled k-space data using one or more receiver coils. The multi-contrast TGV operator exploits the structural similarities of the multi-contrast images to preserve these details in the reconstruction process. The proposed technique yields to improved reconstruction accuracy when compared to widely used parallel imaging reconstruction methods such as SENSE and Total Variation regularized SENSE.

Jose Raya^1,2 and Florian Knoll^1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, ²Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States

In this work we investigate the value of a non-linear phase correction algorithm for model-based reconstruction of diffusion tensor images measured with non-Cartesian multi-shot diffusion-weighted sequences. The approach is used on data acquired with a diffusion-weighted radial spin echo sequence that acquires 2D navigators after each readout. We acquire data for the brain and the knee with resolutions of 1 and 0,7 mm2 respectively. Data reconstructed without phase correction showed systematic deviation of the MD and FA. In summary the use of non-linear phase correction is essential in iterative reconstruction of diffusion data acquired with multishot sequences.

Andrew T Curtis¹, Berkin Bilgic², Kawin Setsompop², Ravi S Menon³, and Christopher K Anand^1
1Computing and Software, McMaster University, Hamilton, Ontario, Canada, ²Martinos Center for Biomedical Imaging, Charlestown, MA, United States,³Robarts Research Institute, London, Ontario, Canada

The recently introduced WAVE encoding modulates the phase/slice gradients such that the read trajectory corkscrews through k-space, sampling additional spatial frequencies. Here we investigate the combination of WAVE encoding with compressed-sensing (CS) via random phase/slice under-sampling patterns and sparsity enforcing reconstruction, which we term Wave-CS. The open source BART toolkit is leveraged for reconstruction. The additional phase encoding and the aliasing generated in the read direction from WAVE was found to provide significant performance benefits in the CS-framework as compared to regular Cartesian sampling, with improved reconstruction quality and faster iterative convergence for matched acceleration factors.

Tom Hilbert^1,2, Damien Nguyen³, Tobias Kober^1,2, Jean-Philippe Thiran², Gunnar Krueger^1,2, and Oliver Bieri^3
1Siemens ACIT – CHUV Radiology, Siemens Healthcare IM BM PI & Department of Radiology CHUV, Lausanne, Switzerland, ²LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³Radiological Physics, Department of Radiology, University of Basel, Basel, Switzerland

Balanced steady-state free-precession (bSSFP) is prone to local field inhomogeneities, typically appearing as signal voids, i.e. banding-artifacts. A new method, termed true constructive interference in steady state (trueCISS), is proposed based on the acquisition of eight highly undersampled bSSFP k-spaces with different radio-frequency (RF) phase increments. A model-based non-linear inversion is used to fit the bSSFP signal model onto the undersampled data, effectively estimating parameter maps that allow synthesizing the genuine bSSFP signal over the whole image, thus without any noticeable banding artifacts.

Eric Y. Pierre¹, Dan Ma¹, Yong Chen², Chaitra Badve², and Mark A. Griswold^1,2
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, ²Department of Radiology, Case Western Reserve University & University Hospitals, Cleveland, Ohio, United States