Qiuting Wen¹, Chandana Kodiweera², and Yu-Chien Wu^1
1Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States, ²Darmonth College, Hanover, NH, United States

High-resolution DWI often relies on multi-shot acquisitions, which suffer from long acquisition time and motion-related phase issues. However, highly correlated information exists in DWIs as they are weighting the same structure. To take advantages of this feature, rotating short-axis DWI was proposed to accelerate DWI acquisition by acquiring only one rotating blade per diffusion direction. In the previous reconstruction, high-resolution DWI was achieved by integrating the full set of DWIs. In this work, we propose a “windowed” composite reconstruction where only a subset of DWIs was selected to reconstruct each high-resolution DWI.  Improved image quality was appreciated in both simulation and human data.

Anne Slawig¹, Tobias Wech¹, Valentin Ratz¹, Johannes Tran-Gia^1,2, Henning Neubauer¹, Thorsten Bley¹, and Herbert Köstler^1
1Departement for Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany, ²Department of Nuclear Medicine, Würzburg, Germany

Banding artefacts in images acquired by bSSFP are a big challenge in fast MRI as they can considerably reduce image quality and deteriorate the diagnostic value. As the steady state tolerates small shifts in frequency, it is possible to acquire a frequency-modulated bSSFP. Unfortunately a simple gridding reconstruction of such a measurement suffers from signal loss. Our study proposes a multi-frequency reconstruction and demonstrates its capability of reconstructing banding-free 3D images while retaining the high signal levels of standard bSSFP.

Adrienne E Campbell-Washburn¹, Robert J Lederman¹, Anthony Z Faranesh¹, and Michael S Hansen^1
1Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States

Balanced SSFP Golden Angle radial imaging uses a rapidly varying gradient scheme and thus is susceptible to image distortion caused by gradient delays and eddy currents. We propose that storing a history of the gradient waveforms in each axis can enable us to better predict our true k-space coordinates during sampling. We use the gradient system impulse response function to predict k-space coordinates and demonstrate reduced image distortion (shading and streaking) in a phantom and in vivo when utilizing the gradient waveform history. This method will be useful for dynamic and real-time imaging with Golden Angle balanced SSFP imaging schemes.

Cihat Eldeniz¹, Yasheng Chen¹, and Hongyu An^1
1Washington University in St. Louis, St. Louis, MO, United States

Breath-hold or navigator-based MR acquisition has been widely used to remove the effect of motion from the images. However, breath holding can be challenging for patients. On the other hand, navigator-based methods suffer from lengthened acquisition time and the disturbance of magnetization history. In this respect, we will developed a self-gated free-breathing MR imaging method to obtain 4D MRI (3D spatial+1D respiratory phases) for deformable motion derivation. 

Ali Ersoz¹ and L Tugan Muftuler^2,3
1Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States, ²Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, ³Center for Imaging Research, Medical College of Wisconsin, Milwaukee, WI, United States

Although radial MRI has favorable properties, a major disadvantage is the image blurring caused by off-resonance effects. This is less tolerable than image distortions typically seen in Cartesian scans.  Linogram MRI, which carries advantages of radial MRI, has an off-resonance behavior similar to Cartesian sampling. Thus, linogram combines the beneficial properties of two sampling techniques and avoids the disadvantages. In this study, we propose a self-calibrated off-resonance correction method for linogram sampling, which doesn’t require a field map. Both experimental phantom and human studies demonstrated that the proposed method significantly improved the image quality and provided sharper images. 

Matthew J. Muckley^1,2, Douglas C. Noll¹, and Jeffrey A. Fessler^1,2
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

Majorize-minimize algorithms are a powerful tool for solving image reconstruction problems with sparsity-promoting regularization; however, when non-Cartesian trajectories are used it becomes challenging to design a suitable majorizer for these methods due to the high density of samples near the center of k-space. We derive a new circulant majorizer that is related to the density compensation function of the k-space trajectory. We then use the frequency localization properties of wavelets and the circulant majorizer to design an algorithm that converges faster than conventional FISTA for reconstructing images from undersampled, non-Cartesian k-space data.

Dinghui Wang¹, Zhiqiang Li¹, and James G. Pipe^1
1Imaging Research, Barrow Neurological Institute, Phoenix, AZ, United States

T2*-weighted (T2*w) gradient-echo (GRE) sequences are commonly used in neuroimaging to depict hemorrhage, calcification and iron deposition. Compared to three-dimensional (3D) GRE sequences, 2D GRE sequences are more sensitive to the deleterious T2* effects at air-tissue interfaces. However, 3D Cartesian high-resolution T2*w GRE sequences usually require long scan times, because of the preferred long TRs and TEs. In this study, we implement a fast, scan efficient 3D T2*w imaging method with a distributed spiral in-out trajectory.

Craig H. Meyer¹, Samuel W Fielden¹, Josef Pfeuffer², John P. Mugler III³, Alto Stemmer², and Berthold Kiefer^2
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, ²Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ³Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, United States

The purpose of this work was to apply a spiral k-space characterization method to a variety of scanner models to assess the consistency of characterization parameters and the ability of the method to yield high-quality spiral images on the different scanners.  Characterization of gradient-system performance on 11 MR scanners yielded only minor variation in parameter values among scanners, and in all cases model-based correction of spiral trajectories yielded very similar image results to reconstruction based on measured trajectories.  These results suggest that model-based reconstruction may represent a viable approach for obtaining high-quality spiral images without the need for characterization of specific spiral-trajectory implementations.  

Sangwon Oh¹, Gigi Galiana¹, Dana Peters¹, and R. Todd Constable^1
1Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States

MRI with non-linear spatial encoding magnetic (SEM) fields was originally introduced to realize faster gradient switching time without peripheral nerve stimulation (PNS) ¹. Since then various MRI encoding method such as O-Space, 4D-RIO, and FRONSAC have been introduced for more efficient accelerated spatial encoding ^{2, 3, 4}.  However, these methods are mostly focused on 2-dimensional MRI and there is uncertainty in its applicability to 3-dimensional MRI. We apply O-Space to 3D MRI and find practical challenges and improvement over 3D radial sequence.

Alex Cerjanic^1,2, Joseph L Holtrop^1,2, Giang Chau Ngo¹, Brent Leback³, Galen Arnold⁴, Mark Van Moer⁴, Genevieve LaBelle^2,5, Jeffrey A Fessler⁶, and Bradley P Sutton^1,2
1Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³PGI Compilers & tools; an NVIDIA brand, Portland, OR, United States, ⁴National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁶Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

PowerGrid is an accelerated, open source, freely available toolkit for iterative reconstruction supporting non-Cartesian trajectories. Using high level compiler directives, GPU accelerated Fourier transform operators were implemented in a high level syntax designed to correlate with the popular Image Reconstruction Toolbox (IRT). A speed-up of up to 8.96x over the unaccelerated IRT reconstruction was obtained using an NVIDIA Tesla K40c accelerator.