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Fa-Hsuan Lin¹, ², Thomas Witzel¹, ³, Graham Wiggins¹, Lawrence Wald¹, John Belliveau¹

¹Massachusetts General Hospital, Charlestown, Massachusetts, USA; ²National Taiwan University, Taipei, Taiwan; ³Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA

We propose a K-space magnetic resonance Inverse Imaging (K-InI) approach to use a highly parallel radio-frequency coil array to achieve high temporal resolution MRI. K-InI solves an under-determined linear system using regularization in parallel MRI reconstruction. K-InI uses auto-calibration technique to estimate the reconstruction coefficients and it can provide coil-by-coil reconstruction to allow for more flexible combination of different channels in the coil array. We demonstrate K-InI using a 3D visual fMRI experiment to achieve 100 ms temporal resolution.

Thimo Grotz¹, Benjamin Zahneisen¹, Arsène Ella¹, Jürgen Hennig¹

¹University Hospital Freiburg, Freiburg, Germany

MREG, also called inverse imaging, was introduced as a new approach to measure activation related MR-signal changes in the brain, with very high temporal resolu-tion. We present a constraint reconstruction based on regularization with arbitrary projections to localize the activation. The results demonstrate that COBRA with very low number of projections can be used to acquire activation maps with reasonable spatial resolution at very high temporal resolution. Signal time courses show excel-lent contrast-to-noise for the observed BOLD response.

Ian C. Atkinson¹, Keith R. Thulborn¹, Aiming Lu¹, Justin Haldar², X J. Zhou¹, Ted Claiborne¹, Zhi-Pei Liang²

¹University of Illinois-Chicago, Chicago, Illinois, USA; ²University of Illinois-Urbana-Champaign, Urbana, Illinois, USA

The sensitivity of ultra-high field MRI enables quantitative imaging of non-proton species such as 23-sodium and 17-oxygen.  Constrained k-space reconstruction techniques can be used to improve the spatial resolution of the acquired data without compromising the ability to quantify the final image.  This approach of enhanced image reconstruction combined with the improved sensitivity of high field broadens the human applications of metabolic MR imaging by minimizing otherwise long acquisition times to achieve adequate spatial resolution for the anatomy and SNR performance for quantification.

Mariya Doneva¹, Holger Eggers², Jürgen Rahmer², Peter Börnert², Alfred Mertins¹

¹University of Luebeck, Luebeck, Germany; ²Philips Research Europe, Hamburg, Germany

We illustrate the feasibility of Compressed Sensing for 3D dynamic imaging using highly undersampled 3D radial acquisition with golden ratio profile ordering. Image reconstruction from a low number of measurements could be very useful for dynamic 3D imaging, to reduce the often long acquisition times and thus improve temporal resolution in 3D MRI. Using CS, the aliasing artifacts were significantly reduced and a high frame rate was achieved, allowing dynamic imaging with good temporal resolution. The described approach could be particularly useful for dynamic studies of joint motion.

Ali Bilgin¹, ², Ted P. Trouard¹, Maria I. Altbach¹, Natarajan Raghunand¹

¹University of Arizona, Tucson, Arizona , USA

Compressed Sensing (CS) theory illustrates that a small number of linear measurements can be sufficient to reconstruct sparse or compressible signals. we introduce a CS theory based method for reconstruction of time-varying radial k-space data by exploiting the spatio-temporal sparsity of Dynamic Contrast Enhanced (DCE) MRI images. The proposed method significantly reduces undersampling artifacts and can provide high temporal and spatial resolution.

Carsten Oliver Schirra¹, ², Sascha Krueger³, Steffen Weiss³, Reza Razavi¹, Tobias Schaeffter¹, Sebastian Kozerke²

¹King's College London, London, UK; ²University and ETH Zurich, Zurich, Switzerland; ³Philips Medical Systems, Hamburg, Germany

With standard dynamic 3D imaging methods sufficient spatial resolution is difficult to achieve at the required temporal rates when visualizing interventional devices. Active catheters lend themselves well to undersampling methods given their confined sensitivity volume. Compressed Sensing allows exploiting the image sparseness inherent to images acquired with active catheter antennae, however the associated iterative reconstruction algorithms are time-expensive. In this work, the feasibility of using Compressed Sensing for accelerating 3D imaging of active catheters is investigated. Dedicated constraints are introduced taking into account the known catheter length and position in order to minimize the number of iterations in reconstruction.

Huimin Wu¹, Walter F. Block¹, Alexey A. Samsonov¹

¹University of Wisconsin-Madison, Madison, USA

Constrained reconstruction methods have been shown to produce significant accelerations to date, but suffer some temporal inaccuracy when vessels with different temporal behaviors are nearby or as the sparsity of the image volume decreases.  We present simulated comparisons of a single pass reconstruction method (Highly constrained Projection Local Reconstruction or HYPRLR) and an iterative constrained reconstruction method termed HYPR Reconstruction by Iterative Estimation (HYPRIT).  We demonstrate increased temporal accuracy for HYPRIT relative to HYPR LR, but also demonstrate how HYPRIT’s performance improves  when using the HYPR LR image as a constraining image. Finally, rapid CE-MRA capabilities are demonstrated.

Ganesh Adluru¹, ², Edward VR DiBella¹

¹University of Utah, Salt Lake City, USA

Constrained reconstruction methods can be used to accelerate the acquisition of cardiac dynamic contrast-enhanced MRI data.  The temporal constraint term is important for determining the quality of reconstructions from undersampled data.  Here we compare and evaluate reconstructions obtained by using an L2-norm and an L1-norm as temporal constraints.  The reconstructions were compared using data with simulated undersampling and using actual undersampled radial data acquired from the scanner.  Using an L1-norm in the temporal constraint helps in obtaining better reconstructions as compared to using an L2-norm in the temporal constraint especially when there is respiratory motion in the data.

André Fischer¹, ², Felix Breuer², Martin Blaimer², Nicole Seiberlich¹, Peter Michael Jakob¹, ²

¹University of Wuerzburg, Wuerzburg, Germany; ²Research Center for Magnetic Resonance Bavaria e.V., Wuerzburg, Germany

The concept of Compressed Sensing offers a new perspective for accelerated magnet resonance imaging. We demonstrate the use of CS in connection with dynamic imaging. The proposed method reconstructs the differences between a certain timeframe and the composite image of a dynamic dataset. By choosing a radial trajectory, the artifacts in the undersampled image are incoherent, and, therefore, beneficial for the CS algorithm. We achieved good reconstructions with as less as 14 projections (192 x 192 matrix size). Hence, this technique is promising for future real-time dynamic applications.

Alexey Samsonov¹, Youngkyoo Jung¹, Andrew L. Alexander¹, Walter F. Block¹, Aaron S. Field¹

¹University of Wisconsin, Madison, Wisconsin, USA

Recently, there has been an emerging interest to accelerate MRI through iterative reconstruction of undersampled data based on compressed sensing theory. We extend the compressed sensing framework via sparsifying images.  The new method utilizes the recent idea in HYPR methods to use sliding window composite images to constrain reconstruction.  At the same time, such enhancement is done within the compressed sensing framework.  We demonstrate that the new method, HighlY constrained back PRojection by Iterative esTimation (HYPRIT), may be a powerful tool for image reconstruction from highly undersampled data.  We demonstrate its potential for accelerated radial diffusion tensor imaging.