ISMRM 21st Annual Meeting & Exhibition ○ 20-26 April 2013 ○ Salt Lake City, Utah, USA
SCIENTIFIC SESSION
Compressed Sensing: Novel Methods & Applications Thursday 25 April 2013
Room 250 BCEF | 10:30 - 12:30 | Moderators: | Justin P. Haldar, Lei Leslie Ying
10:30 | 0602. | Reference Histogram Constrained Artifact Suppression (RHiCA) for Incoherently Undersampled Magnetic Resonance Imaging Thomas Gaass1, Guillaume Potdevin2, Grzegorz Bauman3,4, Peter Noël5, and Axel Haase1 1Zentralinstitut für Medizintechnik, Technische Universität München, Garching, Germany, 2Department of Physics, Technische Universität München, Garching, Germany, 3Department of Medical Physics, German Cancer Research Center, Heidelberg, Germany, 4Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, United States, 5Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany We introduce a novel reconstruction technique (RHiCA), based on the specific signature of incoherent undersampling artifacts in histogram space and relying on a minimization algorithm. Utilizing the histogram of a low resolution reference image we propose to correct the impaired image via its altered histogram. The performance of the Reference Histogram Constrained Artifact (RHiCA) reduction is successfully presented on a numerical phantom and a 3T head MRI. We successfully illustrate, that applying RHiCA leads to an effective suppression of undersampling artifacts in radial MRI acquisitions up to undersampling factors of 6.
10:42 | 0603. | A Unified Tensor Regression Framework for Calibrationless Dynamic, Multi-Channel MRI Reconstruction Joshua D. Trzasko1 and Armando Manduca1 1Mayo Clinic, Rochester, MN, United States Previously, low-rank matrix regression methods have been used to enable "calibrationless" parallel and "training-free" dynamic MRI reconstruction. In this work, we present a novel low n-rank tensor regression framework for calibrationless reconstruction of dynamic and multi-channel MRI data, and demonstrate that previously image-domain strategies arise as instances of this unifying model.
10:54 | 0604. | Accelerating Encoded Simultaneous Multi Slice MRI with Compressed Sensing Sagar Mandava1, Jean-Philippe Galons2, and Ali Bilgin1,3 1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Medical Imaging, University of Arizona, Tucson, AZ, United States, 3Biomedical Engineering, University of Arizona, Tucson, AZ, United States Simultaneous Multislice Acquisition (SIMA) by Hadamard-encoded excitation has been proposed as an alternative to 3D volume imaging (3DFT) when acquiring fewer than 64 slices to avoid ringing and leakage artifacts. As slices are excited simultaneously, SIMA enjoys SNR benefits over slice-by-slice imaging similar to 3DFT. In this work, we investigate the use of compressive sampling strategies within the SIMA framework. In addition to Hadamard and complex Hadamard encoding, we introduce the use of Noiselet encoding in SIMA.
11:06 | 0605. | Compressed Sensing ASL Perfusion Imaging Using Adaptive Nonlinear Sparsifying Transforms Yihang Zhou1,2, Yanhua Wang1,2, Jie Zheng3, and Leslie Ying1,2 1Department of Electrical Engineering, University at Buffalo, Buffalo, NY, United States, 2Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States, 3Department of Radiology, Washington University, St. Louis, MO, United States In this study, a broader family of nonlinear transforms is investigated for sparse representation of dynamic images in compressed sensing (CS). We propose a novel kernel-based CS method that implicitly and adaptively sparsifies the dynamic image series of interest using nonlinear transforms. The proposed method is evaluated using accelerated arterial spin labeled perfusion data. It is shown to be able to better preserve the spatial and temporal information than the conventional CS method with linear transforms.
11:18 | 0606. | Compressed Sensing Reconstruction with an Additional Respiratory-Phase Dimension for Free-Breathing Imaging Li Feng1,2, Jing Liu3, Kai Tobias Block4, Jian Xu5, Leon Axel1,2, Daniel K. Sodickson1,2, and Ricardo Otazo1,2 1Center for Biomedical Imaging, New York University, School of Medicine, New York, New York, United States, 2Sackler Institute of Graduate Biomedical Sciences, New York University, School of Medicine, New York, New York, United States, 3Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 4Center for Biomedical Imaging, NYU Langone Medical Center, New York, New York, United States, 5Siemens Medical Solutions, New York, New York, United States Respiratory motion reduces the temporal sparsity and thus the performance of compressed sensing reconstruction. In this work, we propose a respiratory motion compensation method for compressed sensing reconstruction using golden-angle radial sampling by creating an extra respiratory-phase dimension estimated from the acquired data with self-gating. The additional respiratory-phase dimension improves the performance of compressed sensing for free-breathing imaging due to (a) additional correlation and thus increased overall multidimensional sparsity and (b) higher incoherence, since the dimension is formed by sorting complementary golden-angle radial data. We demonstrate the feasibility of the technique for accelerated free breathing cardiac cine and liver imaging.
11:30 | 0607. | High-Frame-Rate Full-Vocal-Tract Imaging Based on the Partial Separability Model and Volumetric Navigation Maojing Fu1,2, Bo Zhao1,2, Joseph Holtrop2,3, Jamie Perry4, David Kuehn5, Zhi-Pei Liang1,2, and Bradley P. Sutton2,3 1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States, 3Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States, 4Department of Communication Sciences and Disorders, East Carolina University, Greenville, North Carolina, United States, 5Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States Dynamic MRI can provide quantitative assessment on the anatomy and dynamics of the vocal tract, but usually suffers from poor spatial or temporal resolution or poor spatial coverage. This work presents full-vocal-tract dynamic imaging at a frame rate of 102.2 fps with a 2.2 mm × 2.2 mm × 6.5 mm spatial resolution. It is performed by incorporating an optimized 3D volumetric navigation scheme into a Partial Separability (PS) model-based imaging method. Subtle temporal behaviors of the articulator motion and fine 3D anatomy of the vocal tract are well captured and analyzed.
11:42 | 0608. | Ultra-Fast Dynamic MRI for Lung Tumor Tracking Based on Compressed Sensing Manoj K. Sarma1, M. Albert Thomas1, Peng Hu2, Daniel B. Ennis2, and Ke K. Sheng3 1Radiological Sciences, UCLA School of Medicine, Los Angeles, CA, United States, 2Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, 3Radiation Oncology, UCLA School of Medicine, Los Angeles, CA, United States Radiotherapy guided by MRI has afforded the hardware potential to treat a moving tumor more accurately but existing imaging speed is inadequate for 3D real-time lung and lung tumor imaging. By exploiting the intrinsic coherence of the patient anatomy during time, we adapted a k-t SLR compressed sensing method to dramatically reduce the amount of data that is needed to update a new dynamic imaging without losing details. We were able to accurately track moving tumors of nine patients based on images reconstructed with very high data under-sampling ratio up to 5% of the original data.
11:54 | 0609. | Whole Heart Motion Corrected Compressed Sensing for 3D Free Breathing Dynamic Cardiac MRI Muhammad Usman1, Mariya Doneva2, Tobias Schaeffter1, and Claudia Prieto1,3 1King's College London, London, United Kingdom, 2Philips Research Europe, Hamburg, Germany, 3Pontificia Universidad Catolica de Chile, Santiago, Chile asdasd
12:06 | 0610. | Compressive Diffusion MRI – Part 1: Why Low-Rank? Hao Gao1,2, Longchuan Li3, and Xiaoping P. Hu3 1Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia, United States, 2Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, United States, 3Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States This work compares several sparsity models for dynamic MRI with the focus on diffusion MRI. The low-rank model, a global sparsification of diffusion images via SVD, generally was found to be the best model, while the rank-sparsity decomposition was shown to be the best when the diffusion images are non-low-rank.
12:18 | 0611. | Accelerating Compressed-Sensing-Based DCE-MR Image Reconstruction with GPU Jiangsheng Yu1, Yiqun Xue2, and Hee Kwon Song2 1Toshiba Medical Research Institute USA, Cleveland, Ohio, United States, 2University of Pennsylvania, Philadelphia, Pennsylvania, United States Synopsis: Temporally constrained reconstruction based on compressed sensing (CS) has recently been developed for dynamic MR imaging to obtain high temporal resolution without losing image quality. The intensive computation overhead in CS reconstruction has limited the application for clinical data processing where large data sets are generated from multi-slice and multi-channel acquisition. The current work presents a parallelized GPU implementation to accelerate the CS-based image reconstruction in DCE-MRI. The forward and backward gridding operations, which are the most-time consuming part of the conjugate gradient searching, is addressed with a radial-point driven parallelization approach by assigning a thread for each radial point operation. A comparison with the C++ sequential implementation shows an acceleration factor of ~15 was achieved on a moderately GPU-powered laptop computer.