Pauline Wong Worters¹, Kyunghyun Sung¹, Kathryn J Stevens¹, Kevin M Koch², and Brian A Hargreaves^1
1Stanford University, Stanford, CA, United States, ²ASL, GE Healthcare, Waukesha, WI, United States

Multi-spectral imaging (MSI) methods such as MAVRIC, SEMAC and Hybrid have been developed in recent years to provide distortion-free MRI of tissue around metallic implants. However, acquisition times remain lengthy (5-15 minutes) and limit the achievable spatial resolution in routine clinical use. In this work, we demonstrate the feasibility of using compressed sensing (CS) to reduce acquisition time in a retrospective application to patient data with spinal hardware. Results show that retrospective CS-MSI are the same as or better than the original MSI images. We also show that fully sampled MSI and prospectively undersampled T2-weighted CS-MSI (42% scan time reduction) are comparable in terms of image contrast and quality.

Curtis N Wiens¹, Colin M McCurdy^2,3, and Charles A McKenzie^1,3
1Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada, ²Department of Physics, University of Guelph, Guelph, Ontario, Canada, ³Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada

R2^*-corrected chemical shift based fat-water separation techniques have been used to accurately quantify fat. These are time-consuming and require image acceleration techniques. The following work describes a method of separating fat and water from undersampled multi-channel datasets. This method simultaneously applies compressed sensing, parallel imaging, and fat-water separation. To illustrate this technique, net acceleration factors of up to 4 are shown in the abdomen. Including the R2* term improves the accuracy of the fat-water separation. The proposed method improves image quality over sequential parallel imaging and fat-water separation at high acceleration factors.

Jon Furuyama¹, Chris Roberts², and M. Albert Thomas^1
1Radiology, UCLA, Los Angeles, CA, United States, ²School of Nursing, UCLA, Los Angeles, CA, United States

Recently, a four-dimensional Echo-Planar Correlated Spectroscopic Imaging (EP-COSI) sequence was shown to produce spatially localized two-dimensional spectra. Despite the speed improvements of the echo-planar readout, the sequence still requires significant scan time to collect all four dimensions. We show that the use of Compressed Sensing (CS) techniques can reconstruct under-sampled datasets with as little as 33% of the original data. Such a reduction in scan time allows for the deployment of 4D spectroscopic imaging sequences in a clinically feasible time frame. Examples of CS reconstruction are shown in the study of diabetes in the human calf muscle.

Yanjie Zhu^1,2, Yin Wu^1,2, Ed X. Wu^3,4, Leslie Ying⁵, and Dong Liang^1,2
1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China,²Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, China, ³Laboratory of Biomedical Imaging and Signal Processing, ⁴Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, ⁵Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, WI, Milwaukee, United States

The joint sparsity constraint is integrated into the model-based method to improve the accuracy of direct diffusion tensor estimation from highly undersampled k-space data. The method, named model-based method with joint sparsity constraint (MB-JSC), effectively incorporates the prior information on the joint sparsity of different diffusion weighted images in solving the nonlinear equation of tensors. Experimental results demonstrate that the proposed method is able to estimate the diffusion tensors more accurately than the existing method when a high net reduction factor is used.

R. Marc Lebel¹, Jesse Jones², Jean-Christophe Ferré², Samuel Valencerina², Krishna S. Nayak¹, and Meng Law^2
1Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States, ²Department of Radiology, University of Southern California, Los Angeles, CA, United States

Dynamic contrast enhanced (DCE) imaging requires high spatial and temporal resolution and large volume coverage; traditionally, these are mutually exclusive. We present prospective undersampled DCE imaging of brain tumors with high spatiotemporal resolution using l1-SPIRiT (compressed sensing and parallel imaging). We employ multiple spatial and temporal reconstruction constraints, including a novel temporal constraint that promotes low frequency signal changes, to achieve high accelerations without compromising resolution or dynamic information.

Eric Wong^1
1Radiology/Psychiatry, UC San Diego, La Jolla, CA, United States

In clinical MRI, images are often acquired of the same anatomy with multiple forms of contrast. Mutual information has long been used as a criterion for aligning images with different contrast, as it is high for any pair of aligned images. We explore here the maximization of mutual information as a criterion in the joint reconstruction of pairs of under-sampled images with different contrasts. For T1 and T2 weighted images that were under-sampled by a factor of 1.9, maximizing mutual information resulted in excellent reconstructions in less than one second of reconstruction time.

Maojing Fu^1,2, Anthony G. Christodoulou^1,2, Andrew T. Naber³, David P. Kuehn⁴, Zhi-Pei Liang^1,2, and Bradley P. Sutton^2,3
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Beckman Institute for Advanced Science and Technology, Urbana, IL, United States, ³Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States,⁴Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Dynamic MRI can provide a valuable tool to quantitatively assess changes in oropharyngeal dynamics during speech. In this work we present 5-slice dynamic speech imaging at a frame rate of 20 fps with 2.2 mm × 2.2 mm × 8.0 mm spatial resolution. It was successfully performed by incorporating parallel imaging methods, a composite spiral / Cartesian sampling strategy, and a reconstruction scheme exploiting the partial separability and the spatial-spectral sparsity of the speech image sequence. Changing tongue shape is observed over a speech sample in volunteer subjects.

Julia V Velikina¹, and Alexey A Samsonov^2
1Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, United States, ²Radiology, University of Wisconsin - Madison

A novel regularization by a model consistency condition is adapted for application in time-resolved contrast-enhanced intracranial angiography. The temporal behavior model is learned from low resolution training data by principal component analysis. The proposed method is shown to distinguish different filling patterns of healthy and pathological vasculature.

Michael O. Zenge¹, Martin Uecker^2,3, Gerald Mattauch¹, and Jens Frahm^2
1Healthcare Sector, Siemens AG, Erlangen, Germany, ²Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany, ³Electrical Engineering and Computer Science, University of California, Berkeley, United States

Continuous table movement MRI is an emerging technique for a variety of clinical applications. The achievable acceleration with parallel imaging, however, is not yet sufficient to scan an extended FOV in a single breath-hold. Radial scanning in combination with innovative iterative image reconstruction and joint coil estimation promises significantly higher acceleration factors. This method was implemented for moving table abdominal MRI in a single breath-hold and was evaluated in 5 healthy volunteers. It was proven that highly undersampled radial images can be reconstructed with very little streaking artifacts which justifies further investigation in volunteers and patients.

Mohammad H Kayvanrad¹, A. Jonathan McLeod¹, John S. H. Baxter¹, Charles A McKenzie¹, and Terry M Peters^1
1Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada

The similarity between images, in problems involving multiple acquisitions with different imaging parameters, is used as an additional reconstruction constraint beside sparity to further increase the quality of reconstruction/k-space under-sampling. This is of particular interest in reconstruction of T1/T2 maps. From a clinical perspective, this means a reduction in acquisition time.