Kerstin Hammernik¹, Florian Knoll², Daniel K Sodickson², and Thomas Pock^1,3
1Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria, ²Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU School of Medicine, New York, NY, United States, ³Safety & Security Department, AIT Austrian Institute of Technology GmbH, Vienna, Austria

Compressed sensing techniques allow MRI reconstruction from undersampled k-space data. However, most reconstruction methods suffer from high computational costs, selection of adequate regularizers and are limited to low acceleration factors for non-dynamic 2D imaging protocols. In this work, we propose a novel and efficient approach to overcome these limitations by learning a sequence of optimal regularizers that removes typical undersampling artifacts while keeping important details in the imaged objects and preserving the natural appearance of anatomical structures. We test our approach on patient data and show that we achieve superior results than commonly used reconstruction methods.

Tae Hyung Kim¹, Kawin Setsompop², and Justin P. Haldar^1
1Electrical Engineering, University of Southern California, Los Angeles, CA, United States, ²Radiology, Harvard Medical School, Boston, MA, United States

We introduce a novel framework called SENSE-LORAKS for partial Fourier phase-constrained parallel MRI reconstruction.  SENSE-LORAKS combines classical SENSE data modeling with advanced regularization based on the novel low-rank modeling of local k-space neighorhoods (LORAKS) framework.  Unlike conventional phase-constrained SENSE techniques, SENSE-LORAKS enables use of phase constraints without requiring a prior estimate of the image phase or a fully sampled region of k-space that could be used for phase autocalibration.  Compared to previous SENSE-based and LORAKS-based reconstruction approaches, SENSE-LORAKS is compatible with a much wider range of sampling trajectories, which can be leveraged to achieve much higher acceleration rates.

Claudio Santelli¹, Adrian Huber¹, and Sebastian Kozerke^1
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland

Using eigen-decomposition of a modified k-t SPIRiT operator, computationally optimized reconstruction formally translating into auto-calibrated SENSE-like reconstruction of a coil-combined x-f image (k-tESPIRiT) is proposed. 2D and 3D in-vivo experiments show equivalence of k-t SPIRiT and k-t ESPIRiT, and significant reconstruction time speed-up's of the proposed relative to the standard technique.

Li Feng¹, Tiejun Zhao², Hersh Chandarana¹, Daniel K Sodickson¹, and Ricardo Otazo^1
1Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, United States, ²Siemens Medical Solutions, New York, NY, United States

This work proposes a 3D free-breathing MRI technique called variable-density XD-GRASP, which employs stack-of-stars sampling with variable-density k_z-undersampling and motion-resolved sparse reconstruction. The new sampling scheme combines the advantages of conventional stack-of-stars sampling and kooshball-type 3D radial sampling, enabling 3D continuous MRI with flexible slice resolution, robust fat suppression and low sensitivity to eddy currents. The performance of variable-density XD-GRASP is demonstrated for free-breathing liver MRI.

Peng Lai¹, Joseph Yitan Cheng², Shreyas S Vasanawala², and Anja C.S Brau^3
1Global MR Applications and Workflow, GE Healthcare, Menlo Park, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States, ³Global MR Applications and Workflow, GE Healthcare, Munich, Germany

Respiratory gating (RG) is commonly used for free-breathing 3D MRI. Conventional RG based on acceptance/rejection performs hard-threshholding on acquired data and suffers from either increased motion corruption with a large acceptance window or long scan time/increased undersampling artifacts with a small window. This work developed a non-iterative respiratory soft-threshholding method by incorporating the motion-induced error into autocalibrating parallel imaging (ac-PI). The proposed method showed more effective motion suppression on free-breathing 3D cine than conventional respiratory gating on the same datasets. This method can be generalized to suppress other types of motion with full acquisition or ac-PI as well.

Siddharth Srinivasan Iyer¹, Frank Ong¹, and Michael Lustig^1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States

ESPIRiT is a robust, auto-calibrating approach to parallel MR image reconstruction that estimates the subspace of sensitivity maps using an eigenvalue-based method. While it is robust to a range of parameter choices, having parameters that result in a tight subspace yields the best performance. We propose an automatic, parameter free method that appropriately weights the subspace using a shrinkage operator derived from Stein's Unbiased Risk Estimate. We demonstrate the efficacy of our technique by showing superior map estimation without user intervention in simulation and in-vivo data compared to the current default method of subspace estimation.

Ute Goerke^1
1CMRR/Radiology, University of Minnesota, Minneapolis, MN, United States

RASER (rapid acquisition with sequential excitation and refocusing) is an ultrafast imaging technique based on spatiotemporal encoding (SPEN). The excitation with a chirp-pulse with a low bandwidth-time product (R-value) introduces blurring in the SPEN dimension. Superresolution (SR) which removes the blurring fails as a result of the spatially varying B₁-phase produced by radio-frequency coils at ultrahigh fields. A novel iterative phase-correction of the SR-algorithm is presented. It is shown that the spatial resolution and the SNR of blurred RASER images acquired at 7 T are significantly improved employing phase-corrected SR.

Sudhanya Chatterjee¹, Dattesh D Shanbhag¹, Venkata Veerendranadh Chebrolu¹, Uday Patil¹, Sandeep N Gupta², Moonjung Hwang ³, Jeong Hee Yoon⁴, Jeong Min Lee⁴, and Rakesh Mullick^1
1GE Global Research, Bangalore, India, ²GE Global Research, Niskayuna, NY, United States, ³GE Healthcare, Seoul, Korea, Republic of, ⁴Seoul National University Hospital, Seoul, Korea, Republic of

Main aim of this research is to investigate a source separation based approach to remove noise from true signal, while maintaining original tissue enhancement signature. It is based on the hypothesis that there exists overlapping temporal information in the DCE-MRI data, which if identified, can be used for filtering noise out of the true concentration data. We demonstrate the utility of source separation and subsequent weight estimation methodology to filter “noise” from DCE concentration data and impact on the pK model parameters in liver DCE-MRI.

Lucilio Cordero-Grande¹, Emer Hughes¹, Anthony Price¹, Jana Hutter¹, A. David Edwards¹, and Joseph V. Hajnal^1
1Centre for the Developing Brain, King's College London, London, United Kingdom

A framework for retrospectively motion corrected reconstruction of multislice multishot MRI in the presence of 3D rigid motion is developed. The method is able to cope both with within-plane and through-plane motion by estimating the motion states corresponding to the acquired shots and slices. It has been applied to 478 T1 and T2 newborn brain studies, including many severely motion corrupted examples, for which consistent structures have been recovered in more than 96% of cases. Due to its robustness and flexibility, our technique has wide potential application for both clinical and research examinations.

Jasper Schoormans¹, Oliver Gurney-Champion¹, Remy Klaassen², Jurgen H. Runge¹, Sonia I. Gonçalves³, Bram F. Coolen¹, Abdallah G. Motaal¹, Hanneke W.M. van Laarhoven², Jaap Stoker¹, Aart J. Nederveen¹, and Gustav J. Strijkers^4
1Department of Radiology, AMC, Amsterdam, Netherlands, ²Department of Medical Oncology, AMC, Amsterdam, Netherlands, ³Institute for Biomedical Imaging and Life Sciences, University of Coimbra, Coimbra, Portugal, ⁴Department of Biomedical Engineering and Physics, AMC, Amsterdam, Netherlands

We developed a 4D radial fat-suppressed alternating-TR bSSFP sequence with T2-like contrast for abdominal free-breathing imaging of pancreatic cancer patients. The sequence was tested in healthy volunteers and patients with pancreatic cancer and provided images of the abdomen during different respiratory motion states of diagnostic quality.