Joint Annual Meeting ISMRM-ESMRMB 2014 10-16 May 2014 Milan, Italy

Reconstruction & Processing Methods for Quantitative Susceptibility Mapping

Wednesday 14 May 2014
Red 1 & 2  13:30 - 15:30 Moderators: Chunlei Liu, Ph.D., Alexander Rauscher, Ph.D.

13:30 0597.   Differential approach to quantitative susceptibility mapping without background field removal
Tian Liu1, Dong Zhou2, Pascal Spincemaille2, and Yi Wang2
1MedImageMetric LLC, New York, New York, United States, 2Radiology, Weill Cornell Medical College, New York, New York, United States

High quality background field removal is a critical yet challenging step in quantitative susceptibility mapping (QSM), as it involves phase unwrapping, skull stripping, and knowledge of the boundary conditions. In this work, we provide a theoretical analysis of the background field removal and propose to bypass all the steps using a differential equation approach.

13:42 0598.   HARPERELA Phase Processing for Quantitative Susceptibility Mapping
Wei Li1,2, Alexandru V Avram1,3, Bing Wu1,4, Xue Xiao1,5, and Chunlei Liu1,6
1Brain Imaging and Analysis Center, Duke University, Durham, NC, United States, 2Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States, 3National Institute of Health, Bethesda, DC, United States, 4GE Healthcare, Beijing, China,5Tsinghua University, Beijing, China, 6Radiology, Duke University, Durham, NC, United States

3D phase unwrapping and background phase removal are typically two separated preprocessing steps for quantitative susceptibility mapping (QSM). We developed a novel method for integrated phase unwrapping and harmonic background phase removal using Laplacian, named as HARPERELLA. HARPERELLA is fast, robust, easily to implement and yields local tissue phase with similar accuracy to that using the well-known SHARP and PDF methods. To facilitate evaluation and dissemination, we combined HARPERELLA, QSM, susceptibility tensor imaging (STI) algorithms and related graphical user interfaces into a software package, named as “STI Suite”, which is available online for free academic use.

13:54 0599.   Which parameters are optimal? - A comprehensive numerical analysis of background phase correction with SHARP
Ferdinand Schweser1, Pιnar Senay Özbay2,3, Andreas Deistung1, Edsel Daniel Peres Gomez1, Xiang Feng1, Daniel Nanz2, and Jürgen R Reichenbach1
1Medical Physics Group, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany, 2Department of Radiology, University Hospital Zurich, Zurich, Switzerland, 3Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland

Sophisticated harmonic artifact reduction for phase data (SHARP) is regarded as one of the most important techniques in the area of background correction for QSM. The influence of the SHARP parameters has not been investigated in detail so far and they are usually chosen empirically, which may have serious consequences for the comparability of studies relying on SHARP processed phase images. We performed a comprehensive analysis of the impact of different parameter choices on the resulting images and propose a universal definition of the regularization parameter that does not depend on the spherical radius or on the image resolution.

14:06 0600.   
Background Field Removal by Solving the Laplacian Boundary Value Problem
Dong Zhou1, Tian Liu2, Pascal Spincemaille1, and Yi Wang1,3
1Radiology Department, Weill Cornell Medical College, New York, NY, United States, 2Medimagemetric LLC, NY, United States, 3Biomedical Engineering Department, Cornell University, Ithaca, NY, United States

We assume simple boundary conditions and remove the background field by explicitly solving the boundary value problems of Laplace’s or Poisson’s equation. The proposed Laplacian boundary value (LBV) method for background field removal retains data near the boundary and is computationally efficient. Tests on a numerical phantom, an experimental phantom and in in vivo data sets showed that LBV was more effective than the SHARP and PDF methods.

14:18 0601.   
Fast Reconstruction for Regularized Quantitative Susceptibility Mapping
Berkin Bilgic1, Audrey Fan2, Cornelius Eichner1, Stephen Cauley1, Jonathan Polimeni1, Marta Bianciardi1, Elfar Adalsteinsson2, Lawrence Wald1, and Kawin Setsompop1
1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2MIT, Cambridge, MA, United States

A high-resolution whole brain QSM reconstruction can take up to 20 min on a workstation, which poses a limit on QSM usability in clinical and research settings. Herein, we introduce an improved Split-Bregman (SB) L1-regularized dipole inversion algorithm that offers 20× faster reconstruction relative to the standard nonlinear conjugate gradient (NCG) solver. Additionally, we extend SB L1-regularization to admit magnitude-weighting that prevents smoothing across edges identified on the magnitude signal, and solve this more complicated problem 5× faster than the NCG approach. Further, we extend the previously proposed closed-form L2-based inversion to admit magnitude-weighting, and demonstrate 15× acceleration relative to NCG by employing a preconditioner that leads to faster convergence. Utility of the proposed methods is demonstrated in high-resolution (0.6 mm isotropic) 3D GRE data at 3T, as well as multi-echo Simultaneous Multi-Slice (SMS) EPI time-series at 7T.

14:30 0602.   
Modulated closed form solution for quantitative susceptibility mapping - permission withheld
Diana Khabipova1, Rolf Gruetter1,2, and Marques P. José3
1LIFMET, EPFL, Lausanne, Vaud, Switzerland, 2Radiology, University of Geneva, Geneva, Geneva, Switzerland, 3Radiology, University Lausanne, Lausanne, Vaud, Switzerland

Quantitative susceptibility mapping (QSM) has shown the potential delivering iron deposition estimations in deep grey matter structures. Compared to multiple orientation acquisitions, many proposed regularization methods remain time consuming, rely on careful regularization selection and provide inferior results. A recently proposed closed-form solution including an l2-regularization allows fast reconstructions. Our methodology uses additional k-space position dependent modulation, thus avoiding regularizing not ill-posed regions and being effective without showing signs of over-smoothing QSM maps. Optimal modulation enables largely independence from regularization parameters, lower case Greek lambda. Being comparable for lower case Greek lambda values an order of magnitude greater than optimum makes it ideal for unsupervised usage.

14:42 0603.   
Slab Segmented Edge-preserving QSM Method
Fei Cong1, Bo Wang1, Xiaohong Joe Zhou2, Yan Zhuo1, and Yongquan Ye3
1Institute of Biophysics, Chinese Academy of Sciences, Chaoyang District, Beijing, China, 2Department of Radiology and Center for MR Research, University of Illinois Medical Center, Chicago, IL, United States, 3Department of Radiology, School of Medicine, Wayne State University, Detroit, MI, United States

A new edge-preserving method for quantitative susceptibility mapping (QSM) is proposed, which uses 3D slab segmented Wiener filtered phase data as input and the convex half-quadratic regularizing algorithm to suppress the artifacts when solving the inverse problem by using ill-conditioned Green¡¯s function kernel. Compared to current QSM methods, this edge-preserved method reserves the high spatial frequency details for veins in the final susceptibility maps. The comparisons between quadratic regularized and edge-preserved methods of the whole brain are presented, and the feasibility and future work are discussed.

14:54 0604.   Single-Step Quantitative Susceptibility Mapping using Total Generalized Variation and 3D EPI
Kristian Bredies1, Stefan Ropele2, Benedikt A Poser3, Markus Barth4, and Christian Langkammer2
1Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria, 2Department of Neurology, Medical University of Graz, Graz, Austria, 3Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands, 4Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

Combined 3D EPI acquisition combined with single-step TGV reconstruction yielded reliably QSM images of the entire brain with 1mm isotropic resolution in 1 minute acquisition time.

15:06 0605.   The best of both worlds: Improved rapid quantitative susceptibility mapping (QSM) by combining closed-form L2 regularization with SDI
Ferdinand Schweser1, Andreas Deistung1, Xiang Feng1, Edsel Daniel Peres Gomez2, and Jürgen R Reichenbach1
1Medical Physics Group, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany, 2Medical Physics Group, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Germany

Quantitative susceptibility mapping (QSM) is a novel post-processing technique for gradient-echo phase data. Schweser et al. have recently presented that inverse filtering with extreme thresholding of the unit dipole response (SDI) yields within seconds susceptibility maps without noise amplification and with a low level of streaking artifacts. Bilgic et al. recently presented a closed-form solution for rapid L2-regularized QSM with a gradient-based penalty (CF-L2) that provides images with even reduced noise level compared to SDI . In this contribution, we show that combining CF-L2 with SDI can considerably reduce reconstruction artifacts.

15:18 0606.   
A joint background field removal and dipole deconvolution approach for quantitative susceptibility mapping in the liver
Samir D. Sharma1, Diego Hernando1, Debra E. Horng1, and Scott B. Reeder1,2
1Radiology, University of Wisconsin - Madison, Madison, WI, United States, 2Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, United States

Noninvasive quantification of liver iron concentration is important for the detection and staging of iron overload as well as for effective longitudinal monitoring during treatment. Magnetic susceptibility is a fundamental property of tissue that has a well-defined relationship to iron concentration. Quantitative susceptibility mapping (QSM) methods have been developed in the brain to quantify the concentrations of localized iron deposits. The purpose of this work was to develop a QSM technique for the liver and to demonstrate its feasibility for measuring susceptibility in the liver, which may serve as an imaging biomarker of liver iron overload.