ISMRM 21st Annual Meeting & Exhibition 20-26 April 2013 Salt Lake City, Utah, USA

Advances in Image Analysis
Thursday 25 April 2013
Room 255 EF  13:30 - 15:30 Moderators: James C. Gee, Joseph V. Hajnal

13:30 0692.   Dynamic Magnetic Property of Multiple Sclerosis Lesions at Various Ages Measured by Quantitative Susceptibility Mapping -permission withheld
Weiwei Chen1, Susan A. Gauthier2, Ajay Gupta2, Joseph Comunale2, Tian Liu2, Shuai Wang3, Mengchao Pei2, David Pitt4, and Yi Wang2
1Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China, 2Weill Cornell Medical College, New York, NY, United States, 3School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China, 4The Ohio State University, Columbus, Ohio, United States

A total of 293 MS lesions were detected in 12 clinically definite MS patients who underwent twice quantitative susceptibility mapping (QSM) from 8/2011 to 5/2012. This longitudinal study of susceptibilities of MS lesions of different ages using QSM suggests the following findings. 1) There are 6 patterns of lesions manifested in MRI, with various patterns in individual patients. 2) QSM can detect lesions that are not detectable on conventional MRI (pattern Q). 3) Susceptibilities of MS lesions increase at early stage, peak in 1-3 yrs, subsequently decrease in 3-7 yrs, and return to normal after 7 yrs.

13:42 0693.   Imaging Multipole Magnetic Susceptibility Anisotropy in vivo
Chunlei Liu1 and Wei Li1
1Brain Imaging and Analysis Center, Duke University, Durham, NC, United States

A method was demonstrated to extract sub-voxel tissue magnetic response in vivo. A set of magnetic multipoles were obtained by analyzing a single volume of gradient-echo images in the spectral space (p-space). An algorithm was developed for multipole analysis of images acquired with multiple coils. A number of unique findings were described: 1) multipole response of white matter exhibits strong dependence on p-value; 2) multipole response of white matter is anisotropic; 3) multipole tensors provide distinctive contrasts and 4) are indicative of tissue microstructure. Most importantly, this unique information can be obtained from a single volume of GRE images.

13:54 0694.   
Artery-Vein Segmentation in Non-Contrast-Enhanced Flow-Independent 3D Peripheral Angiography
Serena Y. Yeung1, Kie Tae Kwon1, Bob S. Hu1,2, and Dwight G. Nishimura1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Palo Alto Medical Foundation, Palo Alto, CA, United States

Magnetization-prepared 3D SSFP sequences have shown promise for non-contrast-enhanced flow-independent angiography (FIA), where intrinsic tissue parameters such as T1, T2, and chemical shifts are exploited to generate stable vessel contrast even under slow flow conditions. However, an important challenge with this approach is sufficient artery-vein contrast, which is crucial for artery visualization and the diagnosis of arterial disease. In this work, we apply the Maximally Stable Extremal Regions (MSER) detector and k-means clustering to perform unsupervised segmentation and removal of the femoral veins in 3D FIA datasets of the lower extremities.

14:06 0695.   Optimal Enhancement of Brain Structures by Combining Different MR Contrasts: Demonstration of Venous Vessel Enhancement in Multi-Echo Gradient-Echo MRI
Andreas Deistung1, Ferdinand Schweser2, and Jürgen R. Reichenbach2
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

We present a supervised approach for creating image contrast that enhances specific structures of interest. To this end, we apply linear discriminant analysis (LDA) to optimally combine multiple image contrasts generated from complex-valued multi-echo GRE information (magnetic susceptibility, R2*, and S0) to create a novel contrast with increased cerebral venous vessel contrast. This approach can easily be adjusted to emphasize other tissue properties by changing the definition of classes in the training step.

14:18 0696.   
Automatic Bolus Analysis for DCE-MRI Using Radial Golden-Angle Stack-Of-Stars GRE Imaging
Robert Grimm1, Li Feng2, Christoph Forman3, Jana Hutter1, Berthold Kiefer4, Joachim Hornegger1, and Kai Tobias Block5
1Pattern Recognition Lab, FAU Erlangen-Nuremberg, Erlangen, Germany, 2Department of Radiology, New York University Langone Medical Center, New York City, NY, United States, 3Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany, 4Siemens Healthcare, Erlangen, Germany, 5Department of Radiology, NYU Langone Medical Center, New York City, NY, United States

Compressed Sensing reconstruction of DCE-MRI with radial stack-of-stars GRE acquisition suffers from two shortcomings: First, the acquisition cannot be combined with conventional bolus-detection techniques to confirm successful bolus administration. Second, in abdominal examinations only few of the up to 100 reconstructed volumes are relevant for clinical diagnosis. An automatic, k-space based method is presented that addresses both problems. A bolus signal reflecting the course of global contrast enhancement is extracted and used to accurately determine the bolus arrival time. Using population-based estimates for the delay of arterial and venous enhancement in the liver, the corresponding images can be identified.

14:30 0697.   
Articulation Analysis Using Real Time Spiral MRI with TRACER
Bo Xu1, Sam Tilsen2, Pascal Spincemaille3, Madhur Srivastava2, Peter Doerschuk2, and Yi Wang1
1Biomedical Engineering, Cornell University, Ithaca, New York, United States, 2Cornell University, Ithaca, New York, United States, 3Weill Cornell Medical College, New York, New York, United States

In this study, the real-time spiral MRI combined with the TRACER reconstruction is used for articulation analysis. This method maintains coverage and spatial resolution but achieves a high temporal frame rate that is shown to be advantageous for the investigation of speech motor control.

14:42 0698.   Model-Based Super-Resolution of Diffusion MRI for Microstructure Imaging
Alexandra Tobisch1 and Hui Zhang2
1Department of Medical Physics and Bioengineering, University College London, London, United Kingdom, 2Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom

This work develops a super-resolution reconstruction (SRR) technique that constructs isotropic high-resolution diffusion-weighted images (DWI) from multiple anisotropic low-resolution acquisitions. The technique will enable the mapping of tissue microstructure for fine brain structures without the need for prohibitively long imaging time. It advances the state-of-the-art by adopting a model-based approach to directly super-resolve the parameter maps of the underlying tissue microstructure.

14:54 0699.   Experimentally and Computationally Fast Method for Estimation of the Mean Kurtosis
Brian Hansen1, Torben E. Lund1, Ryan Sangill1, and Sune N. Jespersen1,2
1CFIN, Aarhus University, Aarhus C, Denmark, 2Department of Physics, Aarhus University, Aarhus C, Denmark

Diffusion kurtosis imaging is a popular extension of diffusion tensor imaging accounting for nongaussian aspects of diffusion in biological tissue. Recently, several studies have indicated enhanced sensitivity of mean kurtosis (MK) to pathology, including stroke. However, lengthy acquisition time and postprocessing remains an obstacle for the exploration of further clinical applications. Here we propose a very fast acquisition and postprocessing scheme based on a linear combination of 13 diffusion weighted images for estimation of a new mean kurtosis metric, the trace of the kurtosis tensor, which is then shown to have very similar contrast to MK in the human brain.

15:06 0700.   
REKINDLE: Robust Extraction of Kurtosis INDices with Linear Estimation
Chantal M.W. Tax1, Willem M. Otte1, Max A. Viergever1, Rick M. Dijkhuizen1, and Alexander Leemans1
1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands

Diffusion kurtosis imaging (DKI) provides new avenues for an accurate and complete tissue characterization within clinically feasible scanning times. In a clinical setting, however, such benefits are often nullified by numerous acquisition artifacts. In this work, we propose to extend the popular Robust Estimation of Tensors by Outlier Rejection (RESTORE) approach, which is widely used in diffusion tensor imaging (DTI), to DKI. In addition, a linearized framework, coined REKINDLE (Robust Extraction of Kurtosis INDices with Linear Estimation), has been developed that drastically reduces the computational cost without compromising the estimation reliability.

15:18 0701.   Framework for Task-Based Assessment of MR Image Quality
Christian G. Graff1
1Division of Imaging and Applied Mathematics, U. S. Food and Drug Administration, Silver Spring, MD, United States

A computational modeling framework has been developed which is able to analyze and compare image quality across different sequences, trajectories and reconstruction techniques. The image quality metrics are based on practical analysis tasks which emulate the complex uses of clinical MR. Using these metrics we show how even complex reconstruction methods such as compressed sensing can be analyzed in a rigorous manner, which is not possible with traditional image quality metrics.