ISMRM 23rd Annual Meeting & Exhibition • 30 May - 05 June 2015 • Toronto, Ontario, Canada

Scientific Session • Perfusion & Permeability: Contrast Agent Methods

Monday 1 June 2015

Room 701 A

16:30 - 18:30


Hassan Bagher-Ebadian, Ph.D., Stefan A. Reinsberg, Ph.D.

16:30 0190.   
Real-time Automatic Resolution Adaption (AURA) for dynamic contrast-enhanced MRI
Ina Nora Kompan1,2, Benjamin Richard Knowles3, and Matthias Guenther1,2
1Fraunhofer MEVIS, Bremen, Bremen, Germany, 2mediri GmbH, Heidelberg, Baden-Württemberg, Germany, 3Universitätsklinikum Freiburg, Freiburg, Baden-Württemberg, Germany

Pharmacokinetic modeling is used in dynamic contrast-enhanced MRI to quantify tissue physiology. High temporal and spatial resolutions are both needed, but are not compatible. In this work, a resolution adaption sequence is presented which in real-time adapts temporal resolutions to the measured contrast-enhanced signal changes. The sequence is validated using a perfusion phantom and is compared to an only low spatial and only high spatial resolution sequence. For the phantom, the adaptive sequence provides comparable fitting accuracy to the low temporal resolution sequence and high spatial resolution images at the signal peak.

16:42 0191.   Mitigating bias and variance associated with fat signal in quantitative DCE of the breast
James H Holmes1, Kang Wang1, Courtney K Morrison2, Frank R Korosec3, Ersin Bayram4, Roberta M Strigel3, Diego Hernando3, Scott B Reeder3, Edward F Jackson2, and Ryan J Bosca2
1Global MR Applications and Workflow, GE Healthcare, Madison, WI, United States, 2Medical Physics, University of Wisconsin-Madison, WI, United States,3Radiology, University of Wisconsin-Madison, WI, United States, 4Global MR Applications and Workflow, GE Healthcare, Houston, WI, United States

Robust fat suppression in breast dynamic contrast enhanced (DCE)-MRI has been primarily viewed as a critical need for qualitative evaluation. However, current pharmacokinetic models used to quantitate high-resolution acquisitions fail to account for the complicated signals in voxels arising from voxels containing fatty tissue and enhancing water compartments. Such combinations of fat and water are found commonly in, for example, non-mass enhancing lesions of the breast. In this work we demonstrate the influences of fat signal on DCE parameters and evaluate fat suppression and separation methods including a 2 point Dixon method for high-spatiotemporal quantitative DCE acquisitions.

16:54 0192.   In vivo cross-validation study of contrast kinetic model analysis with simultaneous B1/T1 estimation
Jin Zhang1,2, Kerryanne Winters1,2, and Sungheon Gene Kim1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), Dept. Radiology, NYU School of Medicine, New York, NY, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Dept. Radiology, NYU School of Medicine, New York, NY, United States

In our previous studies, we introduced Active-Contrast Encoding MRI (ACE-MRI) which measures both B1 and T1 values along with kinetic parameters from a single DCE-MRI data. The model free approach of ACE-MRI we proposed separates estimation of B1/T1 from estimation of contrast kinetic parameters, and consequently improves parameter estimation accuracy and precision. This in vivo cross-validation study was conducted to compare the contrast kinetic parameters estimated from ACE-MRI data with those estimated from conventional DCE-MRI experiments with separate measurements of B1 and T1.

17:06 0193.   
Improving the Arterial Input Function in Dynamic Contrast Enhanced MRI by fitting the signal in the complex plane
Frank FJ Simonis1, Alessandro Sbrizzi2, Ellis Beld1, Jan JW Lagendijk1, and Cornelis AT van den Berg1
1Radiotherapy, UMC Utrecht, Utrecht, Utrecht, Netherlands, 2Radiology, UMC Utrecht, Utrecht, Netherlands

Acquiring an accurate arterial input function (AIF) is essential in dynamic contrast-enhanced (DCE) MRI analysis. Determining AIFs using MR magnitude data faces challenges due to experimental difficulties such as inflow, B1 non-uniformity and saturation effects. However phase-based AIFs have problems estimating the baseline and the tail of the AIF. Here we demonstrate that fitting the enhancement data in the complex plane can be used in DCE AIF estimation to mitigate noise and bias that arise from solely using phase or magnitude data. The technique is applied to 3T DCE-MRI data of prostate cancer patients

17:18 0194.   
Interleaved Acquisition of a Radial Projection Based AIF with a Multi-slice DCE Experiment
Jen Moroz1, Andrew Yung1, Piotr Kozlowski2,3, and Stefan Reinsberg1
1Physics and Astronomy, UBC, Vancouver, BC, Canada, 2Radiology, UBC, Vancouver, BC, Canada, 3MRI Research Centre, UBC, Vancouver, BC, Canada

This study investigates the potential for interleaving a DCE-MRI experiment with the acquisition of a high temporal resolution AIF using a radial projection-based approach. A mouse tail phantom was scanned with two slice packs: one for the AIF (radial, single slice) and one for the DCE experiment (Cartesian, multi-slice). By interleaving these acquisitions, we maintain a high temporal resolution for the AIF, and sufficient spatial and temporal resolutions for the DCE experiment. This technique is expected to improve the accuracy of fitted model parameters as the AIF is specific to the individual study.

17:30 0195.   
Should DSC-MRI based blood volume and vessel size measures be corrected for contrast agent T2 leakage effects?
Ashley M Stokes1 and C. Chad Quarles1
1Institute of Imaging Science, Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States

Contrast agent (CA) leakage in tumors leads to inaccurate spin echo (SE) cerebral blood volume (CBV) and mean vessel diameter (mVD) measures in DSC-MRI. Here, we propose a new strategy to correct for T2 leakage effects on T1-insensitive ΔR2 timecourses. This simple and efficient approach was validated in rat tumor models by comparing uncorrected and corrected SE CBV and mVD against reference values obtained with an intravascular CA. Correction of both T1 and T2 leakage effects improved the reliability of SE CBV and mVD. This highlights the need for comprehensive correction techniques to more accurately represent non-leaky tumor hemodynamics.

17:42 0196.   
Accelerated DCE MRI using constrained reconstruction based on pharmaco-kinetic model dictionaries
Sajan Goud Lingala1, Yi Guo1, Yinghua Zhu1, Samuel Barnes2, R. Marc Lebel3, and Krishna S Nayak1
1Electrical Engineering, University of Southern California, Los Angeles, California, United States, 2Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States, 3GE Healthcare, Calgary, Canada

DCE-MRI of the brain is a powerful technique to assess the blood brain barrier permeability, and other neuro-vascular parameters. Current clinical DCE-MRI protocols have restricted spatial resolution, and slice coverage due to the slow MRI encoding process. In this work, we propose a novel pharmaco-kinetic dictionary approach to constrain the recovery of concentration profiles from under-sampled DCE -MRI acquisitions. Using the Patlak pharmaco-kinetic model, we construct a dictionary of temporal bases that characterize all possible time-intensity curves. The dictionary bases are extremely tolerant of noise and incoherent under-sampling artifacts as these are poorly described in the dictionary. Our approach enabled faithful reconstruction of upto reduction factor of 20 fold. We demonstrate superior fidelity in recovering Pharmaco-kinetic parameter maps in comparison to image reconstructions that are based on constraints blind to pharmaco-kinetic modeling (such as the temporal total variation constraint).

17:54 0197.   4-D Spatio-Temporal MR Perfusion Deconvolution via Tensor Total Variation
Ruogu Fang1
1School of Computing and Information Sciences, Florida International University, Miami, FL, United States

A 4-D Tensor Total Variation (TTV) deconvolution approach is adapted and evaluated for dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) in the brain. TTV exploits the temporal continuity of the contrast agent concentration and the spatial correlation of the non-random structure of the microvasculature. The algorithm is guaranteed to converge to global optima and outperforms the baseline methods (singular value decomposition-based and Tikhonov regularization) on both synthetic and real data of subjects with glioblastomas, in terms of improving accuracy of residue functions, quantification of perfusion maps, and localization of tumor tissue.

18:06 0198.   Quantification of Water Exchange between Intravascular and Extravascular Compartments using Independent Component Analysis
Hatef Mehrabian1,2, Anne L Martel1,2, Johann Le Floc'h1, Hany Soliman1,3, Arjun Sahgal1,4, and Greg J Stanisz1,2
1Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada, 2Medical Biophysics, University of Toronto, Toronto, Ontario, Canada, 3Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, 4Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

Tumor response to therapy could be assessed using imaging biomarker derived from DCE-MRI. We have previously developed an independent component analysis (ICA)-based algorithm to split DCE-MRI data into its intravascular and extravascular compartments. The objective of current study is to employ these intravascular/extravascular signals in a two-compartment relaxation model of MRI signal and calculate the water exchange rates between these two compartments, as well as the compartment sizes. Our results show that the model can robustly estimate exchange parameters, and that these parameters are more sensitive to changes in the tumor due to radiotherapy compared to conventional pharmacokinetic modeling.

18:18 0199.   
Multi-compartment analysis on water dynamics in rat brain by heavy water perfusion
Zi-Min Lei1, Cheng-He Li1, Sheng-Min Huang1, Chin-Tien Lu1, Kung-Chu Ho2, and Fu-Nien Wang1
1Biomedical Engineering and Environmental Sciences, National Tsing Hua University, HsinChu, Taiwan, 2Nuclear Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan

In this study of rodent brain imaging, using a new strategy of monitoring heavy water tracer by the attenuation of 1H signal, we further re-investigate the heavy water dynamics with a multi-compartmental analysis method, and investigated the spatial distribution of fast and slow compartments of heavy water perfusion.