TRADITIONAL POSTER - ismrm

Poster Sessions
results show that the use of cardiac output to correct DCE-MRI produces parameter estimates which are significantly closer to DCE-CT with reduced
variance; the use of such corrections may significantly benefit DCE-MRI analyses.
3148. A Novel Method for Automatic Estimation of M0 Used by ASL CBF Quantification
Ognjen Zivojnovic 1 , Greg Zaharchuk 2 , Ajit Shankaranarayan 3
1 Stanford University, Stanford, CA, United States; 2 Department of Radiology, Stanford University, Stanford, CA, United States;
3 Applied Sciences Laboratory - West, GE Healthcare, Menlo Park, CA, United States
Calculating quantitative CBF values based on ASL images requires knowledge of M0. Two models exist for estimating its value, a blood based model that
depends on the M0 of CSF, and a tissue based model that requires the re-imaging of the entire volume. This abstract presents a novel method for
automatically estimating M0 based on the blood model in order to take advantage of its faster scan times compared to the tissue based model, as well as to
remove human inconsistencies in selecting the area from which the estimate is made.
3149. Haemal Supplies Correlation Based Hepatic Nodules Identification from Dynamic Contrast-Enhanced
MR Images
Min Sun 1,2 , Xuedong Yang 3 , Dongjiao Lv 4 , Mingyuan Xie 2 , Ling Yang 2 , Chengbo Wang 5 , Xiaoying Wang,
1,3 , Jue Zhang, 1,4 , Jing Fang, 1,4
1 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; 2 Dept. of Electronic Engineering, Chengdu
University of Information Technology, Chengdu, Sichuan, China; 3 Dept. of Radiology, Peking University First Hospital, Beijing,
China; 4 College of Engineering, Peking University, Beijing, China; 5 Dept. of Radiology, University of Virginia, Charlottesville,
Virigina, United States
Early detection of liver nodular lesions is critical in improving patient¡¯s survival rate. Previous studies have shown that for dynamic contrast-enhanced MR
imaging of liver nodules, there exists correlation between nodules¡¯ blood supplies and MR signal changes. In this retrospective study, haemal supplies
correlation based strategy was introduced to identify the suspected hepatic nodules, including DN, RN and SHCC from dynamic contrast-enhanced MR
Images, and the analysis results were in consistence with the clinical diagnosis under double-blind test. The proposed computer aided identification approach
could be helpful to provide valuable information for the detection of hepatic nodules.
3150. Performance and Accuracy of a Morphological MR Marker Localization at Reduced Spatial
Resolutions: Results from Simulated and Real Marker Images
Gregor Thörmer 1 , Nikita Garnov 1 , Jürgen Haase 2 , Thomas Kahn 1 , Michael Moche 1 , Harald Busse 1
1 Diagnostic and Interventional Radiology, Leipzig University Hospital, Leipzig, Germany; 2 Physics and Geosciences Department,
Leipzig University, Leipzig, Germany
MR-visible markers have many potential applications such as an automated mapping of coordinate systems (image/patient registration), stereotactic
planning/monitoring of procedures, and the localization/tracking of devices inside the magnet. In this work, precision, accuracy and update rates of a fully
automatic marker localization based on morphologic image processing have been studied experimentally as well as theoretically (simulation) as a function of
the underlying pixel size. The moderate 3D errors (¡Ö1 mm) observed for the fastest sequence (pixel dimension 4.7 mm) clearly demonstrate that the
presented technique does not necessarily require highly resolved images of the markers (physical dimension ¡Ö4 mm).
3151. Automatic MRI Acquisition Parameters Optimization Using Perceptual Criteria
Javier Jacobsen 1,2 , Sergio Uribe, 2,3 , Cristian Tejos 1,2 , Carlos Sing-Long 1,2 , Pablo Irarrazaval 1,2
1 Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; 2 Biomedical Imaging Center,
Pontificia Universidad Catolica de Chile, Santiago, Chile; 3 Department of Radiology, Pontificia Universidad Catolica de Chile,
Santiago, Chile
The visualization of structures in MRI highly depends on many user defined scan parameters. The selection of them is always done heuristically and requires
a vast experience from the operator. We propose a methodology based on an automatic optimization to find the MRI acquisition parameters that maximize
the visibility of a desired structure. The objective function of our optimization is computed from Visibility Maps (VM) that are designed to measure the
visibility of structures according a perceptual criteria. The method was tested on brain MRI experiments and the optimal parameters found by our method are
in excellent agreement with those found by experienced radiologists.
3152. A Stochastic Framework for Improving the Accuracy of PIESNO
Cheng Guan Koay 1 , Evren Ozarslan 1 , Carlo Pierpaoli 1 , Peter J. Basser 1
1 NIH, Bethesda, MD, United States
Probabilistic Identification and Estimation of Noise (PIESNO) is a recent technique capable of identifying noise-only pixels in magnitude-reconstructed MR
images. The identification criterion and the estimation method used in PIESNO were chosen and constructed for expediency in terms of computational
efficiency and theoretical simplicity rather than for accuracy. Although a strictly theoretical approach to determine the exact level of bias in the estimate of
noise level through PIESNO seems to be intractable, it is still worthwhile to use stochastic framework for determining the level of bias. Here, we present one
such framework for improving the accuracy of PIESNO.
3153. Comparison of SNR Calculation Methods for in Vivo Imaging
Bing Wu 1 , Chunsheng Wang 1 , Yong Pang 1 , Xiaoliang Zhang 1,2
1 Radiology&Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States; 2 UCSF/UC Berkeley
Joint Group Program in Bioengineering, CA, United States
Local and global SNR of in vivo MR images are often measured to evaluate the image quality. Due to the density variation of in vivo images, the motion
during the acquisition and other aspects, the SNR measurement of the in vivo image, especially at high field MRI, is much more complicated. The purpose
of this work is to evaluate and compare SNR calculation methods to provide the reference or guidance for in vivo image SNR measurements.