Electronic Posters: Cardiovascular

Image Processing: Myocardium & Vascular

Hall B Monday 14:00-16:00 Computer 39

14:00 3728. Peak Angiogram Calculations from 4D Flow Imaging

Michael Loecher¹, Kevin Johnson¹, Christopher Francois², Oliver Wieben<sup>1

1</sup>Department of Medical Physics, University of Wisconsin, Madison, WI, United States; ²Department of Radiology, University of Wisconsin, Madison, WI, United States

This study aims to assess an alternative reconstruction method that utilizes the temporal information from a 4D radially encoded flow scan. The method creates and angiogram from dynamic time frames instead of a time averaged reconstruction. While the approach increases background noise, it alleviates the problem of signal drops and voids from reversing flow patterns. The utility of the algorithm was evaluated in a group of 4 volunteers and 6 patients, demonstrating improved signal consistency along the aorta.

14:30 3729. Unsupervised Reconstruction for Ungated Ghost Angiography by Clustering of Image Features

Sotirios A. Tsaftaris^1,2, Erik Offerman³, Robert R. Edelman³, Ioannis Koktzoglou<sup>3

1</sup>Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, United States; ²Radiology, Northwestern University, Chicago, IL, United States; ³Radiology, NorthShore University HealthSystem, Evanston, IL, United States

Ghost magnetic resonance angiography (MRA) has been proposed as an unenhanced and ungated method for angiography. The method requires manual post-processing to identify suitable slices in a large stack from which to create an interpretable angiogram. To maximize the contrast of the final angiogram it is necessary to eliminate slices located within the body and to carefully select the slices that contain conspicuous ghost artifacts. This time-consuming process can also introduce unwanted inter- and intra- observer variability. The purpose of this work was to completely automate the reconstruction process during ungated and non-contrast-enhanced Ghost MRA using image analysis and clustering.

15:00 3730. Level-Set Segmentation of Arterial and Venous Vessels Based on ToF-SWI Data

Andreas Deistung¹, Michal Strzelecki², Andrzej Materka², Jürgen R. Reichenbach<sup>1

1</sup>Medical Physics Group, Department of Diagnostic and Interventional Radiology , Jena University Hospital , Jena, Germany; ²Institute of Electronics, Technical University of Lodz, Lodz, Poland

Non-invasive quantitative assessment of the cerebral vasculature is of high diagnostic and therapeutic interest. The pre-requisite for the quantitative description of blood vessels is voxel-wise classification into vessel and non-vessel structures. In this contribution, we use a hybrid level-set approach that relies on both boundary and region information to segment arterial and venous vessels from simultaneously acquired time-of-flight (ToF) and susceptibility weighted imaging (SWI) data to create a 3D representation of the arterial and venous vasculature.

15:30 3731. Fast Plaque Burden Assessment of the Femoral Artery Using 3D Black-Blood MRI and Automated Segmentation

Bernard Chiu¹, Xihai Zhao¹, Jinnan Wang², Niranjan Balu¹, Chun Yuan¹, William S. Kerwin<sup>1

1</sup>Radiology, University of Washington, Seattle, WA, United States; ²Clinical Sites Research Program, Philips Research North America, Briarcliff Manor, NY, United States

Peripheral arterial disease (PAD) is a serious health issue in the western world. Recent advances in high-resolution MRI have allowed noninvasive and detailed assessment of PAD, including black-blood MRI visualization of the vessel wall. Because the length of a femoral artery is substantial, a long field of view is required to image the femoral artery. Manual outlining of wall boundaries along the entire length of the femoral artery is an arduous task. In this work, we proposed and demonstrated an automatic algorithm that is capable of accurately segmenting the lumen and wall boundaries along the majority of the femoral artery.