ELECTRONIC POSTER - ismrm

implemented. Results are shown on T1 MR liver data with simulated enhancing structures to demonstrate the volume preserving
properties.
14:00 5084. Non-Rigid Motion Compensation in MR Prostate Perfusion Imaging
Gert Wollny 1 , Isabel Casanova 1 , Thomes Hambrock 2 , Andres Santos 1 , Maria Jesus
Ledesma-Carbayo 1
1 BIT-DIE, ETSI Telecomunicación, UPM, Madrid, Spain; 2 Department of Radiology, Radboud University
Nijmegen Medical Centre, Nijmegen, Netherlands
Dynamic Contrast enhance MRI has been established as accurate method in detection and localization of prostate cancer. Time series
of three-dimensional datasets of the prostate are acquired and used to obtain per-voxel signal-intensity vs. time curves. These are then
used to differentiate cancerous from non-cancerous tissue. However, rectal peristalsis and patient movement may result in spatialmismatching
of the serial datasets and, therefore, incorrect enhancement curves. In this work, we present a method based on image
registration to compensate for these movements, and validate the method by comparing pre-and postregistration intensity time curves
to manually obtained ones.
14:30 5085. Validation of Deformable Registration of Prostate MRI with and Without
Endorectal Coil for IMRT Planning
Marnix Christiaan Maas 1 , Corijn Kamerling 1 , Simon van Kranen 1 , Sara Muller 2 , Jelle
Teertstra 2 , Floris Pos 1 , Christoph Schneider 1 , Jan Jakob Sonke 1 , Marcel van Herk 1
1 Radiotherapy, NKI-AVL, Amsterdam, Netherlands; 2 Radiology, NKI-AVL, Amsterdam, Netherlands
Using an endorectal coil (ERC) greatly improves the quality of prostate MR images, but results in displacements and deformations of
the organ and its surrounding tissues, causing systematic errors in intensity modulated radiation therapy (IMRT) planning. We have
implemented an image based method for the deformable registration of endorectal to non-endorectal MR images. Here we present a
validation of this method using markers placed on corresponding anatomical structures in pairs of fixed and deformed images. The
registration method was found to be feasible, and our results suggest that sufficient accuracy for use in radiotherapy planning is
attainable.
15:00 5086. Automatic Segmentation of the Prostate in MR Images Using a Prior Knowledge of
Shape, Geometry and Gradient Information
Yujin Jang 1 , Helen Hong 1 , Hak Jong Lee 2 , Sung Il Hwang 2
1 Division of Multimedia Engineering, Seoul Women's University, Seoul, Korea, Republic of; 2 Department of
Radiology, Seoul National University Hospital of Bundang, Seongnam-si, Korea, Republic of
To segment the prostate in MR images with a poor tissue contrast and shape variation, we propose a reliable and reproducible
segmentation method using a prior knowledge of shape, geometry and gradient information. The prostate surface is generated by 3D
active shape model using adaptive density profile and multiresolution technique. To prevent holes from occurring by the convergence
of the surface shape on the local optima, the hole is eliminated by 3D shape correction using geometry information. In the apex of the
prostate which has a large anatomical variation, the boundary is refined by 2D contour correction using gradient information.
Image Analysis
Hall B Monday 14:00-16:00 Computer 125
14:00 5087. Automated Assessment of Ghost Artifacts in MRI
Sotirios A. Tsaftaris 1,2 , Xiangzhi Zhou 2 , Rohan Dharmakumar 2
1 Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, United States;
2 Radiology, Northwestern University, Chicago, IL, United States
Flow artifacts in MR images can appear as image ghosts within and outside the body cavity. Technical improvements to suppress
these ghosts often rely on expert scoring or on semi-automated methods demanding tissue segmentation to evaluate the efficacy of the
methods. These approaches can be labor/computation intensive, introduce observer bias, or error-prone if tissue segmentation is used.
Herein we propose two fully automated image-processing methods relying on the statistical properties of background pixels to assess
the presence of flow artifacts (appearing as image ghosts) without requiring segmentation. We demonstrate that the automated
methods are as effective as image scoring approaches that rely on expert reviewers.
14:30 5088. Total Variation Denoising with Spatially Dependent Regularization
Florian Knoll 1 , Yiqiu Dong 2 , Christian Langkammer 3 , Michael Hintermüller 2,4 , Rudolf
Stollberger 1
1 Institute of Medical Engineering, Graz University of Technology, Graz, Austria; 2 Institute of Mathematics and
Scientific Computing, University of Graz, Graz, Austria; 3 Department of Neurology, Medical University Graz,
Graz, Austria; 4 Department of Mathematics, Humboldt-University of Berlin, Berlin, Germany
The Total Variation regularization model is popular in MR research. In this model, a regularization parameter controls the trade-off
between noise elimination, and preservation of image details. However, MR images are comprised of multiple details. This indicates
that different amounts of regularization are desirable for regions with fine image details in order to obtain better restoration results.
This work introduces spatially dependent regularization parameter selection for TV based image restoration. With this technique, the