TRADITIONAL POSTER - ismrm

Poster Sessions
3132. Effects of Treatment on Brain Tissue Classification with Serial MRI-Based ISODATA Cluster Analysis
in an Experimental Subarachnoid Hemorrhage Model
Mark J.R.J. Bouts 1 , Ivo A.C.W. Tiebosch 1 , René Zwartbol 1 , Ona Wu 2 , Rick M. Dijkhuizen 1
1 Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands; 2 Athinoula A. Martinos center for biomedical
imaging, Massachusetts General Hospital, Charlestown, MA, United States
Voxel-wise clustering of multiparametric MRI data enables classification of heterogeneous ischemic lesions into distinct categories. Previously, we have
introduced a lesion clustering approach that incorporates temporal T2 and diffusion dynamics for tissue characterization. In the current study we extend this
approach in an experimental subarachnoid hemorrhage model, to evaluate lesion characteristics in a treatment and control group based on temporal changes
in T2, diffusion, and perfusion parameters. Five distinct signatures with different characteristics of cerebrovascular injury were identified and signature
distribution revealed a different prevalence in Interferon-β treated animals compared to controls.
3133. A Multi-Anatomy System for Computing and Centering Field of View from Localizer Images
Vivek Prabhakar Vaidya 1 , Maggie M. Fung 2 , Rakesh Mullick 1 , Robert D. Darrow 3
1 GE Global Research, Bangalore, Karnataka, India; 2 GE Healthcare, Waukesha, WI, United States; 3 GE Global Research, Niskayuna,
NY, United States
A system is demonstrated for automatically deriving and centering oblique scan extents/fields of view (FOV) from localizer scans. Our method differs from
prior work in the field by being marker-less and allowing for automated acquisitions oblique to the input localizer. By constraining acquisition to the precise
extents of the anatomy being sought acquisition time is reduced. This acquisition time reduction is particularly valuable in cardiac and abdominal imaging:
given the need for breath-held scanning. Furthermore, by prescribing an optimal field of view we can also reduce potential wrapping artifacts and improve
the consistency of image representation.
3134. Automated Volume of Interest Evaluation for Sequence Development
Ying Wu 1,2 , Hongyan Du 3 , Fiona Malone 1 , Shawn Sidharthan 1 , Ann Ragin 4 , Robert Edelman 1,5
1 Radiology, NorthShore University HealthSystem, Evanston, IL, United States; 2 Radiology , University of Chicago; 3 NorthShore
University HealthSystem Research Institute, IL, United States; 4 Radiology, Northwestern University; 5 Radiology, University of
Chicago
This investigation compared the standard manual region of interest approach with a volume-of-interest analysis based on automated brain segmentation.
Analysis based on automated VOI successfully detected subtle changes in tissue contrast and was consistently informative for MR sequence optimization.
Results based on the standard ROI approach were ambiguous in different brain regions and individuals, and failed to document changes in image quality
when scanning parameters were alternated in MR sequence optimization. These findings demonstrate the potential benefit of integrating advanced
quantitative image analysis into sequence development routines to improve efficiency and accuracy.
Registration & Image Analysis
Hall B Wednesday 13:30-15:30
3135. Combining Variational and Model Based Techniques to Register MR Finger Images and PET Hand
Data
Derek Magee 1 , Steven Frederick Tanner 2 , Michael Waller 3 , Ai-Lyn Tan 4 , Dennis McGonagle 4 , Alan
Jeavons 3
1 School of Computing, University of Leeds, Leeds, W-Yorkshire, United Kingdom; 2 Division of Medical Physics, University of
Leeds, Leeds, W-Yorkshire, United Kingdom; 3 Medical Physics, St James University Hospital, Leeds, United Kingdom; 4 Academic
Section of Musculoskelatal Disease, Chapel Allerton Hospital, Leeds, W-Yorkshire, United Kingdom
A non-rigid image registration method for co-registering high-resolution PET data and MR images of the hand is described and evaluated. Employing this
protocol to register synthetic data indicated a the mean registration error of less than approximately 1.5 mm. Measurements made in images acquired from
patients with osteoarthritis show that the registration errors are consistent with those made in the study using synthetic data.
3136. Automated Scan Plane Planning for Brain MRI Using 2D Scout Images
Suguru Yokosawa 1 , Yo Taniguchi 1 , Yoshitaka Bito 1 , Hisako Nagao 2 , Miki Tachibana 2 , Mutsumi Ishida 2 ,
Atsushi Shiromaru 2 , Hiroyuki Itagaki 2
1 Central Research Laboratory, Hitachi, Ltd., Kokubunji-shi, Tokyo, Japan; 2 Hitachi Medical Corporation, Kashiwa-shi, Chiba, Japan
We propose a faster automated scan plane planning method for the brain using 2D multi-slice orthogonal three-plane scout images. Our algorithm based
method, uses 2D scout images, which can be acquired rapidly. Furthermore, our algorithm can prescribe scan plane faster than other method that use 3D
data due to the smaller 2D data size. We applied our proposed method to healthy volunteers, and compared automatically defined scan plane position with
those manually defined. The results showed that our method prescribed scan planes quickly and with acceptable accuracy in clinical practice.