ELECTRONIC POSTER - ismrm
ELECTRONIC POSTER - ismrm
ELECTRONIC POSTER - ismrm
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14:30 4144. TMAP @ IFE - A Framework for Guiding and Monitoring Thermal Ablations<br />
Eva Rothgang 1,2 , Wesley David Gilson 2 , Joerg Roland 3 , Joachim Hornegger 1 , Christine<br />
H. Lorenz 2<br />
1 Pattern Recognition Lab, Department of Computer Science, University of Erlangen-Nuremberg, Erlangen,<br />
Germany; 2 Center for Applied Medical Imaging, Siemens Corporation, Corporate Research, Baltimore, MD,<br />
United States; 3 Siemens Healthcare, Erlangen, Germany<br />
Thermal ablations are increasingly used for minimally invasive local treatment of solid malignancies, supplementing systemic<br />
treatment strategies such as chemotherapy and immunotherapy. We present an intuitive application for monitoring thermal treatment<br />
independent of heating source, in real-time, with multiplanar MRI. Systematic quality control of thermal maps is carried out on-line to<br />
ensure reliability of the displayed thermal data. The application is fully integrated into the Interactive Front End (IFE) which allows<br />
real-time MRI-guided placement of the heating device. Thus, the presented application supports the thermal ablation workflow from<br />
placing the ablation device to online monitoring the progress of ablation.<br />
15:00 4145. Imitation of Radiofrequency Ablation Scars with Laser System for MR Guided<br />
Ablation of Atrial Fibrillation<br />
Can Kerse 1,2 , Bulent Oktem 3 , Fatih Omer Ilday 4 , Ergin Atalar, 2,5<br />
1 Department of Electrical and Electronics Engineering , Bilkent University, Ankara, Turkey; 2 UMRAM<br />
(National Research Center for Magnetic Resonance), Ankara, Turkey; 3 Material Science and Nanotechnology<br />
Graduate Program, Bilkent University, Ankara, Turkey; 4 Physics Department, Bilkent University, Ankara,<br />
Turkey; 5 Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey<br />
Fiber delivery of laser energy has no MR compatibility issues and is used with MR guidance in the field. But, it is not widely used as<br />
the cure of Atrial Fibrillation, since there is risk of perforating the myocardial wall. Several diffusing tip designs to emit light in<br />
cylindrical symmetry exist, but, due to their orientation with respect to the cardiac chamber, common RF delivery methods cannot be<br />
applied directly. In our study, we propose a novel multiple fiber laser energy delivery with catheter approach and a system that<br />
imitates the scars created with RF probes under MR guidance.<br />
Thursday 13:30-15:30 Computer 65<br />
13:30 4146. Feasibility of Multipolar Radiofrequency Ablation in the Pig Liver Under<br />
Simultaneous Real-Time MR Thermometry<br />
Matthieu Lepetit-Coiffe 1 , Alexandru Cernicanu 2 , Olivier Seror, 1,3 , Thomas Stein 4 ,<br />
Yasmina Berber 1 , Mario Ries 1 , Chrit T. Moonen 1 , Bruno Quesson 1<br />
1 Laboratoire IMF CNRS UMR 5231 / Universite Bordeaux 2, Bordeaux, France; 2 Philips Healthcare, Suresnes,<br />
France; 3 Service de Radiologie, Hopital Jean Verdier, Bondy, France; 4 Celon/Olympus, Teltow, Germany<br />
The feasibility of real-time MR thermometry for monitoring multipolar RF ablation was demonstrated both ex vivo and in vivo on a<br />
pig liver. The quality of the real-time thermal dose maps appeared satisfactory in the presence of respiratory motion and cyclic<br />
switching between the different pairs of RF electrodes. The large (about 250 cm3) resulting thermal coagulation volume was spatially<br />
homogeneous as predicted by the real-time lethal thermal dose maps and confirmed by the post procedural MR imaging and histology.<br />
14:00 4147. Breath Hold Phase Correction for Water-Fat Separated MR Thermometry Using<br />
B0 Field Changes<br />
Cory Robert Wyatt 1 , Brian J. Soher 2 , James R. MacFall 2<br />
1 Department of Biomedical Engineering, Duke University, Durham, NC, United States; 2 Department of<br />
Radiology, Duke University Medical Center, Durham, NC, United States<br />
Proton resonance frequency shift (PRFS) thermometry of the breast is often confounded not only by the presence of fat in the tissue<br />
but also by respiration-induced B0 changes even in the absence of detectable breast motion. In this work, field fitting techniques used<br />
previously are used to extrapolate fat referenced B0 changes measured using fat-water separation methods to B0 changes in a wateronly<br />
simulated tumor in a fat-water breast phantom. Results show that the B0 map extrapolation method reduces PRFS temperature<br />
errors between breath holds from a maximum of 5.55°C to less than 0.53°C.<br />
14:30 4148. Real-Time Non Subtraction Thermometry Using Artificial Neural Networks<br />
Manivannan Jayapalan 1<br />
1 MR SW & Applications Engg, GE Healthcare, Bangalore, Karnataka, India<br />
Thermal monitoring in focused ultrasound applications is crucial step where MR is most widely used as it provides better thermal<br />
monitoring capability than others. Regular PRF shift technique involves, some form of image subtraction using a baseline pretreatment<br />
images. Subject motion and tissue deformation due to coagulation can severely distort these techniques. Self-referenced<br />
methods require a large area of tissue around the ablation for polynomial fitting and can’t be used when tissue cooling is applied to<br />
sensitive structures. Here a new method of thermal monitoring using Radial Basis Function Neural Network (RBFNN) trained by<br />
orthogonal least square algorithm is proposed. This method eliminates the need for baseline subtraction and also tolerates subject<br />
motion to a great extent. A feed forward, radial basis neural network is used with 2 input, 1 output and a hidden layer where the<br />
number of units in that layer is obtained using orthogonal least square algorithm learning method. Gaussian function is used as kernel<br />
whose centers are obtained through network learning. 2-D surface co-ordinates of phase image in a selected ROI is used as inputs<br />
while its corresponding phase value are used as output to train the network. Then the network is tested, where, the phase values<br />
obtained from the network and the actual values are compared. It was observed that the network output matches very well with the<br />
actual values which clearly proves that the neural networks approximates the phase distribution function very well.