Department of Radiology - Medical Physics

Serhat Ilbey, Ali C. Özen

In a conventional MRI exam the excitation and acquisition events are time-interleaved: MR signal is first generated by a strong radio-frequency excitation and later the weak MRI signal is acquired. Concurrent excitation and acquisition (CEA) MRI is performed by simultaneous excitation of the spins and acquisition of the resulting signal. Concurrent excitation and acquisition (CEA) can overcome these limitations, but CEA is extremely challenging due to the huge difference between transmit and receive signal levels. Therefore, we developed novel techniques to decouple transmit and receive parts of the MRI system, and adapted full-duplex telecommunication techniques to MRI to implement CEA in clinical MRI systems. 

We have developed a novel software tool for optimization of the CEA contrast. With CEA the signal of ultrashort-T₂* species has become detectable. We demonstrated nearly 100 % MR signal acquisition efficiency by CEA. In addition, CEA can be eliminated with virtually no acoustic noise, thus increasing patient comfort. Finally we have used extremely low peak RF powers and reduced the RF-power-deposition-related risks of MRI. CEA MRI is a promising tool for various clinical applications including musculoskeletal imaging, dental imaging, connective tissue and myelinated neurons.

References

Özen, A. C., Bock, M., & Atalar, E. (2015). Active decoupling of RF coils using a transmit array system. Magnetic Resonance Materials in Physics, Biology and Medicine, 28(6), 565–576.

Özen, A.C., Atalar, E., Korvink, J.G., Bock, M. In vivo MRI with Concurrent Excitation and Acquisition using Automated Active Analog Cancellation. Sci Rep 8, 10631 (2018).

Cooperation Partners

University of Minnesota, Center for Magnetic Resonance Research

Johannes Fischer, Agazi Tesfai

This project focuses on MR imaging of ancient human remains, which poses two main problems: The artificial dehydration necessary for the successful preservation vastly reduces signal strength and the increased coupling of the remaining hydrogen nuclei in the solid material leads to very short T₂* relaxation times. For imaging of these specimens we use ultra-short echo time (UTE) and single point imaging (SPI) sequences as well as custom built transmit/receive coils. The coils are simulated and adjusted to the sample with analytic estimation of signal to noise ratio (SNR) depending on coil geometry and specimen.

References

Tesfai, A.S., Fischer, J., Özen, A.C., Eppenberger, P., Oehrstroem, L., Rühli, F., Ludwig, U. and Bock, M., 2020. Multi-parameter Analytical Method for B1 and SNR Analysis (MAMBA): An open source RF coil design tool. Journal of Magnetic Resonance, 319, p.106825.

Özen, A.C., Ludwig, U., Öhrström, L.M., Rühli, F.J. and Bock, M. (2016), Comparison of ultrashort echo time sequences for MRI of an ancient mummified human hand. Magn. Reson. Med., 75: 701-708.

Fischer, J. Özen, A.C., Kurzhunov, D., Reisert, M., Tesfai, A., Rühli, F.J., Ludwig, U. and Bock M. Cross-Modality MR Image Reconstruction: CT-Constrained Anisotropic Diffusion to Preserve Edge Information in MRI of an Ancient Mummified Hand, Proceedings of the 25^th ISMRM annual meeting, Honolulu, Hawaii.

Tesfai, A. Fischer, J., Özen, A.C., and Bock M. Comparison of different RF coil designs for short T2* samples, Proceedings of the 25^th ISMRM Annual Meeting, Honolulu, Hawaii.

Tesfai, A. Fischer, J., Özen, A.C., Ludwig, U. and Bock M. Effects of Reduced Dead Times on the SNR of Tissue with Tissue with Short T2* values, Joint Annual Meeting ISMRM-ESMRMB, Paris, France.

Tesfai, A. Fischer, J., Özen, A.C., Ludwig, U. and Bock M. Multiparameter Analysis Method for B1 Acquisition (MAMBA): A tool for RF coil design and SNR estimation for short T2* samples, Proceedings of the 27^th ISMRM Annual Meeting, Montréal, QC, Canada.

Cooperation Partners

Institute of Evolutionary Medicine (IEM), University of Zürich

AG Koch, University of Marburg

Grant Support

Deutsche Forschungsgemeinschaft (DFG) Grants BO 3025/8-1 and LU 1187 / 6-1

Lars Bielak, Deepa Gunashekar, Leonard Hägele, Michael Bock

In this artificial intelligence project, tools are developed (particularly, deep neural networks) to process and analyze large amounts of data from various patient studies. In particular, the automatic segmentation of head & neck and prostate tumors is connected with the data acquisition techniques in MRI to improve imaging protocols and, thus, to achieve the optimal patient treatment.

The work is linking AI research and computer science with development and optimization of MRI cancer imaging protocols and general data pre- and post-processing. We closely collaborate with clinical partners in the university medical center (Dept. Radiation Oncology) as well as partners from industry (MathWorks).

References

Bielak L, Wiedenmann N, Nicolay NH, et al. Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction. Tomogr. J. Imaging Res. 2019;5:292–299.

Cooperation Partners

Department of Radiation Oncology, University Medical Center Freiburg

MathWorks

Grant Support

Joint Imaging Project, German Consortium for Translational Cancer Research (DKTK)

Klaus Tschira-Stiftung

Ali Özen, Ute Ludwig

MRI pulse sequences for imaging of ancient remains such as Egyptian mummies (in cooperation with the Univ. Zürich)

References

Özen A, et al. Comparison of ultrashort echo time sequences for MRI of an ancient mummified human hand. Magn Reson Med. 2015 Mar 7. [Epub ahead of print]