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Medical Metrology

Department 8.1

Bachelor and Master projects

Compensation of physiological motion for MR applications (batchelor or master project)

Breathing or the beating of the heart can lead to movement of organs in the human body. This physiological motion can strongly impair the quality of MR images. We develop novel approaches to minimise these motion artefacts and to ensure excellent diagnostic accuracy of MRI for a wide range of applications, such as quantitative cardiac MRI or PET-MR.

If you are studying physics, electrical engineering or a comparable area of engineering/natural sciences and you are interested in a summer project or BSc/MSc project in the field of motion compensation, please contact Christoph Kolbitsch (Dr. Christoph Kolbitsch).

Advanced image reconstruction for MRI (master project)

Magnetic Resonance Imaging (MRI) is a versatile medical imaging technique which is widely used in clinical practice. In recent years, great advances have been made in the area of MR image reconstruction introducing novel concepts such as parallel imaging, compressed sensing or machine learning. We are working on novel concepts for speeding up MR image reconstruction utilizing GPUs or optimizing OpenMP implementations.

If you are studying physics, electrical engineering or a comparable area of engineering/natural sciences and you are interested in a summer project or BSc/MSc project in the field of advanced image reconstruction for MRI, please contact Christoph Kolbitsch (Opens window for sending email Dr. Christoph Kolbitsch).

Validierung von HF-Feld Messmethoden am 7T Ultra-Hochfeld MR-Tomographen (Masterarbeit)


In unserer Arbeitsgruppe entwickeln wir neue Methoden für die Magnetresonanzbildgebung (MRI) auf sehr hohen magnetischen Feldern (z.B. B0=7T). Unser Hauptziel ist es, das höhere Signal-Rausch-Verhältnis für in-vivo-Anwendungen am Menschen nutzen zu können. Ein Problem hierbei besteht jedoch in der räumlich inhomogenen Verteilung der Hochfrequenzfelder (HF-Felder), welche zur Anregung der Kernspins notwendig sind, da hierdurch räumliche Variationen der Bildsignalintensität auftreten. Um die Inhomogenitäten der HF-Felder zu quantifizieren und auszugleichen werden HF-Feldkarten gemessen, wobei die Präzision und Genauigkeit dieser Karten für die Bildgebung entscheidend ist. Während Ihrer Tätigkeit unterstützen Sie unsere Arbeitsgruppe beim Erstellen dieser Karten durch verschiedene Messmethoden und deren Vergleich, sowie Optimierung zur Etablierung einer reproduzierbaren Methode.


Ziel der Arbeit ist es, vorhandene Messmethoden zur Bestimmung der HF-Feldverteilungen von Spulen mit 8 Sendeeinheiten am 3 Tesla und 7 Tesla System anzuwenden, sie zu optimieren und die Präzision und Genauigkeit der Methoden zu quantifizieren. Zunächst werden Phantommessungen am 7T MRI Scanner durchgeführt, welche anschließend mit Messungen einer Magnetfeld Sonde sowie numerischen Finite Differenzen Methode verglichen werden. Zuletzt sind in-vivo Kartierungen im menschlichen Kopf geplant.



·         Studium der Physik, Elektrotechnik, Informatik, Medizintechnik oder ein vergleichbarer Studiengang

·         Programmierkenntnisse (bevorzugt in Matlab/Python und C/C++) sind vorteilhaft

·         Interesse an MR-Physik

Ansprechpartner:            Dr. Sebastian Schmitter (sebastian.schmitter@ptb.de, +49 030 3481-7767)

                                               Natalie Schön (natalie.schoen(at)ptb.de, +49 030 3481-7781)

Calculation of 3D RF field maps using neuronal networks in ultrahigh field MRI

Calculation of 3D RF field maps using neuronal networks in ultrahigh field MRI


In our research group, we are developing new methods for magnetic resonance imaging (MRI) at ultrahigh magnetic fields (e.g., 7 Tesla). Our main goal is to be able to use the higher signal-to-noise ratio for in vivo human applications. However, a problem here is the spatially inhomogeneous distribution of the radio frequency (RF) field necessary to excite the nuclear spins that result in spatial variations of the image signal intensity. It has been shown that this spatial inhomogeneity can be successfully compensated by so-called multi-element RF coils: homogeneous signals and contrast can be generated by an optimized superposition of the transmitting fields of e.g. 8 independent RF transmitting elements.

However, an essential prerequisite for the optimization of the resulting field and the subsequent "parallel transmission" is that the RF fields of the individual elements are known. These must be measured for each subject at the beginning of the actual examination and this process takes too long with current measurement times of 5-15 minutes. Thus, it severely hinders the conductance and performance of patient studies at 7 Tesla.


Recently, a new method has been developed within the group, which can predict these RF fields within a few seconds using a neural network. As input of this network a fast localizer image is used, which is acquired anyway at the beginning of each measurement. These predicted maps can then be used to optimize the transmit fields to obtain a homogeneous image intensity in a two-dimensional layer.


The goal of this work is to systematically investigate and extend the accuracy of this method. This objective is divided into three work packages.

  1. In the first step the method shall be systematically investigated for its accuracy compared to conventional mapping methods.
  2. In the second step the network shall be extended so that it can also use 3D data instead of only 2D data so far. This will then allow the RF fields to be determined over a three-dimensional volume.
  3. In the last step, the maps determined in this way will be used to calculate RF pulses that achieve homogeneous excitation of a 3D volume. These pulses are to be implemented in a suitable MR sequence and finally validated in a study with human subjects.




  • Study of physics, electrical engineering, computer science, biomedical engineering or a comparable course of study
  • good programming skills (Python/MATLAB)
  • initial experience in the field of deep learning is advantageous
  • interest in MR physics



Dr. Sebastian Schmitter (Sebastian.Schmitter(at)ptb.de,   +49 030 3481-7767)
web: https://www.ptb.de/cms/en/ptb/fachabteilungen/abt8/fb-81/ag-814.html



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