Graduate Projects

At MD&I, we develop new laboratory device for fingerprinting of magnetic nanoparticles (MNP). These MNP are used in healthcare, environmental research, oil recovery, but also in automotive or textile industry. Our laboratory susceptometry device, the superparamagnetic quantifier (SPaQ), is used for magnetic characterization of MNPs, but also for further development of detection probes based upon our patented magnetic detection method: Differential Magnetometry. Our research line in differential susceptometry concentrates on:

• Characterization of magnetic tracers with respect to e.g. concentration, viscosity of medium, type of medium (e.g. water, glycerin, blood plasma).
• Optimization of magnetic detection (e.g. with respect to excitation frequency, power). 
• Modelling of magnetic properties of the tracers. 
• Redesign SPaQ regarding the cooling system

At MD&I we are developing new handheld devices to localize magnetic nanoparticles in the human body. These handheld probes use a patented nonlinear detection principle (differential magnetometry). The nanoparticles are usually injected intravenously or intra/peri tumoral, and accumulate in a primary tumor and (regional) lymph nodes. The main clinical application we currently focus on is sentinel lymph node biopsy (SLNB), which is intended to dissect lymph nodes with potential metastases. Main clinical focus involves breast cancer, cancer in head and neck region, colorectal cancer, and prostate cancer. Ongoing technical research involves development of detection methods for both open surgery and laparoscopic surgery. The following assignments (not limited to) are presently open:

• DiffMag detection principle uses a set of excitation and detection coils. The detection coils detect the signal originating from magnetic nanoparticle, but also the excitation magnetic field. To compensate for the excitation field, a dedicated active balancing algorithm is used that is facilitated by an additional set of compensation coils. This assignment involves optimization of the active  balancing algorithm. 
• Comparison of DiffMag detection principle to other nonlinear detection principles (e.g. second harmonics). 
• Translate and adapt signal processing & control software from current prototype (Matlab) to C(++) for use with a microcontroller. Good knowledge of C++ and experience with microcontrollers/Arduino is desired. As the system is meant to work as a medical device, both the hardware and software should comply to certain norms (IEC 60601 and IEC 62304).
• The shine through effect is problematic in some clinical applications. In this assignment you will develop and test a new algorithm to distinguish the signal from different sources in the sensitive area of the probe. 

Ongoing medical research involves ex vivo & in vivo validations of detection methods for both open and laparoscopic surgery. The following assignments (not limited to) are presently open:

• Clinical implications of new handheld probes for treatment of head & neck cancer. 
• Effect of applied magnetic field on human tissue with and without magnetic particles (e.g. temperature effects).

In our research group we develop new methods to locate magnetic nanoparticles in the human body. These nanoparticles are usually injected intravenously or intra/peri tumoral, and accumulate in a primary tumor and (regional) lymph nodes. The main clinical application we focus on is a sentinel lymph node biopsy (SLNB), which is intended to resect lymph nodes with potential metastases. Ongoing technical research involves preoperative lymph node staging (lymphography) using multi sequence MRI. The experimental imaging protocol contains preoperative MRI, ex vivo MRI of resected lymph nodes, and histological sections. During this assignment, you will dive into the world of MRI imaging of lymph nodes enhanced with nanoparticles. The following assignments (not limited to) are presently open:

• Ex vivo MRI of resected lymph nodes using a table-top MRI (an experimental, low-cost MRI device). The scope of this assignment 
  concentrates on development of table-top protocols that would allow a fast and cost-effective lymph node staging during the surgery. 
• The scope of this assignment concentrates on digital pathology with an aim to develop Artificial intelligence algorithm for classification lymph nodes (metastatic, non-metastatic), and for classification of relevant tissue regions within one histology image. The scope of this assignment concentrates on co-registering the lymph node images (ex vivo MRI) to histology images, labeling relevant histological regions (e.g. necrosis, metastases, nanoparticles, fat), and lymph node classification using artificial intelligence.