Interview with Jaber Malekzadeh-Najafabadi, TUM

In this instalment of our ‘STARSTEM researchers’ series, we talk to Dr Jaber Malekzadeh-Najafabadi, who recently defended their PhD at the Technical University of Munich (TUM).

The Institute of Biological and Medical Imaging (IBMI or Munich imaging) of the Helmholtz Zentrum München in Munich, Germany, is a multi-disciplinary academic research structure strongly integrated with the Chair of Biological Imaging at the Technical University of Munich. IBMI scientists develop next-generation imaging and sensing methods to measure previously inaccessible properties of living systems, hence, catalyzing breakthroughs in biology and medicine. Comprising 10 inter-disciplinary laboratories and scientists from more than 25 countries, IBMI offer state-of-the-art infrastructure for innovative research. IBMI research aims to shift the paradigm of biological discovery and translation to address major health challenges of our time and develop the medical solutions of tomorrow.

Please introduce yourself.

I am Jaber Malekzadeh-Najafabadi and I started my PhD research at Technical University of Munich (TUM) in 2014 at the department of Electrical and Computer Engineering. Last month, in April, I successfully defended my PhD thesis on ‘Studying nonlinear effects in optoacoustics’. I will continue my research as a postdoctoral researcher at TUM. I am working on optoacoustic imaging and sensing to understand the principles of optoacoustics (OA) and improve the OA data quality. I am mainly interested in data analysis and nonlinear optoacoustics and want to pursue my carrier in the field of biomedical imaging and sensing.

 

TUM is a leader in biological imaging, optical and optoacoustic methods. Can you give us an overview of how TUM contributes to STARSTEM?

At TUM our department of biological imaging is a pioneer in optoacoustic imaging. We have OA systems that work on micro, meso and macroscopic scales, therefore OA is suitable for both preclinical and clinical studies. As a partner in the STARSTEM project, we are responsible for several tasks related to OA imaging of small animals including biodistribution and therapy monitoring, OA data analysis and multimodal image co-registration using large animal MRI and OA data produced by our partners.  I am involved in both the activities. Additionally, we have established a data storage system at TUM. This system is shared with all partners to save all of the data generated during this project. This will assure the long term storage of all the STARSTEM data.

 

What is your role in STARSTEM?

I have three main tasks in STARSTEM – small animal OA data analysis with our biologist, developing an algorithm for image co-registration for OA and MRI, and software development to carryout image registration. I am responsible for reconstructing and analysing the optoacoustic data acquired at TUM. Moreover, I am developing a co-registration algorithm for optoacoustic and MRI images that are acquired by our partner in the University of Cambridge. I am also developing software for the image co-registration algorithm; this work is done with the help of our partners at iThera Medical, University of Cambridge, and NUIG.

 

 

What are the biggest challenges you face with your research?

In my field, I found two unsolved challenges and I have tried to solve them during my PhD. First challenge is the main cause of the nonlinear optoacoustic and the second challenge is fluence correction of multispectral optoacoustic data. This work is also useful in STARSTEM, because both phenomenon affect the acquired optoacoustic data.

In STARSTEM, in addition to OA data analysis, the development of the co-registration algorithm is the main challenge because the optoacoustic and MRI images are separately acquired by two different imaging systems that have different resolutions. Both imaging systems present different biological information. For instance, OA images represent a map of optical absorption coefficient of tissue, whereas MRI images provide information about soft tissue. Moreover, the misalignment in the two modalities is complex and is not linear.

 

Why is STARSTEM important to TUM?

STARSTEM will generate optoacoustic data in the field of regenerative therapy. This will open a new field for optoacoustics in the clinic. Additionally, development of co-registration algorithms for multimodal imaging will help physicians in the clinic by facilitating better decision making, improving personalised therapy, and following the therapy response using minimally-invasive methods.

 

Personally, what has been the best part of working on STARSTEM?

STARSTEM is an international and interdisciplinary project, that brings together leaders in the nano-materials, regenerative medicine,  bio-imaging, physics and electrical engineering fields from across Europe. It has provided a great opportunity for me to collaborate with different partners and enhance my knowledge and network.

 

How has Covid-19 impacted your work as a researcher? Tell us about your experiences.

Our work on developing and validating image co-registration algorithms and software, is largely dependent on the data generated from the small animal and large animal studies that are carried out at partner sites.  Unfortunately, during the lockdown various experiments have been delayed at different institutes including ours. Therefore, due to limited time available, we have to carry out  data analysis and validation of algorithms and software with limited time and data. By putting in extra hours, we are confident that we will generate necessary and meaningful information.