Ioannis Angelos Giapitzakis

Alumni of the Research Group MR Spectroscopy

Main Focus

The last twenty years several techniques have been developed enabling the investigation of the function of human brain, as well as the study of the biochemical procedures taking place in the brain.

Magnetic Resonance Imaging plays an predominant role in this direction. Methods such as functional MRI (f-MRI), Diffusion Tensor Imaging (DTI) and Spectroscopy (MRS) further enlight  our knowledge about the human brain function.

During my  studies I have dealt mainly with fMRI and DTI, gaining significant knowledge and experience regarding the applications of these methods in neurosciences. My previous research concerned the study of myelination process in children with craniosynostosis (Diploma thesis) and the combination of DTI and fMRI to investigate the default mode network (DMN) in people suffering from posterior cortical atrophy (PCA)

However, my main goal was eventually to deal further with the MRS and its applications in neurosciences. The aim of my current PhD research is first the development of a robust protocol of MRS exploiting all the advantages of the Ultra High Fields (UHF) scanners (increased Signal to Noise ratio, higher spectral resolution, increased frequency dispersion of the metabolites). In the last stage, this protocol will be implemented in behavioural sciences and neurosciences. In particular, in studies using pharmacological stimulation

My current scientific interests are focused on the development of innovative methods for MRS @ 9.4 Tesla. The main localization schemes which were developed and optimised for in vivo studies @ 9.4T are metabolite cycled STEAM (MC STEAM) and Metabolite cycled sLASER (MC sLASER). In addition, double inversion recovery technique was optimised for the characterization of the macromolecule baseline of in vivo  human brain @ 9.4T.

PhD project: Functional Magnetic Resonance Spectroscopy at ultra-high field strength

In vivo Proton Magnetic Resonance Spectroscopy (1H-MRS) is a non-invasive method allowing the detection, as well as the quantification of several biochemical compounds in the body. Many previous studies utilized the unique information offered by this technique in order to investigate the changes in the concentration of metabolites in the brain. As a consequence, useful conclusions can be extracted regarding the function and the metabolic procedures taking place in the brain. In addition, recent technological achievements regarding the hardware and the software used in magnetic resonance imaging (MRI) enable the research of the brain metabolite profiles at ultra-high magnetics fields (i.e. ?7T). The advantages of higher static fields (Bo) concern mainly increased Signal to Noise Ratio (SNR) and higher chemical shift dispersion leading to better spectral resolution. Nonetheless, technical limitations due to Bo inhomogeneity, radiofrequency (RF) pulses, as well as chemical shift displacement and lipid contamination demand a better protocol to be established. For this purpose, this PhD project concerns the development and improvement of a protocol for the localization, post-processing and quantification of the MRS Signal. Finally, this protocol will be applied in functional MRS (f-MRS) studies using pharmacological stimulation.

Optimisation of Asymmetric Adiabatic Pulses for Single Voxel Metabolite Cycled 1H-MRS in the Human Brain at 9.4 Tesla

Metabolite cycling is a method in which the metabolites either up-field or down-field of the water peak are alternately inverted [1] using asymmetric adiabatic pulses [2] leaving the water peak highly unaffected. Thus, the subtraction of two consecutive acquisitions leads to a spectrum without the water peak while the sum to a pure water peak spectrum. The advantages of this technique compare to other water suppression techniques are that the water signal can be used for frequency alignment and absolute quantification [3, 4]. The purpose of this research was to optimize the characteristics of the asymmetric adiabatic pulse for metabolite cycling implemented in a STEAM sequence at 9.4 Tesla.

[1] Dreher W., Leibfritz D. MRM. 2005; 54(1):190-195 [2] Hwang T., van Zijl, Garwood M. JMR. 1999; 138(1):173-177 [3] Donnie Cameron et al. Proc 21st ISMRM. 2009:1359 [4] Andreas Hock et al. MRM. 2013; 69(5):1253-1260

Comparison of different methods for combination of multichannel spectroscopy data

Single voxel magnetic resonance spectroscopy (SVS) enables the study of several metabolites in different body parts yielding useful information regarding the underlying biochemical procedures. For reliable quantification and interpretation of the data a high signal to noise ratio (SNR) is required. One solution to this problem is the implementation of phased-array coils. However, one question immediately arising is how the spectra from multiple receive channels can be combined in order to give the best result. In this study three different methods  for the combination of multichannel SVS data [1-4] were compared with respect to multiple evaluation  criteria: 1) Brown’s method [2], 2) singular value decomposition (SVD) method [3] and generalized least squares (GLS) method [4] .

[1] Wright S. NRMB. 1997; 52:394-410 [2] Brown M. MRM. 2004; 52:1207-1213 [3] Bydder M. et al. MRI. 2008; 26:847-850 [4] An L. et al. JMRI. 2013; 37:1445-1450.

Metabolite cycled single voxel 1H spectroscopy at 9.4T

Non-water suppressed metabolite cycled proton magnetic resonance spectroscopy (MC 1H-MRS) has been proven to enhance the frequency resolution and the signal to noise ratio (SNR) of the spectrum at 3 Tesla [1-2]. This is achieved by enabling shot-by-shot frequency and phase alignment due to the simultaneous acquisition of water and metabolite spectra.  Previously the adiabatic inversion pulse for MC 1H-MRS was optimized to exploit these advantages for application in the human brain at 9.4T [3].  In this work, we examine the performance of STEAM [4] based MC 1H-MRS [3,5] compared to water suppressed 1H-MRS using a numerically optimized short water suppression (WS) sequence with respect to spectral resolution and signal-to-noise ratio (SNR) in the human brain at 9.4T.

[1] MacMillan E.L. et al. MRM. 2011; 65:1239-1246 [2] Hock A. et al. MRM. 2013; 69:1253-1260 [3] Giapitzakis I.A. et al. Proc 22nd ISMRM: 2014:2895 [4] Frahm J. et al. MRM. 1989; 79-93 [5] MacMillan E.L. et al. Proc 19th ISMRM. 2011:1412

Curriculum Vitae


Name:          Ioannis-Angelos

Surname:      Giapitzakis

Place of birth: Athens, Hellenic Republic

Birthday:       28 Sep 1988

Nationality:    Hellenic/Italian


2012- Current    Max Planck Institute for Biological Cybernetics ()

Department of

PhD student

PhD title: Magnetic Resonance Spectroscopy in human brain at Ultra High Filed Strength: Methods and Applications

Group Leader-Supervisor:

2011-2012 Imperial College London ()

MSc & DIC in Biomedical Engineering with Medical Physics

Overall grade: 75.9%

Thesis: Co-analysis of resting state functional magnetic resonance imaging and diffusion tensor imaging for correlation of default mode network and ultra-structural deficit in posterior cortical atrophy (Grade: 86.7%)

2006-2011 National Technical University of Athens ()

School of Applied Mathematics and Physical Sciences ()

Diploma in Applied Mathematics and Physical Sciences  (5 years)

Majors: 1) Nuclear Physics and Elementary Particles & 2) Optoelectronics and Laser

Overall Grade: 8.25/10

Thesis: (Grade: 10/10 “Excellent”)

2003-2006 2nd High School of Kallithea, Athens, Greece


2017- Summa and Manga Cum Laude award - ISMRM

2016- Magna Cum Laude award - ISMRM

2012-2016   Max Planck Institute PhD Grant for my doctoral (PhD) studies

2012-2015  Scholarship from the Greek State Scholarships Foundation () and European Social Fund for my doctoral (PhD) studies

2011-2012 Institute of Engineering and Physics in Medicine () award for the best MSc project in Medical Physics

2011-2012 Master of Science (MSc) with Merit

2011-2012  Scholarship from the Greek State Scholarships Foundation () and European Social Fund for my Postgraduate studies

(Updated 14.07.17)

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