MASTER THESIS TOPICS Academic year 2019-2020 - DEPARTMENT OF ELECTRONICS AND INFORMATION SYSTEMS (ELIS) MEDICAL IMAGE AND SIGNAL PROCESSING (MEDISIP)

DEPARTMENT OF ELECTRONICS AND INFORMATION SYSTEMS (ELIS)
 MEDICAL IMAGE AND SIGNAL PROCESSING (MEDISIP)

MASTER THESIS TOPICS
Academic year 2019-2020

MEDISIP

 2

MEDISIP

 3

RESEARCH GOALS OF MEDISIP
• Make medical imaging more quantitative
• Improve acquisitions/reconstructions
 i. Reduce imaging time
 ii. Improve spatial resolution
• Solve artefacts in multimodal integration
• Additional information from multimodal data

• Application fields: small animal and neuroimaging

 4

RESEARCH ACTIVITIES @ MEDISIP

 5

RESEARCH ACTIVITIES @ MEDISIP

 6

ONGOING PHD PROJECTS
• Radiomics-machine learning-brain tumors (partner nuclear medicine/radiology)
• PET imaging in plants (partner Bioengineering)
• PET-MRI novel isotopes (partner KULeuven)
• Dosimetry in radionuclide therapy (Lutetium, partner Bordet)
• High resolution detectors for Total body PET
• Monolithic Time-of-flight detectors for PET

Collaborations
• EEG/Epilepsy with Neurology dept
• Intraoperative PET/CT lumpectomy margin assessment (R. Van den Broucke)

 7

IMAGING
 8

F-18 LABELING OF MICROSPHERES TO ENABLE INTERVENTIONAL
PET FOR MINIMALLY INVASIVE LIVER RADIO-EMBOLISATION
Supervisor: Marek Beliš, Ken Kersemans (UZ Gent)
Promotors: prof. Stefaan Vandenberghe, prof. Christian Vanhove

Background
Targeted radionuclide therapy (TRT) is an established cancer treatment modality. It relies
on cancer specific agents that are labeled with radionuclides for internal radiotherapy. By
the use of disease specific carriers linked to radionuclides emitting particle with a short
range, a high dose of radiation can be delivered to tumors while sparing the unaffected
organs. Imaging the distribution of these radionuclides is required for individual
assessment and planning of TRT.

When we would have theranostic F-18 labeled spheres PET imaging could be used to
combine diagnostic and therapeutic procedures in one procedure. For this reason we
want to study three radiolabelling strategies to introduce PET isotopes (F-18) onto the
surface of the microparticles

 9

Goal
• Investigate the different labeling options.
• Image the labeled microspheres with a high-resolution PET system
 (available at Infinity lab).

An optional area of research is to investigate with flow simulations the flow
of the microspheres in a typical hepatic artery and liver.

Tools: Modeling, hotlab, PET …

Remark: this project is of direct interest from a pharma company delivering
therapeutic microspheres

Timeline: literature study, (simulation), labeling, data analysis

 More information?! stefaan.vandenberghe@ugent.be
 10

HIGH SENSITIVITY SPECT USING 12 ROTATING
PARALLEL COLLIMATED DETECTORS
Supervisor: Marek Beliš, Dr. Bieke Lamber (UZ Gent)
Promotors: prof. Stefaan Vandenberghe, prof. Roel Van Holen

Background
SPECT is the most frequently used techniques and detects single photon
emittors by a mechanical collimator and scintillation detector. The
conventional gamma camera, based on a 40-year old design, is
composed of 2 large (about 40-50 cm) detector heads equipped with large
parallel hole collimators. This limits the sensitivity and spatial resolution of
SPECT imaging. To obtain relevant images, relative long acquisition times
and/or high doses are required. A totally new design based on 12 detector
(CZT) heads has been recently commercialised and first systems are
installed at 4 clinical sites (France). Each head has an axial dimension of
35 cm and a smaller axial dimension of about 5 cm. These detectors can
be brought very close to any body part of the patient to improve spatial
resolution. For small objects also a larger sensitivity can be obtained.

 11

Goal
The aim of this thesis is to characterize in detail how much improvement can be expected from such a design in
typical imaging situations

Tools: Literature, Monte Carlo simulations, MATLAB, SPECT, …

Remark: First 4 systems are installed at sites in France
measurements can be performed on these sites

Timeline: literature study, simulations, data analysis

 More information?! stefaan.vandenberghe@ugent.be
 12

INVESTIGATION OF LYSO BACKGROUND RADIATION IN A
TOTAL-BODY PET
 Conventional PET system

Supervisor: Charlotte Thyssen
Promotors: prof. Stefaan Vandenberghe, prof. Roel Van Holen

Background
Positron Emission Tomography (PET) is a molecular imaging modality that
uses a radioactive tracer to visualize processes occurring inside the body.
However, conventional systems only have a very small length → a lot of the
radiation produced inside the patient is lost …
For this reason MEDISIP wants to develop a total-body PET with a length of 1
meter → ~20x more radiation is caught!!
 Total-body PET system

LYSO, the scintillator crystal of choice, is naturally radioactive → background
radiation present during scanning

 13

Goal
• Mapping out the effect of background radiation in total-body PET
• Monte Carlo simulations of human phantoms with and without
 background in total-body PET

Software: Gate, XCAT, MATLAB/Python, Root, …

Timeline: literature study, Monte Carlo simulations, image
reconstruction, data analysis

 More information?! cathysse.thyssen@ugent.be
 14

MEDIUM-SIZE ANIMAL PET SCANNER: INVESTIGATION OF
IDEAL SCANNER GEOMETRY
Supervisor: Charlotte Thyssen
Promotors: prof. Stefaan Vandenberghe, prof. Roel Van Holen

Background
Today, rats and mice are mostly used for scientific research, however, translation
of the obtained results to humans is not straightforward. For this reason there is
an increased interest in larger animals like rabbits. Preclinical imaging modalities
for these animals are scarce. The idea is to increase the bore size of the
MOLECUBES PET-scanner and to include TOF capabilities.

 15

Goal
• Comparison of different designs for medium-size animal scanners
• Effect of TOF inclusion in medium size animal scanners
• Comparison of different scintillation crystals to reduce cost

Software: Gate, XCAT, MATLAB/Python, Root, …

Timeline: literature study, Monte Carlo simulations, image
reconstruction, data analysis

 More information?! cathysse.thyssen@ugent.be
 16

ACCELERATING MONTE CARLO SIMULATIONS FOR
MEDICAL SCANNER DATA WITH JULIA
Supervisor: Charlotte Thyssen, Tim Besard
Promotors: prof. Bjorn De Sutter, prof. Stefaan Vandenberghe

Background
Monte Carlo simulations are used for simulation of medical imaging data (to optimize
image reconstruction or simulate innovative system designs). The code is based on
the computationally intensive Geant 4 package (CERN). Simulation of realistic
patient data is a very slow process and needs to be run on multiple CPU or GPU, to
obtain data in an acceptable time frame (days/weeks). Acceleration of this code
would benefit a large community of researchers working on improved medical
imaging systems.

 17

Goal
• Identify the critical parts in the library
• Evaluation of the potential of Julia to make Monte Carlo simulations much more efficient and more easily
 accessible

Software: Gate, Julia

Timeline: literature study, Monte Carlo simulations, analysis of simulation code and optimization using Julia

Two different types of simulations will be investigated: the first one relies on voxelized sources for determining
patient interactions (e.g., Dosimetry purposes) and the second is the scanner simulation part.

To reach these goals, we are looking for students with considerable programming experience and a passion for
the latest state-of-the-art programming languages.

 More information?! bjorn.desutter@ugent.be
 18

HIGH-PERFORMANCE YET RAPID IMAGING
RECONSTRUCTION WITH JULIA (1 OR 2 STUDENTS)
Supervisor: Charlotte Thyssen, Tim Besard Reconstruction by back projection
Promotors: prof. Bjorn De Sutter, prof. Stefaan Vandenberghe

Background
After image acquisition, recorded data are obtained as a list of
events or projection data sets. An image reconstruction algorithm
uses this output data from the scanner to calculate the 3D image
of the patient. This step is done in an iterative loop and typically
involves several matrix multiplications resulting in a
computationally intensive algorithm. The image reconstruction
needs to be run on multiple CPU or GPU to be able to keep it
equal to the faster acquisition of the most recent scanners.

 19

Goal
• Migrate the state-of-the-art medical image reconstruction code developed at MEDISIP
• Use Julia to answer the existing open questions

Software: QETIR, Julia

Timeline: literature study, image reconstruction, analysis of reconstruction code and optimization using Julia,
analysis of a second algorithm algorithm (even-based) and comparison to first

Possibility for collaboration with MOLECUBES (UGent Spin-off)

To reach these goals, we are looking for students with considerable programming experience and a passion for
the latest state-of-the-art programming languages.

 More information?! bjorn.desutter@ugent.be
 20

21

22

23

24

25

Deep Learning for Computer-Aided Detection and Diagnosis
of Breast Cancer
Supervisor: Milan Decuyper
Promotor: prof. Roel Van Holen
Background
̶ Breast cancer is the second leading cause of cancer-related
 death among women
̶ Early detection increases the chance of full recovery
̶ Screening mammography is associated with a high risk of
 false positive testing
̶ Computer-aided detection and diagnosis (CAD) systems:
 workload + Accuracy

 26

Goal
Design and train algorithms for computer-aided detection or
diagnosis of abnormalities in mammograms such as calcification and
masses.

Different tasks possible such as:
- Detection of breast cancer
- Segmentation of masses and calcifications
- Diagnosis of masses and calcifications as benign or malignant

Data: CBIS-DDSM database @ The Cancer Imaging Archive.

Software: Python (PyTorch/Tensorflow/Keras/...)

 27

Deep Learning for Computer-Aided Lung Nodule Detection
Supervisor: Milan Decuyper
Promotor: prof. Roel Van Holen

Background
̶ Lung cancer is the leading cause of cancer-related death
 worldwide
̶ Early detection reduces lung cancer mortality
̶ Manual interpretation of lung CT scans is error-prone and time
 intensive.
̶ Computer-aided detection and diagnosis (CAD) systems:
 workload + Accuracy

Goal
Design and train algorithms for computer-aided lung nodule annotation
in CT scans.

Different tasks possible such as:
- Lung Nodule Detection
- Lung Cancer Diagnosis, survival prediction, prediction
 of genomic mutations etc.

Data: LIDC-IDRI, NSCLC-Radiomics and NSCLC-Radiogenomics
 @ The Cancer Imaging Archive.

Software: Python (PyTorch/Tensorflow/Keras/...)

 29

FAST AND EFFICIENT RECONSTRUCTION ALGORITHM FOR MAGNETIC
RESONANCE ELECTRICAL PROPERTIES TOMOGRAPHY (MREPT)
Supervisors: Prakash Parappurath Vasudevan
Promotors: prof. Roel Van Holen, prof. Wout Joseph

Background
MREPT is a technique used to obtain the admittivity (both conductivity and permittivity) of tissues
 ̶ Electrical properties (EP) can be used for Cancer diagnosis, Staging and Grading
 ̶ EPs are critical in applications utilizing EM stimulation for treatment
 ̶ Accurate assessment of EPs are necessary for subject Specific Absorption Rate (SAR) measurements

 B1 Mapping Reconstruction

 Conductivity/Permittivity Image
 30

Goal
• Improve the existing reconstruction algorithm of MREPT
• Test the algorithm using Electromagnetic (EM) field simulation
• Optimize the algorithm for different measurement set-up
• Investigate different B1 mapping methods and compare their performance

Data: Simulated B1 maps, MRI data of Phantoms and Mouse tumour models

Software: MATLAB/Python, Sim4Life (optional)

 For more information: Prakash.ParappuarthVasudevan@UGent.be
 31

RHENIUM-188 SPECTRA SIMULATION FOR SPECT
Supervisor: Marek Beliš
Promotors: prof. Stefaan Vandenberghe

Background
Rhenium-188 (188Re)
 • theranostic agent => β- and γ-emissions
 • 155 keV γ-ray (15 %) suitable for SPECT => single-photon emission
 computed tomography
 • similar to 99mTc
 • several high-energy γ-rays in emission spectrum & Bremsstrahlung
 may complicate quantitative imaging

Collimation is necessary to ensure good reconstruction, therefore parameters
of the collimator affect the quality of images, but also sensitivity etc.

 32

Goal
 • Simulation of 188Re spectra and comparison to 99mTc
 • Search for improvement by changing the parameters of the
 collimator
 • Possible upgrade to more radionuclides

Software: Gate, MATLAB, …

Timeline: literature study, Monte Carlo simulations, data analysis,
3D-modelling

 More information?! Marek.Belis@UGent.be
 33

DEVELOPMENT OF LIGANDS FOR COMPLEXES WITH
RHENIUM
Supervisor: Marek Beliš
 Biomolecule
Promotors: prof. Stefaan Vandenberghe
 Radionuclide – 188Re, 99m
 Tc

Background
Rhenium-188 (188Re)
 Cancer
 • β- and γ-emissions => theranostic agent (suitable both for therapy and cell

 imaging)
 • chemically similar to Tc, but with much more complicated redox
 chemistry

Stability of the radiopharmaceutical is the key aspect for success of targeted
radionuclide therapy (TRNT). Therefore development of ligands stabilizing the
Re is necessary.

 34

Goal
• Synthetical modification of macrocyclic ligands
• Formation of complexes with cold Re and later with 188Re
• Radiolabelling of biomolecules, stability testing

Fields: Organic synthesis, coordination chemistry

Timeline: literature study, synthesis, coordination chemistry, data
analysis

Cooperation with SCK•CEN (Belgian Nuclear Research Centre)

 More information?! Marek.Belis@UGent.be

 35

NEUROENGINEERING
 36

CALIBRATING EEG SOURCE IMAGING USING
EVOKED RESPONSES
Supervisor: Jolan Heyse
Promotors: prof. Pieter van Mierlo

Background
In EEG source imaging, the brain activity underlying the measured EEG is
estimated by modelling the spreading of electrical activity in MR-based
electromagnetic head models. Despite very accurate models that are available
these days, the spatial resolution of EEG source imaging is in the order of cm.
New MRI sequences (ultra-short echo time, UTE) could help to further improve
the head models by refining the tissue segmentation. Evoked potentials (e.g.
finger tapping) can be used to evaluate the performance of ESI with the new
head model.

 37

Goal
• To evaluate the spatial resolution of existing EEG source imaging
 methods and improve it through refinement of the head model.
 Parametrization of the head model and assessing the spatial
 resolution of the EEG source imaging will be done using evoked
 potentials as a ground truth.
• A lot of the work will be practical. The student will obtain his/her
 own data (MRI and EEG) for further analysis.

Software: MATLAB/Python

Timeline: literature study, MRI/EEG experiments, data analysis

 More information?! Pieter.vanMierlo@ugent.be
 38

EEG SIGNALS TREATED AS SOUND
Supervisor: Jolan Heyse
Promotors: prof. dr. ir. Pieter van Mierlo, prof. dr. ir. Nilesh Madhu

Background
Because signal transmission occurs instantaneously in the brain, each EEG
electrode measures the sum of the individual activities of different brain regions.
Individual contributions of the different sources can be obtained by applying a de-
mixing procedure. The EEG signals are further corrupted by different artifacts of
environmental (e.g. 50Hz hum from power supplies) and biological (e.g. eye
blinks, muscle activity etc.) nature. Similar problems have been well-studied for
the multi-microphone recording and processing of audio signals and robust
solutions have been developed for these use-cases.

 39

Goal
• To use algorithms developed for speech/audio processing (e.g.
 beamforming and spectral corrections) to get more information from the
 EEG signals.
• These techniques will be applied to the problem of localizing the
 epileptic focus in epilepsy patients. Seizure recordings often involve
 activity from many brain regions and contain many artifacts because of
 muscle contraction and movement of the patient.

Software: MATLAB/Python

Timeline: literature study, algorithm implementation, data analysis

 More information?! Pieter.vanMierlo@ugent.be
 40

EEG SOURCE IMAGING AND FUNCTIONAL
CONNECTIVITY ANALYSIS OF MONKEY EEG
Supervisor: Jolan Heyse
Promotors: prof. dr. ir. Pieter van Mierlo

Background
In EEG source imaging, the brain activity underlying the measured EEG signals is
estimated. Looking at the activity patterns from different brain regions, functional
connectivity methods can be applied to reconstruct the functional network of the brain
(i.e. how do the brain regions interact with each other?). Many methods exist for
assessing functional connectivity, but they are hard to validate as the ground truth
communicating network is rarely known. A dataset of simultaneous recordings with
scalp and intracranial EEG with electrodes placed inside a monkey's brain can serve
as a validation tool.

 41

Goal
• To validate different functional connectivity methods, plus the added
 value of time lag information, based on the monkey dataset.

Software: MATLAB/Python

Timeline: literature study, data analysis, functional connectivity evaluation

 More information?! Jolan.Heyse@UGent.be
 42

EEG-NEUROFEEDBACK FOR IMPROVED BCI
PERFORMANCE
Supervisor: Jolan Heyse
Promotors: prof. Pieter van Mierlo

Background
Brain computer interfaces (BCI) involve direct communication between the brain and
an external device (e.g. a neuro-prosthetic limb). As EEG provides a direct
measurement of brain activity, it poses a viable candidate as communicating
interface in BCI. However classification of brain signals into the intended tasks is
hampered by the complexity and variability of the underlying activity. Neurofeedback
uses real-time displays of brain activity to teach self-regulation of brain function
and could help to improve BCI performance by teaching the subject to steer brain
activity towards the desired classification area.

 43

Goal
• To establish an EEG-based BCI and evaluate the added value of
 neurofeedback on the performance, or to learn new tasks using
 neurofeedback

Software: MATLAB/Python

Timeline: literature study, experiment design, neurofeedback and BCI
implementation, experiments, data analysis

 More information?! Jolan.Heyse@UGent.be
 44

AGE RELATED CHANGES IN CORTICO-CORTICAL
CONNECTIONS IN PHONEME DISCRIMINATION
Supervisor: Jolan Heyse
Promotors: prof. dr. ir. Pieter van Mierlo, prof. Miet De Letter

Background
Phonemes are perceptually distinct units of sound and can be considered
fundamental building blocks for speech. Discrimination of these phonemes is
important for speech comprehension and has been investigated in an EEG-study
conducted at Ghent University. In this study, aging was associated with
increased latencies and decreased amplitude with age during phonemic
discrimination tasks. However, why this difference was observed is not yet
explained.

 45

Goal
• To investigate the connection between brain regions during phoneme
 discrimination tasks. Functional connectivity analysis will be used to
 reveal information flow in several frequency bands.
• We will investigate these interactions during phoneme discrimination and
 study age-related differences. This will shed light on why elderly have
 more difficulties discriminating phonemes.

Software: MATLAB/Python

Timeline: literature study, data analysis, clinical interpretation

 More information?! Pieter.vanMierlo@ugent.be
 46

INTERHEMISPHERIC CONNECTIVITY OF
SUBCORTICAL NUCLEI DURING WORD TASKS
Supervisor: Jolan Heyse
Promotors: prof. dr. ir. Pieter van Mierlo en prof. dr. Patrick Santens

Background
Deep brain stimulation is an established treatment for patients with Parkinson’s
disease. Here depth electrodes are bilaterally implanted in the subthalamic
nucleus (STN). In literature it has been shown that the stimulation of the STN
has an impact on speech. However, the exact role of the STN during speech and
the coupling between the STNs from both hemispheres remains to be
elucidated.

 47

Goal
• To investigate the communication between the left and right STN during
 word tasks. We will work with intracranial EEG data from patients with
 implanted electrodes. The stimulator is only implanted a couple of days
 after the depth electrodes, which provides us a time frame to measure
 intracranial EEG activity.
• Several word tasks have been recorded in a number of patients, where
 action and non-action words were visually shown to the patients.

Software: MATLAB/Python

Timeline: literature study, data analysis

 More information?! Pieter.vanMierlo@ugent.be
 48

SPIKE SORTING OF SUBTHALAMIC SINGLE NEURON
RECORDINGS IN PARKINSON PATIENTS
Supervisor: Jolan Heyse
Promotors: prof. dr. ir. Pieter van Mierlo en prof. dr. Patrick Santens

Background
Deep brain stimulation of the subthalamic nucleus (STN) is an established
treatment to reduce motor tremors in patients with Parkinson’s disease. A depth
electrode is implanted into the subthalamic nuclei to stimulate the neurons. First
multiple micro-electrodes are inserted into the STN, that are capable to record
multiple single neurons. Based on these recordings, the location is chosen to
implant the macro-electrode that is used for current stimulation. Because the
micro-electrode records the activity of multiple neurons simultaneously, spike
sorting algorithms are used to separate the activity of the neurons.

 49

Goal
• To investigate the micro-electrode recordings in patients with
 Parkinson’s disease. This requires the implementation of spike
 sorting algorithms which allow separating activity from the different
 recorded neurons. Different algorithms will be implemented and
 their performance will be assessed.
• Furthermore the relation between the micro-recordings of neuronal
 activity and the macro-recordings of local field potentials will be
 studied.

Software: MATLAB/Python

Timeline: literature study, data analysis, algorithm evaluation

 More information?! Pieter.vanMierlo@ugent.be
 50

AUTOMATED EPILEPSY DIAGNOSIS FROM ROUTINE
EEG USING MACHINE LEARNING
Supervisor: ir. Tom Van Steenkiste, prof. dr. Dirk Deschrijver
Promotors: prof. dr. ir. Tom Dhaene, prof. dr. ir. Pieter van Mierlo

Background
Epilepsy is a neurological disorder that affects approximately 0.5-1% of the
world’s population. The most important technique to diagnose epilepsy is
electroencephalography (EEG). In the EEG, the occurrence of epileptic spikes,
i.e. brief electrical discharges in the brain, are a hallmark to diagnose epilepsy.
The occurrence of epileptic spikes differs from patient to patient and even within
a patient from time to time. In clinical practice, a routine EEG of 20min duration
is recorded to diagnose epilepsy. Unfortunately, many patients with epilepsy do
not have frequent spikes; therefore the sensitivity of routine EEG to confirm the
diagnosis of epilepsy is only 25-56% and the specificity is 78-98%.

 51

Goal
• To increase the sensitivity and specificity of routine EEG to
 diagnose epilepsy. This will be done by using and developing state-
 of-the-art machine learning techniques to classify routine EEGs
 recorded in Ghent and Geneva University Hospital as epileptic or
 non-epileptic.
• In addition to the detection of epilepsy, classification into subtypes
 can be performed. In a first step, classification in temporal vs extra-
 temporal lobe epilepsy can be done.
• This master thesis is in close collaboration with Epilog, a startup
 company specialized in EEG analysis. The student has the
 opportunity to do an internship at Epilog before the master thesis.

Software: MATLAB/Python

Timeline: literature study, data analysis, machine learning

 More information?! Pieter.vanMierlo@ugent.be
 52

DETECTING THE CAUSE OF DEMENTIA USING EEG
MEASUREMENTS AND MACHINE LEARNING
Supervisor: ir. Tom Van Steenkiste, prof. dr. Dirk Deschrijver
Promotors: prof. dr. ir. Tom Dhaene and prof. dr. ir. Pieter van Mierlo

Background
Dementia is a syndrome of several diseases: Alzheimer’s Disease (AD),
Frontotemporal lobe degeneration (FTD), creutzfeldt-jakob disease (CJD) or
Lewy body disease (LBD). Up to now, there is no medical test to diagnose which
disease is causing the dementia. Some pilot studies have indicated that
electroencephalography (EEG) could be a useful neuroimaging technique to
diagnose the cause of dementia. At the same time, recent advancements in
machine learning and deep learning have resulted in powerful analysis
techniques for medical time-series data. The application of machine learning to
EEG data for detecting the cause of dementia could lead to valuable insights
and models and could optimize patient treatment.

 53

Goal
• To use machine learning to classify EEGs from patients that have
 dementia into AD, FTD, CJD and LBD. A post-mortem confirmed
 database from Antwerp University Hospital is available to address this
 question. The student can explore and develop state-of-the-art machine
 learning algorithms for time-series analysis and can develop custom
 algorithms for EEG data analysis.
• This master thesis is in close collaboration with Epilog, a startup
 company specialized in analyzing EEG data. The student has the
 opportunity to do an internship at Epilog before the master thesis.

Software: MATLAB/Python

Timeline: literature study, data analysis, machine learning

 More information?! Pieter.vanMierlo@ugent.be
 54

EFFECT OF ANTI-EPILEPTIC DRUGS ON FUNCTIONAL
BRAIN CONNECTIONS
Supervisor: Jolan Heyse
Promotors: prof. dr. ir. Pieter van Mierlo, dr. Gregor Strobbe

Background
The first line treatment of epilepsy is antiepileptic drugs (AEDs). In
approximately 60-70% of patients AED mono- or polytherapy have
the desired outcome, namely the patient is seizure-free. Most of the
AEDs go hand in hand with many side-effects such as drowsiness,
dizziness, fatigue, nausea and vomiting. In all patients an AED is
tested without knowing whether the AED will lead to seizure freedom
or not. Furthermore, the side effects cannot be predicted.

 55

Goal
• To assess how antiepileptic drugs affect the functional connectivity
 and whether these alterations are indicative for the side-effects of
 the AEDs.
• Furthermore we will investigate the possibility of predicting who will
 be a drug responder (i.e. seizure-free) or not, based on the
 functional connectivity.

Software: MATLAB/Python

Timeline: literature study, data analysis, machine learning

 More information?! Pieter.vanMierlo@ugent.be
 56

SMALL ANIMALS
 57

USING FUNCTIONAL MRI AND GRAPH THEORY TO
INVESTIGATE ABNORMAL FUNCTIONAL BRAIN NETWORKS IN A
RAT MODEL OF TEMPORAL LOBE EPILEPSY
Supervisor: Emma Christiaen
Promotors: prof. Chris Vanhove, prof. Robrecht Raedt

Background
• Epilepsy is a disease characterized by recurrent seizures
• More insight into the functional brain networks involved can lead to new therapies
• Resting state functional magnetic resonance imaging (fMRI) can be used to identify functionally connected
 brain regions and construct functional networks
• These networks can be analysed and compared using graph theory

 58

Goal
Use graph theory to investigate abnormal functional brain networks in a rat
model of temporal lobe epilepsy
 - use previously acquired resting-state fMRI images
 - preprocess images and do global signal regression
 - construct networks of functionally connected brain regions
 - analyse networks using graph theory

Software: Matlab

 59
 More information?! emma.christiaen@ugent.be

USING RESTING STATE FUNCTIONAL MRI TO IDENTIFY QUASI-
PERIODIC PATTERNS OF FUNCTIONAL CONNECTIVITY IN A
RAT MODEL OF TEMPORAL LOBE EPILEPSY
Supervisor: Emma Christiaen
Promotors: prof. Chris Vanhove, dr. Benedicte Descamps

Background
• Functional magnetic resonance imaging (fMRI) is a functional imaging technique that allows the visualization of
 whole-brain activity
• Resting state functional magnetic resonance imaging (fMRI) can be used to identify functionally connected
 brain regions
• Functional connectivity is usually assumed to be stationary
• In reality it varies over time and recurring patterns can be found (=quasi-periodic patterns)

 60

Goal
 Identify quasi-periodic patterns of functional connectivity using resting state
 fMRI data of the rat brain and investigate how these patterns differ in healthy
 and epileptic animals
 - use previously acquired resting-state fMRI images
 - identify quasi-periodic patterns
 - compare patterns between healthy and
 epileptic animals

 Software: Matlab

More information?! emma.christiaen@ugent.be

 61

DYNAMIC PET IMAGING OF CHEMOGENETIC MODULATION OF
THE HIPPOCAMPUS
Supervisor: Emma Christiaen
Promotors: prof. Chris Vanhove, dr. Benedicte Descamps

Background
• Chemogenetics is a neuromodulation technique that allows very specific activation or inhibition of neurons
• Neuronal activity can be modulated by injecting a drug-like ligand (clozapine)
• Dynamic PET imaging allows monitoring of radioactive tracer uptake over time
• Changing concentration of radioactivity in tissue gives information about underlying mechanisms of diseases or
 interventions

 62

Goal
Use dynamic PET imaging to investigate the effects of chemogenetic
modulation of the hippocampus
 - acquire dynamic 18F-FDG PET images of animals while clozapine is administered -> inhibition of hippocampus
 - visualize changing concentration of radioactivity in brain regions
 - visualize the effect of inhibition of hippocampus

Software: MATLAB, Amide, Amira

 63
 More information?! emma.christiaen@ugent.be

USING DIFFUSION MRI AND TRACTOGRAPHY TO INVESTIGATE
CHANGES IN WHITE MATTER TRACTS IN A RAT MODEL OF
TEMPORAL LOBE EPILEPSY
Supervisor: Emma Christiaen
Promotors: prof. Chris Vanhove, prof. Robrecht Raedt

Background
̶ Epilepsy is a disease characterized by recurrent seizures
 ̶ Diffusion magnetic resonance imaging (dMRI) can be used to identify epileptogenic abnormalities
 ̶ White matter tracts can be mapped using tractography
 ̶ More insight into changes in white matter tracts during the development of epilepsy can lead to new
 biomarkers or therapies

 64

Goal
Use dMRI and tractography to investigate abnormal white matter tracts in a rat model of temporal lobe epilepsy
 - use previously acquired diffusion MRI images
 - preprocess images and do tractography
 - investigate changes in known white matter tracts

Software: MATLAB, ExploreDTI, MRtrix3

 65
 More information?! emma.christiaen@ugent.be

MACHINE LEARNING FOR DISEASE DIAGNOSIS AND
PROGNOSIS IN A RAT MODEL OF TEMPORAL LOBE EPILEPSY

Supervisors: Emma Christiaen, Milan Decuyper
Promotors: prof. Chris Vanhove, prof. Robrecht Raedt

Background
• Epilepsy is a disease characterized by recurrent seizures
• Not clear which patients will develop epilepsy after head trauma
• Need for biomarkers: functional brain networks involved in development of epilepsy
• Resting state functional magnetic resonance imaging (fMRI) can be used to identify functionally connected
 brain regions and construct functional networks

 66

Goal
Use machine learning to identify epileptic rats and to predict their eventual seizure frequency
 - rat model of temporal lobe epilepsy
 - use previously acquired resting-state fMRI images
 - construct networks of functionally connected brain regions
 - extract features and build a classifier

Software: MATLAB, Python

 67
 More information?! emma.christiaen@ugent.be

ARTIFICIAL INTELLIGENCE FOR AUTOMATIC
SEIZURE DETECTION IN EPILEPSY
Supervisor: dr. Lars Emil Larsen
Promotors: dr. Lars Emil Larsen and prof. dr. ir. Pieter van Mierlo

Background
Automatic seizure detection algorithms
• preclinical experiments: save experiments countless hours
• assist clinicians inspecting electroencephalographic data from epilepsy patients
• feedback driven closed-loop neurostimulation techniques for epilepsy

 69

Goal
• Electroencephalographic data will be available from several rodent epilepsy models, which will
 be used to build seizure detection algorithms and compare performance.
• The project will revolve around testing the robustness of select machine learning techniques
 such as random forest classification, support vector machines or neural networks.

Software: MATLAB/Python

Timeline: literature study,

 More information?! Pieter.vanMierlo@ugent.be
 70

ARTIFICIAL INTELLIGENCE FOR AUTOMATIC DETECTION
OF HIGH FREQUENCY OSCILLATIONS IN EPILEPSY
Supervisor: dr. Lars Emil Larsen
Promotors: dr. Lars Emil Larsen and prof. dr. ir. Pieter van Mierlo

Background
Pathological high frequency oscillations (pHFOs)
• hallmark of epileptogenic brain regions
• reflect activity of a diseased brain predisposed to generate epileptic seizures
• exact mechanisms underlying pHFOs are unknown
• their frequency is generally correlated to seizure frequency

pHFOs are more frequent than seizures -> useful surrogate biomarker of disease severity

Quantification of pHFOs can be very labor intensive -> need for automatic detection tool

 72

Goal
• Electroencephalographic data will be available from
 Example of HFO recorded in the epileptic hippocampus:
 several rodent epilepsy models, which will be used to build
 a) raw signal, b) high-pass filtered signal, c) relative time frequency plot
 pHFO detection algorithms and compare performance.
• The project will revolve around testing the robustness of
 select machine learning techniques such as random forest
 classification, support vector machines or neural networks.

Software: MATLAB/Python

Timeline: literature study,

 More information?! Pieter.vanMierlo@ugent.be
 73

EEG CAP DEVELOPMENT FOR SMALL ANIMALS
Supervisor: dr. Lars Emil Larsen
Promotors: prof. dr. ir. Pieter van Mierlo and prof. dr. Robrecht Raedt

Background
• Preclinical validation of medical research on laboratory animals: rat brain model for human brain
• Humans: electroencephalography (EEG) using scalp electrodes
• Rats: small scalp area to place electrodes on => intracranial EEG (electrodes implanted in brain)
• Interesting to capture brain signals of rats from scalp electrodes -> a means to validate methods developed
 for human scalp EEG
• Up to now, examples of scalp EEG for rats in literature are limited

 75

Goal
• Improve this prototype and to eliminate some existing problems. You can work on different aspects, depending
 on what you prefer:
 ✓ How can we make the setup more practical? What is an easy, safe and fast way to fasten the cap to the rat’s head?
 ✓ Skin-electrode impedance could be lowered in order to better pick up the brain signals.
 ✓ What is the best technique and design for the electrodes?
 ✓ Can we improve the impedance with conductive gel?
 ✓ Techniques could be designed and implemented to shield the electrodes and cables from interfering signals, especially in
 MR room.
 ✓ Different materials for the electrodes to improve on MRI compatibility (making the artifact on the MR images smaller).
• Design and implement your improvements or make your own prototype. Finally, the system should be tested
 and you will be able to register EEG data of rats

Software: MATLAB/Python

Timeline: literature study, experiments, data analysis

 More information?! Pieter.vanMierlo@ugent.be
 76

Don’t hesitate to contact us for more information!
 The presentation will be made available
 on medisip.ugent.be

 77