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) Reconstruction by back projection Supervisor: Charlotte Thyssen, Tim Besard 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
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̶ ̶ ̶ ̶ 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
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