Combined study

4 years

prof. Ing. Valentine Provazník, Ph.D.

Chairman :
prof. Ing. Valentine Provazník, Ph.D.
Councillor internal :
doc. Ing. Radim Kolář, Ph.D.
doc. Ing. Jana Kolářová, Ph.D.
doc. Ing. Daniel Schwarz, Ph.D.
Councillor external :
prof. Mgr. Jiří Damborský, Dr.
prof. Pharm.Dr. Petr Babula, Ph.D.
Prof. José Millet Roig
prof. Ewaryst Tkacz, Ph.D.,D.Sc.
prof. MUDr. Marie Nováková, Ph.D.
prof. Dr. Marcin Grzegorzek

The doctoral studies of a student follow the Individual Study Plan (ISP), which is defined by the supervisor and the student at the beginning of the study period. The ISP is obligatory for the student, and specifies all duties being consistent with the Study and Examination Rules of BUT, which the student must successfully fulfill by the end of the study period. The duties are distributed throughout the whole study period, scored by credits/points and checked in defined dates. The current point evaluation of all activities of the student is summarized in the “Total point rating of doctoral student” document and is part of the ISP. At the beginning of the next study year the supervisor highlights eventual changes in ISP. By October, 15 of each study year the student submits the printed and signed ISP to Science Department of the faculty to check and archive.
Within mainly the first four semesters the student passes the exams of compulsory, optional-specialized and/or optional-general courses to fulfill the score limit in Study area, and concurrently the student significantly deals with the study and analysis of the knowledge specific for the field defined by the dissertation thesis theme and also continuously deals with publishing these observations and own results. In the follow-up semesters the student focuses already more to the research and development that is linked to the dissertation thesis topic and to publishing the reached results and compilation of the dissertation thesis.
By the end of the second year of studies the student passes the Doctor State Exam, where the student proves the wide overview and deep knowledge in the field linked to the dissertation thesis topic. The student must apply for this exam by April, 30 in the second year of studies. Before the Doctor State Exam the student must successfully pass the exam from English language course.
In the third and fourth year of studies the student deals with the required research activities, publishes the reached results and compiles the dissertation thesis. As part of the study duties is also completing a study period at an abroad institution or participation on an international research project with results being published or presented in abroad or another form of direct participation of the student on an international cooperation activity, which must be proved by the date of submitting the dissertation thesis.
By the end of the winter term in the fourth year of study the full-time students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The combined students submit the elaborated dissertation thesis by the end of winter term in the fifth year of study. The final dissertation thesis is expected to be submitted by the student by the end of the fourth or fifth year of the full-time or combined study form, respectively.
In full-time study form, during the study period the student is obliged to pass a pedagogical practice, i.e. participate in the education process. The participation of the student in the pedagogical activities is part of his/her research preparations. By the pedagogical practice the student gains experience in passing the knowledge and improves the presentation skills. The pedagogical practice load (exercises, laboratories, project supervision etc.) of the student is specified by the head of the department based on the agreement with the student’s supervisor. The duty of pedagogical practice does not apply to students-payers and combined study program students. The involvement of the student in the education process within the pedagogical practice is confirmed by the supervisor in the Information System of the university.

1. Advanced methods of medical image analysis in modern CT scanners

   Computed tomography scanners are one of the most used modalities for diagnosing various diseases and pathologies. Nowadays, the development and clinical use of modern CT scanners enabling multi-energy X-ray imaging using multilayer detectors or even single photon level imaging are taking place. At the same time, the devices provide a range of parametric images, such as monoenergetic images, material decomposition images, etc. It appears that this information increases the diagnostic yield of CT imaging modalities, with a significant dose reduction, which is in the interest of the wide medical community. The topic will aim to development of advanced image processing and analysis methods involving machine learning and deep learning approaches with a scope for multiparametric images acquired by multilayer CT detectors. The student will focus on the development, implementation, and validation of preprocessing, segmentation, detection, classification, and prediction tasks considering the character of multiparametric images. The proposed complex computer-aided diagnostic tool will help increase diagnostic accuracy and reproducibility, speed of the examinations and decrease the inter-/intra-expert variability and routine workload. The topic will be solved at the Department of Biomedical Engineering. However, cooperation with our external partners is expected – national clinical institutions (FN Brno, VFN Prague, FNUSA/ICRC Brno) and foreign institutions (IRST IRCCS Meldola Italy, Philips Healthcare Netherlands, DKFZ Heidelberg Germany), allowing clinical evaluation of the results and their discussion with medical experts. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Chmelík Jiří, Ing., Ph.D.

1. round (applications submitted from 01.04.2023 to 30.04.2023)

1. Artificial intelligence methods in perfusion imaging

   The topic of this dissertation is focused on design of new advanced methods of perfusion imaging based on artificial intelligence, mostly on deep learning. The project will include studying of actual approaches to reconstruction of perfusometric image data, calculation of concentration curves and their fitting with pharmacokinetic models of the second generation. Furthermore, the project will cover design of new deep learning procedures for the selected parts of the perfusion-analysis processing chain, or for the complete processing task. The design will be based on the current simulator of MR perfusion data acquisition, its appropriate extensions, available real datasets and on standard validated perfusion-analysis methods of the software PerfLab, available at ISI CAS. Evaluation of the proposed methods will be carried out on available real datasets. The work will be done in cooperation with the team of Luxembourg Institute of Health.

   Tutor: Jiřík Radovan, doc. Ing., Ph.D.

2. Computational modelling of heart failure using methods of artificial intelligence

   Heart diseases is a global cause of death; by World Health Organization (WHO) it causes 30% mortality worldwide. Therefore, assessment of a risk of heart failure is of vast importance in both treated patients and healthy population. The topic is focused on predicting of heart failure in hospitalized patients or patients in a home care. The applied methodology will contain work with high-dimensional data: laboratory values and biometric values with their changes in time as well as continuous data from electrocardiograph acquired by several kind of devices (from clinical ECG devices to wearables for home-care monitoring). The applicant will have to find proper pre-processing approaches for specific data and experiment with machine learning methods. Then machine learning methods will be used to find optimal model. The applicant will be motivated to use deep-learning methods whenever allowed by dataset. Developed method are expected to assess a risk of heart failure in short (days) and long (months) terms. This project will be solved at the Institute of Scientific Instruments of the Czech Academy of Sciences and is connected to the ongoing project of Technological Agency of the Czech Republic.

   Tutor: Plešinger Filip, Ing., Ph.D.