Biomedical Imaging: Course syllabus

Transcription

1 Biomedical Imaging: Course syllabus Dr. Felipe Orihuela Espina Term: Spring 2015 Table of Contents Description... 1 Objectives... 1 Skills and Abilities... 2 Notes... 2 Prerequisites... 2 Evaluation and assessment... 3 Contents... 3 Research Projects... 5 References... 5 Description This course is part of the line in Biosignal processing and medical computing of the MSc in Computational Sciences program of the Dept. of Computational Sciences at INAOE. The course overviews different aspects of biomedical imaging. This includes topics on image formation and reconstruction, processing, analysis and interpretation. The course aims to offer the student an understanding of the generic concepts underpinning the physical processes of imaging, and their practical realisations in specific imaging modalities and imaging systems. Emphasis is put on neuroimages following the department research priorities on electroencephalography (EEG) and functional Near Infrared Spectroscopy (fnirs). Objectives Provide an overview of physical processes of imaging biological tissues. Provide the students with mathematical and computational tools to analyse and interpret a range of biomedical images.

2 To introduce fundamental neuroscience concepts and describe different neuroimaging approaches. Skills and Abilities On successful completion of this module, the student should be able to: Understand basic and intermediate concepts in biomedical imaging transversal across imaging modalities. Given a certain biomedical image, s/he will be able to design and apply a processing and analysis strategy to extract new biomedical knowledge. Interpret the biomedical image confining its conclusions to assumptions made through the reconstruction and analytical process. Given a certain biomedical demand, to imaginatively propose image construction methods to obtain structural or functional representation. To creatively and critically carry out research in the field of biomedical imaging. Notes This course is loosely inspired in the course on Imaging and Image Analysis given at the School of Computer Science at the University of Birmingham, and I would like to particularly acknowledge the contribution of Prof. Ela Claridge. The course is scheduled for 40h. Density of hours per week will depend on the number of available weeks. Ideally, the student may attend the course whilst s/he is working on his/her thesis. All the information and additional material related to the course will be available in the course webpage: Prerequisites Knowledge of English and Spanish, both written and conversational is required. Basic algebra and statistics is necessary. The student is expected to have gain this knowledge in the compulsory course of Mathematics for Computing required in this MSc program. Basic knowledge of signal and image processing is necessary. The student is expected to have gain this knowledge in the course of Digital Signal Processing as part of the first term of the line in biosignal processing and medical computing required in this MSc program.

3 Elementary research skills are required. The student is expected to have gain this knowledge in the compulsory course of Methodology seminar I required in this MSc program. Although the course does not assumes knowledge of any particular programming language, intermediate programming skills in some language is extremely convenient. Elementary knowledge of physics principles are not assumed, but are also highly convenient. Evaluation and assessment Research project (50%) Written examination (50%) 1 single exam Class attendance is not accounted. Contents All contents indicated in this syllabus will be covered during the course. However the order of presentation may differ, for clarity purposes. 0. Presentation (3h) 1. Introduction to Biomedical Imaging (4.5h) a. Basic definitions (biomedical imaging, body planes, structural and anatomical imaging) b. Physics concepts (e.g. wave equations, energy transport, chromophores and contrasts) c. Image formation and reconstruction, and levels of analysis d. The temporal-spatial-signal matrix e. Examples of imaging systems 2. Image formation and acquisition principles (10.5h) a. Fundamental models of image formation i. Kinds of radiation and imaged properties b. The imaging system i. Point spread function ii. Imaging filters: Monochromatic, colour, multi-spectral and hyperspectral images iii. Resolution (pixel, spatial, radiometric/magnitude,spectral, temporal, superresolution)

4 c. Image quality and uncertainties in image formation (digitization, quantum efficiency, metamerism, calibration, CNR, SNR) d. Major imaging modalities i. Magnetic Resonance Imaging ii. Optical Imaging (inc. X-Ray, OCT, NIRS, microscopy, confocal imaging, one and two photon imaging, fluoroscopy, CT) iii. Electrical and magnetic imaging (inc. EEG/MEG, EMG, ECG, etc) iv. Ultrasound 3. Image reconstruction (3h) a. Inverse problem and the Jacobian b. Regularization 4. Image processing and analysis (6h) a. Registration b. Feature extraction; edge detection, Hough transform c. Filtering; Noise removal and signal enhacement d. Segmentation e. Domain transformation; Fourier and Wavelets 5. Image interpretation (3h) a. Data mining b. Knowledge discovery c. Interpreting statistics d. Interpretation guidelines 6. Advanced topics on Neuroimaging (9h) a. The neuron i. Metabolism b. The brain and the central nervous system i. Anatomy ii. Histophysiology 1. Blood irrigation iii. Neurovascular coupling iv. Working principles 1. Segregation and Integration 2. Connectivity (Structural, Functional and Effective) 3. The resting state network c. Neuroimages (EEG, fnirs, fmri, PET/SPECT) d. Analysis and Interpretation

5 i. Typical processing in fmri ii. Typical processing in fnirs iii. Analysis; Analytical Modelling, Statistical Parametric Mappings, Graph Theory, Topological, Others Research Projects The student is free to propose his/her own topic for a project; but it must be consensuated with the lecturer. Alternatively, s/he may choose from one of the following: Segmentation of stroke lesions in T1 weighted MRI. o Data will be provided by the lecturer o Approach may be semi-automatic Segmentation of adult head tissues in T1 weighted MRI for the construction of a geometrically realistic optical head model. o May be atlas based, otherwise data will be provided by the lecturer o Approach may be semi-automatic Parallelization of radiation transport simulation software MOCARTS o MOCARTS software and documentation will be provided by the lecturer ICA based removal of blinking artefacts for EEG o Data will be provided by the lecturer Detection of body and optode movement artefacts in fnirs o Data will be provided by the lecturer It is acceptable to work in groups of up to 3 students. However in the final document, the contribution of each student has to be clearly indicated. Upon conclusion of the project, the following evidence has to be produced: A hard printed technical report describing the project research question, hypothesis, goal, achievements and contribution, introduction and state of the art, materials and methods, results, discussion and conclusions. A CD with all intermediate and final results, code (if appropriate), and a digital copy of the documentation. References 1. Related scientific literature 2. Gonzalez R. C. y Woods, R. E. Digital Image Processing Prentice Hall 3 rd Ed. (2007), 976 pgs. 3. Proakis and Manolakis Digital Signal Processing Prentice Hall 4 th Ed. (2006), 1004 pgs. 4. Davies, E.R. Computer and Machine Vision: Theory, Algorithms, Practicalities Academic Press 4 th Ed. (2012), 912 pgs.

6 5. Bushberg, J. T., Seibert, J. A., Leidholt, E. M. and Boone, J. M. The essential physics of medical imaging Wolters Kluwer and Lippincott Williams & Wilkins 3 rd Ed. (2012) 6. Frackowiack et al Human Brain Function Academic Press 2 nd Ed. (2004), 1144 pgs. 7. Nunez, P. and Srinivasan, R. Electrical fields of the brain: the neurophysics of EEG Oxford University Press 2 nd Ed. (2006) 611 pgs.