Course content

The taught part of the course is delivered in the first two terms and consists of 8 modules, each with 15 credits. Students also undertake a research project in the third term and during the summer, this carries 60 credits (one third of the total). The modules are described below, note this description is based on the academic year 2008/9 and we reserve the right to vary the syllabus.

Students are also taken to see clinical  and computing systems at use in a Hospital environment. Students have access to a study room equipped with PCs that have MATLAB, Visual Studio, ITK and Protege installed. The course makes use of Moodle virtual learning environments for the delivery of lecture notes, coursework, data files, quizzes and the uploading of coursework and project reports.

Term 1

Foundations of Anatomy and Scientific Computing

Anatomy and Physiology.

This course provided by the UCL Anatomy Department specifically for students on the MSc's in the Medical Physics Department. The course comprises of lectures and dissecting room demonstrations. Topics covered include:

• Organisation of the Body: Anatomical Planes and Terms, body cavities, abdominopelvic regions, cells tissues and organs, body systems.
• Development and Homeostasis: Gamete formation, fertilisation, implantation, embryonic and fetal development, birth and lactation, growth and ageing. Homeostasis.
• Nervous system:nervous tissue, signalling, reflex, nerve impulse, action potential, synapses, neurotransmitters. Central, peripheral, autonomic, sympathetic, parasympathetic nervous systems. 
• Endocrine system: glands, hypothalamus, pituitary, thyroid, hormones, pancreas, adrenal.
• Locomotor system: bones, skeleton, joints, joint spaces, muscle types, fibres, myofibrils, sarcomeres.
• Cardiovascular System: Heart, circulation, blood vessels, hepatic system, blood pressure, lymphatic system.
• Respiratory System: Respiratory tract and tree, alveoli, pharynx, larynx, inspiration and expiration.
• Digestive and urinary systems: mouth, stomach, duodenum, small intestine, pancreas, liver,large intestine, colon, kidneys.

Scientific Computing

This course is specific to students on the MSc in Medical Image Computing. It covers areas of Scientific Computing relevant to medical imaging, using MATLAB for examples and coursework. Topics include:

• MATLAB, 
• linear algebra: matrices, matrix factorization, example from MRI parallel imaging,
• signal processing: filtering, Fourier and sampling theory,
• image interpolation,
• Eigenvalue decomposition, singular value decomposition, principal component analysis, diffusion tensor imaging,
• radiological coordinate systems, geometrical transformations, geometrical information in DICOM files, positioning of images in 3D patient coordinates
• data fitting: linear and non-linear
• optimisation: least-squares non-linear, Levenberg-Marquadt.

Assessment: multiple choice questions and dissecting-room spot test (Anatomy), coursework (Scientific Computing).

 

Computer Assisted Radiology

This module is provided by Dr Paul Taylor from CHIME, specifically for the Medical Image Computing MSc. The module provides an introduction to the field of computer assisted radiology. It explains how the delivery of this branch of healthcare is changing in response to the potential of new technology. The focus of the course is on the techniques which underpin the most promising applications for improving radiologists’ capacity to interpret images. Topics included:

• Applications of Computers in Radiology, Human Perception vs Computer Vision, Clinical Decision-Making vs Automated Reasoning.
• Image Texture and Risk Assessment.
• Computer Aided Diagnosis (CAD)  in mammography, lung, chest-Xray, cervical smear.
• Abnormality Detection. Normal vs Abnormal features. Detecting local features (calcs, nodules, stellate lesions). Detecting global features (change, asymmetry, complex appearance).
• Content-based Image Retrieval: Applications of CBIR in radiology, Image annotation, controlled clinical terminologies,
• Probabilistic systems, Uncertainty, Bayesian Networks (theory and practical).
• Machine Learning, neural nets, support vector machines.
• Decision Support Systems.
• Semantic Web Technologies: Semantic web, ontologies, practical (Protege).

Assessment: coursework (essay).

 

Physics for Imaging and Therapy

This module is intended as a pre-requisite for the term 2 modules on Medical Imaging. This module includes:

• Interactions,
• Detectors,
• Sources,
• Dosimetry,
• Introduction to MRI,
• Introduction to nuclear medicine,
• Radiation Protection
• Statistics.

Assessment: Examination.

 

Image Processing

This module is provided by the Computer Science Department (COMPGV12) and shared with students on the MSc in Computer Graphics, Vision and Imaging. The module is delivered as a series of lectures with supporting practical sessions. Topics include:

• digital images: digital camera, data types, 2D representation of images
• characteristics of grey-level digital images: discrete sampling, quantisation, noise.
• segmentation: thresholding, connected components labelling, region growing, split and merge algorithms, ROC analysis.
• image transformations: grey-level transformations, histogram equalization, geometrical transformations, polynomial warps,
• morphological operations: erosion and dilation binary images, open, close, thinning, medial axis transforms, introduction to grey-level morphology,
• feature characterisation:region properties, moment features, boundary coding, Fourier descriptors, line descriptors.
• image filtering: linear and non-linear filtering, image convolution, separable convolutions, sub-sampling and interpolation as convolution operations.
• edge detection: alternative approaches, edge enhancement by differentiation, Canny, performance evaluation,
• corner detection:image structure tensor, relationship to autocorrelation, Harris corner-detector,
• colour: colour representation, colour metrics, pixelwise operations, colour invariants and consistency.
• template matching: similarity and dissimilarity metrics, L2 metric and relationship to cross-correlation, image search and multi-resolution algorithms, 2D object detection, recognition, location.

 Assessment: coursework and examination.

 

Term 2

Medical Imaging (Ionising) & Medical Imaging (Non-Ionising)

These two modules are common to all the Medical Physics MSc courses. They provide an in-depth background in the following areas:

• Diagnostic Radiology,
• Computer Tomography (CT),
• Nuclear Medicine,
• Positron Emission Tomography (PET),
• Image reconstruction,
• MRI,
• Ultrasound,
• Optical Imaging.

Assessment: unseen written examinations.

 

Information Processing in Medical Imaging

The essence of medical image computing is to derive information form medical images for clinical diagnosis, therapy or to improve our understanding of function and disease. This module focusses on algorithms and software for obtaining this information. The module is provided by members of the Centre for Medical Image Computing and is also offered as an option for students on the MSc in Computer Graphics, Vision and Imaging. Topics include:

• Introduction: medical imaging modalities, clinical challenges, statistics relating to medical imaging (t-tests, Bland Altman,  sensitivity and specificity, ROC), clinical trials.
• Image registration: rigid, non-rigid, fluid, free-form deformation, registration theory and practice.
• Image segmentation and classification.
• Statistical Parametric Mapping (SPM): neuroimaging practical.
• Insight Toolkit (ITK): C/C++, templates, CMake, ITK.
• High Performance Computing: Cluster computing, graphics cards (GPUs) and the NVidia CUDA language.

Assessment: Coursework based on SPM and ITK.

 

Image Directed Analysis and Therapy

The module is specific to the MSc in Medical Image Computing and covers the extraction of information from medical images and how this is increasingly being used to guide interventions and therapy. Topics include:

• Image guided interventions,
• Dynamic measures from PET imaging,
• Dynamic measures from DCE-MRI,
• Visualisation (surface and volume rendering, applications such as virtual endoscopy),
• Imaging biomarkers,
• MRI Diffusion tensor imaging and tractography.

Assessment: Coursework (oral presentation and essay based on published research).

 

Research Project 

Assessment: Oral presentation, poster presentation, written report.

 

Industrial Projects

Industrial companies are encouraged to contact us if they are interested in providing research projects. There are tax incentives to encourage research in UK SMEs and offering a project may qualify under this scheme (see the Research and Development Tax Credits web site).