HL2012 3D Image Reconstruction in Medicine 6.0 credits

The course includes the following elements:

• An introduction to digital image processing, including digital image filtering both in room and frequency domains, Fourier Transform, Radon Transform, image restoration and registration
• The presentation of Gauss and Poisson noise, the sonogram, Fourier slice theorem
• Different image reconstruction techniques such as the filtered back projection technique, iterative methods, algebraic methods, Maximum Likelihood, ordered subsets as well as a Maximum a Posteriori

In parallel, students will work in small groups with a project aimed as solving a 3D image reconstruction problem and implementing the solution in Matlab code, in addition to writing a report, publishing it on the World Wide Web and presenting it orally for other students and researchers.

The course also includes a seminar work where each (small) group of students reads research articles on a topic in the area of Medical Image Reconstruction, and discusses it orally in a seminar.

Three dimensional (3D) imaging plays a central role in medical imaging. 3D images are used for both diagnosis and treatment. For several imaging modalities, data are acquired digitally as one or 2D projections of the object. In order to obtain a 3D image from these projections, a reconstruction operation must be implemented, this course deals with basic methods for digital image processing and commonly used methods for 3D reconstructions. The course is organized as a project course that provides practical knowledge about 3D image reconstruction in medicine. It also provides experience in problem solving as well as presenting research result both orally and in writing.

Upon completion of this course the participant should understand:

• Digital image registration and factors affecting image quality
• Image filtering in space and frequency domains
• Image restoration
• 3D image reconstruction

Bachelor’s degree in Engineering Physics, Electrical Engineering, Computer Science or equivalent. Basic knowledge of anatomy.

The course literature consists of current research articles that will be given out when the course starts, in addition to the book:
Gonzalez, Woods & Eddins, Digital Image Processing Using Matlab, Prentice Hall 2004, ISBN 0130085197

If the course is discontinued, students may request to be examined during the following two academic years.

• ANN1 - Exercises, 1.5 credits, grading scale: P, F
• PRO1 - Project, 4.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Passed seminar work (ANN1; 1.5 cr.) grading P/F.
Passed project work (PRO1; 4.5 cr.) grading A-F.

Rodrigo Moreno

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Home Page of the Course

http://medim.sth.kth.se/hl2012/