BME I5000: Medical Imaging

Course overview and schedule:
Syllabus

Tutorials:
Linear Algebra: Open Course Work from MIT

Digital Signal Processing: graphic demonstrations in matlab
Short overview of geometric transforms.
Video series explaining basics of NMR/MRI technology.

Python:

• Basic Python Intro
• Editors
• We recommend Spyder or Pycharm as introductory editors, but additional information on editors is available here
• Datacamp Courses:
• Intro to Python
• Image Processing in Python
• Biomedical Image Analysis in Python
• Python Packages:
• Numpy
• Matplotlib
• Pandas
• Pillow

Lectures:

1. Introduction
2. X-ray Imaging
3. Intensity manipulations
4. Computed Tomography
5. 2D reconstruction from Projections
6. Nuclear Imaging
7. Maximum Likelihood Reconstruction
8. Nuclear Magnetic Resonance
9. Magnetic Resonance Imaging
10. Fourier Transform Review
11. Point Spread Function, Inverse Filtering,Wiener Filtering, Sharpening, ...
12. Deep networks for image segmentation

• Images for homework 1: blood1.tif, cell.tif, penny.mat.
• Rice image: rice.png
• Homework 5: Brain MRI, Example animation script
• CT image: head 1, head 2, mouse.
• Fan beam projection: MicroCT of mouse femur
• Head x-ray: head-xray1, head-xray2
• MRI in .hdr/.img format: hdr, img
• BME 310 segmentation exercise: data
• Image for homework 4: homodimer_staining.tiff
• Sinogram for homework 8 (expected source image is (225,225)): sinogram.tiff