Data Analysis with Python for Medical Physicists

Start Date: Apr 21st, 2022 09:30

End Date: Apr 23rd, 2022 14:00

URL: https://mamp.org.mt/python-april-2022-registration-page/

Location: Online

Data Analysis with Python for Medical Physicists

Course Aim

The aim of this course is for participants to learn skills in Python that a medical physicist should know. By the end of the course, participants will be competent to implement data analysis using this programming language and use them in different applications.

Target Group

Attendants should be clinical or academic medical physicists who are interested in learning and developing new skills in the programming language, Python. Individuals who have limited to no background of Python, or would like to refresh their skills are encouraged to apply.

Course Lecturer

Eric Pace, Lead Medical Physicist, Medical Physics Expert, Medical Imaging Department, Mater DeiHospital, Malta

 

Course Content

•Overview of programming basics and control flow

•Classes and methods as the building blocks of object oriented programming. Explain how these may be applied in the context of managing typical medical physics data. This will help develop skills beyond a simple linear execution of code.

•Reading unstructured data that is often produced by CT, MRI and other modalities

•Structure data in a more machine readable format for storage and retrieval –this assists in preparing data for fast insertion into databases (although databases themselves will not be discussed)

•Using an Integrated Development Environment (IDE) such as PyCharm

•Introduce the concept of panels as a wrapper over matrix data. Medical physicists often have columnar data (2D matrix) which needs to be cleaned, filtered, processed and plotted.

•Plotting of data as line, bar, box and scatter plots.

•Use PyDICOM package to work with DICOM files (extract information from DICOM headers and write any modifications back to DICOM files).

 

This course will not:

•Explore the use of databases and SQL

•Cover numerical methods or statistical techniques

Attachments