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After the successful participation in the module the student is able to:
- prepare self-dependently an x-ray based radiography and CT experiment
- operate a clinical CT and perform measurements under supervision
- perform data record, analysis, and evaluation
- document the experiments including methods, results, and discussion fulfilling the requirement for a scientific publication
- perform literature research

Welcome to the course for the BEMP lab course 01 on clinical CT, where we will show you the basics of computed tomography, and give you some insights in state-of-the-art clinical and especially spectral CT. This lab course is divided into three parts in presence. For each part you will have prepare before the lab course -- we have prepared a separate manual for each part --, evaluate your data, work with python scripts, answer CT related questions, and summarize your work in a report.

Computed tomography (CT) is a non-destructive X-ray imaging method, which allows to visualize three-dimensional X-ray absorption properties of an object. In part 1, you will learn the basics of computed tomography in a laboratory environment. In the first part of the lab course, you will learn the basics about X-ray imaging and computed tomography using a simple laboratory CT setup. Next to theoretical and experimental basics of X-ray imaging and radiography, you will get an idea how CT reconstruction using filtered backprojection (FBP) works and how typical reconstruction artifacts look like. You can also scan your own samples. Part 1 will take place in the TUM Physics Department in Garching Forschungszentrum.

In part 2, we will focus on clinical CT systems with respect to image quality and dose. Clinical CT systems are a standard diagnostic tool in radiology. The method is fast, comparably cheap, and provides three-dimensional isotropic attenuation of different tissue. CT is not only used in emergency medicine, where a whole body scan can be performed below 20 seconds with state-of-the-art CT systems, but also for different diagnostic questions like staging of cancer patients, determination of kidney stones, or diagnosis of cardio-vascular diseases with the use of contrast agent. However, CT comes at the cost of radiation exposure. In part 2, you will perform measurements at a clinical CT scanner at the TUM Klinikum rechts der Isar in Munich. First, we will measure a test sample to get familiar with the CT scanner. You can also measure your own samples (not too much metal) if you are interested. The next question will be spatial resolution. You will learn how to determine the spatial resolution of a CT system. The third task is to assess noise using quantitative samples. You will evaluate noise. Dose is determined by noise and resolution and essential to be kept at a minimum in patient care. Here, you will assess dose of a scan.

In part 3, you fill focus on state-of-the-art dual-energy CT (DECT) imaging. DECT, also known as spectral CT, is a computed tomography technique that separates X-ray spectra, allowing for quantitative assessment of materials that have different attenuation properties at different energies. In this part of the BEMP clinical CT lab course, you will learn the basics about DECT using image-based material decomposition on micro CT data as well as a simple simulation of data for projection-based decomposition. Also, you will look into clinical data sets and investigate the diagnostic potential of DECT. Next to theoretical and experimental basics, you will get an idea of how data acquisition, material decomposition in projection space, filtered backprojection (FBP), and algebraic reconstruction (ART) work in combination. You will create your own DECT measurement datasets and simulation data from a digital phantom. This part will also take place at the TUM Klinikum rechts der Isar in Munich.

No preconditions in addition to the requirements for the Master's program. PH2001 might be helpful.

The lab course is carried out in groups of usually 3 participants. The groups are working self-dependently as far as possible. A supervisor supports the group in case that questions or problems arise. Students have to prepare for the course by studying introductory material like the manual of each part and additional literature, perform the experiments for each part, work the given Python script tasks. The experimental data has to be analyzed and evaluated. For each part, a report has to be written according to scientific standards. Eventually, the lab course has to be presented in a colloquium followed by a scientific discussion.

see the recommended literature on Moodle.