Development of a Matlab analyzation toolkit for 3D lookup tables
IDP by: Chen Hsuan Shih
Advisor:
Prof. Nassir Navab
Supervision by:
Philipp Matthies,
Jose Gardiazabal and
Aslı Okur
Abstract
Gamma cameras are frequently used nowadays in nuclear functional imaging procedures in cancer diagnostics and interventions. In order to be able to obtain 3D reconstructed images of a radioactive distribution using mini gamma cameras, tracking and modeling of these devices is needed. Tracking is currently performed optically, but for robotic SPECT acquisitions mechanical tracking will be used due to its higher accuracy.
To model the gamma cameras acquisition characteristics, a big amount of measurements of a radioactive point source in front of the gamma camera at different positions has been acquired and now this large dataset called lookup table has to be analyzed to improve future acquisitions and quality of the reconstructed 3D images. We are interested in evaluating this dataset to extract important parameters of the camera and aid in characterizing di fferent cameras or di fferent collimators without having to repeat all these measurements, which is very time-consuming (it takes a couple of weeks) and error prone. It also helps us to come up with an analytical mathematical model, which we could use to further improve the 3D reconstructions in speed and quality.
More speci fically, a Matlab based graphical user interface should be developed to load, process, analyze and visualize lookup table datasets. Parameters like the homogeneity, ellipsoidal shape of single pixels at diff erent distances and sparseness should be obtained and conclusions regarding lookup table acquisition and its usage should be derived.
Project Outline
1. Introduction
- Getting familiar with nuclear functional imaging
- Getting familiar with intra-operative SPECT using mini gamma cameras
- Obtain dataset and git access
2. Parameters of Interest
- Homogeneity of the di fferent pixels
- Direction of view of each pixels
- Symmetry and sparseness of the dataset
- Shape of the field of view
3. Analysis and Visualization
- (Semi-)Automatic calculation of the parameters of interest
- Fitting of analytical functions to the lookup table data
- Similarities between pixels dependent on their positions
4. Evaluation and Conclusion
- Exploit symmetries and sparseness of the dataset to optimize future acquisitions
5. Documentation and Presentation
- Written report of about 20 pages covering the work done
- 20min oral presentation plus discussion in our seminar