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Table of contents 1. Introduction 2. Multi-level Technology Transfer Infrastructure 2.1 Level 1: University Education – Encourage the Idea of becoming an Entrepreneur 2.2 Level 2: Post Graduate Education – Improve your skills and focus it on a product family. 2.3 Level 3: Birth of a Company – Focus your skills on a product and a market segment. 2.4 Level 4: Ready to stand alone – Set up your own business 2.5 Level 5: Grow to be Strong – Develop your business 2.6 Level 6: Competitive and independent – Stay innovative. 3. Samples 3.1 Sample 1: Laser Processing and Consulting Centre, LBBZ 3.2 Sample 2: Prototyping Centre, CP 4. Funding - Waste money or even lost Money? 5. Conclusion
The Crystal Clear Collaboration has developed a modular system for a small animal PET scanner (ClearPET). The modularity allows the assembly of scanners of different sizes and characteristics in order to satisfy the specific needs of the individual member institutions. The system performs depth of interaction detection by using a phoswich arrangement combining LSO and LuYAP scintillators which are coupled to Multichannel Photomultipliers (PMTs). For each PMT a free running 40 MHz ADC digitizes the signal and the complete scintillation pulse is sampled by an FPGA and sent with 20 MB/s to a PC for preprocessing. The pulse provides information about the gamma energy and the scintillator material which identifies the interaction layer. Furthermore, the exact pulse starting time is obtained from the sampled data. This is important as no hardware coincidence detection is implemented. All single events are recorded and coincidences are identified by software. The system in Jülich (ClearPET Neuro) is equipped with 10240 crystals on 80 PMTs. The paper will present an overview of the data acquisition system.
The small animal PET scanners developed by the Crystal Clear Collaboration (ClearPETtrade) detect coincidences by analyzing timemarks which are attached to each event. The scanners are able to save complete single list mode data which allows analysis and modification of the timemarks after data acquisition. The timemarks are obtained from the digitally sampled detector pulses by calculating the baseline crossing of the rising edge of the pulse which is approximated as a straight line. But the limited sampling frequency causes a systematic error in the determination of the timemark. This error depends on the phase of the sampling clock at the time of the event. A statistical method that corrects these errors will be presented