1 Automatic Calibration of MiCES Modules Craig Dowell University of Washington IRL Lab Seminar...

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Automatic Calibration of MiCES Modules

Craig DowellUniversity of Washington

IRL Lab SeminarNovember 19, 2009

MiCES Automatic Calibration November 2009

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Outline

• Quick Hardware Overview

• Graphical Analysis Tools

• Calibration Process

• Some Interesting Results

• Short Q & A


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MiCES Hardware Overview

• PET scanners have large numbers of detectors;

• Detectors arranged in concentric rings;

• Rings are composed of rows (of four PSPMTs each);

• Four (PS)PMTs per “cassette”;

• Cassette controlled by a “Rabbit Digital Board”;

• Two modules per digital board (master, slave);

• Two PMTs per module;

• From calibration perspective modules are independent;

• Calibration works per-module, or per-2-PMTs.


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• Fifteen settings (“knobs”) per module – Calibration means finding best settings for each of these knobs;

– For some definition of best.

Xx 2


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• High Voltage Setting– Affects both PMTs

– Used in calibration to position photopeak in histogram of PMT 0 (currently “best” means bin 155 – constant in cal code);

– Finest grained control (one bit = 5 bins).

• ASIC Gain (A, B, C & D) Setting– Allows for differences between PMTs

– Used in calibration to position photopeak in histogram of PMT 1 (currently “best” means bin 155);

– X+, X-, Y+, Y- are always set together;

– Not as fine grained (one bit = 15 bins).


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• CFD Threshold Setting– Used to adjust low energy side of histograms to common position “best” means bin

35;

– Cut out low energy noise but leave interesting signal.

• TDC Offset, TDC Gain Settings– Offset adjusts low side of TDC voltage divider network;

• “Best” means uniform distribution WRT lowest bins.

– Gain adjusts high side of TDC voltage divider network;• “Best” means uniform distribution WRT highest bins.

– Adjusts shape of TAC “ramp”;

– Offset too high means no low time-values;

– Gain too low means no high time-values.


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• Feedback for calibration is via histograms– Implemented in FPGA;

– Available to Rabbit processor;

– PMT divided into four quadrants;• Edge pixels not accumulated;

• Single histogram per quadrant;

• 256 energy bins per histogram.

– Timing information in fifth virtual “quadrant”;

• 32 bins of fine grained start time.


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Graphical Analysis Tools

• Written for the calibration project– C++;

– Really C;

– Some is similar code as in Rabbit calibration functions;

– Help make sense of odd histo data.

• Runs on Mac– Xcode + gnu toolchain + gnuplot

• Runs on Windows– Cygwin + gnu toolchain + gnuplot

• Freely available– Feel free to use any code you want


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• rabbit-histo-fit – Create energy histograms from module histogrammer raw data

and perform same fits as calibration code.


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• rabbit-tdc-plot – Create time histograms from module histogrammer raw data and

calculate same average and tolerance as calibration code.


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• crystal-map – Create surface plots of hit counts from MiCES map files;

– Not the same thing as an orgthog display, but similar.


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• map-read – Similar to rabbit-histo-plot but operates on MiCES map files;

– Used to check end-to-end results.


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• Orthog Display – Existing tool written in IDL;

– Combines position and energy information.


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Calibration

• First step is to adjust HV – Set default values for HV, ASIC 0 gain and take histo of PMT 0 and run fits to find

photopeak bin of each quadrant;

– Find average photopeak for PMT 0;

– Make small change in HV;

– Take histo of PMT 0 and run fits to find new photopeak bin of each quadrant;


– Assume responses to HV changes are linear, and calculate new HV to place average photopeak in desired bin (155);

– Set new HV and remember setting.

_ _

_ _ _

small change bits required change bits

measured after measured before bins desired measured before bins


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Calibration

• Second step is to adjust gain for PMT 1– Set default values for ASIC 1 gain;

– Take histo of PMT 1 and run fits to find photopeak bin of each quadrant;


– Make small change in ASIC 1 gain;

– Take histo of PMT 1 and run fits to find new photopeak bin of each quadrant;


– Assume responses to gain changes are linear, and calculate new gain to place average photopeak in desired bin (155);

– Set new ASIC 1 gain and remember setting.


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Calibration

• Third, fourth steps are to set CFD threshold for PMT 0 and 1– Set CFD threshold to max;

– Take histo of PMT and find low energy edge of backscatter peak in each quadrant – calculate average over quadrants

– Make small downward change in CFD threshold;

– Take histo of PMT and find low energy edge of backscatter peak in each quadrant – calculate average over quadrants;

– Assume responses to CFD threshold are linear, and calculate new gain to place average edge in desired bin (35);

– Set new CFD threshold and remember setting;

– Repeat for second PMT.


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Calibration

• Fifth, sixth steps are to set TDC offset and gain for PMT 0 and 1– Set TDC offset and gain to default

– Take histo of PMT and find average counts in center bins;

– If lowest bin not in range, binary search (change setting, take histo) for TDC offset that puts lowest bin closest to average;

– Take histo of PMT and find average counts in center bins;

– If highest bin not in range, binary search (change setting, take histo) for TDC gain that puts highest bin closest to average;

– Set new TDC offset and gain and remember settings;

– Repeat for second PMT.


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Calibration

• Final step is to write settings to flash– HV DAC setting;

– ASIC 0 gain (individual channels A, B, C, D);

– ASIC 0 CFD Threshold;

– ASIC 0 TDC offset;

– ASIC 0 TDC gain;

– ASIC 1 gain (individual channels A, B, C, D);

– ASIC 1 CFD Threshold;

– ASIC 1 TDC offset;

– ASIC 1 TDC gain.

• Total of fifteen knobs adjusted

• Values can be restored from flash at power-on


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Calibration

• Before and after energy histograms


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Calibration

• Before and after time histograms


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Interesting Results 1

• Already seen one– Funny shaped photopeak;

– Backscatter peak larger than photopeak.


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• Why?– Each PSPMT is really an array of miniature PMTs;

– Gain of each miniature PMT can vary by more than 30% (source Hamamatsu).


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• Why?– Rabbit histograms give finer granularity;

– Another case: three different photopeaks?


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• Why?– Orthog display shows

phenomenon clearly;

– Notice gain drop overabout one sixth of PMTwidth in saggital slice;

– Six X-direction anodesper PMT.


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• This means different PMT quadrants exhibit very different photopeaks.– Quadrant 1


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• This means different PMT quadrants exhibit very different photopeaks– Quadrant 2


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• Implications– Can’t just look for highest count and assume that is photopeak –

it might be a backscatter peak;

– Differences between quadrants within one PMT are often greater than between different PMTs;

– If looking for unscattered annihilation photons by filtering on 511 KeV photons, you want to look for hits around the photopeak – but positions of photopeaks have 30% tolerance;

• Can do calibration on end-to-end system using map files to come up with compensation values since map files have pixel resolution;

• Not possible with current FPGA histogrammer.

– It’s hard to tell where the cut value is between a good module/PMT and a bad one.


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• Delamination of Crystals– It is suspected that high temperatures in the lab caused

delamination of crystals;

– crystal-map tool shows reduced hit counts quite clearly.


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• Human eyes are good when in doubt– What is to be made of this histo?

– Perhaps a photopeak at bin 24, another photopeak at bin 40 corresponding to another anode, and reinforcing backscatter peaks at bin 7?

– What would scanner energy filters make of this spectrum?

– Could be sorted out withend-to-end calibration –does that exist?

– Is this module good orbad? This may confuse thecalibration code and is why “manual override” is implemented.


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• Human eyes are good when in doubt– What is to be made of this histo?

– Hardware problem?

– No Lorentzian-likephotopeak, so chi-squarewill be huge.

– I provide a “test” functionwith some initial cut values on parameterslike chi-square of fit.

– Needs furtherdevelopment.

– Right now, main calibration routines will just print large chi-square statistics and will continue as much as possible.


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Interesting Results n

• There are many “novel” histograms laying around in the system;

• When I started this project, I went through many of them to try and get my bearings and was often completely baffled;

• I think many of the odd histograms were due to various failures in the system. Dan tells me that cables are notoriously fickle;

• I tried to make the calibration code fairly robust, but I am (and my code is) still surprised by what sometimes happens.


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Resources

• I have passed around an “Orange Book” that describes all of this in excruciating detail;

• It shows where all of the analysis code lives, on Windows and Mac, and how to build it;

• Shows how to run and interpret all of the outputs of the various pieces of code;

• Includes a CD-ROM with all of the code, manual and this presentation on it;

• This is all freely usable in the sense of GPL. If you think any of my bits may be useful, feel free to use them in any way you want.


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Lessons Learned

• The hardest part was understanding why so many histograms were so “oddly” shaped;

• Spend time up front writing tools and scripts for gathering, reducing and presenting data – it is well worth it;

• Looking at lots of histograms with human eyes is important – run code on as many different modules as possible, as soon as possible;

• Don’t be afraid to throw things away if they aren’t working out;

• It’s a research device, expect it to be broken sometimes.


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Automatic Calibration of MiCES Modules

Short Q and A

1 Automatic Calibration of MiCES Modules Craig Dowell University of Washington IRL Lab Seminar...

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