Dr. Jansen, or: How I Stopped Worrying and Learned to Love the Barium

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After a marathon build session, the first images from the open source CT scanner are here! The story…

The Detector
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Recall that in the last update, the stock Radiation Watch Type 5 silicon photodiode high energy particle detector was found to be calibrated for Cesium, with a detection threshold likely somewhere near 80keV. This was too high to detect the ~22keV emissions of the Cadmium-109 source, and so I put together an external comparator that could adjust the threshold down to the noise floor. After testing the circuit on a protoboard, I designed a tiny board that sits on the back of the Type 5, and through the use of a 10-turn potentiometer allows you to recalibrate the threshold down to the noise floor.

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I designed some mounting plates that could mount to the linear carriages for the source and detector.

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Here, the detector is mounted onto an offset mounting plate, which in turn connects to the detector carriage. The wiring harness breaking out all the detector pins feeds through the center of the carriage to a fixed mount point on the bore that acts as a strain relief. Looks great!

The Source

Even with the upgraded extra-sensitive detector, I was still seeing many fewer detections than I was expecting — albeit about an order of magnitude more than without the enhancement. A kind fellow on the Radiation Watch facebook group made a spice simulation model based on the helpful schematics that the folks at Radiation Watch make available, and his simulations suggested that the noise floor for this circuit is around 30keV. This means that with Cadmium 109, whose primary emissions are around 22keV, I was likely still missing the majority of the emissions, and getting many fewer counts than I was expecting.

Enter the Barium-133. There are a number of radioisotope check sources that are commonly available, but many of them have very high energy emissions in the many hundreds (or thousands) of keV — likely far too high energy to be usefully absorptive for everyday objects. The emission spectra I’ve seen for the tubes in commercial CT scanners tend to have broad spectrum emissions centered around 60-70keV, and the datasheet for the silicon photodiode suggests it’s most sensitive from 10keV to 30keV, where the sensitivity drops off afterwards. A higher detection efficiency means that we can get by with a less intense source, and with check sources that are barely detectable over background a foot away, it’s a battle for signal, and every photon counts.

Barium-133 has primary emissions around the 33keV range, and seems to be one of the few commonly available radioisotopes (aside from Cadmium-109) with such low emissions. To give the system the best possible chance of working, I ordered a 10uCi Ba133 source (up from the 1uCi Cd109 source I was using previously). With the source 10cm away from the detector, with the background rate at 20 counts per minute, the Cd109 source reads about 70 (so a delta of 50), and the Ba133 source reads around 1500 (!), so we’re definitely detecting many more of the lower energy emissions, and this should have a much better signal-to-noise ratio, and decrease the acquisition time required for collecting good data.

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The Ba133 source also comes as a sealed 25mm disc. I designed a sandwich mount for these source discs that contains between 3-6mm of lead shielding at a variety of angles, and a very rough approximation of a lead collimator with a 3mm hole drilled in the front to give some directionality to the source. Testing out a few angles, this appears to have brought the reading down to about 60 cpm at 15cm away, except for directly ahead, where the intensity is about 550cpm at 15cm. Sounds great!

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Putting it all together

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I have to confess, I’m a bit of a late sleeper (and a night owl), but I was so excited about finally putting everything together and collecting the first data, that I woke up early Saturday. After a marathon 13-hour build session, I finished designing and fabricating the source and detector mounts, and putting the bore back together.

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With one of the bore covers removed, these pictures make it a little easier to see the complete linear axis mechanisms that are contained within the bore. You can thank my dad for discouraging my rampant hot glue use at a young age, and encouraging me to design things that were easily serviceable. I’ve given a few students the same talk when I see them wielding a hot glue gun for one of their projects… ;)

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Putting it all back together — looks beautiful!

And now, the data!

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After the marathon build session, I took the very first data from the instrument — a quick absorption image straight up the center of this apple. Data was low resolution and noisy, but fantastic for the very first data from the instrument.

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A very tired, but very pleased person after collecting the first data off the scanner around 1am.

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I had some time Monday evening to write some basic firmware for collecting images, storing them to an SD card, specifying the size and resolution parameters, the integration time for the detector, and so forth. In probably one of the strangest things I’ve ever done, and feeling very much like Doc Brown, I went to the grocery store and found a few vegetables that have internal structure and might be interesting to scan. I decided to start with the avocado…

I’d previously determined empirically that the optimal integration time for this setup is about 90 seconds per pixel — that tends to give a stable count of around 550cpm +/- 4 cpm. Lower integration times will give proportionately more noise, but be much quicker to scan.

The avocado is about 10cm by 12cm, and so to capture a first test image I set it to a 5mm resolution with a relatively fast 10 second integration time per point (bringing the total acquisition time to 20 x 24 x 10 seconds, or just over an hour).

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And it worked! The image is certainly a bit noisy (as expected), but it looks great. The table and the avocado are clearly visible, and the seed might also be in there, but we’ll need a bit higher integration time to see if that’s real structure, or just noise.

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Overlaying the scan atop the picture, the scan is a perfect fit!

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The integration time for the first image was only 10 seconds per pixel, and so I setup a longer scan with an integration time of 60 seconds per pixel. Beautiful! This still isn’t quite at the empirically determined sweet spot of 90 seconds, but it really cleaned up the noise in the first image.

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The same data, with log scaling rather than linear scaling. I’m not entirely certain whether avocado pits are more or less absorptive to 33keV photons than the surrounding avocado, so it’s not clear whether we’re seeing lots of absorption at the center because of the seed, or because there’s 10cm of fruit between the source and detector…

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But I’d love to see some internal structure. So tonight I put the bell pepper on, which is about the same size as the avocado, and set it to an integration time of 20 seconds.

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And the result! It definitely looks like a bell pepper, and you can clearly see the seed bundle inside. Incredibly cool!

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The same image, log scaled instead of linear scaled.

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And the overlay. Looks beautiful!

What a fantastic few days for the open source CT scanner, and the initial data looks great. There’s still plenty to do — now that the source and detector are working, I can finish designing the Arduino shield with four stepper controllers (two for the linear axes, one for the table, and one for the rotary axis). The source is also currently collimated in only the most liberal of senses, and in practice the detection volume for a given pixel is likely a pyramid that starts from the ~3mm source aperture and meets the ~1cm square detector — so the images should sharpen up a good deal by better controlling the beam shape. Once all of that is working, and I add an accelerometer sensor to the rotational axis to sense it’s angle, I should be able to scan from 180 degrees around the sample, and test the ability of the instrument in computed tomography mode, backing out the internal structure of a given slice from a bunch of 1D images. Very exciting!

Thanks for reading!

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