Thursday, June 04, 2015

CT Brush and CancerZap!: two video games for computed tomography dose minimization

Dick Gordon sent me the following:

Dear Igor,

Graham Alvare and I just published:

Author: Alvare, G.; Gordon, R.
Year: 2015
Title: CT Brush and CancerZap!: two video games for computed tomography dose minimization
Journal: Theor Biol Med Model
Volume: 12
Issue: 1
Paper: #7

doi: 10.1186/s12976-015-0003-425962597

Abstract: BACKGROUND: X-ray dose from computed tomography (CT) scanners has become a significant public health concern. All CT scanners spray x-ray photons across a patient, including those using compressive sensing algorithms. New technologies make it possible to aim x-ray beams where they are most needed to form a diagnostic or screening image. We have designed a computer game, CT Brush, that takes advantage of this new flexibility. It uses a standard MART algorithm (Multiplicative Algebraic Reconstruction Technique), but with a user defined dynamically selected subset of the rays. The image appears as the player moves the CT brush over an initially blank scene, with dose accumulating with every "mouse down" move. The goal is to find the "tumor" with as few moves (least dose) as possible. RESULTS: We have successfully implemented CT Brush in Java and made it available publicly, requesting crowdsourced feedback on improving the open source code. With this experience, we also outline a "shoot 'em up game" CancerZap! for photon limited CT. CONCLUSIONS: We anticipate that human computing games like these, analyzed by methods similar to those used to understand eye tracking, will lead to new object dependent CT algorithms that will require significantly less dose than object independent nonlinear and compressive sensing algorithms that depend on sprayed photons. Preliminary results suggest substantial dose reduction is achievable.



We have obviously challenged the compressive sensing community in suggesting that humans do it better. Of course, our goal is automating human computing, but the result may be an algorithm that surpasses CS. We hope that some of your readers will try to prove us right or wrong. Either way, we invite them to play the CT Brush video game and give us some feedback and/or improved code. It's open source. Thanks.

Yours, -Dick Gordon
Thanks Dick !

The code is here:

My take: yes, adaptive sampling is likely to be very effective and finding optimal sampling strategy using gaming is very interesting to say the least. Recall that the previous phase transitions found for CT imaging is good for non adaptive sampling and adaptive sampling is likely to bring improvement to those.
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