

When you need a CT scan ask for this - Mitt
http://freepress.intel.com/community/news/blog/2012/03/12/computing-power-speeds-safer-ct-scans

======
bh42222
_The answer to the question Baker posed lay in a set of mathematical rules
called an algorithm._

I never cease to be amazed just how low the math level is in our culture. And
how acceptable it is to be publicly that math ignorant.

 _It was difficult, for example, to change the algorithm, which worked best in
a single-threaded environment, into one that could take advantage of multi-
core processors._

Aha, so the parallelized it!

 _"We have been able to reduce X-ray doses to previously unthinkable levels,"
said Professor Johan de Mey, head of the radiology department of University
Hospital in Brussels, Belgium. That is opening up the benefits of CT scans to
a wider variety of patients._

Great!

The terrible way in which this article explains what was done, is doubly
ironic considering how it is an article about math and programming literally
_saving lives_.

~~~
eaurouge
Using the same phrases you consider "terrible", the writer was able to make
this piece accessible to a wider audience. I would suggest that articles like
this, which describe technological breakthroughs in language the lay public
would understand, are good for the science and engineering communities.

By the way, the article isn't about "math and programming saving lives", it is
about an advancement in technology that reduces the amount of radiation
patients are exposed to (in CT scans) so that there's a lower risk of
radiation poisoning.

------
hogu
My advisors work is in this - part of it is making better use of the
statistical properties of the noise in the reconstruction of the image.

~~~
J3L2404
Stochastic Resonance

<http://en.wikipedia.org/wiki/Stochastic_resonance>

~~~
hogu
That was not what we were doing - It was more like weighting measurements
proportional to their statistical variance, so that you penalize noisier
measurements. In this process, you typically apply some sort of regularization
to the objective function so that it converges to a smooth solution. There are
some interactions between the weights chosen and standard regularization that
creates non-uniform spatial resolution propeties, so much of the work was
spent designing roughness penalties that played nice with the weightings.

~~~
J3L2404
Hmm. Pretty much the inverse of SM.

