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Radiation Risks

November 8, 2013 — 1 Comment

A recent discussion with a colleague on the Neurodome project centered on the acquisition of data by computed tomography (CT). Specifically, we sought volunteers for a non-medical imaging study. Volunteers were difficult, if not impossible, to obtain. Not only did we hope to find a person without cavities or implants, but we needed someone who was willing to be exposed to a certain dosage of radiation. Our conversation rapidly evolved into a treatise on radiation exposure and health risks. CT, which exposes patients to X-rays, indeed carries a certain health risk. What are these risks? How significant are they? I’d like to attempt to answer some (but not all) of these questions here.

To properly elucidate health risks related to cancer exposure, we must answer a number of important questions. What is the person’s health status? Does this person have any underlying genetic mutations? What type of study is being performed (that is, what is being scanned and for how long, along with the width/shape/angle of the beam)? For the purposes of this discussion, let us consider an otherwise healthy human undergoing standard scans.

In the links I provide below, you will note two units of radiation exposure. I’d like to clarify these so that you can more easily explore the topic independently from this post. The first unit, the gray (Gy), measures energy per kilogram. The second, the sievert (Sv), measures absorbed dose per kilogram. This is rooted in models by medical physicists that attempt to adjust for the effects of radiation dependent upon tissue type. In these models, you might be surprised by what is most likely to cause cancer after radiation exposure. While DNA damage is not insignificant, the major contributor is water. When water molecules absorb ionizing radiation (all radiation in this post is ionizing, unless otherwise specified), they emit free radicals (usually OH-), which in turn have damaging effects. Thus, the amount of water in a tissue is often related to a higher damaging dose per unit energy.

How much radiation are you exposed to in a given period of time? This handy chart should answer most of those questions. You might be surprised by the amount of exposure from certain activities. A chest CT is ~7 mSv, which is not much greater than the 4 mSv exposure from background radiation in a given year. As a former resident of central Pennsylvania, I was surprised to see that radiation exposure from the Three Mile Island incident resulted in an average of only 80 µSv. On the other hand, workers at Fukushima were exposed to a dose of 180 mSv! These doses are interesting from an academic point of view, but what real risks do they carry?

When we talk about radiation exposure, health risks come in two flavors. The first, deterministic effects, are those that result from a cumulation of radiation exposure. Below a certain threshold, adverse effects are minimal or non-existent. Above this threshold, health problems arise. The threshold differs between people and the health condition we are considering. Examples include hair loss, skin necrosis, sterility, and death. The second, stochastic effects, are those effects that have an increased probability of occurring with increased radiation exposure. The best example of this is cancer. With low exposure, one has a lower risk of cancer. With high exposure, this risk increases. We often consider this to be a linear relationship, in that a unit increase in radiation exposure results in a unit increase in cancer risk. For example, 100 mSv of radiation, increases one’s lifetime risk of cancer by 0.5%. Unlike deterministic effects, there is no threshold associated with stochastic effects. There is controversy over the linear model of cancer risk, and more research is needed.

An example against the linear model of cancer risk is exposure to radiation at high altitudes. Though this differs from a CT scan in many ways, one would still expect an increased risk of cancer to be associated with exposure to radiation at higher altitudes. However, those who live at high altitudes or those who work at high altitudes (like commercial airline pilots) do not exhibit a greater prevalence of cancer. To put this into perspective, a single round-trip flight across the continental United States results in the same radiation exposure as a chest X-ray. This begs an interesting question: How much risk do medical scans carry? 

The answer, as you can see, is fairly complicated. If you want to know how much radiation exposure a particular study carries, there’s a great resource to calculate this. This website assumes the linear threshold hypothesis to be true and, as I pointed out, it very well might not be true. That being said, any stochastic risks associated with medical scans are often far outweighed by the risks of ignoring a medical condition. In the case of Neurodome, the opposite is sadly true.

That being said, I’d be a happy volunteer.


For the first part of this series and to learn a bit more about 3D reconstruction of computed tomography (CT) slices, check out NEURODOME I: Introduction and CT Reconstruction. Our Kickstarter is now LIVE!

“As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature. Man must explore. And this is exploration at its greatest.” – Cdr. David Scott, Apollo 15


It is official. Our Kickstarter for NEURODOME has launched. I have already described a bit about my role in the project and described CT reconstruction. Future posts will delve into fMRI imaging and reconstruction, along with additional imaging modalities and perhaps a taste of medical imaging in space. You might be surprised at the number of challenges astronauts had to take while aboard rockets, shuttles, and the ISS. All of this will be part of the NEURODOME series.

With our launch, we hope to raise enough funds to develop a planetarium show that illustrates our desire to explore. To do so, real data will be used in the fly-throughs. Our first video, The Journey Inward, provides a basic preview of what you might expect.

I will continue to post about this project but, for now, read about NEURODOME on our website and, if you can, help fuel our mission!

Those who work closely with me know that I am part of a project entitled Neurodome ( The concept is simple. To better understand our motivations to explore the unknown (e.g. space), we must look within. To accomplish this, we are creating a planetarium show using real data: maps of the known universe, clinical imaging (fMRI, CT), and fluorescent imaging of brain slices, to name a few. From our web site:

Humans are inherently curious. We have journeyed into space and have traveled to the bottom of our deepest oceans. Yet no one has ever explained why man or woman “must explore.” What is it that sparks our curiosity? Are we hard-wired for exploration? Somewhere in the brain’s compact architecture, we make the decision to go forth and explore.

The NEURODOME project is a planetarium show that tries to answer these questions. Combining planetarium production technology with high-resolution brain imaging techniques, we will create dome-format animations that examine what it is about the brain that drives us to journey into the unknown. Seamlessly interspersed with space scenes, the NEURODOME planetarium show will zoom through the brain in the context of cutting edge of astronomical research. This project will present our most current portraits of neurons, networks, and regions of the brain responsible for exploratory behavior.

To embark upon this journey, we are launching a Kickstarter campaign next week, which you will be able to find here. Two trailers and a pitch video showcase our techniques and our vision. For now, you can see our “theatrical” trailer, which combines some real data with CGI, below. Note that the other trailer I plan to embed in a later post will include nothing but real data.

I am both a software developer and curator of clinical data in this project. This involves acquisition of high-resolution fMRI and CT data, followed by rendering of these slices into three-dimension objects that can be used for our dome-format presentation. How do we do this? I will begin by explaining how I reconstructed a human head from sagittal sections of CT data. In a later post, I will describe how we can take fMRI data of the brain and reconstruct three-dimensional models by a process known as segmentation.

How do we take a stack of images like this:


(click to open)

and convert it into three-dimensional objects like these:

These renders allow us to transition, in a large-scale animation, from imagery outside the brain to fMRI segmentation data and finally to high-resolution brain imaging. The objects are beneficial in that they can be imported into most animation suites. To render stacks of images, I created a simple script in MATLAB. A stack of 131 saggital sections, each with 512×512 resolution, was first imported. After importing the data, the script then defines a rectangular grid in 3D space. The pixel data from each of these CT slices is interpolated and mapped to the 3D mesh. For example, we can take the 512×512 two-dimensional slice and interpolate it so that the new resolution is 2048×2048. Note that this does not create new data, but instead creates a smoother gradient between adjacent points. If there is interest, I can expand upon the process of three-dimensional interpolation in a later post.

I then take this high-resolution structure mapped to the previously-defined three-dimensional grid and create an isosurface. The function takes volume data in three dimensions and a certain isovalue. An isovalue in this case corresponds to a particular intensity of our CT data. The script searches for all of these isovalues in three dimensions and connects the dots. In doing so, a surface in which all of the points have the same intensity is mapped. These vertices and faces are sent to a “structure” in our workspace. The script finally converts this structure to a three-dimensional “object” file (.obj). Such object files can then be used in any animation suites, such as Maya or Blender. Using Blender, I was able to create the animations shown above. Different isovalues correspond to different parts of the image. For example, a value/index of ~1000 corresponds to skin in the CT data, and a value/index of ~2400 corresponds to the bone intensity. Thus, we can take a stack of two-dimensional images and create beautiful structures for exploration in our planetarium show.

In summary the process is as follows:

  1. A stack of saggital CT images is imported into MATLAB.
  2. The script interpolates these images to increase the image (but not data) resolution.
  3. A volume is created from the stack of high-resolution images.
  4. The volume is “sliced” into a surface corresponding to just one intensity level.
  5. This surface is exported to animations suites for your viewing pleasure.

This series will continue in later posts. I plan to describe more details of the project, and I will delve into particulars of each post if there is interest. You can find more information on this project at