Exposure Indices, Dose, and Image Quality

Reading Time: 7 minutes read

Alright – let’s admit it, X-Ray Machines are pretty smart these days.  So smart, in fact, they can adjust hardware and technique settings automatically, taking some of the weight off us rad techs (more formally known as Radiologic Technologists).  But just because the machine can handle it, doesn’t mean we can sit back and relax.  Technologists are still responsible for producing a diagnostic image so understanding the relationship between exposure indices, dose, and image quality is crucial to making sure we don’t just take pictures – but that we take the best pictures!  Let’s break it down, shall we?

What are Exposure Indices?

First, let’s talk about Exposure Indices (EIs).  You’ve probably seen them on the screen post exposure, correct?  These numbers tell us how much radiation was used to create that image and they provide valuable feedback for determining if an image is diagnostic.  Dose is dose is dose…  What I mean is that whatever Actual Exposure (EI) is used, our goal is to use the right dose to get to the Target Exposure (EI_T) that has been pre-programmed into the unit.  Target Exposures are adjustable which means they might vary from site to site due to the varied Radiologist (Rads) preferences and tolerance for noise.  Which is why it’s important to work closely with your Rads when getting new Xray units or when working to optimize image quality.  The best targets are set based on a review of a facility’s actual exposures and are meant to minimize dose and noise while maximizing image quality.

How are Exposure Indices determined?

Deviation Index is the international standard indicator.  The DI was initiated by the International Electrotechnical Commission (the IEC) and the American Association of Physicists in Medicine (AAPM) among other world representatives.  I firmly believe it is the easiest of all EI’s to understand.  There is a logarithm calculation at work behind the scenes and while I don’t want to discount this amazing work, I also don’t want to get lost in the weeds!  In the simplest terms, the Deviation Index (DI) is the measure of how much the Actual Exposure (EI) deviatedfrom the Target Exposure (EI_T).  An optimal exposure is indicated by a DI of 0.  Which makes sense – the actual exposure didn’t deviate from the target.  So, when you are experiencing discrepancies between the image quality and the DI it might be time to adjust those targets.

So, why should we care about these EI numbers?  Well, first, the EI isn’t just a number.  It is a key indicator that tells you about the dose you’ve used and whether it is an optimal exposure for a quality image.  If the DI is way off, it could mean you’ve either under or overexposed your image, potentially compromising the diagnostic quality or unnecessarily increasing radiation dose.  An optimal EI means the image should have just enough radiation to produce a clear, diagnostic-quality image, without unnecessary radiation exposure to the patient.  But if there is a discrepancy between the image quality and the DI (either the image is diagnostic but the DI is way off, or the DI is optimal but the quality is way off) the target exposure settings likely need adjustment.  Simple, right?  Now let’s take it a step further.

Dose and Its Effect on Image Quality

Let’s set the record straight: Dose and Image Quality are connected – but not quite in the way you might think.  A lot of us were trained on film, where the density (or brightness – a term you might be more familiar with in the digital world) indicates the overall lightness or darkness of an image and is controlled by the dose (kVp / mAs).  More radiation = more density.

But Digital Radiography (DR)?  That’s a whole new ball game.  These days, dose doesn’t directly control the brightness (or density) of an image – that job is taken over by the computer!  Image quality is digitally controlled with image processing to ensure the best quality.  Xray images aren’t really “too light” or “too dark” anymore.  Using the right amount of radiation, the system will help correct the brightness and contrast of the image automatically.  So, whether you’ve accidentally over or underexposed the image, the computer can help bring it to the sweet spot of a diagnostic image.  We still need to be careful – Too little exposure can result in mottle (more noise), and too much exposure can still result in artifacts (less noise but possible saturation), so we need to aim for that “optimal” amount of radiation.

Optimizing Image Clarity

Now, let’s talk about noise – and no, I’m not talking about the random guy who talks way too loud at the movie theater!  In the world of imaging, noise refers to random variations in the image that make it look grainy, fuzzy, or unclear.  When noise happens, it can hide important details, like fractures or tumors, making it harder to interpret the image.

Why does noise happen?

• Underexposure: When the dose is too low on film, the image might look bright and grainy.  In DR, the image may be mottled because the system tries to compensate, but it can’t pull out the necessary detail.
• Overexposure: In film, too much radiation can cause parts of the image to be too dark, making details disappear.  In DR overexposure can result in saturation or can introduce random fluctuations that distort the image.
• Poor Equipment or Calibration: A low-quality detector or one that isn’t properly calibrated can introduce noise into your image, especially when you’re trying to capture subtle details.

Noise is like the background chatter in a room – you’re trying to focus on important details, but the noise makes everything harder to hear.  To minimize noise, we need to optimize the radiation dose.

Post Processing Functions can also play a role in Image Clarity.  It’s not magic (though sometimes it feels like it).  Below are just a few of the digital techniques used to adjust how the image appears on screen.

• Windowing refers to the range of grayscale that gets displayed.  By adjusting the window, you can control how much detail you see in the image.
• Leveling, on the other hand, is more about setting the center of that grayscale.  If the ”level” is too high or too low, parts of your image may become too bright or too dark.

Here’s a fun (and true!) fact: the reason we can sometimes “save” an image that might have been over or underexposed is because of the incredible flexibility of digital radiography.  Adjusting the post processing can help fine-tune the image to bring out the details you need.  But it’s far from perfect.  If you’ve gone too far, you might still see some weird artifacts or noise creeping into the image.

How Image Processing Algorithms Help

Here’s where things get even cooler: Image Processing Algorithms work as the engine powering the imaging software to produce optimal image quality.  This behind-the-scenes “engine” turns raw data into usable images and make sure the image is as crisp and diagnostic as possible.  Algorithms are specific to body parts and automatically adjust brightness, contrast, sharpness, and can even reduce noise, all to bring out the most important details.

For example:

• Noise reduction algorithms smooth out grainy images, making important stuff clearer.
• Contrast and edge enhancement algorithms sharpen the lines around bones, fractures, or soft tissues, making them stand out more.
• Histogram equalization adjusts the image’s overall brightness and contrast, helping bring out details in areas that might have been too light or too dark otherwise.

In short, Image Processing Algorithms (and the imaging scientists that created them) are the unsung heroes of digital radiography.  They help us deal with things like noise, improper techniques, and poor contrast while making sure the image is ready for interpretation in record time.

How This All Ties Together

The goal of radiology is to always minimize dose in order to maximize image quality.  We’ve got this whole new era of digital radiography where the machines are clever, but it’s still up to us to make sure we understand the tools at our disposal.  Exposure indices, dose, noise, and image processing all play critical roles in ensuring that the images we capture are not just images, but diagnostic tools.  Radiologic Technologists are all part of an intricate, evolving system that’s key to helping doctors diagnose, treat, and heal patients.  And, trust me, even though I sometimes feel like I’m still figuring out the “magic” of it all, I know that every small detail counts – and that’s what makes us vital to the healthcare team.

Now, go forth, bring your passion to work, and image the future!