The e2v sensors, size 1 (S1) and size 2 (S2), use the Quick Ray application named for the corresponding size (Quick Ray S1 or Quick Ray S2) to acquire images. The QuickRay software saves the x-ray images captured by the e2v sensor, which are automatically imported by MacPractice, or remain in the import folder for the MacPractice user to import, depending on QuickRay setup and MacPractice Preferences.
Click the Setup button on the QuickRay application window to access the QuickRay Preferences.
In the resulting Preferences window, set the target folder path with the Pictures Folder Path button. Optionally, set an alternative filename with the Pictures Filename button.
In the "How to generate files" field, it is recommended that the "Increment filename" option is selected instead of "One File" to prevent an image from being overwritten should there be an importing delay.
Using e2v Intraoral X-ray Sensors with MacPractice
After setting preferences in both MacPractice and QuickRay, select a visit for the patient in the DR ability of MacPractice and open the QuickRay application.
Take an X-ray image with the sensor to save it to the target folder. If the target folder in the QuickRay Preferences has been set as a MacPractice import folder, MacPractice will automatically import the image from the target folder, where the image will be deleted.
The imported image will display using the layout that has been set on the selected visit. Take the next X-ray to repeat the process until the layout is complete.
Configuring MacPractice Preferences for e2v
To set MacPractice to automatically import images from the QuickRay target folder, set the same target folder as the auto-import folder in the General tab of the Digital Radiography node in MacPractice Preferences. Click the green plus button, select the target folder from the resulting window and click the Open button. The path will be added to the list of Auto import folders. Make sure that the checkbox is marked in the Enabled column.
When using QuickRay with an import folder, you can select how you want layouts for the images to open in the Image Layout Well Placement dropdown.
The Digital Imaging System and Image Quality
Each element in the total digital radiography system plays a role in the final product and affects the perceived image quality, which affects the final perceived diagnostic quality. All digital radiography devices should be properly calibrated according to the manufacturer's instructions to maintain maximum uniformity and efficiency in the diagnostics of digital radiographs. Notate any settings applied to the devices in the office or MacPractice presets, layouts, or manual filter settings to maintain image consistency across all systems.
The Digital Radiography Device
The digital radiography device should first be calibrated for optimum exposure. An imaging procedure should be established in a manner that produces diagnostic images with the lowest radiation dose for the patient. Calibration directly affects the diagnostic quality of the output images and the post-processing outcome.
For example, images with exposure that is too low may exhibit a high amount of "quantum mottle", which will appear visually on the images as a high noise image. Digital radiography images with an exposure that is too high will be of good quality with low noise, however the increase in exposure is also an increase in exposure to the patient. This process should be understood in the context of the device that is calibrated for optimal exposure.
The quality of the original image obtained from the digital radiography device, as well as any calibrations or preprocessing settings of that device, will impact that image that MacPractice initially obtains from the device. Calibrating the digital radiography device for optimum quality will result in better post processed images with MacPractice.
Before editing any image with MacPractice, understand what preprocessing methods the Digital Radiography device may be applying and view the images in their raw state to become familiar with the initial quality of the original image. Image artifacts, non-uniformity, scratches, noise, and other imperfections are often seen in the original image. While MacPractice can be used to improve the final diagnostic quality of an image, the results will greatly vary based on any such artifacts or imperfections in the initial image. To improve the quality of the final image product, it is best to start at the source. In Digital Radiography, this is the original input created by the digital radiography device.
Image Enhancement and Assessment
Image Quality Assessment
- Detail: In medical imaging, detail is the general visibility of blur or sharpness in an image. The amount of visible detail in an image is increased by sharpness and reduced by blurring. Blurring can occur if any motion is present, the receptor is not properly calibrated, the focal spot used is inappropriate to the procedure, or the pixel count is too low (low resolution images). Detail is impacted by Noise in that enhancing the detail in an image can often enhance the perceived value of noise.
- Noise: Also called mottle or grain, Noise is the random variation of brightness in an image that is unrelated to the variations of brightness within the anatomical subject matter. Noise reduces the visibility of the anatomical structures in the digital radiography image, especially in subjects with low contrast. Noise is naturally occurring in all digital images and cannot be eliminated, however the camera used to capture the image can be calibrated to reduce the amount of noise in the image. Increasing Detail in an image often increases the visible noise. Likewise, reducing Contrast in an image makes Noise more visible to the human eye.
- Contrast: Contrast is the variation of white and black values that form the image. Contrast is often seen as the most important quality metric for diagnostic imaging. Nearly any type of post-processing, filtering, or image manipulation will have an impact on the image's contrast. Increasing detail by enhancing sharpness or decreasing noise by increasing smoothness in an image will change the contrast of each affected pixel.
- Radiopaque: Radiopaque anatomical structures are those that limit the passage of radiant energy and therefore appear as white or light values in the resulting x-ray. The lightness of radiopaque structures depends upon the resistance of the structure to radiant energy. Examples: bone, enamel, amalgam, and other metals.
- Radiolucent: Radiolucent anatomical structures allow the passage of radiant energy and therefore appear as darker (gray to black) values in the resulting x-ray. The darkness of radiolucent structures depends upon the resistance of the structure to radiant energy. Examples: soft tissues, cavities, nonmetallic restorations such as acrylic or silicate.
The Filter Setting menu is accessed in the DR ability in MacPractice through the Image Preview window. Click an image in a visit to preview the image. Next, click the Filter Settings icon on the preview toolbar to open the Filter Settings menu.
The Filter Setting Menu contains the Presets menu, the various Filter tools, the Other menu (which contains options for adjustments to orientation, cropping, and so on), the Annotations Menu, and the Histogram graph.
In builds 5.0+ of MacPractice, the toolbar has Toolboxes that can be clicked on in order to reveal tools and filters. The left seated toolbox will reveal the tools and the right seated toolbox reveals the filters.
When collapsed, the image window will appear as thus:
In this following screenshot, you can see the tools on the left side and the filters on the right side with the appropriate "Toolbars" button enabled.
The following contextual guide will attempt to explain the MacPractice Imaging filters and presets as tools that Digital Radiographers can use in creating more uniform post processed digital radiography images.
The Histogram graph is useful in distinguishing potential issues with an image that might otherwise depend on subjectivity. The Histogram is a graph that shows an approximation of the tonal range or brightness values of the pixels in an image. It plots the number of pixels in the image on the vertical axis with the level of brightness on the horizontal axis. In terms of a DR image as a matrix of pixels, the histogram plots the image area or number of pixels with the range of exposure that creates the image. In the context of a tool for a radiographer attempting to make more diagnostic images, the histogram allows for visualization of contrast.
A histogram displaying graphing mostly to the right of the graph will be a high key image. Most of the colors in the image are bright pixels. A spike in the far right may indicate that the range of white pixels that express highlights in the image are not as variable as they could be. Within the image, this can appear as an overexposed image or an image that is too bright or washed out. For a radiographer, the image may appear to not have a truly diagnostic variation in the radiopaque structures, such as calcifications, enamel, or amalgam (metals).
Similarly, the histogram displays the black value pixels to the left of the graph. A histogram with most of the range to the left of the graph will be a low key image. Most of the colors in the image will be darker. An image with a spike on the far left side of the histogram graph may not have enough variation in shadow details. An image of this nature can appear too dark or underexposed. For a radiographer, this image may not offer a clear range of radiolucent structures such as soft tissues, cavities, and nonmetallic restorations (Acrylic or Silicate, for example).
A narrow histogram will display for images with a low dynamic range. To the observer, this will appear as a low contrast, flat image. To the radiographer, this image may not appear to have a diagnostic range of radio-density. The radiopaque and radiolucent anatomical structures may not offer diagnostic variation in this image.
The same histogram principles will apply with color images, however each color channel will be charted separately. The histogram displays the tonal range of the red, green, and blue colors within the image. The specific colors can be displayed singularly with the Channel menu.
Below the histogram graph are two sliders: Input and Output. These sliders work to compress the tonal range of the image. The Input slider adjusts the dark areas of the image while the Output slider adjusts the highlights. Each of these sliders have two nodes. The left node on each slider is the Minimum node while the right node on the slider is the Maximum node.
- On the Input (Dark) slider, moving the Input Minimum node to the right will result in darker shadows, whereas moving the Input Maximum node to the left will result in lighter highlights.
- On the Output (Highlight) slider, moving the Output Minimum node to the right results in lighter dark areas. Moving the Output Maximum node to the left results in darker light areas.
Assessing the image quality of diagnostic images is a subjective process that will rely on the personal preferences of the observer. This guide will use the widely accepted quality characteristics of medical images (Detail, Contrast, and Noise), as well as the various characteristics and radio-density of anatomical subject matter to define solutions within the MacPractice Digital Radiography Filters and tools. The subjective process of creating more diagnostic images is highly dependent on the initial quality of the original image, as well as the subjective role of the viewer.
What it looks like: The Detail of a Digital Radiography image appears as the amount of blur or sharpness in an image.
What it does: Unsharp Mask enhances the visibility by the way of contrast in images where the visibility is limited by the largeness of the area of contrast, such as chest imaging. Unsharp Mask first removes detail by creating a blurred copy of the original image by replacing each pixel with the average value of the neighboring pixels. This Unsharp Mask is then subtracted from the original image. The result of this calculation is an enhancement to Detail by reduction in large area contrast background in relationship to the contrast detail.
What it does not do: The amount of detail in an image directly relates to the resolution, orientation, and sensitivity of the Digital Radiography sensor. For instance, if the DR Device's focal spot is too close to the anatomy, lack of detail (blur) can occur. The focal to patient distance is also closely related to patient exposure. Proper calibration and understanding your DR device is vital to finding the appropriate focal length for a procedure. Similarly, if motion to the DR device or imaging subject is present when the image is obtained, lack of detail or blur can occur. The pixel size of the original image can also play a role of creating blur. Pixel counts that are too low (Low resolution) may appear blurry. No amount of post-processing can correct Detail in these respects.
DR Filter: Sharpen Luminance
The example below shows an image before and after the Sharpen Luminance filter was used.
What it does: The Sharpen Luminance filter increases the overall detail of an image by increasing the amount of contrast in line edges. Sharpen Luminance works within the luminance channel of an image to sharpen detail without impacting the color channel, which can introduce color artifacts to the image.
What it does not do: Just as with Unsharp Mask, the Sharpen Luminance filter cannot create detail that doesn't exist due to improper focal length, motion blur, or a lack of resolution.
What it looks like: Noise appears in images as random variations of brightness unrelated to the uniform variation in image brightness created by anatomical structures in the image. Noise reduces the visibility of the anatomical structures in the Digital Radiography image.
Types of Noise:
Fixed Pattern Noise can be caused by long exposures at a low speed, producing some pixels that have more contrast than the neighboring pixels. Fixed Pattern noise is less random than Quantum Noise.
DR Filter: Noise Reduction, Median, and Pseudo Median Filters
The example below display the original image with simulated noise, followed by the same image using the Noise Reduction filter, Median filter, and Pseudo Median filter.
What it does: Each method of Noise Reduction reduces the amount of noise in an image by smoothing graininess. The Noise Reduction filter smooths an image by bringing the value of each pixel into closer harmony with the neighboring pixels. It also reduces the amount of blurriness applied by the noise reduction level smoothing when the Sharpness slider is used. These increase the visibility of low-contrast anatomical structures. The Median filter reduces noise by replacing each pixel with the median value of the neighboring pixels. The Pseudo Median filter works similarly to the Median filter, but considers all possible medians rather than sorting in the final calculation and therefore does not eliminate as much fixed noise.
What it doesn't do: It is impossible to completely eliminate noise from an image. Using Noise Reduction is an act of compromise, as it increases blurring and decreases detail information in the image. Noise can be reduced by increasing the exposure of the Digital Radiography Device, however this will also increase patient dose. Noise is also more visible in low contrast images or in image locations where dramatic contrasts occur, such as within the closeup example above.
What it looks like: Contrast is the variation in white and black density that forms the image. Contrast is often seen as the most important quality metric for diagnostic imaging. Nearly any type of post-processing, filtering, or image manipulation will have an impact on the image's contrast. Referencing the Histogram as adjustments are made can help to avoid over manipulating the contrast of an image.
The Contrast of Digital Radiography images is most often adjusted to address overexposure or underexposure. Since digital images possess a high dynamic range, adjusting the contrast can produce more diagnostic images.
DR Filter: Auto Levels
The example below shows a poorly contrasted original image before and after the Auto Levels filter was used.
By viewing the Histogram for the image, it appears as though the range of values expressed are not very wide.
First, the Auto Levels filter is applied to adjust the levels on the Histogram and to maximize the dynamic range.
Once the Auto Level filter is applied, the Histogram appears to display a wider expression of dynamic range.
The actual image appears with more diagnostic Contrast, but some of the pixels are far to bright to display accurate Detail for diagnostics.
A Gamma filter is applied to create more control over the brightness of the pixels, allowing more Detail to be expressed.
When the Gamma Filter is applied at a power of 1.76, the overly brightened pixels are now more balanced. This image is now a diagnostic image that makes a good compromise of the Quality Characteristics: Contrast, Noise, and Detail.
As a final step, based entirely on subjective personal preference, a very slight Noise Reduction is applied to compensate from the noise that was highlighted in the previous transitions.
The resulting image has been made more diagnostic and is more subjectively pleasing to the radiographer.