An image-processing method has been developed to improve the visibility of tube and catheter features in portable chest
x-ray (CXR) images captured in the intensive care unit (ICU). The image-processing method is based on a multi-frequency
approach, wherein the input image is decomposed into different spatial frequency bands, and those bands that
contain the tube and catheter signals are individually enhanced by nonlinear boosting functions. Using a random
sampling strategy, 50 cases were retrospectively selected for the study from a large database of portable CXR images
that had been collected from multiple institutions over a two-year period. All images used in the study were captured
using photo-stimulable, storage phosphor computed radiography (CR) systems. Each image was processed two ways.
The images were processed with default image processing parameters such as those used in clinical settings (control).
The 50 images were then separately processed using the new tube and catheter enhancement algorithm (test). Three
board-certified radiologists participated in a reader study to assess differences in both detection-confidence performance
and diagnostic efficiency between the control and test images. Images were evaluated on a diagnostic-quality, 3-megapixel monochrome monitor. Two scenarios were studied: the baseline scenario, representative of today's workflow
(a single-control image presented with the window/level adjustments enabled) vs. the test scenario (a control/test image
pair presented with a toggle enabled and the window/level settings disabled). The radiologists were asked to read the
images in each scenario as they normally would for clinical diagnosis. Trend analysis indicates that the test scenario
offers improved reading efficiency while providing as good or better detection capability compared to the baseline
scenario.
An observer study was conducted on a randomly selected sampling of 152 digital projection radiographs of varying
body parts obtained from four medical institutions for the purpose of assessing a new workflow-efficient imageprocessing
framework. Five rendering treatments were compared to measure the performance of a new processing
algorithm against the control condition. A key feature of the new image processing is the capability of processing without
specifying the exam. Randomized image pairs were presented at a softcopy workstation equipped with two diagnosticquality
flat-panel monitors. Five board-certified radiologists and one radiology resident independently reviewed each
image pair blinded to the specific processing used and provided a diagnostic-quality rating using a subjective rank-order
scale for each image. In addition, a relative preference rating was used to indicate rendering preference. Aggregate results
indicate that the new fully automated processing is preferred (sign test for median = 0 (α = 0.05): p < 0.0001 preference
in favor of the control).
Lower x-ray exposures are commonly used in radiographic exams to reduce the patient radiation dose. An unwanted side effect is that the noise level increases as the exposure level is reduced. Image enhancement techniques increasing image contrast, such as sharpening and dynamic range compression tend to increase the appearance of noise. A Gaussian filter-based noise suppression algorithm using an adaptive soft threshold has been designed to reduce the noise appearance in low-exposure images. The advantage of this technique is that the algorithm is signal-dependent, and therefore will only impact image areas with low signal-to-noise ratio. Computed radiography images captured with lower exposure levels were collected from clinical sites, and used as controls in an observer study. The noise suppression algorithm was applied to each of the control images to generate test images. Hardcopy printed film versions of control and test images were presented side-by-side on a film alternator to six radiologists. The radiologists were asked to rate the control and test images using a 9-point diagnostic quality rating scale and a 7-point delta-preference rating scale. The results showed that the algorithm reduced noise appearance, which was preferred, while preserving the diagnostic image quality. This paper describes the noise suppression algorithm and reports on the results of the observer study.
Image processing is used to transform raw digital radiographic image data, captured using CR (computed radiography) and DR (flat panel direct digital radiography) systems into a display-ready form. Ideally, an image-processing algorithm automatically renders an image for display, based on aims derived from observer performance studies. Establishing the rendering aim for different exam types, however, can be complex because the effects on image appearance introduced by the various steps in the rendering process are interdependent. This paper describes a new rendering algorithm that provides orthogonal control, to the first order, of five fundamental attributes of perceived image quality. These attributes are brightness, latitude, detail contrast, sharpness, and appearance of noise. The detail contrast and sharpness can be controlled in a density-dependent manner. The algorithm uses a multifrequency-band decomposition wherein the bands of the decomposition are manipulated, and the reconstructed image is passed through a tone-scale process that prepares the image for display. The rendering method is implemented in software on a workstation that enables interactive control of these image quality attributes in order to facilitate the determination of rendering aims for different exam types.
Display processing is used to transform digital radiography raw data in log-signal units to display values for presentation using a workstation or film printer. Radiographic appearance with respect to subject latitude and detail contrast varies significantly depending on the signal equalization and grayscale rendition used for processing. A human observer study was conducted to define the latitude and detail contrast that is judged optimal for a broad spectrum of chest radiographs. Raw data for 12 chest radiographs acquired with storage phosphor digital radiography systems were transformed using 52 different combinations of latitude and detail contrast. For specific latitude values, contrast was adjusted by varying the equalization gain. Three radiologists at three different medical centers evaluated the images. Each image was compared to a reference image using a calibrated display on a computer workstation. For PA views, processing that produced a detail contrast of 3.14 ((Delta) D/(Delta) logE) and latitude of 1.47 ((Delta) logE for (Delta) D = 1.75) was determined to be best for all cases and was achieved with an equalization gain of 2.64. For lateral views, a detail contrast of 3.42 and latitude of 1.17 was best for all cases (gain = 2.29). For individual cases, the preferred processing varied from the global average primarily with respect to latitude.
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