Craig W. Cornelius

Toward Clinically Relevant Standardization of Image Quality

In recent years, notable progress has been made on standardization of medical image presentations in the definition and implementation of the Digital Imaging and Communications in Medicine (DICOM) Grayscale Standard Display Function (GSDF). In parallel, the American Association of Physicists in Medicine (AAPM) Task Group 18 has provided much needed guidelines and tools for visual and quantitative assessment of medical display quality. In spite of these advances, however, there are still notable gaps in the effectiveness of DICOM GSDF to assure consistent and high-quality display of medical images. In additions the degree of correlation between display technical data and diagnostic usability and performance of displays remains unclear. This article proposes three specific steps that DICOM, AAPM, and ACR may collectively take to bridge the gap between technical performance and clinical use: (1) DICOM does not provide means and acceptance criteria to evaluate the conformance of a display device to GSDF or to address other image quality characteristics. DICOM can expand beyond luminance response, extending the measurable, quantifiable elements of TG18 such as reflection and resolution. (2) In a large picture archiving and communication system (PACS) installation, it is critical to continually track the appropriate use and performance of multiple display devices. DICOM may help with this task by adding a Device Service Class to the standard to provide for communication and control of image quality parameters between applications and devices, (3) The question of clinical significance of image quality metrics has rarely been addressed by prior efforts. In cooperation with AAPM, the American College of Radiology (ACR), and the Society for Computer Applications in Radiology (SCAR), DICOM may help to initiate research that will determine the clinical consequence of variations in image quality metrics (eg, GSDF conformance) and to define what constitutes image quality from a diagnostic perspective. Implementation of these three initiatives may further the reach and impact of DICOM toward quality medicine.

Antiscatter stationary-grid artifacts automated detection and removal in projection radiography images

Antiscatter grids absorb scattered radiation and increase X- ray image contrast. Stationary grids leave line artifacts or create Moire patterns on resized digital images. Various grid designs were investigated to determine relevant physical properties that affect an image. A detection algorithm is based on grid peak determination in the images averaged 1D Fourier spectrum. Grid artifact removal is based on frequency filtering in a spatial dimension orthogonal to the grid stripes. Different filter design algorithms were investigated to choose the transfer function that maximizes the suppression of grid artifacts with minimal image distortion. Algorithms were tested on synthetic data containing a variety of SNRs and grid spatial inclinations, on radiographic data containing phantoms with and without grids, and on a set of real CR images. Detector and filter performance were optimized by using Intel Signal Processing Library, resulting in a time of about 3 sec to process a 2Kx2.5K CR image on a Pentium II PC> There are no grid artifacts and no image blur revealed on processed images as evaluated by third party technical and medical experts. This automated grid artifact suppression method is built into a new version of Kodak PACS Link Medical Image Manager.

Automatic hanging protocol for chest radiographs

Chest radiography is one of the most widely used techniques in diagnostic imaging. It makes up at least one third of all conventional diagnostic radiographic procedures in hospitals. However, in both film-screen and computed radiography, images are often digitized with the view and orientation unknown or mislabeled, which causes inefficiency in displaying them in the picture archive and communication system (PACS). Hence, the goal of this work is to provide a robust, efficient, and automatic hanging protocol for chest radiographs. To achieve it, the method star ts with recognition by extracting a set of distinctive features from chest radiographs. Next, a well-defined probabilistic classifier is used to train and classify the radiographs. Identifying the orientation of the radiographs is performed by an efficient algorithm which locates the neck, heart, and abdomen positions in radiographs. The initial experiment was performed on radiographs collected from daily routine chest exams in hospitals, and it has shown promising results.