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Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" nodule�3 mm," " nodule

Purpose: Temporal subtraction is used to detect the interval change in chest radiographs and aid radiologists in patient diagnosis. This method registers two temporally different images by geometrically warping the lung region, or ''lung mask,'' of a previous radiographic image to align with the current image. The gray levels of every pixel in the current image are subtracted from the gray levels of the corresponding pixels in the warped previous image to form a temporal subtraction image. While temporal subtraction images effectively enhance areas of pathologic change, misregistration of the images can mislead radiologists by obscuring the interval change or by creating artifacts that mimic change. The purpose of this study was to investigate the utility of mutual information computed between two registered radiographic chest images as a metric for distinguishing between clinically acceptable and clinically unacceptable temporal subtraction images.Methods: A radiologist subjectively rated the image quality of 138 temporal subtraction images using a 1 (poor) to 5 (excellent) scale. To objectively assess the registration accuracy depicted in the temporal subtraction images, which is the main factor that affects the quality of these images, mutual information was computed on the two constituent registered images prior to their subtraction to generatemore » a temporal subtraction image. Mutual information measures the joint entropy of the current image and the warped previous image, yielding a higher value when the gray levels of spatially matched pixels in each image are consistent. Mutual information values were correlated with the radiologist's subjective ratings. To improve this correlation, mutual information was computed from a spatially limited lung mask, which was cropped from the bottom by 10%-60%. Additionally, the number of gray-level values used in the joint entropy histogram was varied. The ability of mutual information to predict the clinical acceptability of a temporal subtraction image was evaluated through receiver operating characteristic (ROC) analysis. Results: The mean correlation coefficient obtained between mutual information computed on constituent images and the subjective rating of temporal subtraction image quality was 0.785. ROC analysis yielded an average A{sub z} value of 0.852 for the ability of mutual information to distinguish between temporal subtraction images of clinically acceptable and clinically unacceptable quality. Conclusions: The results of this study establish a relationship between mutual information and temporal subtraction registration accuracy and demonstrate the ability of mutual information to objectively indicate the presence of misregistration artifacts.« less

Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key "spiral-scanning" technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the "north pole" to the "south pole." The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the "optimal" outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.

Temporal subtraction and dual-energy imaging are two enhanced radiography techniques that are receiving increased attention in chest radiography. Temporal subtraction is an image processing technique that facilitates the visualization of pathologic change across serial chest radiographic images acquired from the same patient; dual-energy imaging exploits the differential relative attenuation of x-ray photons exhibited by soft-tissue and bony structures at different x-ray energies to generate a pair of images that accentuate those structures. Although temporal subtraction images provide a powerful mechanism for enhancing visualization of subtle change, misregistration artifacts in these images can mimic or obscure abnormalities. The purpose of this study was to evaluate whether dual-energy imaging could improve the quality of temporal subtraction images. Temporal subtraction images were generated from 100 pairs of temporally sequential standard radiographic chest images and from the corresponding 100 pairs of dual-energy, soft-tissue radiographic images. The registration accuracy demonstrated in the resulting temporal subtraction images was evaluated subjectively by two radiologists. The registration accuracy of the soft-tissue-based temporal subtraction images was rated superior to that of the conventional temporal subtraction images. Registration accuracy also was evaluated objectively through an automated method, which achieved an area-under-the-ROC-curve value of 0.92 in the distinction between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. By combining dual-energy soft-tissue images with temporal subtraction, misregistration artifacts can be reduced and superior image quality can be obtained.

Radiologists routinely compare multiple chest radiographs acquired from the same patient over time to more completely understand changes in anatomy and pathology. While such comparisons are achieved conventionally through a side-by-side display of images, image registration techniques have been developed to combine information from two separate radiographic images through construction of a 'temporal subtraction image'. Although temporal subtraction images provide a powerful mechanism for the enhanced visualization of subtle change, errors in the clinical evaluation of these images may arise from misregistration artifacts that can mimic or obscure pathologic change. We have developed a computerized method for the automated assessment of registration accuracy as demonstrated in temporal subtraction images created from radiographic chest image pairs. The registration accuracy of 150 temporal subtraction images constructed from the computed radiography images of 72 patients was rated manually using a five-point scale ranging from '5-excellent' to '1-poor'; ratings of 3, 4, or 5 reflected clinically acceptable subtraction images, and ratings of 1 or 2 reflected clinically unacceptable images. Gray-level histogram-based features and texture measures are computed at multiple spatial scales within a 'lung mask' region that encompasses both lungs in the temporal subtraction images. A subset of these features is merged through amore » linear discriminant classifier. With a leave-one-out-by-patient training/testing paradigm, the automated method attained an A{sub z} value of 0.92 in distinguishing between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. A second linear discriminant classifier yielded an A{sub z} value of 0.82 based on a feature subset selected from an independent database of digitized film images. These methods are expected to advance the clinical utility of temporal subtraction images for chest radiography.« less

Purpose: The clinical imaging infrastructure has been designed for the consistent execution of standard‐of‐care imaging examinations. Many imaging protocols for clinical research, however, have unique requirements that may burden the established infrastructure. Our purpose was to develop a solution to the many imaging demands and challenges presented by clinical research.Methods: We have created the Human Imaging Research Office (HIRO) at our institution. The HIRO is responsible for coordinating the acquisition, collection, analysis, and maintenance of images and related data associated with clinical research studies involving human subjects. The purpose of the HIRO is to coordinate, harmonize, and streamline the imaging aspects of clinical research, so that these non‐standard examinations can be performed as required by the study protocol. In effect, the HIRO is becoming the institutional resource for a broad spectrum of research imaging needs. The activities and services of the HIRO include site validation and image quality verification, development of standardized research‐specific requisition and reporting forms, scheduling, implementation of research billing codes, study‐specific image‐basedmeasurements, collection of customized imagedatabases, and distribution of anonymized images to researchers and study sponsors.Results: The HIRO has helped facilitate the acquisition of images for clinical research for 117 active, pending, or recently closed clinical research studies. These clinical research studies are being conducted by 60 unique principal investigators across 14 different sections and departments within the Medical Center. The HIRO has filled over 650 requests for de‐identified images, accounting for over 42.3 million images Conclusions: Imaging for clinical research often has requirements that differ substantially from those of clinical imaging. The complex infrastructure for standard‐of‐care imaging typically is stressed by the unique technical and administrative needs of a research protocol. The HIRO is able to respond effectively to the dynamic imaging needs of clinical research. SGA, HM, and MLG receive royalties and licensing fees through the University of Chicago related to computer‐aided diagnosis. HM is a consultant for Riverain Medical.

Purpose:Radiologists routinely compare multiple radiographs of the same patient to identify interval change in anatomy and pathology. The temporal subtraction images that facilitate this comparison have been limited to posteroanterior radiographs. Since the lateral view provides diagnostically important information, the purpose of this study was to develop an automated method for the subtraction of temporally sequential lateral chest radiographs and to evaluate the quality of the resulting images.Method and Materials: An automated method was developed to digitally subtract pairs of lateral chest radiographs. First, multiple rotated versions of the "previous" lateral radiograph were generated. For each of the rotated previous images, the apex of the lungs was identified through gray‐level profile analysis. A 251 × 215 mm subimage was then extracted near the apex and used as a template for cross‐correlation with the “current” radiograph. The translation and rotation parameters that yielded the highest correlation were used to rigidly transform the “previous” radiograph, which was then subtracted from the current radiograph to yield the temporal subtraction image. 30 pairs of lateral chest radiographs were subtracted. An observer rated the quality of each temporal subtraction image using a 1 (“very poor”) to 5 (“excellent”) scale. Results: The average registration accuracy rating was 4.0. 83% (25/30) of the subtraction images were rated as “acceptable” or better (3.0 or higher). Conclusion: Our technique for the automated subtraction of temporally sequential lateral chest radiographs generates a high percentage of subtraction images with acceptable registration accuracy. Such a method is expected to provide a valuable supplement to the conventional posteroanterior temporal subtraction images that have proven beneficial in the diagnostic evaluation of interval change in chest radiography. S.A. and H.M. hold warrants to shares in R2 Technology, Inc.; H.M. consultant for Riverain Medical and Median Technologies.