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Digital radiography and display systems have revolutionized radiologic practice in recent years and have enabled clinical application of advanced image processing techniques. These include dual energy subtraction and temporal subtraction, both of which can improve diagnostic accuracy for abnormal findings in chest radiographs, especially for subtle lesions such as early lung cancer or focal pneumonia. Dual energy radiography exploits the differential attenuation of low-energy x-ray photons by calcium to produce separate images on the bones and soft tissues, which provides improved detection and characterization of both calcified and noncalcified lung lesions. Dual energy subtraction radiography is currently available from 2 of the major vendors and is in clinical use at many institutions in the United States. Temporal subtraction is a complementary technique that enhances interval change, by using a previous radiograph as a subtraction mask, so that unchanged normal anatomy is suppressed, whereas new abnormalities are enhanced. Though it is not yet a product in the United States, temporal subtraction is available for clinical use in Japan. Temporal subtraction can be combined with energy subtraction to reduce misregistration artifacts, and also has potential to improve computer-aided detection of nodules and other types of lung disease.

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.

RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers.MATERIALS AND METHODS: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading.RESULTS: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future.CONCLUSIONS: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

RATIONALE AND OBJECTIVES: The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on computed tomography (CT) scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured.MATERIALS AND METHODS: Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule >or=3 mm (diameter)," "nodule or=3 mm." During the subsequent "unblinded read" phase, the blinded read results of all four radiologists were revealed to each radiologist, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus.RESULTS: After the initial blinded read phase, 71 lesions received "nodule >or=3 mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. After the unblinded reads, a total of 59 lesions were marked as "nodule >or=3 mm" by at least one radiologist. Twenty-seven (45.8%) of these lesions received such marks from all four radiologists, three (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist.CONCLUSION: The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules >or=3 mm. Nevertheless, substantial variability remains across radiologists in the task of lung nodule identification.

The LIDC is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource. A unique multi-center data collection process and communication system were developed to share image data and to capture the location and spatial extent of lung nodules as marked by expert radiologists. A two-phase data collection process was designed to allow multiple radiologists at different centers to asynchronously review and annotate each CT image series. Four radiologists reviewed each case using this process. In the first or "blinded" phase, each radiologist reviewed the CT series independently. In the second or "unblinded" review phase, the results from all four blinded reviews are compiled and presented to each radiologist for a second review. This allows each radiologist to review their own annotations along with those of the other radiologists. The results from each radiologist's unblinded review were compiled to form the final unblinded review. There is no forced consensus in this process. An XML-based message system was developed to communicate the results of each reading. This two-phase data collection process was designed, tested and implemented across the LIDC. It has been used for more than 130 CT cases that have been read and annotated by four expert readers and are publicly available at (http://ncia.nci.nih.gov). A data collection process was developed, tested and implemented that allowed multiple readers to review each case multiple times and that allowed each reader to observe the annotations of other readers.

We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500 x 500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448 x 448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7 x 7 regions of interest (ROIs: 64 x 64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128 x 128 matrix size), each having its central part (64 x 64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical classification provided improved detection of pulmonary nodules on chest radiographs for 924 lung nodule cases.

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 a linear discriminant classifier. With a leave-one-out-by-patient training/testing paradigm, the automated method attained an A(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(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.

The aim of the study is to investigate the effect of a computer-aided diagnostic (CAD) scheme on radiologist performance in the detection of lung cancers on chest radiographs.We combined two independent CAD schemes for the detection and classification of lung nodules into one new CAD scheme by use of a database of 150 chest images, including 108 cases with solitary pulmonary nodules and 42 cases without nodules. For the observer study, we selected 48 chest images, including 24 lung cancers, 12 benign nodules, and 12 cases without nodules, from the database to investigate radiologist performance in the detection of lung cancers. Nine radiologists participated in a receiver operating characteristic (ROC) study in which cases were interpreted first without and then with computer output, which indicated locations of possible lung nodules, together with a five-color scale illustrating the computer-estimated likelihood of malignancy of the detected nodules.Performance of the CAD scheme indicated that sensitivity in detecting lung nodules was 80.6%, with 1.2 false-positive results per image, and sensitivity and specificity for classification of nodules by use of the same database for training and testing the CAD scheme were 87.7% and 66.7%, respectively. Average area under the ROC curve value for detection of lung cancers improved significantly (P = .008) from without (0.724) to with CAD (0.778).This type of CAD scheme, which includes two functions, namely detection and classification, can improve radiologist accuracy in the diagnosis of lung cancer.

Rationale and Objectives To evaluate how computer-aided diagnosis (CAD) can improve radiologists’ recommendations for management of possible early lung cancers on CT. Materials and Methods Twenty-eight lung cancers and 28 benign lesions were employed. Each group of 28 lesions was classified into subgroups of two sizes (9 between 6 and 10 mm and 19 between 11 and 20 mm) and three patterns (8 with pure ground glass opacity [GGO], 12 with mixed GGO and 8 solid lesions). Sixteen radiologists participated in the observer study, first without and then with CAD. Radiologists’ recommendations, including (1) follow-up in 12 months, (2) in 6 months, (3) in 3 months, or (4) biopsy, were compared at three levels of their malignancy probability ratings (low: 1%–33%; medium: 34%–66%; high: 67%–99%) for 896 observations (56 lesions by the 16 radiologists) in the two size subgroups and three patterns. Results The number of recommendations changed by radiologists by use of CAD was 163 (18%) among all 896 observations. Among these changed recommendations, the fraction showing a beneficial effect from CAD was 68% (111/163), and the fraction showing a beneficial effect regarding biopsy recommendations was 69% (48/70). With CAD, the radiologists’ performance regarding biopsy recommendations was significantly improved for 43 lung cancers (31 changed to biopsy versus 12 changed away from biopsy; P = .003) and was also improved for 27 benign lesions (10 changed to biopsy versus 17 changed away from biopsy; P = .18). Most of the cancers with improved recommendations were solid lesions or mixed GGO and relatively large. Conclusion CAD has the potential to improve the appropriateness of radiologists’ recommendations for small malignant and benign lesions on CT scans.

We carried out an observer performance study for evaluating the performance of 16 radiologists without and with a computer-aided diagnostic (CAD) scheme for determination of the likelihood of malignancy of lung nodules on HRCT in a database of 28 primary lung cancers and 28 benign nodules. The results of our observer study showed that radiologists’ performance was improved with the CAD scheme, and their performance with the CAD scheme was greater than either radiologists alone or computer alone. Our purpose in this study was to analyze radiologists’ responses with the CAD scheme in their task of differentiation between malignant and benign nodules on HRCT. Our results indicated that the average change in radiologists’ ratings (difference between radiologists' ratings with the CAD scheme and radiologists' initial ratings) was strongly related to (A) the likelihood of malignancy (the computer output) and also (B) the difference of their initial ratings from the computer outputs, where the correlation coefficients were 0.93 and 0.90, respectively. Our detailed analysis showed that radiologists changed the majority of their ratings in agreement with the computer results, and the majority of these changes contributed to the improvement in their performance. They were able to maintain some of their correct ratings despite incorrect computer results. For some cases, they increased their confidence in their judgments above the computer output. Thus, the improvement in radiologists' performance above the computer performance was produced by the synergistic effect of the radiologists' decision making and the computer outputs.

This study investigated the effect of a high sensitivity in computer-aided diagnosis (CAD) for detecting lung nodules in chest radiographs when extremely subtle cases were presented to radiologists.The chest radiographs used in this study consisted of 36 normal images and 54 abnormals containing solitary lung nodules, of which 25 were extremely subtle and 29 were very subtle. Receiver operating characteristic analysis for detecting lung nodules was performed without and with CAD. The levels of CAD output were simulated with a hypothetical ideal performance of 100% sensitivity, but with three or four false positives per image. Six radiologists participated in an observer study in which cases were interpreted first without and then with the use of CAD.The average A(z) values for radiologists without and with CAD were 0.682 and 0.808, respectively. The performance of radiologists was improved significantly when high sensitivity was used (P = .0003). However, the radiologists were not able to recognize some extremely subtle nodules (5 of 54 nodules by all radiologists), even with the correct CAD output; these nodules were then considered as non-actionable. None of 306 computer-false positives was incorrectly regarded as a nodule by all radiologists, but 63 false positives were incorrectly identified by one or more radiologists.The accuracy of radiologists in the detection of some extremely subtle solitary pulmonary nodules can be improved significantly when the sensitivity of a CAD scheme can be made to be at an extremely high level. However, all of the six radiologists failed to identify some nodules (about 10%), even with the correct output of the CAD.

We investigated the effect of the number of cases included in an image database on development of a computer-aided diagnosis (CAD) scheme for the detection of lung nodules, in terms of the performance of the CAD scheme. A total number of 1000 chest radiographs with nodules was used in this study. All images were divided randomly into subsets consisting of the same number of cases from different sources. The subsets we used in this study were 10 sets of 100 cases, 5 sets of 200 cases, and 2 sets of 500 cases. The entire database and all of the subsets were tested by use of the same CAD scheme, but with different parameter settings for consistency tests. When the sensitivities of the CAD scheme for each subset were kept at a level of 70.0 %, the numbers of false positives per image were 0.1 for 100 cases, 0.6 for 200 cases, 2.9 for 500 cases, and 6.2 for 1000 cases. Therefore, the performance of the CAD scheme in detecting lung nodules was strongly affected by the number of cases used. We conclude that a large-scale image database is needed for reliable evaluation of the performance of CAD.

A problem in most current computer-aided diagnostic (CAD) scheme is the relatively large number of false positives that are incorrectly reported as nodules by the scheme. Therefore, in this study, we developed a multiple-templates matching technique to significantly reduce the number of false positives in our CAD scheme. With this technique applied to our CAD scheme for detection of pulmonary nodules in chest radiographs, we removed a large number of false positives (44.3%) with reduction of a small number of true positives (2.3%). We believe that this technique has the potential to significantly improve the performance of many different CAD schemes for detection of various lesions in medical images including nodules in chest radiographs, masses and microcalcifications in mammograms, nodules, colon polyps, liver tumors, and aneurysms in CT images.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules.Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features. The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis.Performance of the ANN was considerably greater with objective features (area under the ROC curve, Az = 0.854) than with subjective features (Az = 0.761). Performance of the ANN was also greater than that of the radiologists (Az = 0.752).The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in the distinction of benign and malignant solitary pulmonary nodules.

To determine the effect of computer-aided diagnosis (CAD) on the accuracy of pulmonary nodule detection.Twenty abnormal chest radiographs, each with a single nodule, and 20 normal radiographs were digitized with a laser scanner. These images were analyzed by using a computer program that indicates areas that may represent pulmonary nodules. The radiographs were displayed on computer workstations in randomized order, and an observer test was performed. One hundred forty-six observers participated, including 23 chest radiologists, 54 other radiologists, 27 radiology residents, and 42 nonradiologists. Cases were interpreted first without and then with the use of CAD. The observers' responses were recorded on a continuous confidence rating scale. Detection accuracy both with and without CAD was evaluated with receiver operating characteristic analysis.The detection accuracy was significantly higher for all categories of observers when CAD was used (chest radiologists, P = 8 x 10(-6); other radiologists, P = 2 x 10(-16); radiology residents, P = 6 x 10(-7); and nonradiologists, P = 8 x 10(-9)).CAD has the potential to improve diagnostic accuracy in the detection of lung nodules on digital radiographs.

The purpose of this study was to develop methods for automatic 3D-segmentation and automatic quantification of vascular structures in CT angiographic studies, e.g., abdominal aortic aneurysms. Methods for segmentation were developed based on thresholding, maximum gradient, and second derivative techniques. All parameters for the segmentation are generated automatically, i.e. no user interaction is necessary for this process. Median filtering of all images is initially performed to reduce the image noise. The algorithm then automatically identifies the starting point inside the aorta for the volume growing. The segmentation of the vascular tree is achieved in two steps. First, only the aorta and small parts of branch vessels are segmented by using strong restrictions in the parameters for threshold and gradient. A description of the aorta is generated by fitting the detected outer border of the aorta with an ellipse. This description includes centerline, direction, contour, eccentricity, and area. In the second step, segmentation parameters are changed automatically for segmentation of branch vessels. A shaded surface display of the segmented structures is then generated. The segmentation of the aorta appears accurate, is fast, and the 3D display can be manipulated in real time. The quantitative description of the aorta is reliable giving reproducible information. Total CPU time for the segmentation and description is less than five minutes on a standard workstation. Time-consuming manual segmentation and parameterization of vascular structures are obviated, with 3D visualization and quantitative results available in minutes instead of hours. This technique for segmentation and description of the aorta and renal arteries shows the feasibility of computer assisted diagnosis in CT angiographic studies without user interaction. Besides the description, a rapid 3D view of the vessels is generated, often needed by the physician and normally only achievable by time consuming manual segmentation.

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.