Improved detection of small lung cancers with dual-energy subtraction chest radiography

Portable Single-Exposure Dual-Energy X-ray Detector for Improved Point-of-Care Diagnostic Imaging

INTRODUCTION

Dual-energy subtraction (DES) imaging is well known to reduce anatomical noise and enable material classification. The current approaches to DES imaging have trade-offs, such as motion artifacts, low sensitivity because of losses in a mid-filter, and lack of portability. Recently, a portable triple-layer flat-panel detector (FPD) was proposed for use in single-shot DES imaging that can provide improved sensitivity and removal of motion artifacts in a point-of-care setting. The purpose of this study is to evaluate the feasibility of such a detector. Various image quality metrics and clinical images are provided.

MATERIALS AND METHODS

An FDA-cleared single-exposure DES FPD consisting of three stacked sensors was used for all measurements. This detector generates three images per exposure: A digital radiography (DR) image, i.e., as would be produced with a conventional detector, and two DES images, bone and soft tissue. To evaluate DR image quality, detective quantum efficiency (DQE) and modulation transfer function were measured for multiple radiation quality beams. Digital radiography and DES images obtained from this FPD were evaluated in previously reported fixed and portable clinical studies. Digital radiography and DES images from case studies are presented for qualitative assessment.

RESULTS

Modulation transfer function and DQE were measured across multiple radiation quality beams for the DR image. The DES images showed good tissue separation and uniformity with no visible motion or alignment artifacts. The DES images, when read in conjunction with the DR image, resulted in increased reader confidence and revealed abnormalities or details that were sometimes overlooked in the DR image.

CONCLUSIONS

The proposed panel produces high-quality DR images as indicated by the DQE and modulation transfer function. The DES images have been shown to improve sensitivity in clinical applications and increase reader confidence. This detector can enable DES in portable or otherwise difficult applications, opening new doors for improved patient care.

Effectiveness of one-shot dual-energy subtraction chest radiography with flat-panel detector in distinguishing between calcified and non-calcified nodules

The purpose of this study was to evaluate the added value of the soft tissue image obtained by the one-shot dual-energy subtraction (DES) method using a flat-panel detector compared with the standard image alone in distinguishing calcified from non-calcified nodules on chest radiographs. We evaluated 155 nodules (48 calcified and 107 non-calcified) in 139 patients. Five radiologists (readers 1 - 5) with 26, 14, 8, 6 and 3 years of experience, respectively, evaluated whether the nodules were calcified using chest radiography. CT was used as the gold standard of calcification and non-calcification. Accuracy and area under the receiver operating characteristic curve (AUC) were compared between analyses with and without soft tissue images. The misdiagnosis ratio (false positive plus false negative ratios) when nodules and bones overlapped was also examined. The accuracy of all radiologists increased after adding soft tissue images (readers 1 - 5: 89.7% vs. 92.3% [P = 0.206], 83.2% vs. 87.7% [P = 0.178], 79.4% vs. 92.3% [P < 0.001], 77.4% vs. 87.1% [P = 0.007], and 63.2% vs. 83.2% [P < 0.001], respectively). AUCs for all the readers improved, except for reader 2 (readers 1 - 5: 0.927 vs. 0.937 [P = 0.495], 0.853 vs. 0.834 [P = 0.624], 0.825 vs. 0.878 [P = 0.151], 0.808 vs. 0.896 [P < 0.001], and 0.694 vs. 0.846 [P < 0.001], respectively). The misdiagnosis ratio for nodules that overlapped with the bone decreased after adding soft tissue images in all readers (11.5% vs. 7.6% [P = 0.096], 17.6% vs. 12.2% [P = 0.144], 21.4% vs. 7.6% [P < 0.001], 22.1% vs. 14.5% [P = 0.050] and 35.9% vs. 16.0% [P < 0.001], respectively), particularly that of readers 3 - 5. In conclusion, the soft tissue images obtained using one-shot DES with a flat-panel detector have added value in distinguishing calcified from non-calcified nodules on chest radiographs, especially for less experienced radiologists.

Evaluation of the clinical utility of temporal subtraction using bone suppression processing in digital chest radiography

Rationale and objectives

To evaluate the usefulness of temporal subtraction using the bone suppression method in digital chest radiography for the detection of pulmonary lesions.

Materials and methods

The images of 31 patients with pulmonary lesions and 19 normal cases were included in the study. Conventional and bone suppression temporal subtraction were performed in the 50 cases selected and used for an observer performance study. Five radiologists participated in the study, and the differences between using conventional and bone suppression temporal subtraction were assessed using jackknife free-response receiver operating characteristic analysis.

Results

The average figure-of-merit values for all radiologists increased significantly using the bone suppression method, from 0.619 (conventional) to 0.696 (p = 0.032). The average sensitivity for detecting pulmonary lesions improved from 67.9% to 75.4%, and the average number of false-positive per case decreased from 0.336 to 0.252 using bone suppression temporal subtraction.

Conclusion

Bone suppression temporal subtraction processing can assist with the detection of subtle pulmonary lesions in digital chest radiographs.

Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs

Importance

Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures.

Objectives

To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs.

Design, Setting, and Participants

This decision analytical modeling study used data from 3 centers between November 2015 and July 2019 from 1449 patients. The DLBS nodule-detection algorithm was trained using single-center data (institute 1) of 998 chest radiographs. The DLBS algorithm was validated using 2 external data sets (institute 2, 246 patients; and institute 3, 205 patients). Statistical analysis was performed from March to December 2021.

Exposures

DLBS nodule-detection algorithm.

Main Outcomes and Measures

The nodule-detection performance of DLBS model was compared with the convolution neural network nodule-detection algorithm (original model). Reader performance testing was conducted by 3 thoracic radiologists assisted by the DLBS algorithm or not. Sensitivity and false-positive markings per image (FPPI) were compared.

Results

Training data consisted of 998 patients (539 men [54.0%]; mean [SD] age, 54.2 [9.82] years), and 2 external validation data sets consisted of 246 patients (133 men [54.1%]; mean [SD] age, 55.3 [8.7] years) and 205 patients (105 men [51.2%]; mean [SD] age, 51.8 [9.1] years). Using the external validation data set of institute 2, the bone-suppressed model showed higher sensitivity compared with that of the original model for nodule detection (91.5% [109 of 119] vs 79.8% [95 of 119]; P < .001). The overall mean of FPPI with the bone-suppressed model was reduced compared with the original model (0.07 [17 of 246] vs 0.09 [23 of 246]; P < .001). For the observer performance testing with the data of institute 3, the mean sensitivity of 3 radiologists was 77.5% (95% [CI], 69.9%-85.2%), whereas that of radiologists assisted by DLBS modeling was 92.1% (95% CI, 86.3%-97.3%; P < .001). The 3 radiologists had a reduced number of FPPI when assisted by the DLBS model (0.071 [95% CI, 0.041-0.111] vs 0.151 [95% CI, 0.111-0.210]; P < .001).

Conclusions and Relevance

This decision analytical modeling study found that the DLBS model was more sensitive to detecting pulmonary nodules on chest radiographs compared with the original model. These findings suggest that the DLBS model could be beneficial to radiologists in the detection of lung nodules in chest radiographs without need of the specialized equipment or increase of radiation dose.

Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction

OBJECTIVE

To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs).

MATERIALS AND METHODS

A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed.

RESULTS

BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules.

CONCLUSION

BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

Computer-Aided Detection of Seven Chest Pathologies on Standard Posteroanterior Chest X-Rays Compared to Radiologists Reading Dual-Energy Subtracted Radiographs

RATIONALE AND OBJECTIVES

Retrospective performance evaluation of a computer-aided detection (CAD) system on standard posteroanterior (PA) chest radiographs (PA-CXR) in detection of pulmonary nodules, infectious consolidation, pneumothorax, pleural effusion, aortic calcification, cardiomegaly and rib fractures compared to radiologists analyzing PA-CXR including dual-energy subtraction radiography (further termed as DESR).

MATERIALS AND METHODS

PA-CXR/DESR images of 197 patients were included. All patients underwent chest CT (gold standard) within a short interval (mean 28 hours). All images were evaluated by three blinded readers for the presence of pulmonary nodules, infectious consolidation, pneumothorax, pleural effusion, aortic calcification, cardiomegaly, and rib fractures. Meanwhile PA-CXR were analyzed by a CAD software. CAD results were compared to the majority result of the three readers. Sensitivity and specificity were calculated. McNemar's test was applied to test for significant differences. Interobserver agreement was defined using Cohen's kappa (κ).

RESULTS

Sensitivity of the CAD software was significantly higher (p < 0.05) for detection of infectious consolidation and pulmonary nodules (67.9% vs 26.8% and 54% vs 35.6%, respectively; p < 0.001) compared to radiologists analyzing DESR images. For the residual evaluated pathologies no statistical significant differences could be found. Overall, mean inter observer agreement between the three radiologists was moderate (k = 0.534). The best interobserver agreement could be reached for pneumothorax (k = 0.708) and pleural effusion (k = 0.699), while the worst was obtained for rib fractures (k = 0.412).

CONCLUSION

The CAD system has the potential to improve the detection of infectious consolidation and pulmonary nodules on CXR images.

Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose

OBJECTIVES

To compare image quality and radiation dose between dual-energy subtraction (DES)-based bone suppression images (D-BSIs) and software-based bone suppression images (S-BSIs).

METHODS

Chest radiographs (CXRs) of forty adult patients were obtained with the two X-ray devices, one with DES and one with bone suppression software. Three image quality metrics (relative mean absolute error (RMAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)) between original CXR and BSI for each of D-BSI and S-SBI groups were calculated for each bone and soft tissue areas. Two readers rated the visual image quality for original CXR and BSI for each of D-BSI and S-SBI groups. The dose area product (DAP) values were recorded. Paired t test was used to compare the image quality and DAP values between D-BSI and S-BSI groups.

RESULTS

In bone areas, S-BSIs had better SSIM values than D-BSI (94.57 vs. 87.77) but worse RMAE and PSNR values (0.50 vs. 0.20; 20.93 vs. 34.37) (all p < 0.001). In soft tissue areas, S-BSIs had better SSIM values than D-BSI (97.56 vs. 91.42) but similar RMAE and PSNR values (0.29 vs. 0.27; 31.35 vs. 29.87) (all p < 0.001). Both readers gave S-BSIs significantly higher image quality scores than D-BSI (p < 0.001). The mean DAP in software-related images (0.98 dGy·cm²) was significantly lower than that in the DES-related images (1.48 dGy·cm²) (p < 0.001).

CONCLUSION

Bone suppression software significantly improved the image quality of bone suppression images with a relatively lower radiation dose, compared with dual-energy subtraction technique.

KEY POINTS

• Bone suppression software preserves structure similarity of soft tissues better than dual-energy subtraction technique in bone suppression images. • Bone suppression software achieves superior image quality for lung lesions than dual-energy subtraction technique in bone suppression images. • Bone suppression software can decrease the radiation dose over the hardware-based image processing technique.

The detection of lung cancer using massive artificial neural network based on soft tissue technique

BACKGROUND

A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtle nodules using x-ray images.

METHOD

Such an issue has been resolved by creating MANN (Massive Artificial Neural Network) based soft tissue technique from the lung segmented x-ray image. A soft tissue image recognizes nodule candidate for feature extortion and classification. X-ray images are downloaded using Japanese society of radiological technology (JSRT) image set. This image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represents subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists.

RESULT

A proposed CAD scheme without soft tissue technique attained 66.42% (93/140) sensitivity and 66.76% accuracy having 2.5 false positives per image. Utilizing soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) and accuracy is 72.96% at one false positive rate).

CONCLUSION

In particular, a proposed CAD system determine sensitivity and accuracy in support of subtle nodules (sensitivity is 14/42 = 33.33% and accuracy is 33.66%) is statistically higher than CAD (sensitivity is 13/42 = 30.95% and accuracy is 30.97%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and specificity.

Photon-counting spectral basis component material decomposition for musculoskeletal radiographs

As a very fast and non-invasive examination, conventional X-ray radiography is well established as the first line diagnostic imaging method of the human bone system. While major bone injuries such as fractures and dislocations are usually easily detectable on conventional X-ray images, more subtle injuries such as microfractures are often missed, leading to mistreatment and potential long-term consequences. The technology of Photon-Counting Dual-Energy Radiography (PCDER) yields the possibility to decompose conventional X-ray images into basis material images such as bone- and soft-tissue-equivalence images. The obtained basis material images offer significant advantages in terms of image contrast and image details over the raw attenuation image which shows an overlap of bone and soft tissue. Whereas the advantages of bone- and soft-tissue-equivalence images have been broadly discussed referring to bone subtraction images in the detection of pulmonary diseases, this method has not been considered for the analysis of musculoskeletal images until present. In this study we show that basis component equivalence images have high potential to improve the diagnostic accuracy of the detection of minor bone lesions during clinical trauma imaging. A reader study performed by three experienced radiologists compares the image quality of basis material images to a standard radiograph image of a non-fractured cadaveric hand.

Added Value of Bone Suppression Image in the Detection of Subtle Lung Lesions on Chest Radiographs with Regard to Reader's Expertise

BACKGROUND

Chest radiographs (CXR) are the most commonly used imaging techniques by various clinicians and radiologists. However, detecting lung lesions on CXR depends largely on the reader's experience level, so there have been several trials to overcome this problem using post-processing of CXR. We investigated the added value of bone suppression image (BSI) in detecting various subtle lung lesions on CXR with regard to reader's expertise.

METHODS

We applied a software program to generate BSI in 1,600 patients in the emergency department. Of them, 80 patients with subtle lung lesions and 80 patients with negative finding on CXR were retrospectively selected based on the subtlety scores on CXR and CT findings. Ten readers independently rated their confidence in deciding the presence or absence of a lung lesion at each of 960 lung regions on the two separated imaging sessions: CXR alone vs. CXR with BSI.

RESULTS

The additional use of BSI for all readers significantly increased the mean area under the curve (AUC) in detecting subtle lung lesions (0.663 vs. 0.706; P < 0.001). The less experienced readers were, the more AUC differences increased: 0.067 (P < 0.001) for junior radiology residents; 0.064 (P < 0.001) for non-radiology clinicians; 0.044 (P < 0.001) for senior radiology residents; and 0.019 (P = 0.041) for chest radiologists. The additional use of BSI significantly increased the mean confidence regarding the presence or absence of lung lesions for 213 positive lung regions (2.083 vs. 2.357; P < 0.001) and for 747 negative regions (1.217 vs. 1.195; P = 0.008).

CONCLUSION

The use of BSI increases diagnostic performance and confidence, regardless of reader's expertise, reduces the impact of reader's expertise and can be helpful for less experienced clinicians and residents in the detection of subtle lung lesions.

Dose reduction and image quality improvement of chest radiography by using bone-suppression technique and low tube voltage: a phantom study

OBJECTIVE

To clarify the relationship between entrance surface dose (ESD) and physical image quality of original and bone-suppressed chest radiographs acquired using high and low tube voltages.

METHODS

An anthropomorphic chest phantom and a 12-mm diameter spherical simulated nodule with a CT value of approximately + 100 HU were used. The lung field in the chest radiograph was divided into seven areas, and the nodule was set in a total of 66 positions. A total of 264 chest radiographs were acquired using four ESD conditions: approximately 0.3 mGy at 140 and 70 kVp and approximately 0.2 and 0.1 mGy at 70 kVp. The radiographs were processed to produce bone-suppressed images. Differences in contrast and contrast-to-noise ratio (CNR) values of the nodule between each condition and between the original and bone-suppressed images were analyzed by a two-sided Wilcoxon signed-rank test.

RESULTS

In the areas not overlapping with the ribs, both contrast and CNR values were significantly increased with the bone-suppression technique (p < 0.01). In the bone-suppressed images, these values of the three conditions at 70 kVp were equal to or significantly higher than those of the condition at 140 kVp. There was no apparent decrease in these values between the ESD of approximately 0.3 and 0.1 mGy at 70 kVp.

CONCLUSION

By using the shortest exposure time and the lowest tube voltage possible not to increase in blurring artifact and image noise, it is possible to improve the image quality of bone-suppressed images and reduce the patient dose.

KEY POINTS

• The effectiveness of bone-suppression techniques differs in areas of lung field. • Image quality of bone-suppressed chest radiographs is improved by lower tube voltage. • Applying lower tube voltage to bone-suppressed chest radiographs leads to dose reduction.

Relationship Between Tube Voltage and Physical Image Quality of Pulmonary Nodules on Chest Radiographs Obtained Using the Bone-Suppression Technique

RATIONALE AND OBJECTIVES

Image quality of chest radiographs is affected by tube voltage. This study aimed to clarify the relationship between tube voltage and physical image quality of pulmonary nodules on bone-suppressed chest radiographs.

MATERIALS AND METHODS

An anthropomorphic chest phantom and a spherical simulated nodule, with a 12-mm diameter and approximately +100 Hounsfield units were used. The lung field of the phantom was divided into three areas, based on the overlap with the ribs in the chest radiograph. Ten positions of the simulated nodule were defined in each area. One hundred and twenty chest radiographs were acquired using four tube voltages (70 kVp, 90 kVp, 110 kVp, and 130 kVp) for a total of 30 nodule positions and were processed to create bone-suppressed images. Differences in contrast and contrast-to-noise ratio (CNR) were analyzed for all pairs of the four tube voltages using a two-sided Wilcoxon signed-rank test.

RESULTS

In the area not overlapping with ribs, a statistically significant difference was observed only in contrast between tube voltage of 70 kVp and 90 kVp (p = 0.01). In the area overlapping with one or two ribs, the contrast and CNR tended to be higher at a lower tube voltage. In particular, the p values between the contrast at 70 kVp and that at the other three tube voltage settings were less than 0.01.

CONCLUSION

For a relatively dense nodule, the contrast and CNR in the bone-suppressed chest radiograph were significantly improved with lower tube voltage in the lung field overlapping with the ribs.

Micro-CT myelography using contrast-enhanced digital subtraction: feasibility and initial results in healthy rats

PURPOSE

The spinal subarachnoid space (SSAS) is vital for neural performance. Although models of spinal diseases and trauma are used frequently, no methods exist to obtain high-resolution myelograms in rodents. Thereby, our aim was to explore the feasibility of obtaining high-resolution micro-CT myelograms of rats by contrast-enhanced dual-energy (DE) and single-energy (SE) digital subtraction.

METHODS

Micro-CT contrast-enhanced DE and SE imaging protocols were implemented with live adult rats (total of 18 animals). For each protocol, contrast agents based on iodine (Iomeron® 400 and Fenestra® VC) and gold nanoparticles (AuroVist™ 15 nm) were tested. For DE, images at low- and high-energy settings were acquired after contrast injection; for SE, one image was acquired before and the other after contrast injection. Post-processing consisted of region of interest selection, image registration, weighted subtraction, and longitudinal alignment.

RESULTS

High-resolution myelograms were obtained with contrast-enhanced digital subtraction protocols. After qualitative and quantitative (contrast-to-noise ratio) analyses, we found that the SE acquisition protocol with Iomeron® 400 provides the best images. 3D contour renderings allowed visualization of SSAS and identification of some anatomical structures within it.

CONCLUSION

This in vivo study shows the potential of SE contrast-enhanced myelography for imaging SSAS in rat. This approach yields high-resolution 3D images without interference from adjacent anatomical structures, providing an innovative tool for further assessment of studies involving rat SSAS.

Memory bias in observer-performance literature

The objective of our study was to determine how authors of published observer–performance experiments dealt with memory bias in study design. We searched American Journal of Roentgenology online and Radiology using “observer study” and “observer performance.” We included articles from 1970 or later that reported an observer performance experiment using human observers. We recorded the methods used by the authors to order presentation of the conditions being tested and images within sets for viewing. We recorded use and length of any time gap between viewings. We included 110 experiments. Forty-five used methods not subject to memory bias. Of 68 remaining experiments, 30 (44.1%) ordered the viewing of tested conditions to decrease memory bias. Fifteen (22.1%) ordered the tested conditions in ways that may create memory bias. Eleven (16.2%) intermixed the tested conditions. Forty-three (63.2%) used random or pseudorandom ordering of images within sets. Forty-six (67.6%) used a time gap (median 14 days) between viewings. Six (8.8%) did not use a time gap. Thirty-six (52.9%) did not indicate what methods they used in at least one studied parameter. Therefore, we conclude that 22.1% of the experiments could improve their methods of ordering tested conditions. Completeness of reporting could be improved by including more details regarding methods of ameliorating memory bias.

Bone subtraction radiography in adult patients with cystic fibrosis

Background Bone subtraction radiography allows reading pulmonary changes of chest radiographs more accurately without superimposition of bones. Purpose To evaluate the value of bone subtraction chest radiography using dual energy (DE) bone subtracted lung images compared to conventional radiographs (CR) in adult patients with cystic fibrosis (CF). Material and Methods Forty-nine DE radiographs of 24 patients (16 men) with CF (mean age, 32 years; age range, 18-71 years) were included. Lung function tests were performed within 10 days of the radiographs. Two radiologists evaluated all CR, DE, and CR + DE radiographs using the modified Chrispin-Norman score (CNS) and a five-point score for the confidence. Findings were statistically evaluated by Friedman ANOVA and Wilcoxon matched-pairs test. Results There was significant difference of CNS between CR and DE ( P = 0.044) as well as CR and CR + DE ( P < 0.001). CNS of CR images showed moderate correlation with FEV1% (R = 0.287, P = 0.046) while DE and CR + DE correlated poorly with FEV1% (R = 0.023, P = 0.874 and R = 0.04, P = 0.785). A higher confidence was achieved with bone-subtracted radiographs compared to radiographs alone (median, CR 3.3, DE 3.9, CR + DE 4.1, for both P < 0.001). Conclusion DE radiographs are reliable for the evaluation of adult patients with CF in acute exacerbation. For yearly surveillance, CR and DE radiographs may play a limited role. However, in clinical routine, DE radiographs are useful for adult CF patients and may depict more accurately inflammatory changes than CR.

Diagnostic accuracy and added value of dual-energy subtraction radiography compared to standard conventional radiography using computed tomography as standard of reference

PURPOSE

To retrospectively evaluate diagnostic performance of dual-energy subtraction radiography (DESR) for interpretation of chest radiographs compared to conventional radiography (CR) using computed tomography (CT) as standard of reference.

MATERIAL AND METHODS

A total of 199 patients (75 female, median age 67) were included in this institutional review board (IRB)-approved clinical trial. All patients were scanned in posteroanterior and lateral direction with dual-shot DE-technique. Chest CT was performed within ±72 hours. The system provides three types of images: bone weighted-image, soft tissue weighted-image, herein termed as DESR-images, and a standard image, termed CR-image (marked as CR-image). Images were evaluated by two radiologists for presence of inserted life support lines, pneumothorax, pleural effusion, infectious consolidation, interstitial lung changes, tumor, skeletal alterations, soft tissue alterations, aortic or tracheal calcification and pleural thickening. Inter-observer agreement between readers and diagnostic performance were calculated. McNemar's test was used to test for significant differences.

RESULTS

Mean inter-observer agreement throughout the investigated parameters was higher in DESR images compared to CR-images (kDESR = 0.935 vs. kCR = 0.858). DESR images provided significantly increased sensitivity compared to CR-images for the detection of infectious consolidations (42% vs. 62%), tumor (46% vs. 57%), interstitial lung changes (69% vs. 87%) and aortic or tracheal calcification (25 vs. 73%) (p<0.05). There were no significant differences in sensitivity for the detection of inserted life support lines, pneumothorax, pleural effusion, skeletal alterations, soft tissue alterations or pleural thickening (p>0.05).

CONCLUSION

DESR increases significantly the sensibility without affecting the specificity evaluating chest radiographs, with emphasis on the detection of interstitial lung diseases.

Color radiography in lung nodule detection and characterization: comparison with conventional gray scale radiography

Background

To compare the capability of lung nodule detection and characterization between dual-energy radiography with color-representation (DCR) and conventional gray scale chest radiography (GSR).

Methods

A total of 130 paired chest radiographs (DCR and GSR) obtained from 65 patients (14 with normal scans and 51 with pulmonary nodules) were evaluated. After analysis, 45 non-calcified and 21 calcified nodules were identified. DCR was obtained by adding color space within material-decomposed data (blue for high attenuation and red for low attenuation) and by compounding the manipulated data to one color image. Three radiologists marked suggested nodules on radiographic images and assessed the level of confidence of lesion presence and probability of nodule calcification by using a nine-point rating scale. The jackknife active free-response receiver operating characteristics (JAFROC) analysis was used to evaluate lesion detectability, and multi-reader multi-case receiver operating characteristics (MRMC ROC) analysis was used for the evaluation of the accuracy of nodule calcification prediction.

Results

Figures of merit (FOM) from JAFROC was 0.807 for DCR and 0.811 for GSR, respectively; nodule detectability was not significantly different between DCR and GSR (p = 0.93). Areas under curve (AUC) from MRMC ROC were 0.944 for DCR and 0.828 for GSR, respectively; performance of DCR in predicting lung nodule calcification was significantly higher than that of GSR (p = 0.04).

Conclusions

DCR showed similar performance in terms of lung nodule detection compared with GSR. However, DCR does provide a significant benefit in predicting the presence of nodule calcification.

Comparison of dual energy subtraction chest radiography and traditional chest X-rays in the detection of pulmonary nodules

BACKGROUND

Dual energy subtraction (DES) radiography is a powerful but underutilized technique which aims to improve the diagnostic value of an X-ray by separating soft tissue from bones, producing two different images. Compared to traditional chest X-rays, DES requires exposure to higher doses of radiation but may achieve higher accuracy. The objective of this study was to assess the clinical benefits of DES radiography by comparing the speed and accuracy of diagnosis of pulmonary nodules with DES versus traditional chest X-rays.

METHODS

Five radiologists and five radiology residents read the DES and traditional chest X-rays of 51 patients, 34 with pulmonary nodules and 17 without. Their accuracy and speed in the detection of nodules were measured using specialized image display software.

RESULTS

DES radiography reduced reading time from 13 to 10 sec (P<0.0001) in staff and from 21 to 15 sec in residents (P<0.0001). There was also a small increase in sensitivity 0.58 to 0.67 overall (P<0.10) with no change in specificity (0.85 overall).

CONCLUSIONS

By eliminating rib shadows in soft tissue images, DES improved the speed and accuracy of radiologists in the diagnosis of pulmonary nodules.

Real-time markerless lung tumor tracking in fluoroscopic video: Handling overlapping of projected structures

PURPOSE

Fluoroscopic imaging is a well-suited technique for online visualization of tumor motion in the thoracic region. Template-based approaches for tumor tracking in such images are commonly used. However, overlapping of different structures, mainly bones, can lead to limited visibility of the projected tumor shape, which in turn can negatively affect the performance of the tracking method. In this study, a method based on multiple-template matching was developed, providing fast and robust detection of tumor motion even under the influence of occurring tumor overlaps.

METHODS

A cohort of 14 patients with varying tumor sizes and locations was investigated. Image data from eight of these patients were used for evaluation. Based on the requirement of tumor visibility, the remaining datasets did not qualify for tracking. Generation of multiple templates was improved by implementation of an algorithm for automated selection of reference images containing the most characteristic tumor appearances. Various measures were taken to ensure real-time capability of the algorithm. A prematching step was introduced in order to reduce dispensable comparison operations by selecting the most appropriate template. Subsequent matching was further optimized by using prior knowledge about likely tumor motion to effectively limit necessary matching tasks.

RESULTS

Tracking accuracy of the developed multiple-template method was compared with that of single-template. Mean errors of the multiple-template approach were 0.6 ± 0.6 mm in left-right and 0.9 ± 0.9 mm in superior-inferior direction in the isocenter plane. The single-template approach achieved mean errors of 0.7 ± 0.7 mm in left-right and 1.5 ± 1.3 mm in superior-inferior direction. These results derive from evaluation against manual tumor tracking performed by four expert observers. Computational times needed for tumor detection in a single fluoroscopic frame ranged between 1 and 29 ms depending on the tumor size and motion amplitude.

CONCLUSIONS

This study shows that in case of tumor overlapping with dense structures, multiple-template tracking provides more accurate results than a single-template approach. The developed algorithm shows promising results in terms of suitability for real-time application and robustness against frequently changing overlapping.

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

Role of digital tomosynthesis and dual energy subtraction digital radiography in detecting pulmonary nodules

OBJECTIVE

Digital tomosynthesis (DT) and dual-energy subtraction digital radiography (DES-DR) are known to perform better than conventional radiography in the detection of pulmonary nodules. Yet the comparative diagnostic performances of DT, DES-DR and digital radiography (DR) is not known. The present study compares the diagnostic performances of DT, DES-DR and DR in detecting pulmonary nodules.

SUBJECTS AND METHODS

The institutional Review Board approved the study and informed written consent was obtained. Fifty-five patients (30 with pulmonary nodules, 25 with non-nodular focal chest pathology) were included in the study. DT and DES-DR were performed within 14 days of MDCT. Composite images acquired at high kVp as part of DES-DR were used as DR images. Images were analyzed for presence of nodules and calcification in nodules. Interpretations were assigned confidence levels from 1 to 5 according to Five-Point rating scale. Areas under the receiver operating characteristic curves were compared using Z test.

RESULTS

A total of 110 (88 non-calcified, 22 calcified) nodules were identified on MDCT. For detection of nodules, DR showed cumulative sensitivity and specificity of 25.45% and 67.97%, respectively. DT showed a cumulative sensitivity and specificity of 60.9% and 85.07%, respectively. The performance was significantly better than DR (p<0.003). DES-DR showed sensitivity and specificity of 27.75% and 82.64%, not statistically different from those of DR (p-0.92). In detection of calcification, there was no statistically significant difference between DT, DES-DR and DR.

CONCLUSIONS

DT performs significantly better than DES-DR and DR at the cost of moderate increase in radiation dose.

[Detection of lung nodules. New opportunities in chest radiography]

BACKGROUND

Chest radiography still represents the most commonly performed X-ray examination because it is readily available, requires low radiation doses and is relatively inexpensive. However, as previously published, many initially undetected lung nodules are retrospectively visible in chest radiographs.

STANDARD RADIOLOGICAL METHODS

The great improvements in detector technology with the increasing dose efficiency and improved contrast resolution provide a better image quality and reduced dose needs.

METHODICAL INNOVATIONS

The dual energy acquisition technique and advanced image processing methods (e.g. digital bone subtraction and temporal subtraction) reduce the anatomical background noise by reduction of overlapping structures in chest radiography. Computer-aided detection (CAD) schemes increase the awareness of radiologists for suspicious areas.

RESULTS

The advanced image processing methods show clear improvements for the detection of pulmonary lung nodules in chest radiography and strengthen the role of this method in comparison to 3D acquisition techniques, such as computed tomography (CT).

ASSESSMENT

Many of these methods will probably be integrated into standard clinical treatment in the near future. Digital software solutions offer advantages as they can be easily incorporated into radiology departments and are often more affordable as compared to hardware solutions.

Separation of bones from chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing

Most lung nodules that are missed by radiologists as well as computer-aided detection (CADe) schemes overlap with ribs or clavicles in chest radiographs (CXRs). The purpose of this study was to separate bony structures such as ribs and clavicles from soft tissue in CXRs. To achieve this, we developed anatomically specific multiple massive-training artificial neural networks (MTANNs) combined with total variation (TV) minimization smoothing and a histogram-matching-based consistency improvement method. The anatomically specific multiple MTANNs were designed to separate bones from soft tissue in different anatomic segments of the lungs. Each of the MTANNs was trained with the corresponding anatomic segment in the teaching bone images. The output segmental images from the multiple MTANNs were merged to produce an entire bone image. TV minimization smoothing was applied to the bone image for reduction of noise while preserving edges. This bone image was then subtracted from the original CXR to produce a soft-tissue image where bones were separated out. This new method was compared with conventional MTANNs with a database of 110 CXRs with nodules. Our new anatomically specific MTANNs separated rib edges, ribs close to the lung wall, and the clavicles from soft tissue in CXRs to a substantially higher level than did the conventional MTANNs, while the conspicuity of lung nodules and vessels was maintained. Thus, our technique for bone-soft-tissue separation by means of our new MTANNs would be potentially useful for radiologists as well as CADe schemes in detection of lung nodules on CXRs.

Bone mineral density and geometry parameters determined in vitro from dual-energy digital radiography images in the assessment of bone maximal load of reindeer femora

BACKGROUND

Dual-energy digital radiography (DEDR) has been shown to be a potential method to determine bone mineral density (BMD) and predict maximal load with similar accuracy as standard bone densitometry using DXA (dual-energy X-ray absorptiometry). In addition to bone density, bone geometry has also been shown to have effect on bone fragility and fracture risk.

PURPOSE

To examine the combination of BMD and geometry parameters, as determined from a DEDR experiment, to predict bone maximal load.

MATERIAL AND METHODS

Reindeer femora (n = 47) were imaged at two energies (79 kVp and 100 kVp) using a clinical digital radiography system. BMD was determined in four regions from these images using the DXA calculation principle. Various geometrical parameters were determined from the 79 kVp image. Femora were mechanically tested using axial loading configuration. Pearson correlation coefficients were determined between geometrical parameters and BMDs or maximal load. Multiple stepwise linear regression analysis was used to find the best combination to predict bone maximal load.

RESULTS

From the geometrical parameters, femoral shaft diameter (FSD) and femoral neck axis length (FNAL) correlated best with the maximal load (r = 0.629 and r = 0.446, P < 0.01, respectively). The best combination of parameters to predict bone fragility was BMD at Ward's triangle, FSD and FNAL (r = 0.787, P < 0.05), whereas the correlation coefficient between BMD at Ward's triangle and maximal load was 0.653 (P < 0.05).

CONCLUSION

The combination of DEDR-based BMD and geometrical parameters predicts reindeer bone maximal load with reasonable accuracy and the combined analysis improves the prediction of maximal load compared to BMD prediction only.

Computerized detection of lung nodules by means of "virtual dual-energy" radiography

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of "virtual dual-energy" (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated "extremely subtle" or "very subtle" by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially.

Improved detection of focal pneumonia by chest radiography with bone suppression imaging

OBJECTIVE

To evaluate radiologists' ability to detect focal pneumonia by use of standard chest radiographs alone compared with standard plus bone-suppressed chest radiographs.

METHODS

Standard chest radiographs in 36 patients with 46 focal airspace opacities due to pneumonia (10 patients had bilateral opacities) and 20 patients without focal opacities were included in an observer study. A bone suppression image processing system was applied to the 56 radiographs to create corresponding bone suppression images. In the observer study, eight observers, including six attending radiologists and two radiology residents, indicated their confidence level regarding the presence of a focal opacity compatible with pneumonia for each lung, first by use of standard images, then with the addition of bone suppression images. Receiver operating characteristic (ROC) analysis was used to evaluate the observers' performance.

RESULTS

The mean value of the area under the ROC curve (AUC) for eight observers was significantly improved from 0.844 with use of standard images alone to 0.880 with standard plus bone suppression images (P < 0.001) based on 46 positive lungs and 66 negative lungs.

CONCLUSION

Use of bone suppression images improved radiologists' performance for detection of focal pneumonia on chest radiographs.

KEY POINTS

Bone suppression image processing can be applied to conventional digital radiography systems. Bone suppression imaging (BSI) produces images that appear similar to dual-energy soft tissue images. BSI improves the conspicuity of focal lung disease by minimizing bone opacity. BSI can improve the accuracy of radiologists in detecting focal pneumonia.

Small lung cancers: improved detection by use of bone suppression imaging--comparison with dual-energy subtraction chest radiography

PURPOSE

To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography.

MATERIALS AND METHODS

Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance.

RESULTS

The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001).

CONCLUSION

Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose.

Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization

OBJECTIVE

The purpose of this article is to evaluate radiologists' ability to detect subtle nodules by use of standard chest radiographs alone compared with bone suppression imaging used together with standard radiographs.

MATERIALS AND METHODS

The cases used in this observer study comprised radiographs of 72 patients with a subtle nodule and 79 patients without nodules taken from the Japanese Society of Radiological Technology nodule database. A new image-processing system was applied to the 151 radiographs to create corresponding bone suppression images. Two image reading sets were used with an independent test method. The first reading included half of the patients (a randomly selected subset A) showing only the standard image and the remaining half (subset B) showing the standard image plus bone suppression images. The second reading entailed the same subsets; however, subset A was accompanied by bone suppression images, whereas subset B was shown with only the standard image. The two image sets were read by three experienced radiologists, with an interval of more than 2 weeks between the sessions. Receiver operating characteristic (ROC) curves, with and without localization, were obtained to evaluate the observers' performance.

RESULTS

The mean value of the area under the ROC curve for the three observers was significantly improved, from 0.840 with standard radiographs alone to 0.863 with additional bone suppression images (p = 0.01). The area under the localization ROC curve was also improved with bone suppression imaging.

CONCLUSION

The use of bone suppression images improved radiologists' performance in the detection of subtle nodules on chest radiographs.

Dual energy subtraction digital radiography improves performance of a next generation computer-aided detection program

PURPOSE

Computer-aided detection (CAD) has shown potential to assist physicians in the detection of lung nodules on chest radiographs, but widespread acceptance has been stymied by high false-positive rates. Few studies have examined the potential for dual energy subtraction (DES) to improve CAD performance.

MATERIALS AND METHODS

Institutional review board approval was obtained, the requirement for informed consent was waived because the study was retrospective, and practices conformed to Health Insurance Portability and Accountability Act regulations. The CAD program was applied retrospectively to dual energy posteroanterior (PA) chest radiographs of 36 patients (17 women, 19 men, mean age 69 y) with 48 pathology proven lung nodules. Results were analyzed to determine the stand-alone CAD program false-positive rates, and sensitivity by nodule subtlety and location. Statistical analysis was performed using the chi(2) or Fisher exact tests for independence of sensitivities between standard PA and DES radiography. Differences in the mean false-positives per image (FPPI) between radiographic modalities were determined using the paired Students t test, and bootstrap confidence intervals were obtained to confirm results.

RESULTS

The sensitivity of the CAD program with the standard PA was 46% (22 of 48 nodules) compared with 67% (32 of 48 nodules) using the DES soft tissue or bone-subtracted view (P=0.064). The average number of FPPI identified by CAD was significantly lower using DES (FPPI(soft tissue) = 1.64) when compared with the standard PA chest radiograph (FPPI(PA) = 2.39) (P<0.01).

CONCLUSIONS

DES has the potential to improve stand-alone CAD performance by both increasing sensitivity for certain subtle lung cancer lesions and decreasing overall CAD false-positive rates.

Bone images from dual-energy subtraction chest radiography in the detection of rib fractures

OBJECTIVE

To assess the sensitivity and image quality of chest radiography (CXR) with or without dual-energy subtracted (ES) bone images in the detection of rib fractures.

MATERIALS AND METHODS

In this retrospective study, 39 patients with 204 rib fractures and 24 subjects with no fractures were examined with a single exposure dual-energy subtraction digital radiography system. Three blinded readers first evaluated the non-subtracted posteroanterior and lateral chest radiographs alone, and 3 months later they evaluated the non-subtracted images together with the subtracted posteroanterior bone images. The locations of rib fractures were registered with confidence levels on a 3-grade scale. Image quality was rated on a 5-point scale. Marks by readers were compared with fracture localizations in CT as a standard of reference.

RESULTS

The sensivity for fracture detection using both methods was very similar (34.3% with standard CXR and 33.5% with ES-CXR, p=0.92). At the patient level, both sensitivity (71.8%) and specificity (92.9%) with or without ES were identical. Diagnostic confidence was not significantly different (2.61 with CXR and 2.75 with ES-CXR, p=0.063). Image quality with ES was rated higher than that on standard CXR (4.08 vs. 3.74, p<0.001).

CONCLUSIONS

Despite a better image quality, adding ES bone images to standard radiographs of the chest does not provide better sensitivity or improved diagnostic confidence in the detection of rib fractures.

Performance of radiologists in detection of small pulmonary nodules on chest radiographs: effect of rib suppression with a massive-training artificial neural network

OBJECTIVE

A massive-training artificial neural network is a nonlinear pattern recognition tool used to suppress rib opacity on chest radiographs while soft-tissue contrast is maintained. We investigated the effect of rib suppression with a massive-training artificial neural network on the performance of radiologists in the detection of pulmonary nodules on chest radiographs.

MATERIALS AND METHODS

We used 60 chest radiographs; 30 depicted solitary pulmonary nodules, and 30 showed no nodules. A stratified random-sampling scheme was used to select the images from the standard digital image database developed by the Japanese Society of Radiologic Technology. The mean diameter of the 30 pulmonary nodules was 14.7 +/- 4.1 (SD) mm. Receiver operating characteristic analysis was used to evaluate observer performance in the detection of pulmonary nodules first on the chest radiographs without and then on the radiographs with rib suppression. Seven board-certified radiologists and five radiology residents participated in this observer study.

RESULTS

For all 12 observers, the mean values of the area under the best-fit receiver operating characteristic curve for images without and with rib suppression were 0.816 +/- 0.077 and 0.843 +/- 0.074; the difference was statistically significant (p = 0.019). The mean areas under the curve for images without and with rib suppression were 0.848 +/- 0.059 and 0.883 +/- 0.050 for the seven board-certified radiologists (p = 0.011) and 0.770 +/- 0.081 and 0.788 +/- 0.074 for the five radiology residents (p = 0.310).

CONCLUSION

In the detection of pulmonary nodules, evaluation of a combination of rib-suppressed and original chest radiographs significantly improved the diagnostic performance of radiologists over the use of chest radiographs alone.

Temporal subtraction chest radiography

Radiologist are commonly required to compare a sequence of two or more chest radiographs of a given patient obtained over a period of time, which may range from a few hours to many years. In such cases, the task is one of detecting interval change. In the case of patients who have had a previous chest radiograph, an opportunity exists to enhance selectively areas of interval change, including regions with new or altered pathology, by using the previous radiographs as a subtraction mask. With temporal subtraction, the previous image is superimposed and registered with the current image, using automated two-dimensional warping to compensate for any differences in positioning. A "difference image" is then created, by subtracting the previous from the current radiograph. In this temporal subtraction image, areas that are unchanged appear as uniform gray, while regions of new opacity, such as due to pneumonia or cancer, appear as prominent dark foci on a lighter background. By cancelling out the complex anatomical background, temporal subtraction can provide dramatically enhanced visibility of new areas of disease.