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

Cycle-generative adversarial network-based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy

PURPOSE

Lung tumor tracking during stereotactic radiotherapy with the CyberKnife can misrecognize tumor location under conditions where similar patterns exist in the search area. This study aimed to develop a technique for bone signal suppression during kV-x-ray imaging.

METHODS

Paired CT images were created with or without bony structures using a 4D extended cardiac-torso phantom (XCAT phantom) in 56 cases. Subsequently, 3020 2D x-ray images were generated. Images with bone were input into cycle-consistent adversarial network (CycleGAN) and the bone suppressed images on the XCAT phantom (BSIphantom ) were created. They were then compared to images without bone using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Next, 1000 non-simulated treatment images from real cases were input into the training model, and bone-suppressed images of the patient (BSIpatient ) were created. Zero means normalized cross correlation (ZNCC) by template matching between each of the actual treatment images and BSIpatient were calculated.

RESULTS

BSIphantom values were compared to their paired images without bone of the XCAT phantom test data; SSIM and PSNR were 0.90 ± 0.06 and 24.54 ± 4.48, respectively. It was visually confirmed that only bone was selectively suppressed without significantly affecting tumor visualization. The ZNCC values of the actual treatment images and BSIpatient were 0.763 ± 0.136 and 0.773 ± 0.143, respectively. The BSIpatient showed improved recognition accuracy over the actual treatment images.

CONCLUSIONS

The proposed bone suppression imaging technique based on CycleGAN improves image recognition, making it possible to achieve highly accurate motion tracking irradiation.

Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs

Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.

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.

DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs

Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance automated disease detection. We evaluate this hypothesis using a custom ensemble of convolutional neural network models, which we call DeBoNet, that suppresses bones in frontal CXRs. First, we train and evaluate variants of U-Nets, Feature Pyramid Networks, and other proposed custom models using a private collection of CXR images and their bone-suppressed counterparts. The DeBoNet, constructed using the top-3 performing models, outperformed the individual models in terms of peak signal-to-noise ratio (PSNR) (36.7977±1.6207), multi-scale structural similarity index measure (MS-SSIM) (0.9848±0.0073), and other metrics. Next, the best-performing bone-suppression model is applied to CXR images that are pooled from several sources, showing no abnormality and other findings consistent with COVID-19. The impact of bone suppression is demonstrated by evaluating the gain in performance in detecting pulmonary abnormality consistent with COVID-19 disease. We observe that the model trained on bone-suppressed CXRs (MCC: 0.9645, 95% confidence interval (0.9510, 0.9780)) significantly outperformed (p < 0.05) the model trained on non-bone-suppressed images (MCC: 0.7961, 95% confidence interval (0.7667, 0.8255)) in detecting findings consistent with COVID-19 indicating benefits derived from automatic bone suppression on disease classification. The code is available at https://github.com/sivaramakrishnan-rajaraman/Bone-Suppresion-Ensemble.

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.

The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging

Background

The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR).

Methods

A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient. These were first analyzed using a CCR image, then with CCR plus deep BSI, then with CCR plus DES radiography, and finally with DTS images. Receiver-operating characteristic curves were used to evaluate the performance of the 6 observers in the detection of pulmonary nodules.

Results

For the 6 observers, the average area under the curve improved significantly from 0.717 with CCR to 0.848 with CCR plus deep BSI (P<0.01), 0.834 with CCR plus DES radiography (P<0.01), and 0.939 with DTS (P<0.01). Comparisons between CCR and CCR plus deep BSI found that the sensitivities of the assessments by the 3 residents increased from 53.2% to 69.5% (P=0.014) for nodules located in the upper lung field, from 30.6% to 44.6% (P=0.015) for nodules that were partially/completely obscured by the bone, and from 33.2% to 45.8% (P=0.006) for nodules <10 mm.

Conclusions

The deep BSI technique can significantly increase the sensitivity of radiology residents for solitary pulmonary nodules compared with CCR. Increased detection was seen mainly for smaller nodules, nodules with partial/complete obscuration, and nodules located in the upper lung field.

Lung cancer screening by single-shot dual-energy subtraction using flat-panel detector

Purpose

The purpose of this study was to evaluate the usefulness of single-shot dual-energy subtraction (DES) method using a flat-panel detector for lung cancer screening

Materials and methods

The subjects were 13,315 residents (5801 males and 7514 females) aged 50 years or older (50–97 years, with an intermediate value of 68 years) who underwent lung cancer screening for a period of 1 year and 6 months from January 2019 to June 2020. We investigated whether the number of lung cancers detected, the detection rate, and the rate of required scrutiny changed, when DES images were added to the judgment based on conventional chest radiography.

Results

When DES images were added, the number and percentage of cancer detection increased from 16 (0.12%) to 23 (0.17%) (P < 0.05). Five of the newly detected 7 lung cancers were in the early stages of resectable cancer. The rate of participants requiring scrutiny increased slightly from 1.1 to 1.3%.

Conclusion

DES method improved the detection of lung cancer in screening. The increase in the percentage of participants requiring scrutiny was negligible.

Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings

Chest X-rays (CXRs) are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors. This study aims to build a deep learning (DL)-based bone suppression model that identifies and removes these occluding bony structures in frontal CXRs to assist in reducing errors in radiological interpretation, including DL workflows, related to detecting manifestations consistent with tuberculosis (TB). Several bone suppression models with various deep architectures are trained and optimized using the proposed combined loss function and their performances are evaluated in a cross-institutional test setting using several metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and multiscale structural similarity measure (MS-SSIM). The best-performing model (ResNet-BS) (PSNR = 34.0678; MS-SSIM = 0.9828) is used to suppress bones in the publicly available Shenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a large collection of publicly available CXRs. The CXR-pretrained model is then fine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of Shenzhen and Montgomery TB CXR collections to classify them as showing normal lungs or TB manifestations. The performances of these models are compared using several performance metrics such as accuracy, the area under the curve (AUC), sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC), analyzed for statistical significance, and their predictions are qualitatively interpreted through class-selective relevance maps (CRMs). It is observed that the models trained on bone-suppressed CXRs (Shenzhen: AUC = 0.9535 ± 0.0186; Montgomery: AUC = 0.9635 ± 0.0106) significantly outperformed (p < 0.05) the models trained on the non-bone-suppressed CXRs (Shenzhen: AUC = 0.8991 ± 0.0268; Montgomery: AUC = 0.8567 ± 0.0870).. Models trained on bone-suppressed CXRs improved detection of TB-consistent findings and resulted in compact clustering of the data points in the feature space signifying that bone suppression improved the model sensitivity toward TB classification.

The potential application of dual-energy subtraction radiography for COVID-19 pneumonia imaging

X-ray imaging plays a crucial role in the confirmation of COVID-19 pneumonia. Chest X-ray radiography and CT are two major imaging techniques that are currently adopted in the diagnosis of COVID-19 pneumonia. However, dual-energy subtraction radiography is hardly discussed as potential COVID-19 imaging application. More advanced X-ray radiography equipment often supports dual-energy subtraction X-ray radiography. Dual-energy subtraction radiography enables the calculation of pseudo-radiographs, in which bones are removed and only soft-tissues are highlighted. In this commentary, the author would like to draw the attention to the potential use of dual-energy subtraction X-ray radiography (i.e. soft-tissue pseudo-radiography) for the assessment and the longitudinal follow-up of COVID-19 pneumonia.

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.

Detectability of pulmonary nodules on chest radiographs: bone suppression versus standard technique with single versus dual monitors for visualization

PURPOSE

To evaluate the diagnostic accuracy of bone suppression imaging (BSI) in the detection of pulmonary nodules on chest radiographs (CXRs) and the effect of visualization method (single or dual monitors) on diagnostic accuracy.

MATERIALS AND METHODS

Ten observers interpreted the CXRs of 100 patients: 50 with a T1 lung cancer nodule and 50 without nodules. Each standard CXR was first read alone and then in combination with the corresponding BSI. Two sessions of viewing were conducted: (1) the standard CXR and BSI were placed side by side on dual monitors and (2) both images were shown on the same monitor in alternation. The nodule location, confidence level, and interpretation time were recorded and analyzed statistically.

RESULTS

When BSI was added, the area under the receiver operating characteristic curve (AUC) improved with dual monitors and a single monitor. The AUC was not significantly different between the dual-monitor and single-monitor sessions; however, the specificity with BSI and dual monitors decreased. The total interpretation time was significantly shorter with a single monitor than with dual monitors.

CONCLUSIONS

The use of BSI improved detectability of T1 lung cancer nodules on CXRs; however, specificity and reading time were affected by the visualization method.

Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection

Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.

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.

Automatic delineation of ribs and clavicles in chest radiographs using fully convolutional DenseNets

BACKGROUND AND OBJECTIVE

In chest radiographs (CXRs), all bones and soft tissues are overlapping with each other, which raises issues for radiologists to read and interpret CXRs. Delineating the ribs and clavicles is helpful for suppressing them from chest radiographs so that their effects can be reduced for chest radiography analysis. However, delineating ribs and clavicles automatically is difficult by methods without deep learning models. Moreover, few of methods without deep learning models can delineate the anterior ribs effectively due to their faint rib edges in the posterior-anterior (PA) CXRs.

METHODS

In this work, we present an effective deep learning method for delineating posterior ribs, anterior ribs and clavicles automatically using a fully convolutional DenseNet (FC-DenseNet) as pixel classifier. We consider a pixel-weighted loss function to mitigate the uncertainty issue during manually delineating for robust prediction.

RESULTS

We conduct a comparative analysis with two other fully convolutional networks for edge detection and the state-of-the-art method without deep learning models. The proposed method significantly outperforms these methods in terms of quantitative evaluation metrics and visual perception. The average recall, precision and F-measure are 0.773 ± 0.030, 0.861 ± 0.043 and 0.814 ± 0.023 respectively, and the mean boundary distance (MBD) is 0.855 ± 0.642 pixels of the proposed method on the test dataset. The proposed method also performs well on JSRT and NIH Chest X-ray datasets, indicating its generalizability across multiple databases. Besides, a preliminary result of suppressing the bone components of CXRs has been produced by using our delineating system.

CONCLUSIONS

The proposed method can automatically delineate ribs and clavicles in CXRs and produce accurate edge maps.

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.

Computer-aided detection in chest radiography based on artificial intelligence: a survey

As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

Missed Lung Cancer

The chest radiograph is one of the most commonly used imaging studies and is the modality of choice for initial evaluation of many common clinical scenarios. Over the last two decades, chest computed tomography has been increasingly used for a wide variety of indications, including respiratory illnesses, trauma, oncologic staging, and more recently lung cancer screening. Diagnostic radiologists should be familiar with the common causes of missed lung cancers on imaging studies in order to avoid detection and interpretation errors. Failure to detect these lesions can potentially have serious implications for both patients as well as the interpreting radiologist.

Added Value of Bone Subtraction in Dual-energy Digital Radiography in the Detection of Pneumothorax: Impact of Reader Expertise and Medical Specialty

RATIONALE AND OBJECTIVES

This study aimed to determine the value of dual-energy thoracic radiography in the diagnosis of pneumothorax considering the reader's experience.

MATERIALS AND METHODS

Forty patients with a suspected pneumothorax, imaged with dual-energy chest radiographs, were divided into two groups: those with pneumothorax as the final diagnosis (n = 19) and those without (n = 21). The images were analyzed by 36 readers (5 interns, 16 residents, 15 senior physicians) for the presence or absence of pneumothorax during three readout sessions at 2-week intervals: standard images alone (session 1), dual-energy images with bone subtraction alone (session 2), and a combination of the two (session 3).

RESULTS

The number of correct responses increased 13.3% between sessions 1 and 2 (P < .001) and 9.4% between sessions 1 and 3 (P < .001). The mean sensitivity for pneumothorax detection was higher in sessions 2 (82%) and 3 (79%) compared to session 1 (70%). There was no statistically significant difference in specificity between the sessions. The number of correct responses for small volume pneumothoraces was higher in sessions 2 (10.6 ± 1.8) and 3 (10.1 ± 2.0) than in session 1 (8.9 ± 2.3), with a statistically significant difference between sessions 1 and 2 (P = .002) and between sessions 1 and 3 (P = .048).

CONCLUSION

Bone subtracted dual-energy thoracic radiographs improve the detection sensitivity of pneumothorax, including in cases of small pneumothoraces, regardless of the reader's level or expertise.

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.

Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph

Aim:

The most ubiquitous chest diagnostic method is the chest radiograph. A common radiographic finding, quite often incidental, is the nodular pulmonary lesion. The detection of small lesions out of complex parenchymal structure is a daily clinical challenge. In this study, we investigate the efficacy of the computer-aided detection (CAD) software package SoftView™ 2.4A for bone suppression and OnGuard™ 5.2 (Riverain Technologies, Miamisburg, OH, USA) for automated detection of pulmonary nodules in chest radiographs.

Subjects and Methods:

We retrospectively evaluated a dataset of 100 posteroanterior chest radiographs with pulmonary nodular lesions ranging from 5 to 85 mm. All nodules were confirmed with a consecutive computed tomography scan and histologically classified as 75% malignant. The number of detected lesions by observation in unprocessed images was compared to the number and dignity of CAD-detected lesions in bone-suppressed images (BSIs).

Results:

SoftView™ BSI does not affect the objective lesion-to-background contrast. OnGuard™ has a stand-alone sensitivity of 62% and specificity of 58% for nodular lesion detection in chest radiographs. The false positive rate is 0.88/image and the false negative (FN) rate is 0.35/image. From the true positive lesions, 20% were proven benign and 80% were malignant. FN lesions were 47% benign and 53% malignant.

Conclusion:

We conclude that CAD does not qualify for a stand-alone standard of diagnosis. The use of CAD accompanied with a critical radiological assessment of the software suggested pattern appears more realistic. Accordingly, it is essential to focus on studies assessing the quality-time-cost profile of real-time (as opposed to retrospective) CAD implementation in clinical diagnostics.

A novel bone suppression method that improves lung nodule detection : Suppressing dedicated bone shadows in radiographs while preserving the remaining signal

PURPOSE

Suppressing thoracic bone shadows in chest radiographs has been previously reported to improve the detection rates for solid lung nodules, however at the cost of increased false detection rates. These bone suppression methods are based on an artificial neural network that was trained using dual-energy subtraction images in order to mimic their appearance.

METHOD

Here, a novel approach is followed where all bone shadows crossing the lung field are suppressed sequentially leaving the intercostal space unaffected. Given a contour delineating a bone, its image region is spatially transferred to separate normal image gradient components from tangential component. Smoothing the normal partial gradient along the contour results in a reconstruction of the image representing the bone shadow only, because all other overlaid signals tend to cancel out each other in this representation.

RESULTS

The method works even with highly contrasted overlaid objects such as a pacemaker. The approach was validated in a reader study with two experienced chest radiologists, and these images helped improving both the sensitivity and the specificity of the readers for the detection and localization of solid lung nodules. The AUC improved significantly from 0.596 to 0.655 on a basis of 146 images from patients and normals with a total of 123 confirmed lung nodules.

CONCLUSION

Subtracting all reconstructed bone shadows from the original image results in a soft image where lung nodules are no longer obscured by bone shadows. Both the sensitivity and the specificity of experienced radiologists increased.

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.

Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs

RATIONALE AND OBJECTIVES

To evaluate the performance of a computer-aided detection (CAD) system with bone suppression imaging when applied to unselected consecutive chest radiographs (CXRs) with computed tomography (CT) correlation.

MATERIALS AND METHODS

This study included 586 consecutive patients with standard or portable CXRs who had a chest CT scan on the same day. Among the 586 CXRs, 438 had various abnormalities, including 46 CXRs with 66 lung nodules, and 148 CXRs had no significant abnormalities. A commercially available CAD system was applied to all 586 CXRs. True nodules and false positives (FPs) marked on CXRs by the CAD system were evaluated based on the corresponding chest CT findings.

RESULTS

The CAD system marked 47 of 66 (71%) lung nodules in this consecutive series of CXRs. The mean FP rate per image was 1.3 across all 586 CXRs, with 1.5 FPs per image on the 438 abnormal CXRs and 0.8 FPs per image on the 148 normal CXRs. A total of 41% of the 752 FP marks were related to non-nodule pathologic findings.

CONCLUSIONS

A currently available CAD system marked 71% of radiologist-identified lung nodules in a large consecutive series of CXRs, and 41% of "false" marks were caused by pathologic findings.

[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.

Multi-reader multi-case studies using the area under the receiver operator characteristic curve as a measure of diagnostic accuracy: systematic review with a focus on quality of data reporting

INTRODUCTION

We examined the design, analysis and reporting in multi-reader multi-case (MRMC) research studies using the area under the receiver-operating curve (ROC AUC) as a measure of diagnostic performance.

METHODS

We performed a systematic literature review from 2005 to 2013 inclusive to identify a minimum 50 studies. Articles of diagnostic test accuracy in humans were identified via their citation of key methodological articles dealing with MRMC ROC AUC. Two researchers in consensus then extracted information from primary articles relating to study characteristics and design, methods for reporting study outcomes, model fitting, model assumptions, presentation of results, and interpretation of findings. Results were summarized and presented with a descriptive analysis.

RESULTS

Sixty-four full papers were retrieved from 475 identified citations and ultimately 49 articles describing 51 studies were reviewed and extracted. Radiological imaging was the index test in all. Most studies focused on lesion detection vs. characterization and used less than 10 readers. Only 6 (12%) studies trained readers in advance to use the confidence scale used to build the ROC curve. Overall, description of confidence scores, the ROC curve and its analysis was often incomplete. For example, 21 (41%) studies presented no ROC curve and only 3 (6%) described the distribution of confidence scores. Of 30 studies presenting curves, only 4 (13%) presented the data points underlying the curve, thereby allowing assessment of extrapolation. The mean change in AUC was 0.05 (-0.05 to 0.28). Non-significant change in AUC was attributed to underpowering rather than the diagnostic test failing to improve diagnostic accuracy.

CONCLUSIONS

Data reporting in MRMC studies using ROC AUC as an outcome measure is frequently incomplete, hampering understanding of methods and the reliability of results and study conclusions. Authors using this analysis should be encouraged to provide a full description of their methods and results.

Influence of study design in receiver operating characteristics studies: sequential versus independent reading

Observer studies to assess new image processing devices or computer-aided diagnosis techniques are often performed, but little is known about the effect of the study design on observer performance results. We investigated the effect of the sequential and independent reading design on observer study results with respect to reader performance and their statistical power. For this we performed an observer study for the detection of lung nodules with bone-suppressed images (BSIs) compared with original chest radiographs. In a fully crossed observer study, eight observers assessed a series of 300 radiographs four times, including one assessment of the original radiograph with sequential BSI and two independent reading sessions with BSI. Observer performance was compared using multireader multicase receiver operating characteristics. No significant difference between the effect of BSI in the sequential and the independent reading sessions could be found ([Formula: see text]; [Formula: see text]). Compared with the original radiographs, increased performance with BSI was significant in the sequential and one of the independent reading sessions ([Formula: see text]; [Formula: see text]), and nonsignificant in the other independent reading session ([Formula: see text]). A strong increase of uncorrelated variance components was found in the independent reading sessions, masking the ability to demonstrate differences in observer performance across modalities. Therefore, the sequential reading design is the preferred design because it is less burdensome and has more statistical power.

Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images

PURPOSE

To evaluate the added value of computer-aided detection (CAD) for lung nodules on chest radiographs when radiologists have bone-suppressed images (BSIs) available.

MATERIALS AND METHODS

Written informed consent was waived by the institutional review board. Selection of study images and study setup was reviewed and approved by the institutional review boards. Three hundred posteroanterior (PA) and lateral chest radiographs (189 radiographs with negative findings and 111 radiographs with a solitary nodule) in 300 subjects were selected from image archives at four institutions. PA images were processed by using a commercially available CAD, and PA BSIs were generated. Five radiologists and three residents evaluated the radiographs with BSIs available, first, without CAD and, second, after inspection of the CAD marks. Readers marked locations suspicious for a nodule and provided a confidence score for that location to be a nodule. Location-based receiver operating characteristic analysis was performed by using jackknife alternative free-response receiver operating characteristic analysis. Area under the curve (AUC) functioned as figure of merit, and P values were computed with the Dorfman-Berbaum-Metz method.

RESULTS

Average nodule size was 16.2 mm. Stand-alone CAD reached a sensitivity of 74% at 1.0 false-positive mark per image. Without CAD, average AUC for observers was 0.812. With CAD, performance significantly improved to an AUC of 0.841 (P = .0001). CAD detected 127 of 239 nodules that were missed after evaluation of the radiographs together with BSIs pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates.

CONCLUSION

CAD improved radiologists' performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral radiographs and BSIs. Still, most of the true-positive CAD candidates are dismissed by observers.

New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs

OBJECTIVE

To investigate two new methods of using computer-aided detection (CAD) system information for the detection of lung nodules on chest radiographs. We evaluated an interactive CAD application and an independent combination of radiologists and CAD scores.

METHODS

300 posteroanterior and lateral digital chest radiographs were selected, including 111 with a solitary pulmonary nodule (average diameter, 16 mm). Both nodule and control cases were verified by CT. Six radiologists and six residents reviewed the chest radiographs without CAD and with CAD (ClearRead +Detect™ 5.2; Riverain Technologies, Miamisburg, OH) in two reading sessions. The CAD system was used in an interactive manner; CAD marks, accompanied by a score of suspicion, remained hidden unless the location was queried by the radiologist. Jackknife alternative free response receiver operating characteristics multireader multicase analysis was used to measure detection performance. Area under the curve (AUC) and partial AUC (pAUC) between a specificity of 80% and 100% served as the measure for detection performance. We also evaluated the results of a weighted combination of CAD scores and reader scores, at the location of reader findings.

RESULTS

AUC for the observers without CAD was 0.824. No significant improvement was seen with interactive use of CAD (AUC = 0.834; p = 0.15). Independent combination significantly improved detection performance (AUC = 0.834; p = 0.006). pAUCs without and with interactive CAD were similar (0.128), but improved with independent combination (0.137).

CONCLUSION

Interactive CAD did not improve reader performance for the detection of lung nodules on chest radiographs. Independent combination of reader and CAD scores improved the detection performance of lung nodules.

ADVANCES IN KNOWLEDGE

(1) Interactive use of currently available CAD software did not improve the radiologists' detection performance of lung nodules on chest radiographs. (2) Independently combining the interpretations of the radiologist and the CAD system improved detection of lung nodules on chest radiographs.

Bone suppressed images improve radiologists' detection performance for pulmonary nodules in chest radiographs

OBJECTIVES

To assess the effect of bone suppression imaging on observer performance in detecting lung nodules in chest radiographs.

MATERIALS AND METHODS

Posteroanterior (PA) and lateral digital chest radiographs of 111 (average age 65) patients with a CT proven solitary nodule (median diameter 15 mm), and 189 (average age 63) controls were read by 5 radiologists and 3 residents. Conspicuity of nodules on the radiographs was classified in obvious (n = 32), moderate (n = 32), subtle (n = 29) and very subtle (n = 18). Observers read the PA and lateral chest radiographs without and with an additional PA bone suppressed image (BSI) (ClearRead Bone Suppression 2.4, Riverain Technologies, Ohio) within one reading session. Multi reader multi case (MRMC) receiver operating characteristics (ROC) were used for statistical analysis.

RESULTS

ROC analysis showed improved detection with use of BSI compared to chest radiographs alone (AUC = 0.883 versus 0.855; p = 0.004). Performance also increased at high specificities exceeding 80% (pAUC = 0.136 versus 0.124; p = 0.0007). Operating at a specificity of 90%, sensitivity increased with BSI from 66% to 71% (p = 0.0004). Increase of detection performance was highest for nodules with moderate and subtle conspicuity (p = 0.02; p = 0.03).

CONCLUSION

Bone suppressed images improve radiologists' detection performance for pulmonary nodules, especially for those of moderate and subtle conspicuity.

Comparison of dual-energy subtraction and electronic bone suppression combined with computer-aided detection on chest radiographs: effect on human observers' performance in nodule detection

OBJECTIVE

The objective of our study was to compare the effect of dual-energy subtraction and bone suppression software alone and in combination with computer-aided detection (CAD) on the performance of human observers in lung nodule detection.

MATERIALS AND METHODS

One hundred one patients with from one to five lung nodules measuring 5-29 mm and 42 subjects with no nodules were retrospectively selected and randomized. Three independent radiologists marked suspicious-appearing lesions on the original chest radiographs, dual-energy subtraction images, and bone-suppressed images before and after postprocessing with CAD. Marks of the observers and CAD marks were compared with CT as the reference standard. Data were analyzed using nonparametric tests and the jackknife alternative free-response receiver operating characteristic (JAFROC) method.

RESULTS

Using dual-energy subtraction alone (p = 0.0198) or CAD alone (p = 0.0095) improved the detection rate compared with using the original conventional chest radiograph. The combination of bone suppression and CAD provided the highest sensitivity (51.6%) and the original nonenhanced conventional chest radiograph alone provided the lowest (46.9%; p = 0.0049). Dual-energy subtraction and bone suppression provided the same false-positive (p = 0.2702) and true-positive (p = 0.8451) rates. Up to 22.9% of lesions were found only by the CAD program and were missed by the readers. JAFROC showed no difference in the performance between modalities (p = 0.2742-0.5442).

CONCLUSION

Dual-energy subtraction and the electronic bone suppression program used in this study provided similar detection rates for pulmonary nodules. Additionally, CAD alone or combined with bone suppression can significantly improve the sensitivity of human observers for pulmonary nodule detection.

[Dose reduction and adequate image quality in digital radiography: a contradiction?]

CLINICAL/METHODICAL ISSUE

Dose reduction and adequate image quality in digital radiography - a contradiction?

STANDARD RADIOLOGICAL METHODS

Digital radiography has already replaced traditional screen-film systems.

METHODICAL INNOVATIONS

Substantial improvements in both dose efficiency and spatial resolution demonstrate the rapid developments in digital radiography.

PERFORMANCE

Needle-detector systems have shown up to a 50% dose reduction compared to traditional screen-film systems. There is also a dose reduction capability of up to 50% comparing direct radiography (DR) systems to computed radiography (CR) systems for chest X-rays. However, despite the most recent achievements of CR technology, the dose efficiency of DR systems (caesium iodide flat-panel detector) is unparalleled.

ACHIEVEMENTS

The progress in detector technology has contributed to dose reduction and improved image quality, while saving time and providing a higher examination rate.

PRACTICAL RECOMMENDATIONS

The use of dose indicators and longitudinal dose control are important to avoid substantial accidental dose increase. The dose applied to patients should fall markedly below the defined diagnostic reference levels within the European Union. Regular quality control, as well as continuous education and training of medical and technical personnel, contribute to ensure that the ALARA (as low as reasonably achievable) principle is consistently followed.

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.