Detection Of Subtle Lung Nodules Research Articles

Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise

Stereoscopic chest biplane correlation imaging (stereo∕BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo∕BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes. Measurements for the eMTF were taken for two phantom sizes with an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI. Stereo∕BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by ∼20%. For both phantoms, at isodose, eDQE(0) for stereo∕BCI was ∼100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by ∼10 times for stereo∕BCI. Increasing the dose did not improve eDQE. The detectability index for stereo∕BCI was ∼100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose. The findings indicate that stereo∕BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.

Standard versus inverted digital luminescence radiography in detecting pulmonary nodules: A ROC analysis

ObjectiveObserver performance tests were conducted to compare the effect of presenting digital luminescence radiography (DLR) monitor images in positive mode (“bones black”) or negative mode (“bones white”) in the detectability of subtle lung nodules. MethodsFive radiologists independently reviewed digital radiographs of 55 patients with either (a) a single, small (6–12mm), non- calcified peripheral nodule confirmed by chest CT (n=47) or (b) normal finding (n=8) confirmed by chest CT, respectively. Mean size of pulmonary nodules was 8.3mm (range 6–12mm, median 7mm). Radiographs were displayed either in standard mode (bones white) or inverse intensity (bones black). A total of 550 observations resulted. For the evaluation ROC analysis was performed using a specialized computer algorithm. ResultsThe standard presentation mode showed a sensitivity of 61.7% and a specificity of 72.5%, the inverse presentation mode a sensitivity of 68.1% and a specificity of 75.0%. ROC- analysis showed that the difference between the standard mode (Az- value 0.694) and the inverse mode (Az- value 0.810) was significant in favour of the inverse presentation mode (p=0.001). This finding was especially observed in more experienced radiologist (Az- values 0.716 vs. 0.909, P<0.001). ConclusionOur study demonstrates a significant advantage of the inverse mode in the detection of small pulmonary nodules compared with the commonly used negative mode when interpreted by more experienced radiologists. Advance in knowledgeInverse intensity images (“bones black”) may have some advantages in the detection of small pulmonary nodules in experienced readers when presented on a stand-alone display.

TH-E-217BCD-02: Defining Performance-Based, Size-Specific, Optimized Protocols for Pediatric CT

Purpose: In pediatric chest CT, the detection of subtle lung nodules represents a challenging clinical task, especially with dose reduction. Our purpose was to correlate the diagnostic performance of this task with radiation estimates to develop a framework for the definition of size-specific optimized protocols. Methods: The relationships between diagnostic performance and radiation estimates were derived for nine pediatric age groups (mean age: 0.2, 0.7, 1.4, 2.5, 4.1, 6.2, 8.2, 11.3, 14.7 years) using a 4-mm nodule as the representative task. First, diagnostic performance (area under ROC) was related to contrast-to-noise ratio (CNR) based on an earlier observer study. CNR was then related to patient diameter and imaging parameters using phantom data. Diameter, age, and imaging parameters were further used to obtain radiation estimates (CTDIvol, SSDE, effective dose, risk index) using Monte Carlo data. Clinical operating points were selected for the nine age groups as equal-slope points on the plots of AUC versus radiation estimates. Results: AUC initially increased rapidly with radiation estimate, and then approached a plateau. To achieve the same AUC, more dose (and greater risk) was needed to scan a larger child. When a dose estimate (CTDIvol, SSDE, effective dose) was used for optimization, the equal-slope points generally corresponded to equal-risk points, resulting in an AUC decrease of ∼0.14 from the youngest to the oldest age group. When risk index was used for optimization, the equal-slope points resulted in CTDIvol ratio of 1:6, SSDE ratio of 1:4, effective dose ratio of 1:4, and risk index ratio of 1:2 between the youngest and the oldest, corresponding to a moderate AUC difference of 0.08. Conclusions: We developed an integrated framework for performance-based, size-specific, protocol optimization in pediatric CT. By accounting for the risk difference between age groups, the concept of risk index has innovative applications in pediatric protocol optimization.

Biplane Correlation Imaging: A Feasibility Study Based on Phantom and Human Data

The objective of this study was to implement and evaluate the performance of a biplane correlation imaging (BCI) technique aimed to reduce the effect of anatomic noise and improve the detection of lung nodules in chest radiographs. Seventy-one low-dose posterior-anterior images were acquired from an anthropomorphic chest phantom with 0.28° angular separations over a range of ±10° along the vertical axis within an 11 s interval. Similar data were acquired from 19 human subjects with institutional review board approval and informed consent. The data were incorporated into a computer-aided detection (CAD) algorithm in which suspect lesions were identified by examining the geometrical correlation of the detected signals that remained relatively constant against variable anatomic backgrounds. The data were analyzed to determine the effect of angular separation, and the overall sensitivity and false-positives for lung nodule detection. The best performance was achieved for angular separations of the projection pairs greater than 5°. Within that range, the technique provided an order of magnitude decrease in the number of false-positive reports when compared with CAD analysis of single-view images. Overall, the technique yielded ~1.1 false-positive per patient with an average sensitivity of 75%. The results indicated that the incorporation of angular information can offer a reduction in the number of false-positives without a notable reduction in sensitivity. The findings suggest that the BCI technique has the potential for clinical implementation as a cost-effective technique to improve the detection of subtle lung nodules with lowered rate of false-positives.

Improved Detection of Subtle Lung Nodules by Use of Chest Radiographs With Bone Suppression Imaging: Receiver Operating Characteristic Analysis With and Without Localization

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

Influence of rib structure on detection of subtle lung nodules

Purpose To access the influence of anatomic noise on the detectability of subtle lung nodules depicted on chest radiographs. Material and methods From normal chest radiography images, 132 square regions were extracted, of which the centers were on the upper margin of a rib, the inside of a rib, the lower margin of a rib, and the central region between two adjoining ribs. Simulated nodules were digitally superimposed at the centers of these extracted square images. Twelve radiologists viewed 50 soft-copy images consisting of these 792 processed images, including the noise-added images. The observer's confidence level for the square images containing single nodules was used as an index of observer performance. Results Results indicated statistically reliable effects of the relationship between rib structures and nodule positions on the detection performance ( P < 0.001). The nodule detectability on the images with a center located between two adjoining ribs was significantly the best, whereas it was significantly the worst on the noise-added images with a center located between two adjoining ribs. Conclusion The rib structures overlying a subtle lung nodule on chest X-ray images have a detrimental effect on nodule detection performance as anatomic noise, regardless of the nodule location on ribs.

Nodule detection in digital chest radiography: effect of system noise

Apart from the image content that is the reproduction of anatomy and possible lesions, an X-ray image also contains system noise due to the limited number of photons and other internal noise sources in the system (image plate artefacts, electronic noise, etc.). The aim of this study was to determine the extent to which the system noise influences the detection of subtle lung nodules in five different regions of the chest. This was done by conducting a receiver operating characteristic (ROC) study with five observers on two different sets of images; clinical chest X-ray images and images of a LucAl phantom at similar dose levels found in the different regions of the chest. In both image types, mathematically simulated nodules (with a full-width-at-fifth-maximum of 10 mm) were added to the images at varying contrast levels. As a measure of the influence of system noise on the detection of subtle lung nodules, the ratio between the contrast needed to obtain an area under the ROC curve of 0.80 in the system noise images to that needed in the clinical images was used. The contrast ratio between system noise images and clinical images ranged from approximately 0.02 (in the hilar region) to 0.18 (in the lower mediastinal region). The maximum difference in contrast needed for the corresponding system noise images, collected at the lowest and the highest dose represented in the anatomical image, was a factor of 2. These results indicate that probably no region in a chest X-ray image is limited by the number of quanta to the detector for the detection of 10 mm lung nodules when a radiation dose corresponding to a system with speed class 200 (leading to a detector dose of approximately 9 muGy behind the parenchyma) is used.

Nodule detection in digital chest radiography: part of image background acting as pure noise

There are several factors that influence the radiologist's ability to detect a specific structure/lesion in a radiograph. Three factors that are commonly known to be of major importance are the signal itself, the system noise and the projected anatomy. The aim of this study was to determine to what extent the image background acts as pure noise for the detection of subtle lung nodules in five different regions of the chest. A receiver operating characteristic (ROC) study with five observers was conducted on two different sets of images, clinical chest X-ray images and images with a similar power spectrum as the clinical images but with a random phase spectrum, resulting in an image background containing pure noise. Simulated designer nodules with a full-width-at-fifth-maximum of 10 mm but with varying contrasts were added to the images. As a measure of the part of the image background that acts as pure noise, the ratio between the contrast needed to obtain an area under the ROC curve of 0.80 in the clinical images to that in the random-phase images was used. The ratio ranged from 0.40 (in the lateral pulmonary regions) to 0.83 (in the hilar regions) indicating that there was a large difference between different regions regarding to what extent the image background acted as pure noise; and that in the hilar regions the image background almost completely acted as pure noise for the detection of 10 mm nodules.

Subtle lung nodules: influence of local anatomic variations on detection.

To assess the influence of local anatomic noise on the detection of subtle lung nodules depicted on chest radiographs. Six 8 x 8-cm lung regions were extracted from digital chest radiographs obtained in healthy subjects. Simulated nodules emulating the radiographic characteristics of subtle tissue-equivalent lesions 3.2-6.4 mm in diameter (equivalent to 0.1-0.4 mm in contrast-diameter product [CD]) were added to the images. On multiple renditions of each image, nodules were inserted at slightly different locations within 6 mm of the center; this process allowed different local background patterns to overlie the nodules. An observer detection study involving 15 experienced radiologists was performed. The authors performed analysis of variance and pairwise t test analyses to determine variations in nodule detectability related to nodule location and size on each image. Results indicated a strong correlation between nodule size and observer detection score and significant variation in nodule detectability as a function of location. Changes in nodule position caused observer score variations that were equivalent to the variation caused by an up to 185% change in nodule CD (78% average over all six images), an up to 68% change in diameter (32% average), and an up to 28% change in area under the receiver operating characteristic curve (Az) (14% average). Local anatomic variations surrounding and overlying a subtle lung nodule on a chest radiograph that are created by the projection of anatomic features in the thorax, such as ribs and pulmonary vessels, can greatly influence the detection of nodules, altering the Az by as much as 28%.

Detection of lung nodules on digital chest radiographs: potential usefulness of a new contralateral subtraction technique.

To evaluate the potential usefulness of a contralateral subtraction technique developed for radiologists' performance in the detection of subtle lung nodules on chest radiographs. Fifty chest radiographs (25 normal and 25 abnormal with a subtle lung nodule) that were digitized with a 0.175-mm pixel size and 4,096 gray levels were used. Twelve radiologists (10 attending and two residents) participated in observer tests and read both original and contralateral subtraction images with a sequential testing method. Radiologists' performance was evaluated by means of receiver operating characteristic analysis with use of a continuous rating scale. The beneficial and detrimental effects of the contralateral subtraction technique on the radiologists' performance were also evaluated. The area under the receiver operating characteristic curve values obtained without and with contralateral subtraction images were 0.926 and 0.962, respectively. Results indicated that the contralateral subtraction images significantly (P <.05) improved diagnostic accuracy, particularly for radiologists with limited experience. The contralateral subtraction technique can assist radiologists in the correct identification of subtle lung nodules on chest radiographs.

Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs.

To assess the relative influence of quantum mottle and structured lung patterns (anatomic noise) on the detection of subtle lung nodules on chest radiographs. Sixty 8 x 8-cm lung pattern images were extracted from digital chest radiographs in healthy individuals. Sixty quantum mottle images of the same size and quantum noise level were extracted from uniformly exposed digital radiographs. Simulated nodules with various peak contrast-diameter products (CD) that emulated subtle tissue-equivalent lung nodules were numerically superimposed at the center on three-fourths of the images. Printouts were independently viewed and scored by five experienced radiologists. The area under the receiver operating characteristic curve (Az) was estimated as a measure of the detectability of the nodules. At a fixed observer performance level (e.g., Az = 0.8), much smaller and lower-contrast nodules were detected on quantum mottle images (1-mm diameter, CD = 0.01 mm), compared with those on anatomic images (4.5-mm diameter, CD = 0.20 mm). The findings generally agreed with the signal-to-noise ratio calculations based on statistical observer models. The detection of subtle lung nodules on chest radiographs is limited by anatomic noise.

The performance of digital x‐ray imaging systems in detection of subtle lung nodules

Subtle lung nodules (SLNs) are lesions that are commonly missed in radiologic examinations of the chest. This thesis explores three factors that influence the detection of these lesions: contrast‐diameter characteristics of SLNs, physical performance of the imaging system, and structured noise in the chest radiographs. The contrast‐diameter characteristics of SLNs were determined from radiographs containing confirmed lesions. The nodules were found to have similar subject contrast profiles with diverse variability in peak contrast (0.02–0.13) and diameter (3–16 mm). The physical performance of a digital chest radiographic system was assessed by measuring its Modulation Transfer Function (MTF), Noise Power Spectrum (NPS), and Detective Quantum Efficiency (DQE) responses. The results showed that the system only minimally blurs the appearance of SLNs, and that for a typical chest exposure, the quantum mottle is low enough not to obscure the visibility of even the smallest SLNs. The relative influence of the structured and quantum noise was explored in a Receiver Operating Characteristic (ROC) study in which simulated tissue‐equivalent nodules were superimposed on images of either structured or quantum noise backgrounds. The detection threshold =0.8) was achieved for nodules 4.5 mm in diameter against structured backgrounds, and for nodules 1.0 mm in diameter against quantum noise backgrounds. The results indicate that anatomical structured noise is the main factor limiting the detection of SLNs in chest radiographs.