Paediatric CT: the effects of increasing image noise on pulmonary nodule detection

Pediatric Pulmonary Nodules: Imaging Guidelines and Recommendations

Incidental pulmonary nodules are not infrequently identified on computed tomography imaging in the pediatric population and can be a challenge in suggesting appropriate follow-up recommendations. An evidence-based and practical imaging approach for diagnosis and appropriate directed management is essential for optimal patient care. This article provides an up-to-date review of the pediatric pulmonary nodule literature and suggests a practical algorithm to manage pulmonary nodules in the pediatric population.

Comparison of 0.3-mSv CT to Standard-Dose CT for Detection of Lung Nodules in Children and Young Adults With Cancer

Background: CT is the imaging modality of choice to identify lung metastasis. Objective: The purpose of this study was to evaluate the performance of reduced-dose CT for detection of lung nodules in children and young adults with cancer. Methods: This prospective study enrolled patients 4-21 years old with known or suspected malignancy who were undergoing clinically indicated chest CT. Study participants underwent an additional investigational reduced-dose chest CT in the same imaging encounter. Separated deidentified CT examinations were reviewed in blinded fashion by three independent radiologists. One reviewer performed a subsequent secondary review to match nodules between the standard- and reduced-dose examinations. Diagnostic performance was computed for the reduced-dose examinations, using clinical examinations as reference standard. Intraobserver and interobserver agreement were calculated using Cohen's Kappa. Results: A total of 78 patients (44 male, 34 female; mean age 15.2±3.8 years) were enrolled. Mean estimated effective dose was 1.8±1.1 mSv for clinical CT and 0.3±0.1 mSv for reduced-dose CT, an 83% reduction. Forty-five (58%) patients had 162 total lung nodules (mean size 3.4±3.3 mm) detected on the clinical CT examinations. A total of 92% of nodules were visible on reduced-dose CT. Sensitivity and specificity of reduced-dose CT for nodules ranged from 63%-77% and 80%-90% respectively across the three reviewers. Intraobserver agreement between clinical and reduced-dose CT was moderate to substantial for presence of nodules (κ=0.45-0.67), and good to excellent for number of nodules (κ=0.68-0.84) and nodule size (κ=0.69-0.86). Interobserver agreement for the presence of nodules was moderate for both reduced-dose (κ=0.53) and clinical (κ=0.54) CT. A median of 1 nodule was present on clinical CT in patients with a falsely negative reduced-dose CT examination. Conclusion: Reduced-dose CT depicts greater than 90% of lung nodules in children and young adults with cancer. Reviewers identified the presence of nodules with moderate sensitivity and high specificity. Clinical Impact: CT performed at 0.3 mSv mean effective dose has acceptable diagnostic performance for lung nodule detection in children and young adults and has the potential to reduce patient dose or expand CT utilization (e.g., to replace radiography in screening or monitoring protocols).

Generation of realistic virtual nodules based on three-dimensional spatial resolution in lung computed tomography: A pilot phantom study

PURPOSE

The aim of this feasibility study using phantoms was to propose a novel method for obtaining computer-generated realistic virtual nodules in lung computed tomography (CT).

METHODS

In the proposed methodology, pulmonary nodule images obtained with a CT scanner are deconvolved with the point spread function (PSF) in the scan plane and slice sensitivity profile (SSP) measured for the scanner; the resultant images are referred to as nodule-like object functions. Next, by convolving the nodule-like object function with the PSF and SSP of another (target) scanner, the virtual nodule can be generated so that it has the characteristics of the spatial resolution of the target scanner. To validate the methodology, the authors applied physical nodules of 5-, 7- and 10-mm-diameter (uniform spheres) included in a commercial CT test phantom. The nodule-like object functions were calculated from the sphere images obtained with two scanners (Scanner A and Scanner B); these functions were referred to as nodule-like object functions A and B, respectively. From these, virtual nodules were generated based on the spatial resolution of another scanner (Scanner C). By investigating the agreement of the virtual nodules generated from the nodule-like object functions A and B, the equivalence of the nodule-like object functions obtained from different scanners could be assessed. In addition, these virtual nodules were compared with the real (true) sphere images obtained with Scanner C. As a practical validation, five types of laboratory-made physical nodules with various complicated shapes and heterogeneous densities, similar to real lesions, were used. The nodule-like object functions were calculated from the images of these laboratory-made nodules obtained with Scanner A. From them, virtual nodules were generated based on the spatial resolution of Scanner C and compared with the real images of laboratory-made nodules obtained with Scanner C.

RESULTS

Good agreement of the virtual nodules generated from the nodule-like object functions A and B of the phantom spheres was found, suggesting the validity of the nodule-like object functions. The virtual nodules generated from the nodule-like object function A of the phantom spheres were similar to the real images obtained with Scanner C; the root mean square errors (RMSEs) between them were 10.8, 11.1, and 12.5 Hounsfield units (HU) for 5-, 7-, and 10-mm-diameter spheres, respectively. The equivalent results (RMSEs) using the nodule-like object function B were 15.9, 16.8, and 16.5 HU, respectively. These RMSEs were small considering the high contrast between the sphere density and background density (approximately 674 HU). The virtual nodules generated from the nodule-like object functions of the five laboratory-made nodules were similar to the real images obtained with Scanner C; the RMSEs between them ranged from 6.2 to 8.6 HU in five cases.

CONCLUSIONS

The nodule-like object functions calculated from real nodule images would be effective to generate realistic virtual nodules. The proposed method would be feasible for generating virtual nodules that have the characteristics of the spatial resolution of the CT system used in each institution, allowing for site-specific nodule generation.

Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening

Currently, lung cancer screening by low-dose chest CT is implemented in the United States for high-risk persons. A disadvantage of lung cancer screening is the large number of small-to-intermediate sized lung nodules, detected in around 50% of all participants, the large majority being benign. Accurate estimation of nodule size and growth is essential in the classification of lung nodules. Currently, manual diameter measurements are the standard for lung cancer screening programs and routine clinical care. However, European screening studies using semi-automated volume measurements have shown higher accuracy and reproducibility compared to diameter measurements. In addition to this, with the optimization of CT scan techniques and reconstruction parameters, as well as advances in segmentation software, the accuracy of nodule volume measurement can be improved even further. The positive results of previous studies on volume and diameter measurements of lung nodules suggest that manual measurements of nodule diameter may be replaced by semi-automated volume measurements in the (near) future.

Impact of incremental increase in CT image noise on detection of low-contrast hypodense liver lesions

RATIONALE AND OBJECTIVES

To determine the impact of incremental increases in computed tomography (CT) image noise on detection of low-contrast hypodense liver lesions.

MATERIAL AND METHODS

We studied 50 CT examinations acquired at image noise index (NI) of 15 and hypodense liver lesions and 50 examinations with no lesions. Validation of a noise addition tool to be used in the evaluation of the CT examinations was performed with a liver phantom. Using this tool, three 100-image sets were assembled: an NI of 17.4 (simulating 75% of the original patient radiation dose), 21.2 (simulating 50% dose), and 29.7 (simulating 25%). Three readers scored certainty of lesion presence using a five-point Likert scale.

RESULTS

For original images (NI 15) plus images with NI of 17.4 and 21.2, sensitivity was >90% threshold (range, 95%-98%). For images with NI of 29.7, sensitivity was just below the threshold (89%). Reader Az values for receiver operating characteristic curves were good for original, NI 17.4, and NI 21.2 images (0.976, 0.973, and 0.96, respectively). For NI of 29.7, the Az decreased to 0.913. Detection sensitivity was <90% for both lesion size < 10 mm (85%) and lesion-to-liver contrast <60 Hounsfield units (85%) only at NI 29.7.

CONCLUSIONS

For low-contrast lesion detection in liver CT, image noise can be increased up to NI 21.2 (a 50% patient radiation dose reduction) without substantial reduction in sensitivity.

Detection of pulmonary nodules at paediatric CT: maximum intensity projections and axial source images are complementary

BACKGROUND

Maximum intensity projection (MIP) images might be useful in helping to differentiate small pulmonary nodules from adjacent vessels on thoracic multidetector CT (MDCT).

OBJECTIVE

The aim was to evaluate the benefits of axial MIP images over axial source images for the paediatric chest in an interobserver variability study.

MATERIALS AND METHODS

We included 46 children with extra-pulmonary solid organ malignancy who had undergone thoracic MDCT. Three radiologists independently read 2-mm axial and 10-mm MIP image datasets, recording the number of nodules, size and location, overall time taken and confidence.

RESULTS

There were 83 nodules (249 total reads among three readers) in 46 children (mean age 10.4 ± 4.98 years, range 0.3-15.9 years; 24 boys). Consensus read was used as the reference standard. Overall, three readers recorded significantly more nodules on MIP images (228 vs. 174; P < 0.05), improving sensitivity from 67% to 77.5% (P < 0.05) but with lower positive predictive value (96% vs. 85%, P < 0.005). MIP images took significantly less time to read (71.6 ± 43.7 s vs. 92.9 ± 48.7 s; P < 0.005) but did not improve confidence levels.

CONCLUSION

Using 10-mm axial MIP images for nodule detection in the paediatric chest enhances diagnostic performance, improving sensitivity and reducing reading time when compared with conventional axial thin-slice images. Axial MIP and axial source images are complementary in thoracic nodule detection.

Development and evaluation of a software tool for the generation of virtual liver lesions in multidetector-row CT datasets

RATIONALE AND OBJECTIVES

Development and evaluation of a software tool for the insertion of simulated hypodense liver lesions in multidetector-row computed tomography (CT) datasets.

MATERIALS AND METHODS

Forty software-generated hypodense liver lesions were inserted at random locations in 20 CT datasets by using the "alpha blending" technique and compared with 40 real metastatic lesions. The location, diameter (5-20 mm) and density of the simulated lesions were individually adjusted to closely resemble real lesions in each patient. Three blinded readers evaluated all 80 lesions twice in a 2-week interval using a five-point Likert confidence scale under standardized conditions. Nonparametric tests were used to statistically evaluate possible differences in scoring between real and simulated lesions. The correctness of the observer rating for real and simulated lesions was compared to chance distribution using the chi-squared statistics. The inter- and intraobserver variability was determined using Kendall's coefficient of concordance.

RESULTS

The observer study did not reveal significant differences between the scoring for real versus simulated lesions for any of the readers (P > .05). The distribution of correct and false scoring of the lesions was not significantly different from chance distribution (P > .05). Inter- and intraobserver agreement was poor (Kendall W coefficient = 0.12/0.13).

CONCLUSION

The proposed algorithm is suitable for creating realistic virtual liver lesions in CT datasets.

Lung nodule detection in pediatric chest CT: quantitative relationship between image quality and radiologist performance

PURPOSE

To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT.

METHODS

The study included clinical chest CT images of 30 pediatric patients (0-16 years) scanned at tube currents of 55-180 mA. Calibrated noise addition software was used to simulate cases at three nominal mA settings: 70, 35, and 17.5 mA, resulting in quantum noise of 7-32 Hounsfield Unit (HU). Using a validated nodule simulation technique, lung nodules with diameters of 3-5 mm and peak contrasts of 200-500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display).

RESULTS

AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU.

CONCLUSIONS

We quantified the relationship between image quality and the performance of radiologists in detecting lung nodules in pediatric CT. The relationship can guide CT protocol design to achieve the desired diagnostic performance at the lowest radiation dose.]

Pediatric MDCT: towards assessing the diagnostic influence of dose reduction on the detection of small lung nodules

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the effect of reduced tube current (dose) on lung nodule detection in pediatric multidetector array computed tomography (MDCT).

MATERIALS AND METHODS

The study included normal clinical chest MDCT images of 13 patients (aged 1-7 years) scanned at tube currents of 70 to 180 mA. Calibrated noise addition software was used to simulate cases as they would have been acquired at 70 mA (the lowest original tube current), 35 mA (50% reduction), and 17.5 mA (75% reduction). Using a validated nodule simulation technique, small lung nodules of 3 to 5 mm in diameter were inserted into the cases, which were then randomized and rated independently by three experienced pediatric radiologists for nodule presence on a continuous scale ranging from zero (definitely absent) to 100 (definitely present). The observer data were analyzed to assess the influence of dose on detection accuracy using the Dorfman-Berbaum-Mets method for multiobserver, multitreatment receiver-operating characteristic (ROC) analysis and the Williams trend test.

RESULTS

The areas under the ROC curves were 0.95, 0.91, and 0.92 at 70, 35, and 17.5 mA, respectively, with standard errors of 0.02 and interobserver variability of 0.02. The Dorfman-Berbaum-Mets method and the Williams trend test yielded P values for the effect of dose of .09 and .05, respectively.

CONCLUSION

Tube current (dose) has a weak effect on the detection accuracy of small lung nodules in pediatric MDCT. The effect on detection accuracy of a 75% dose reduction was comparable to interobserver variability, suggesting a potential for dose reduction.