Rongping Zeng

A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom

A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation.

Objective assessment of image quality and dose reduction in CT iterative reconstruction

PURPOSE Iterative reconstruction (IR) algorithms have the potential to reduce radiation dose in CT diagnostic imaging. As these algorithms become available on the market, a standardizable method of quantifying the dose reduction that a particular IR method can achieve would be valuable. Such a method would assist manufacturers in making promotional claims about dose reduction, buyers in comparing different devices, physicists in independently validating the claims, and the United States Food and Drug Administration in regulating the labeling of CT devices. However, the nonlinear nature of commercially available IR algorithms poses challenges to objectively assessing image quality, a necessary step in establishing the amount of dose reduction that a given IR algorithm can achieve without compromising that image quality. This review paper seeks to consolidate information relevant to objectively assessing the quality of CT IR images, and thereby measuring the level of dose reduction that a given IR algorithm can achieve. METHODS The authors discuss task-based methods for assessing the quality of CT IR images and evaluating dose reduction. RESULTS The authors explain and review recent literature on signal detection and localization tasks in CT IR image quality assessment, the design of an appropriate phantom for these tasks, possible choices of observers (including human and model observers), and methods of evaluating observer performance. CONCLUSIONS Standardizing the measurement of dose reduction is a problem of broad interest to the CT community and to public health. A necessary step in the process is the objective assessment of CT image quality, for which various task-based methods may be suitable. This paper attempts to consolidate recent literature that is relevant to the development and implementation of task-based methods for the assessment of CT IR image quality.

Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study.

Due to the limited number of views and limited angular span in digital breast tomosynthesis (DBT), the acquisition geometry design is an important factor that affects the image quality. Therefore, intensive studies have been conducted regarding the optimization of the acquisition geometry. However, different reconstruction algorithms were used in most of the reported studies. Because each type of reconstruction algorithm can provide images with its own image resolution, noise properties and artifact appearance, it is unclear whether the optimal geometries concluded for the DBT system in one study can be generalized to the DBT systems with a reconstruction algorithm different to the one applied in that study. Hence, we investigated the effect of the reconstruction algorithm on the optimization of acquisition geometry parameters through carefully designed simulation studies. Our results show that using various reconstruction algorithms, including the filtered back-projection, the simultaneous algebraic reconstruction technique, the maximum-likelihood method and the total-variation regularized least-square method, gave similar performance trends for the acquisition parameters for detecting lesions. The consistency of system ranking indicates that the choice of the reconstruction algorithm may not be critical for DBT system geometry optimization.

Benefit of Overlapping Reconstruction for Improving the Quantitative Assessment of CT Lung Nodule Volume

RATIONALE AND OBJECTIVES The aim of this study was to quantify the effect of overlapping reconstruction on the precision and accuracy of lung nodule volume estimates in a phantom computed tomographic (CT) study. MATERIALS AND METHODS An anthropomorphic phantom was used with a vasculature insert on which synthetic lung nodules were attached. Repeated scans of the phantom were acquired using a 64-slice CT scanner. Overlapping and contiguous reconstructions were performed for a range of CT imaging parameters (exposure, slice thickness, pitch, reconstruction kernel) and a range of nodule characteristics (size, density). Nodule volume was estimated with a previously developed matched-filter algorithm. RESULTS Absolute percentage bias across all nodule sizes (n = 2880) was significantly lower when overlapping reconstruction was used, with an absolute percentage bias of 6.6% (95% confidence interval [CI], 6.4-6.9), compared to 13.2% (95% CI, 12.7-13.8) for contiguous reconstruction. Overlapping reconstruction also showed a precision benefit, with a lower standard percentage error of 7.1% (95% CI, 6.9-7.2) compared with 15.3% (95% CI, 14.9-15.7) for contiguous reconstructions across all nodules. Both effects were more pronounced for the smaller, subcentimeter nodules. CONCLUSIONS These results support the use of overlapping reconstruction to improve the quantitative assessment of nodule size with CT imaging.

Statistical analysis of lung nodule volume measurements with CT in a large‐scale phantom study

PURPOSE To determine inter-related factors that contribute substantially to measurement error of pulmonary nodule measurements with CT by assessing a large-scale dataset of phantom scans and to quantitatively validate the repeatability and reproducibility of a subset containing nodules and CT acquisitions consistent with the Quantitative Imaging Biomarker Alliance (QIBA) metrology recommendations. METHODS The dataset has about 40 000 volume measurements of 48 nodules (5-20 mm, four shapes, three radiodensities) estimated by a matched-filter estimator from CT images involving 72 imaging protocols. Technical assessment was performed under a framework suggested by QIBA, which aimed to minimize the inconsistency of terminologies and techniques used in the literature. Accuracy and precision of lung nodule volume measurements were examined by analyzing the linearity, bias, variance, root mean square error (RMSE), repeatability, reproducibility, and significant and substantial factors that contribute to the measurement error. Statistical methodologies including linear regression, analysis of variance, and restricted maximum likelihood were applied to estimate the aforementioned metrics. The analysis was performed on both the whole dataset and a subset meeting the criteria proposed in the QIBA Profile document. RESULTS Strong linearity was observed for all data. Size, slice thickness × collimation, and randomness in attachment to vessels or chest wall were the main sources of measurement error. Grouping the data by nodule size and slice thickness × collimation, the standard deviation (3.9%-28%), and RMSE (4.4%-68%) tended to increase with smaller nodule size and larger slice thickness. For 5, 8, 10, and 20 mm nodules with reconstruction slice thickness ≤0.8, 3, 3, and 5 mm, respectively, the measurements were almost unbiased (-3.0% to 3.0%). Repeatability coefficients (RCs) were from 6.2% to 40%. Pitch of 0.9, detail kernel, and smaller slice thicknesses yielded better (smaller) RCs than those from pitch of 1.2, medium kernel, and larger slice thicknesses. Exposure showed no impact on RC. The overall reproducibility coefficient (RDC) was 45%, and reduced to about 20%-30% when the slice thickness and collimation were fixed. For nodules and CT imaging complying with the QIBA Profile (QIBA Profile subset), the measurements were highly repeatable and reproducible in spite of variations in nodule characteristics and imaging protocols. The overall measurement error was small and mostly due to the randomness in attachment. The bias, standard deviation, and RMSE grouped by nodule size and slice thickness × collimation in the QIBA Profile subset were within ±3%, 4%, and 5%, respectively. RCs are within 11% and the overall RDC is equal to 11%. CONCLUSIONS The authors have performed a comprehensive technical assessment of lung nodule volumetry with a matched-filter estimator from CT scans of synthetic nodules and identified the main sources of measurement error among various nodule characteristics and imaging parameters. The results confirm that the QIBA Profile set is highly repeatable and reproducible. These phantom study results can serve as a bound on the clinical performance achievable with volumetric CT measurements of pulmonary nodules.

Is the outcome of optimizing the system acquisition parameters sensitive to the reconstruction algorithm in digital breast tomosynthesis

There exist various reconstruction algorithms for digital breast tomosynthesis (DBT). However, when optimizing the data acquisition parameters for better image quality in terms of a specific task, researchers usually pick one of their favorite or available reconstruction algorithms. It is unclear whether using a different reconstruction algorithm would yield a different conclusion in the system optimization, thereby yielding a different optimized acquisition configuration. We look into this problem through simulation and present our preliminary results in this report.

Relating fisher information to detectability of changes in nodule characteristics with CT

Fisher information provides a bound on the variance of any unbiased estimate for estimation tasks involving nonrandom parameters. In addition, a Fisher information approximation for ideal-observer detectability has been derived. We adopt and generalize such an approximation to establish a method to assess a systems ability to detect small changes in lesion characteristics. By representing the lesion by a size parameter, the ability to detect small changes can be approximated by a function involving the size difference and the Fisher information. A concept, termed the approximated least required difference (ALRD), is introduced and evaluated as an upper bound for assessing a systems power in size discrimination. We present a simulation study for lung nodules as an example to illustrate such a framework, where the image model incorporates a simulated CT imaging system, a thorax background and parameterized nodules. The noise is assumed to be multivariate Gaussian and the noise power spectrum (NPS) method is used to estimate the covariance matrix for the Fisher information calculation. In addition to bounding performance, our results also provide insights into factors, including nodule characteristics and acquisition parameters, that influence ALRD performance. This framework can be extended to connect other discrimination and estimation tasks, facilitating objective assessment and optimization of quantitative imaging systems.

Volume estimation of multidensity nodules with thoracic computed tomography

Abstract. This work focuses on volume estimation of “multidensity” lung nodules in a phantom computed tomography study. Eight objects were manufactured by enclosing spherical cores within larger spheres of double the diameter but with a different density. Different combinations of outer-shell/inner-core diameters and densities were created. The nodules were placed within an anthropomorphic phantom and scanned with various acquisition and reconstruction parameters. The volumes of the entire multidensity object as well as the inner core of the object were estimated using a model-based volume estimator. Results showed percent volume bias across all nodules and imaging protocols with slice thicknesses <5  mm ranging from −5.1% to 6.6% for the entire object (standard deviation ranged from 1.5% to 7.6%), and within −12.6% to 5.7% for the inner-core measurement (standard deviation ranged from 2.0% to 17.7%). Overall, the estimation error was larger for the inner-core measurements, which was expected due to the smaller size of the core. Reconstructed slice thickness was found to substantially affect volumetric error for both tasks; exposure and reconstruction kernel were not. These findings provide information for understanding uncertainty in volumetry of nodules that include multiple densities such as ground glass opacities with a solid component.

WE-D-18A-06: Estimating Local Noise Power Spectrum From a Few CT Scans.

PURPOSE The traditional way to estimate CT NPS is by averaging the power spectrum of many noisy scans. When only a few scans are available, regions of interests are often extracted from different locations to obtain sufficient samples to estimate NPS, thus ignoring the nonstationarity of CT noise. This approach to estimating NPS does not accurately reflect the location-variant characteristics of CT noise. The purpose of this work is to develop a method to estimate local NPS from only a few CT scans. METHODS As a result of the FBP reconstruction algorithm, the CT NPS presents the following radial property: the shape of the radial profile of the NPS is almost constant, determined by the reconstruction filter, and the magnitude varies with angle depending on the object attenuation map. Therefore, a shape function and an angular magnitude function are sufficient to describe the CT NPS. Based on this property, the dimensionality of the NPS is greatly reduced and we are able to derive a radial NPS method to estimate the NPS from only a few scans. RESULTS We applied the radial NPS method to simulated CT scans of a nonuniform object. The results showed that the local NPS estimated from only 6 scans using the radial method was very close to the NPS estimated using the traditional method from 400 scans, according to normalized mean squared error (NMSE) and the signal detectability based on an NPS-prewhitening Hoteling model observer. We also applied this method to physical phantom scans. Good accuracy was again achieved from only 6 scans using the radial NPS method. CONCLUSION The radial NPS method was shown to be accurate and efficient in estimating the local NPS of FBP reconstructed CT images. This method is readily extendable to estimating the NPS of helical CT scans.