Ricardo S. Avila

Model-Based analysis of local shape for lesion detection in CT scans

Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.

An open-source toolkit for the volumetric measurement of CT lung lesions

An open source lesion sizing toolkit has been developed with a general architecture for implementing lesion segmentation algorithms and a reference algorithm for segmenting solid and part-solid lesions from lung CT scans. The CT lung lesion segmentation algorithm detects four three-dimensional features corresponding to the lung wall, vasculature, lesion boundary edges, and low density background lung parenchyma. These features form boundaries and propagation zones that guide the evolution of a subsequent level set algorithm. User input is used to determine an initial seed point for the level set and users may also define a region of interest around the lesion. The methods are validated against 18 nodules using CT scans of an anthropomorphic thorax phantom simulating lung anatomy. The scans were acquired under differing scanner parameters to characterize algorithm behavior under varying acquisition protocols. We also validated repeatability using six clinical cases in which the patient was rescanned on the same day (zero volume change). The source code, data sets, and a running application are all provided under an unrestrictive license to encourage reproducibility and foster scientific exchange.

Size and Growth Assessment of Pulmonary Nodules: Consequences of the Rounding

Objectives The aim of this study was to understand the effect of rounding nodule size measurements on the frequency of positive results in the setting of lung cancer screening. Methods Four methods for determining nodule size were compared, including rounding each individual length and width measurement and also rounding the overall average. These were applied to the International Early Lung Cancer Action Program database, in which we determined the frequency of a positive result by using standard size thresholds of 6.0 mm on baseline screening and 3.0 mm on repeat scanning. We also explored how rounding influences the ability to measure growth according to a predefined cutoff value of 1.5 mm as required in the Lung Computed Tomography Screening Reporting and Data System (Lung‐RADS) (version 1.0). Results Each method for rounding increased the frequency of positive results compared with that with no rounding. The largest increases—28.9% and 22.3% for the baseline and repeat round, respectively—occurred when rounding was used for both the individual length and width measurements and again for the final diameter. If the 1.5‐mm increase in size were used for determining growth, a 4‐mm nodule would need to have a volume doubling time of 130.6 days or less to demonstrate growth in 6 months (Lung‐RADS category 3) whereas a 6‐mm nodule would need volume doubling time of 93.2 days to demonstrate growth in 3 months (Lung‐RADS category 4A). In addition, rounding can have the effect of having nodules that appear to not be growing meet the criteria for growth and vice versa. Conclusions Rounding influences the frequency of positive results and growth assessment, substantially decreasing the efficiency of screening.

A Quantitative Method for Estimating Individual Lung Cancer Risk

RATIONALE AND OBJECTIVES Lung cancer is caused primarily by repeated exposure to carcinogenic particulate matter and noxious gasses with high particulate deposition localized to airway bifurcations and the lung periphery. The quantitative measurement and analysis of these sites has the potential to stratify lung cancer risk. The aim of this preliminary study was to assess the performance of a new method for estimating individual lung cancer risk based on the analysis of airway bifurcations on high-resolution (HR) computed tomographic (CT) scanning and spirometry. MATERIALS AND METHODS One hundred eight subjects with spirometry and thin-slice CT data were selected from a CT screening study including 15 patients with early lung cancer and 93 age-matched and pack-year-matched controls. A subset of seven patients with cancer and 72 controls were scanned with 1-mm CT slice thickness, representing an HR case subset. A quantitative lung cancer risk index method was developed on the basis of airway bifurcation x-ray attenuation combined with the ratio of forced expiratory volume in 1 second to forced vital capacity. Cochran-Mantel-Haenszel and conditional logistic regression tests were used to analyze performance. RESULTS Cochran-Mantel-Haenszel crude analysis revealed a cancer detection sensitivity and specificity of 67% and 72% for all cases and 100% and 73% for the HR case subset, respectively. Conditional logistic regression showed that a 0.0328 increase in lung cancer risk index was associated with odds ratios of 1.84 (95% confidence interval, 1.18-2.85) for the full data set (P = .0067) and 2.89 (95% confidence interval, 1.02-8.19) for the HR subset (P = .0467). CONCLUSIONS A preliminary evaluation of a new lung cancer risk estimation method based on thin slice CT and spirometry showed a statistically significant association with lung cancer.

Open Source and Open Science: how it is changing the medical imaging community

The Open Science movement advances the idea that the results of scientific research must be made available as public resource. Limiting access to scientific information hinders innovation, complicates validation, and wastes valuable socio-economic resources. Open Science is an effective way of overcoming the nearsightedness of the contemporary obsession with intellectual property. The practice of Open Science is based on three pillars: Open Access, Open Data, and Open Source. Given that the practice of medical image research pertains to a field that affects the health condition of the public, it is of paramount importance to introduce the concepts of Open Science in domains such as animal research, drug discovery, clinical trials, computer assisted diagnosis and computer assisted treatment

Automated coronary CT angiography plaque-lumen segmentation

We are investigating the feasibility of a computer-aided detection (CAD) system to assist radiologists in diagnosing coronary artery disease in ECG gated cardiac multi-detector CT scans having calcified plaque. Coronary artery stenosis analysis is challenging if calcified plaque or the iodinated blood pool hides viable lumen. The research described herein provides an improved presentation to the radiologist by removing obscuring calcified plaque and blood pool. The algorithm derives a Gaussian estimate of the point spread function (PSF) of the scanner responsible for plaque blooming by fitting measured CTA image profiles. An initial estimate of the extent of calcified plaque is obtained from the image evidence using a simple threshold. The Gaussian PSF estimate is then convolved with the initial plaque estimate to obtain an estimate of the extent of the blooming artifact and this plaque blooming image is subtracted from the CT image to obtain an image largely free of obscuring plaque. In a separate step, the obscuring blood pool is suppressed using morphological operations and adaptive region growing. After processing by our algorithm, we are able to project the segmented plaque-free lumen to form synthetic angiograms free from obstruction. We can also analyze the coronary arteries with vessel tracking and centerline extraction to produce cross sectional images for measuring lumen stenosis. As an additional aid to radiologists, we also produce plots of calcified plaque and lumen cross-sectional area along selected blood vessels. The method was validated using digital phantoms and actual patient data, including in one case, a validation against the results of a catheter angiogram.

Tumor volume measurement error using computed tomography imaging in a phase II clinical trial in lung cancer.

Abstract. To address the error introduced by computed tomography (CT) scanners when assessing volume and unidimensional measurement of solid tumors, we scanned a precision manufactured pocket phantom simultaneously with patients enrolled in a lung cancer clinical trial. Dedicated software quantified bias and random error in the X,Y, and Z dimensions of a Teflon sphere and also quantified response evaluation criteria in solid tumors and volume measurements using both constant and adaptive thresholding. We found that underestimation bias was essentially the same for X,Y, and Z dimensions using constant thresholding and had similar values for adaptive thresholding. The random error of these length measurements as measured by the standard deviation and coefficient of variation was 0.10 mm (0.65), 0.11 mm (0.71), and 0.59 mm (3.75) for constant thresholding and 0.08 mm (0.51), 0.09 mm (0.56), and 0.58 mm (3.68) for adaptive thresholding, respectively. For random error, however, Z lengths had at least a fivefold higher standard deviation and coefficient of variation than X and Y. Observed Z-dimension error was especially high for some 8 and 16 slice CT models. Error in CT image formation, in particular, for models with low numbers of detector rows, may be large enough to be misinterpreted as representing either treatment response or disease progression.

Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening

After years of assessment through controlled clinical trials, low-dose CT screening for lung cancer is becoming part of clinical practice. As with any cancer screening test, those undergoing lung cancer screening are not being evaluated for concerning signs or symptoms, but are generally in good health and proactively trying to prevent premature death. Given the resultant obligation to achieve the screening aim of early diagnosis while also minimizing the potential for morbidity from workup of indeterminate but ultimately benign screening abnormalities, careful implementation of screening with conformance to currently recognized best practices and a focus on quality assurance is essential. In this review, we address the importance of each component of the screening process to optimize the effectiveness of CT screening, discussing options for quality assurance at each step. We also discuss the potential added advantages, quality assurance requirements and current status of quantitative imaging biomarkers related to lung cancer screening. Finally, we highlight suggestions for improvements and needs for further evidence in evaluating the performance of CT screening as it transitions from the research trial setting into daily clinical practice.

A new method for predicting CT lung nodule volume measurement performance

Purpose: To evaluate a new approach for predicting nodule volume measurement bias and variability when scanning with a specific CT scanner and acquisition protocol. Methods: A GE LightSpeed VCT scanner was used to scan 3 new rolls of 3M 3/4 x 1000 Inch Scotch Magic tape with a routine chest protocol (120 kVp, 100 mA, 0.4 s rotation, .98 pitch, STANDARD kernel) at three different slice thicknesses and spacings. Each tape scan was independently analyzed by fully automated image quality assessment software, producing fundamental image quality characteristics and simulated lung nodule volume measurements for a range of sphere diameters. The same VCT scanner and protocol was then used to obtain 10 repeat CT scans of an anthropomorphic chest phantom containing multiple Teflon spheres embedded in foam (diameters = 4.76mm, 6.25mm, and 7.94mm). The observed volume of the spheres in the 30 (3 reconstructions per scan) repeat scans was provided by independently developed nodule measurement software. Results: The predicted vs observed mean volume (mm3 ) and CV for 3 slice thicknesses and sphere sizes was obtained. For 0.625mm slice thickness scans the predicted vs observed values were (44.3,0.91)-vs-(48.2,1.17), (110.4,0.51)-vs-(124.1,0.47), and (219.9,0.29)-vs-(250.1,0.34), for 4.76mm, 6.25mm, and 7.94mm spheres respectively. For 1.25mm slice thickness the corresponding values were (42.1,0.98)-vs-(47.6,1.35), (106.9,0.56)-vs-(123.1,0.61), and (214.8,0.32)-vs-(248.8,0.41). For 2.5mm slice thickness the corresponding values were (23.9,9.53)-vs-(36.8,12.50), (77.6,3.84)-vs-(110.5,3.20), and (173.0,1.57)-vs-(233.9,1.32). Conclusion: Volume measurement bias and variability for lung nodules based on nodule size and acquisition protocol can potentially be predicted using a new method that utilizes fundamental image characteristics and simulation.

A New Platform for Quantitative CT Imaging Dose Optimization

We propose a new set of calibration tools for achieving personalized CT dose optimization in quantitative imaging studies. Small dosimeters placed with a compact CT phantom offers insight into dose and image quality tradeoffs.