ES01.01 Image Quality Characteristics and Nodule Growth Measurement, Medical Physics and Machine Parameters

Abstract

Modern CT scanners are routinely being used to determine the malignancy potential of small sub-centimeter pulmonary nodules. Increasingly, this involves CT scanning and quantitative volume measurement of lung nodules over short time intervals (e.g. 3 or 6 months) to determine whether a change in nodule size consistent with malignant growth has occurred. Although it may appear that current CT scanners are more than capable of reliably performing these quantitative measurements with high quality due to their ability to obtain sub-millimeter resolution lung images, many clinical sites are not taking the steps needed to achieve consistent high quality small lung nodule measurement results. A study of volume measurement performance in a phase II clinical trial observed multiple clinical sites using CT scanners which resulted in errors in volume change measurements as high as 43% [1]. In addition, a 2016 crowd-sourcing study of CT scanner image quality performance using the site’s low dose CT lung cancer screening acquisition protocol revealed that 37% of sites used insufficient slice thickness (<= 1.25mm slice thickness is needed) and only 19% of sites used the needed slice thickness and a reconstruction kernel that avoided excessive smoothing and avoided high levels of edge enhancement [2]. Poor CT image acquisition performance has the potential to result in poor lung nodule volume measurement performance which can negatively impact patient care by contributing to unnecessary biopsies and delays in early lung cancer diagnosis. To address these issues the RSNA’s Quantitative Imaging Biomarkers Alliance (QIBA®) has developed the QIBA CT Small Lung Nodule Profile that provides a comprehensive set of specifications to ensure that a clinical site attains a minimum level of quantitative imaging performance necessary to achieve a specified lung nodule volume measurement accuracy. The Small Lung Nodule Profile outlines six fundamental image quality characteristics that are needed throughout the full scanner field of view to support precise volumetric measurement of small lung nodules. These characteristics are (1) Edge Enhancement, (2) Three-Dimensional Resolution, (3) Resolution Aspect Ratio, (4) CT linearity, (5) Spatial Warping, and (6) Noise. In general, CT scanners achieve highest fundamental image quality performance at scanner iso-center with some scanners and image acquisition protocols exhibiting large losses in image quality performance as a function of distance from scanner iso-center [3]. These fundamental image quality properties can now be quickly and easily measured by a technologist at any clinical site using a new image quality measurement phantom and fully automated and cloud-based phantom analysis software. To determine the clinical impact of achieved CT image quality performance, a new set of modeling and simulation tools has been developed that can create simulated CT images given the image quality characteristics for a CT scanner and image acquisition protocol [4]. Quantitative measurement software can then be applied to these images resulting in expected measurement performance for a clinical task, such as the bias and precision of solid lung nodule volume change measurement for virtual lung nodules of different sizes. Having these estimates of a CT scanners performance can further be used to one day quantitatively determine the minimum time interval needed in order to be able to distinguish malignant nodule volume growth from a stable lung nodule. Regularly performing these measurements also has the potential to offer numerous advantages to lung cancer screening sites including the ability to determine if scans from two different CT scanner models will produce sufficiently similar image quality and measurement performance. In summary, a new set of phantoms and cloud-based software tools is available that enables more careful control and optimization of CT lung cancer imaging performance based on fundamental image quality properties. These new tools provide several new opportunities for clinical sites to more precisely perform CT lung cancer imaging studies and measurements. [1] Henschke CI, Yankelevitz DF, Yip R, Archer A, Zahlmann G, Krishnan K, Helba B, Avila R, “Tumor volume measurement error using computed tomography imaging in a phase II clinical trial in lung cancer.” Journal of Medical Imaging 3(3), 035505 (Jul–Sep 2016). [2] Avila R, Yankelevitz D, Yip R, Henschke C, “P1.03-021 Initial Results from A Novel and Low Cost Method For Measuring CT Image Quality,” January 2017. Journal of Thoracic Oncology 12(1):S554-S555. [3] Avila R, Subramaniam R, Henschke C, Yankelevitz D, “Hot Topic: Clinical Implications of CT Image Quality Variation in Low Dose Lung Cancer Screening Scans,” 4th World Congress of Thoracic Imaging Proceedings, Journal of Thoracic Imaging, accepted for oral presentation, 2017. [4] Avila R, Jirapatnakul A, Subramaniam R, Yankelevitz D, “A new method for predicting CT lung nodule volume measurement performance,” SPIE Medical Imaging Proceedings, 2017. "lung cancer screening", "Image Quality"