KEYWORDS: Breast, Digital breast tomosynthesis, Imaging systems, Breast density, Anatomy, Diagnostics, Tomography, Systems modeling, Spherical lenses, Quantum modeling
PurposeDigital phantoms are one of the key components of virtual imaging trials (VITs) that aim to assess and optimize new medical imaging systems and algorithms. However, these phantoms vary in their voxel resolution, appearance, and structural details. We investigate whether and how variations between digital phantoms influence system optimization with digital breast tomosynthesis (DBT) as a chosen modality.MethodsWe selected widely used and open-access digital breast phantoms created with different methods and generated an ensemble of DBT images to test acquisition strategies. Human observer performance was evaluated using localization receiver operating characteristic (LROC) studies for each phantom type. Noise power spectrum and gaze metrics were also employed to compare phantoms and generated images.ResultsOur LROC results show that the arc samplings for peak performance were ∼2.5 deg and 6 deg in Bakic and XCAT breast phantoms, respectively, for the 3-mm lesion detection task and indicate that system optimization outcomes from VITs can vary with phantom types and structural frequency components. In addition, a significant correlation (p<0.01) between gaze metrics and diagnostic performance suggests that gaze analysis can be used to understand and evaluate task difficulty in VITs.ConclusionOur results point to the critical need to evaluate realism in digital phantoms and ensure sufficient structural variations at spatial frequencies relevant to the intended task. Standardizing phantom generation and validation tools may help reduce discrepancies among independently conducted VITs for system or algorithmic optimizations.
Parametric response mapping (PRM) is a voxel-based quantitative CT imaging biomarker that measures the severity of chronic obstructive pulmonary disease (COPD) by analyzing both inspiratory and expiratory CT scans. Although PRM-derived measurements have been shown to predict disease severity and phenotyping, their quantitative accuracy is impacted by the variability of scanner settings and patient conditions. The aim of this study was to evaluate the variability of PRM-based measurements due to the changes in the scanner types and configurations. We developed 10 human chest models with emphysema and air-trapping at endinspiration and end-expiration states. These models were virtually imaged using a scanner-specific CT simulator (DukeSim) to create CT images at different acquisition settings for energy-integrating and photoncounting CT systems. The CT images were used to estimate PRM maps. The quantified measurements were compared with ground truth values to evaluate the deviations in the measurements. Results showed that PRM measurements varied with scanner type and configurations. The emphysema volume was overestimated by 3 ± 9.5 % (mean ± standard deviation) of the lung volume, and the functional small airway disease (fSAD) volume was underestimated by 7.519 % of the lung volume. PRM measurements were more accurate and precise when the acquired settings were photon-counting CT, higher dose, smoother kernel, and larger pixel size. This study demonstrates the development and utility of virtual imaging tools for systematic assessment of a quantitative biomarker accuracy.
Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0 to 5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.
KEYWORDS: Lung, Data modeling, Computed tomography, Scanners, 3D modeling, Computer simulations, Modulation transfer functions, Chest imaging, Medicine, Medical research
Virtual Imaging Trials, known as VITs, provide a computational substitute for clinical trials. These traditional trials tend to be sluggish, costly, and frequently deficient in definitive evidence, all the while subjecting participants to ionizing radiation. Our VIT platform meticulously mimics essential components of the imaging process, encompassing everything from virtual patients and scanners to simulated readers. Within the scope of this intended research, we aim to authenticate our virtual imaging trial platform by duplicating the results of the National Lung Screening Trial (NLST) for lung cancer screening through the emulation of low-dose computed tomography (CT) and chest radiography (CXR) procedures. The methodology involves creating 66 unique computational phantoms, each with inserted simulated lung nodules. Replicating NLST CT imaging via Duke Legacy W20 scanner matched essential properties. Virtual imaging was done through DukeSim. A LUNA16-trained virtual reader, combining a 3D RetinaNet model (front-end) with a ResNet-10 false positive reduction model (back-end), evaluated the virtually imaged data, ensuring rigorous assessment. The back-end model achieved a sensitivity of over 95% at fewer than 3 false positives per scan for both the clinical and virtual imaged CTs. Notably, nodule diameter-based analysis showcases even higher sensitivity for nodules measuring 10 mm or more. In conclusion, the integration of diverse computational and imaging techniques, culminating in a virtual reader, demonstrates promising sensitivity. To capture both arms of the trial, future research will compare virtual reader performance on CT with CXR. This affirms the transformative potential of virtual imaging trials in advancing evidence-based medicine, offering an efficient and ethically conscious approach to medical research and development.
KEYWORDS: Breast, Digital breast tomosynthesis, Medical imaging, Image filtering, Breast imaging, Tomography, Image processing, Cancer, Analytical research, Digital imaging
Texture features have been explored and studied over the last decades providing technical insights that have helped improve a wide variety of fields involving medical and other types of imaging. However there remains a need to examine estimation details, and robustness as significant and new information could be uncovered. Understanding the interactions of imaging system variation and object features in texture formation can provide a corner stone in the advancement of new image processing techniques and acquisition technologies. In this work, we evaluate these questions for digital breast tomosynthesis (DBT) a partial angle tomographic breast imaging system. Recently, our group showed for the first time a correlation between second order texture features and human observer detection performance in digital images. We also showed that second order texture features commonly used as “radiomic” metrics can change with multiple acquisition and reconstruction methods. Here we focus on issues related to robustness in estimating these features. Specifically, we aim to understand how Haralick’s GLCM texture features, used in radiomic models as predictors, change under different estimation conditions in simulated DBT images. We attempt to understand and analyze the effects that different breast densities, pixel distance offsets, ROI window sizes and filtering have on GLCM texture features calculations.
KEYWORDS: Sensors, Iodine, Mammography, Signal attenuation, Breast, Signal to noise ratio, Tissues, Photon counting, Breast imaging, X-rays, Dual energy imaging
Contrast-enhanced spectral mammography (CESM) is being implemented to overcome the limitations of conventional mammography where tumor visualization is obstructed by overlapping glandular tissue. CESM exploits the spectral properties of a contrast agent by subtracting two images one obtained above and other below the K-edge energy. The most common approach requires dual-exposure where two images are obtained with differ- ent incident spectra. However, this comes at the expense of increased patient dose and susceptibility to motion artifacts. We propose the use of photon counting spectral detectors to simultaneously obtain multiple images with single-exposure. This is demonstrated using a wide area CdTe Medipix3RX detector to acquire images of iodine contrast agent in an anthropomorphic breast imaging phantom. The electronic thresholds in the detector replace the traditional physical filters. Our results show single-exposure CESM for the detection of iodine with concentrations as low as 2.5 mg/mL of a 10 mm diameter target in a 5 cm thick heterogeneous background. These results demonstrate the viability of photon counting detectors for low dose contrast-enhanced mammography.
Our previous work on DBT image texture indicates that certain texture features may impact human observer performance for the task of low-contrast mass detection. Despite this, little is yet known about these texture statistics in the context of medical imaging. In this study, we investigate the factors that influence texture features in simulated DBT images. Specifically, we explore whether or not changes in quantum noise and anatomical variations are reflected in image texture curves. Our findings concerning the effects of Wiener filtration and changes in DBT system parameters indicate that texture statistics are affected by both anatomical variations and quantum noise.
KEYWORDS: Gold, Signal attenuation, Sensors, Data modeling, Calcium, Electronic filtering, Iodine, Filtering (signal processing), Computed tomography, Signal to noise ratio
When using a photon counting detector (PCD) for material decomposition problems, a major issue is the low-count rate per energy bin which may lead to high image-noise with compromised contrast and accuracy. We recently proposed a multi-step algorithmic method of material decomposition for spectral CT, where the problem is formulated as a series of simpler and dose efficient decompositions rather than solved simultaneously. While the method offers higher flexibility in the choice of energy bins for each material type, there are several aspects that should be optimized for effective utility of these methods. A simple domain of four materials: water, calcium, iodine and gold was explored for testing these. The results showed an improvement in accuracy with low-noise over the single-step method where the materials were decomposed simultaneously. This paper presents a comparison of contrast-to-noise ratio (CNR) and retrieval accuracy in both single-step and multi-step methods under varying acquisition and reconstruction parameters such as Wiener filter kernel size, pixel binning, signal size and energy bin overlap.
In x-ray breast images, anatomical variations have been characterized by slope of the noise power spectrum (NPS) that follows an inverse power-law relationship. Prior literature has reported that this slope (β) changes with imaging modality (DBT vs. mammography) and with different reconstruction algorithms and filters for the same breast structures. In this paper, we assessed the relative contributions of anatomic and quantum noise in the estimated magnitude of β. This is achieved via simulations with varying levels of quantum noise and examining contributions of noise filters. The calculations were performed on simulated DBT images from anthropomorphic software breast phantoms under varying acquisition and reconstruction/filter parameters. Our results indicate that variations in β cannot be solely considered as an indicator of reduced “anatomic noise” and hence potentially improved detectability in those images; presence of quantum noise and view aliasing artifacts in anatomical region always lowered the value of β.
KEYWORDS: Digital breast tomosynthesis, Digital imaging, Image processing, Breast, Tomography, Breast imaging, Imaging systems, Image display, Signal detection, Statistical analysis, Medical imaging, Tumor growth modeling, X-ray computed tomography
Understanding factors that influence search and localization of signals in tomographic breast imaging can allow for the development of efficient system design and image displays. Several acquisition, reconstruction and display parameters are known to influence signal (mass or microcalcification) detection. In this abstract we examine variation in relevant image texture features with respect to digital breast tomosynthesis (DBT) acquisition parameters. We shall relate the impact of these changes in detection via correlations against results obtained from human observer localization ROC (LROC) studies. Our methods included calculation and analysis of these texture features at randomly sampled ROIs in select image sets.
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