Ms. Cindy McCabe Profile

Cindy McCabe

PhD Student

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Publications (8)

Proceedings Article | 2 April 2024 Paper

Quantitative trabecular bone morphometry using photon-counting CT

G. Shi, A. Sivakumar, E. Abadi, C. McCabe, F. Quevedo Gonzalez, R. Breighner, J. Carrino, J. Siewerdsen, W. Zbijewski

Proceedings Volume 12930, 1293004 (2024) https://doi.org/10.1117/12.3006949

KEYWORDS: Bone, Voxels, Spatial resolution, Image segmentation, Image resolution, In vivo imaging, X-ray computed tomography, Computed tomography, Spine, Head

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The recently introduced Photon Counting CT (PCCT) offers major advances in spatial resolution and material discrimination compared to conventional multi-detector CT. We investigate whether these new capabilities may enable accurate in vivo quantification of the trabecular microstructure of human bone. Human femoral bone was imaged using reference HR-pQCT (isotropic 60 μm voxels) and PCCT operated in a High Resolution mode (HR, 80 μm in-plane voxel size. 200 μm slice thickness) and in a Calcium-selective mode (CA, isotropic 390 μm voxels). 468 spherical Regions-of-Interest (ROIs) of 5 mm diameter were placed at corresponding locations in the HR-pQCT and PCCT volumes. The bone voxels of HR-pQCT and CA PCCT ROIs were segmented (binarized) using global Otsu thresholding; local Bernsen segmentation was used for HR PCCT. Trabecular thickness (TbTh), spacing (TbSp), number (TbN), and bone volume fraction (BV/TV) were measured in the binarized ROIs. The performance of PCCT morphometrics was evaluated in terms of correlation coefficient and numerical agreement with HR-pQCT. For ROIs with mean TbTh⪆250 μm (approaching the nominal resolution of HR PCCT), the average trabecular measurements obtained from HR PCCT achieved excellent correlations with the reference HR-pQCT: 0.88 for BvTv, 0.89 for TbTh, 0.81 for TbSp and 0.78 for TbN. For ROIs with mean TbTh of 200 μm – 250 μm, the correlations were slightly worse, ranging from 0.61 for TbTh to 0.84 for BvTv. The spatial resolution of CA PCCT in its current implementation is insufficient for microarchitectural measurements, but the material discrimination capability appears to enable accurate estimation of BvTv (correlation of 0.89 to HR-pQCT). The results suggest that the introduction of PCCT may enable microstructural evaluation of the trabecular bone of the lumbar spine and hip, which are inaccessible to current in vivo high-resolution bone imaging technologies. The findings of this work will inform the development of clinical indications for PCCT trabecular bone assessment.

Proceedings Article | 1 April 2024 Poster + Paper

A comparative study on the utility and choice parameters of photon-counting CT for bone quantification

Cindy McCabe, Steve Bache, Gengxin Shi, Ryan Breighner, Wojciech Zbijewski, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12925, 129252U (2024) https://doi.org/10.1117/12.3006830

KEYWORDS: Bone imaging, Bone, Scanners, Spatial resolution, Computed tomography, Image sharpness, Biological imaging

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The measurements of bone macro- and microstructures provide major insight into bone health and risk fracture. The accuracy of these measurements is limited when Conventional CT is used. However, CT technology has advanced with the introduction of photon-counting detectors that offer significant improvements in spatial resolution. This advancement offers potential improvements in resolving trabecular microstructures, and the quantification of bone through current or emerging biomarkers. Additionally, the spectral separation available in photon-counting CT (PCCT) may further aid in quantification. The purpose of this study was to objectively investigate PCCT capabilities in accurate quantification of bone macro- and micro-structures. To do so, 5 human bone specimens were scanned using a PCCT scanner (NAEOTOM Alpha, Siemens) and an energy-integrating CT (EICT) (FORCE, Siemens). Each specimen was imaged at a CTDIvol of 4 and 8mGy, and then reconstructed with 2 matrix sizes and at least 2 kernels. For PCCT, a 70keV virtual mono-energetic image series was acquired to evaluate the potential benefits of spectral maps. The same specimens were also scanned using a high-resolution peripheral quantitative CT to provide a ground truth for the bone metrics. Each image series was analyzed in terms of bone mineral density (BMD) and trabecular bone volume to total bone volume. PCCT demonstrated major improvements (5.5% compared to 17% error for EICT) in quantifying bone microstructures (BV/TV). However, the BMD measurements remained similar across imaging conditions and scanners, and did not significantly change by the PCCT spatial resolution enhancement. For BV/TV measurements, PCCT T3D was the most accurate when the sharpest kernel available and 1024-matrix size for (error: 5.53%±4.72%) were used. Similarly, EICT images were the most accurate for BV/TV measurements (error: 16.70%±10.55%) when a medium-sharpness kernel and 1024-matrix size were used. The overall results suggest that PCCT technology can further improve trabecular bone measurements and thus enhance the clinical decision making for patients with bone disease.

Proceedings Article | 1 April 2024 Presentation + Paper

Synthesizing heterogeneous lung lesions for virtual imaging trials

Cindy McCabe, Justin Solomon, W. Paul Segars, Ehsan Abadi, Ehsan Samei

Proceedings Volume 12925, 129251N (2024) https://doi.org/10.1117/12.3006199

KEYWORDS: Lung, Mathematical modeling, Computed tomography, 3D modeling, Simulations, Radiomics, Cooccurrence matrices, Computational modeling

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Proceedings Article | 1 April 2024 Paper

Virtual NLST: towards replicating national lung screening trial

Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe, Amareswararao Kavuri, Lavsen Dahal, Brian Harrawood, Milo Fryling, Mojtaba Zarei, Saman Sotoudeh-Paima, Fong Chi Ho, Dhrubajyoti Ghosh, Sheng Luo, W. Paul Segars, Ehsan Abadi, Kyle Lafata, Ehsan Samei, Joseph Lo

Proceedings Volume 12925, 129252F (2024) https://doi.org/10.1117/12.3006915

KEYWORDS: Lung, Data modeling, Computed tomography, Scanners, 3D modeling, Computer simulations, Modulation transfer functions, Chest imaging, Medicine, Medical research

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Proceedings Article | 7 April 2023 Presentation + Paper

Harmonizing CT images via physics-based deep neural networks

Mojtaba Zarei, Saman Sotoudeh-Paima, Cindy McCabe, Ehsan Abadi, Ehsan Samei

Proceedings Volume 12463, 124631Q (2023) https://doi.org/10.1117/12.2654215

KEYWORDS: Computed tomography, Education and training, Modulation transfer functions, Lung, Chronic obstructive pulmonary disease, Radiomics, Image quality, Neural networks, Scanners, Point spread functions

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The rendition of medical images influences the accuracy and precision of quantifications. Image variations or biases make measuring imaging biomarkers challenging. The objective of this paper is to reduce the variability of computed tomography (CT) quantifications for radiomics and biomarkers using physics-based deep neural networks (DNNs). The proposed framework used the knowledge of ground truth through a virtual imaging trial (VIT) methodology to harmonize the different renditions of CT scans across variations in reconstruction kernel and dose. This harmonization was done by developing a generative adversarial network (GAN) model, informed by pixel values and patient-based modulation transfer function (MTF) estimates. To train the network, the VIT platform was used to acquire CT images from a set of forty computational human models (XCAT). Models included varying levels of pulmonary diseases including lung nodules and emphysema. The patient models were imaged with a validated CT simulator (DukeSim) modeling a commercial CT scanner operating across a range of dose (20-100 mAs) and reconstruction kernels (15 from smooth to sharp). The harmonized virtual images were evaluated in three different ways: 1) visual assessment of the images, 2) bias and variation in density-based biomarkers, and 3) bias and variation in morphological-based biomarkers. The harmonization improved the bias and variability of the test set images yielding a structural similarity index of 95±1%, a normalized mean squared error of 10.2±1.5%, and a peak signal-to-noise ratio of 31.8±1.5 dB compared to variations in these metrics when no harmonization was applied (87±9%, 14.2±4%, and 29.6±2, respectively). Emphysema-based imaging biomarkers of LAA-950 (-1.5±1.8%), Perc15 (13.65±9.3 HU), and Lung mass (0.1±0.3 g) had more precise and consistent quantifications compared to values when images were not harmonized (9.9±12%, -36.0±53 HU, and 0.2±0.4g, respectively). These results suggest that the method can be promising to improve consistency in multi-center studies when reliable and consistent quantification of data from multiple systems are required.

Proceedings Article | 7 April 2023 Presentation + Paper

A systematic assessment of photon-counting CT for bone mineral density and microarchitecture quantifications

Cindy McCabe, Thomas Sauer, Mojtaba Zarei, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12463, 1246303 (2023) https://doi.org/10.1117/12.2654028

KEYWORDS: Bone, Diseases and disorders, Scanners, Computed tomography, X-ray computed tomography, Bone imaging, Osteoporosis, Image sharpness, Biological imaging

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Proceedings Article | 4 April 2022 Presentation + Paper

Development and clinical applications of a virtual imaging framework for optimizing photon-counting CT

Ehsan Abadi, Cindy McCabe, Brian Harrawood, Saman Sotoudeh-Paima, William Segars, Ehsan Samei

Proceedings Volume 12031, 120311Q (2022) https://doi.org/10.1117/12.2612079

KEYWORDS: Monte Carlo methods, Sensors, Imaging systems, Chronic obstructive pulmonary disease, Computed tomography, Scanners, Lung, Systems modeling, Signal detection, Modulation transfer functions

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The purpose of this study was to develop a virtual imaging framework that simulates a new photon-counting CT (PCCT) system (NAEOTOM Alpha, Siemens). The PCCT simulator was built upon the DukeSim platform, which generates projection images of computational phantoms given the geometry and physics of the scanner and imaging parameters. DukeSim was adapted to account for the geometry of the PCCT prototype. To model the photon-counting detection process, we utilized a Monte Carlo-based detector model with the known properties of the detectors. We validated the simulation platform against experimental measurements. The images were acquired at four dose levels (CTDIvol of 1.5, 3.0, 6.0, and 12.0 mGy) and reconstructed with three kernels (Br36, Br40, Br48). The experimental acquisitions were replicated using our developed simulation platform. The real and simulated images were quantitatively compared in terms of image quality metrics (HU values, noise magnitude, noise power spectrum, and modulation transfer function). The clinical utility of our framework was demonstrated by conducting two clinical applications (COPD quantifications and lung nodule radiomics). The phantoms with relevant pathologies were imaged with DukeSim modeling the PCCT systems. Different imaging parameters (e.g., dose, reconstruction techniques, pixel size, and slice thickness) were altered to investigate their effects on task-based quantifications. We successfully implemented the acquisition and physics attributes of the PCCT prototype into the DukeSim platform. The discrepancy between the real and simulated data was on average about 2 HU in terms of noise magnitude, 0.002 mm^-1 in terms of noise power spectrum peak frequency and 0.005 mm^-1 in terms of the frequency at 50% MTF. Analysis suggested that lung lesion radiomics to be more accurate with reduced pixel size and slice thickness. For COPD quantifications, higher doses, thinner slices, and softer kernels yielded more accurate quantification of density-based biomarkers. Our developed virtual imaging platform enables systematic comparison of new PCCT technologies as well as optimization of the imaging parameters for specific clinical tasks.

Proceedings Article | 4 April 2022 Presentation + Paper

Optimization of imaging parameters of an investigational photon-counting CT prototype for lung lesion radiomics

Cindy McCabe, Mojtaba Zarei, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12033, 1203337 (2022) https://doi.org/10.1117/12.2612973

KEYWORDS: Image segmentation, Lung, Computed tomography, Lung cancer, Photon counting, Cancer

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