Photon-counting CT versus conventional CT for COPD quantifications: intra-scanner optimization and inter-scanner assessments using virtual imaging trials

Sotoudeh-Paima, Saman; Segars, W. Paul; Samei, Ehsan; Abadi, Ehsan

doi:10.1117/12.2613003

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…To match the spatial resolution, MTF was measured in each CT case using the methods in [8]. A reference MTF was defined based on a previous study that identified optimum settings in emphysema measurements [5]. The images were transferred to the reference MTF using,…”

Section: Physics-based Harmonizermentioning

confidence: 99%

“…Previous studies have demonstrated the existing correlation between emphysema quantification with its histopathology [3], [4]. Despite the increased use of CT quantifications in assessing the presence and severity of emphysema or monitoring its progression, acquiring robust quantifications remains a major challenge [5]. Over the years, CT radiation dose has been reduced in accordance with the principle of "as low as reasonably achievable".…”

Section: Introductionmentioning

confidence: 99%

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CT-HARMONICA: physics-based CT harmonization for reliable lung density quantification

Sotoudeh-Paima¹,

Samei²,

Abadi³

2023

Medical Imaging 2023: Computer-Aided Diagnosis

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Pulmonary emphysema is a form of Chronic Obstructive Pulmonary Disease (COPD) and a chronic lung condition that results in a breakdown of alveoli walls. Quantitative Computed Tomography (CT) is increasingly used to assess the presence or progression of emphysema. CT quantifications are affected by the acquisition protocols and scanner makes and models. This variability is a major concern for cross-sectional and longitudinal disease characterizations with largescale, multi-institutional datasets. Therefore, CT images need to be harmonized to reflect the patient condition and not the attributes of the imaging systems. The purpose of this study was to develop a physics-based harmonization framework that transforms CT images to a reference quality index (iso resolution and noise conditions) enabling robust emphysema quantifications across varied CT conditions. The harmonizer was developed using a virtual imaging trial (VIT) platform taking advantage of ground truth knowledge and control over CT parameters in VIT. The developed harmonizer was applied to clinical data from the COPDGene dataset to demonstrate its clinical utility. The established imaging biomarkers of "LAA-950" and "Perc15" were selected for emphysema quantifications. Results demonstrated that the harmonizer improved the quantification performance by reducing the bias in "LAA-950" from 7.03 (𝐶𝐼: [6.38, 7.68]) to 0.14 (𝐶𝐼: [0.08, 0.20]) after matching for kernel and from 2.48 (𝐶𝐼: [2.21, 2.76]) to −0.34 (𝐶𝐼: [−0.48, −0.20]) after matching for noise settings on the COPDGene dataset. This developed harmonization framework provides robust emphysema quantifications, enabling objective disease characterizations in large-scale, multi-center, and longitudinal studies.

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Section: Physics-based Harmonizermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CT-HARMONICA: physics-based CT harmonization for reliable lung density quantification

Sotoudeh-Paima¹,

Samei²,

Abadi³

2023

Medical Imaging 2023: Computer-Aided Diagnosis

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“…Forty unique computational patient models (XCAT) [10] with variable body attributes were enrolled. Ten XCAT phantoms contains variable and patient-informed emphysema models [14]. Phantoms had two different renditions of emphysematous parenchyma, each with varying severity, distribution, and size.…”

Section: Training Images and Paired Ground Truth Datamentioning

confidence: 99%

Harmonizing CT images via physics-based deep neural networks

Zarei

Sotoudeh-Paima

McCabe

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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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.

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