A scanner-specific framework for simulating CT images with tube current modulation

Jadick, Giavanna; Abadi, Ehsan; Harrawood, Brian P.; Sharma, Shobhit; Segars, W. Paul; Samei, Ehsan

doi:10.1088/1361-6560/ac2269

Cited by 15 publications

(15 citation statements)

References 38 publications

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“…The DukeSim CT simulator was used to image the human phantoms, by generating scanner-specific CT projection images of voxelized computational phantoms that take into account the physical and geometric characteristics of the scanners [16][17][18][19][20]. In this study, a clinical PCCT and an energy-integrating CT (EICT) (NAEOTOM Alpha, SOMATOM Definition Flash, Siemens) were simulated by DukeSim to generate the CT projections of the phantoms.…”

Section: Virtual Acquisitionsmentioning

confidence: 99%

“…In this study, a clinical PCCT and an energy-integrating CT (EICT) (NAEOTOM Alpha, SOMATOM Definition Flash, Siemens) were simulated by DukeSim to generate the CT projections of the phantoms. Virtual acquisitions were performed at 2 dose levels (CTDIvol of 6.3 and 12.6 mGy) using scanner-specific x-ray spectra (120kV) with a body bowtie filter at a pitch of 1.0 with "average" tube current modulation (TCM) and a reference diameter of 31.4 cm [15,18]. The projection images were reconstructed using vendor-specific reconstruction software (ReconCT, Siemens) with the weighted filtered back projection (wFBP) for EICT images and prior-based noise reduction (PNR).…”

Section: Virtual Acquisitionsmentioning

confidence: 99%

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Development and application of a virtual imaging trial framework for airway quantifications via CT

Sotoudeh-Paima

Segars

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death worldwide, characterized by emphysema and bronchitis. Airway measurements reflect the severity of bronchitis and other airway-related diseases. Airway structures can be objectively evaluated with quantitative computed tomography (CT). The accuracy of such quantifications is limited by the spatial resolution and image noise characteristics of the imaging system and can be potentially improved with the emerging photon-counting CT (PCCT) technology. This study evaluated the quantitative performance of PCCT against energy-integrating CT (EICT) systems for airway measurements, and further identified optimum CT imaging parameters for such quantifications. The study was performed using a novel virtual imaging framework by developing the first library of virtual patients with bronchitis. These virtual patients were developed based on CT images of confirmed COPD patients with varied bronchitis severity. The human models were virtually imaged at 6.3 and 12.6 mGy dose levels using a scanner-specific simulator (DukeSim), synthesizing clinical PCCT and EICT scanners (NAEOTOM Alpha, FLASH, Siemens). The projections were reconstructed with two algorithms and kernels at different matrix sizes and slice thicknesses. The CT images were used to quantify clinically relevant airway measurements ("Pi10" and "WA%") and compared against their ground truth values. Compared to EICT, PCCT provided more accurate Pi10 and WA% measurements by 63.1% and 68.2%, respectively. For both technologies, sharper kernels and larger matrix sizes led to more reliable bronchitis quantifications. This study highlights the potential advantages of PCCT against EICT in characterizing bronchitis utilizing a virtual imaging platform.

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Section: Virtual Acquisitionsmentioning

confidence: 99%

Section: Virtual Acquisitionsmentioning

confidence: 99%

Development and application of a virtual imaging trial framework for airway quantifications via CT

Sotoudeh-Paima

Segars

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

View full text Add to dashboard Cite

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“…DukeSim accounts for various scanner‐specific features, including polychromatic X‐ray source, bowtie filter, focal spot wobbling, tube current modulation, and poly‐energetic detector response. To generate sinograms, DukeSim has a ray‐tracing and a Monte Carlo module to account for both primary and scatter signals 6–8 . In previous works, DukeSim has been validated against experimental data acquired from various scanners and imaging conditions with cylindrical phantoms of various diameters using a measurements of image contrast, noise texture, and spatial resolution 6,9 .…”

Section: Introductionmentioning

confidence: 99%

“…To generate sinograms, DukeSim has a ray-tracing and a Monte Carlo module to account for both primary and scatter signals. [6][7][8] In previous works, DukeSim has been validated against experimental data acquired from various scanners and imaging conditions with cylindrical phantoms of various diameters using a measurements of image contrast, noise texture, and spatial resolution. 6,9 However, CT images of cylindrical phantoms do not reflect in a way similar to the varying structures of the human chest, thereby indicating a need for further investigations regarding the accuracy and reliability of DukeSim in terms of physiological measurements for more realistic and anthropomorphic phantoms.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] In previous works, DukeSim has been validated against experimental data acquired from various scanners and imaging conditions with cylindrical phantoms of various diameters using a measurements of image contrast, noise texture, and spatial resolution. 6,9 However, CT images of cylindrical phantoms do not reflect in a way similar to the varying structures of the human chest, thereby indicating a need for further investigations regarding the accuracy and reliability of DukeSim in terms of physiological measurements for more realistic and anthropomorphic phantoms. For lung imaging applications in particular, a CT simulation needs to be validated against a corresponding anthropomorphic chest phantom, including ribs, spine, mediastinum, and lungs, and in terms of the quantifications that are relevant to the pulmonary imaging applications.…”

Section: Introductionmentioning

confidence: 99%

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Task‐based validation and application of a scanner‐specific CT simulator using an anthropomorphic phantom

et al. 2022

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Background Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner‐specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. Purpose To validate a scanner‐specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. Methods An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner‐specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20–300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground‐truth values modeled in the computational chest phantom. Results The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%–18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84–94 HU, lung volume of 797–3785 cm3, and lung mass of −800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6–84 HU, lung volume of 66–3785 cm3, and lung mass of 170–1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. Conclusion We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

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A systematic assessment and optimization of photon‐counting CT for lung density quantifications

Sotoudeh‐Paima,

Segars,

Ghosh

et al. 2024

Medical Physics

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BackgroundPhoton‐counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy‐integrating computed tomography (EICT) scanners.PurposeTo systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT.MethodsThis cross‐sectional study involved a retrospective analysis of subjects scanned (August‐December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner‐specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth.ResultsClinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low‐attenuation areas below ‐950 HU (LAA‐950) by up to 15.7 ± 6.9% and 11.8 ± 5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5 mm), pixel size (0.49 to 0.98 mm), reconstruction technique (70 keV‐VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3 ± 2.0, 1.4 ± 0.6, 2.2 ± 0.3, 4.2 ± 0.8, and 9.1 ± 1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6 ± 1.0 and 9.6 ± 3.4 HU, compared to EICT A, and by 4.8 ± 0.8 and 7.4 ± 2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects’ mean lung density (p < 0.05), measured by PCCT at optimum setting under the conditions studied.ConclusionPhoton‐counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy‐integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.

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A scanner-specific framework for simulating CT images with tube current modulation

Cited by 15 publications

References 38 publications

Development and application of a virtual imaging trial framework for airway quantifications via CT

Development and application of a virtual imaging trial framework for airway quantifications via CT

Task‐based validation and application of a scanner‐specific CT simulator using an anthropomorphic phantom

A systematic assessment and optimization of photon‐counting CT for lung density quantifications

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