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

Abadi, Ehsan; McCabe, Cindy; Harrawood, Brian P.; Sotoudeh-Paima, Saman; Segars, W. Paul; Samei, Ehsan

doi:10.1117/12.2612079

Cited by 9 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The plaques, containing different tissue types, can be combined with the library of XCAT phantoms to simulate any number of cases for use in VITs in cardiac CT imaging. We have also presented initial simulations of these atherosclerotic plaques combined with XCAT phantoms with our EID-CT 31 and PCD-CT 64 simulator to illustrate their utility. We unified tools from prior work 26,27,30,36,37,[64][65][66] to create a framework for reproducible, simulated cardiac imaging of realistic virtual coronary artery pathologies.…”

Section: Discussionmentioning

confidence: 99%

“…We have also presented initial simulations of these atherosclerotic plaques combined with XCAT phantoms with our EID-CT 31 and PCD-CT 64 simulator to illustrate their utility. We unified tools from prior work 26,27,30,36,37,[64][65][66] to create a framework for reproducible, simulated cardiac imaging of realistic virtual coronary artery pathologies. These tools enable cardiac CT optimization by creating a framework for easy parameterization of realistic cardiac motion in XCAT, integrated CAD models, and CT acquisition and reconstruction-essential for optimizing and determining the robustness of image acquisition and reconstruction parameters.The results demonstrate the fundamental limitations of acquiring, quantifying, and determining the composition of CAD from cardiac CT with unoptimized acquisition protocols.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…We unified tools from prior work 26,27,30,36,37,64–66 to create a framework for reproducible, simulated cardiac imaging of realistic virtual coronary artery pathologies. These tools enable cardiac CT optimization by creating a framework for easy parameterization of realistic cardiac motion in XCAT, integrated CAD models, and CT acquisition and reconstruction—essential for optimizing and determining the robustness of image acquisition and reconstruction parameters.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Development of physiologically‐informed computational coronary artery plaques for use in virtual imaging trials

Sauer,

Buckler,

Abadi

et al. 2024

Medical Physics

Self Cite

View full text Add to dashboard Cite

BackgroundAs a leading cause of death, worldwide, cardiovascular disease is of great clinical importance. Among cardiovascular diseases, coronary artery disease (CAD) is a key contributor, and it is the attributed cause of death for 10% of all deaths annually. The prevalence of CAD is commensurate with the rise in new medical imaging technologies intended to aid in its diagnosis and treatment. The necessary clinical trials required to validate and optimize these technologies require a large cohort of carefully controlled patients, considerable time to complete, and can be prohibitively expensive. A safer, faster, less expensive alternative is using virtual imaging trials (VITs), utilizing virtual patients or phantoms combined with accurate computer models of imaging devices.PurposeIn this work, we develop realistic, physiologically‐informed models for coronary plaques for application in cardiac imaging VITs.MethodsHistology images of plaques at micron‐level resolution were used to train a deep convolutional generative adversarial network (DC‐GAN) to create a library of anatomically variable plaque models with clinical anatomical realism. The stability of each plaque was evaluated by finite element analysis (FEA) in which plaque components and vessels were meshed as volumes, modeled as specialized tissues, and subjected to the range of normal coronary blood pressures. To demonstrate the utility of the plaque models, we combined them with the whole‐body XCAT computational phantom to perform initial simulations comparing standard energy‐integrating detector (EID) CT with photon‐counting detector (PCD) CT.ResultsOur results show the network is capable of generating realistic, anatomically variable plaques. Our simulation results provide an initial demonstration of the utility of the generated plaque models as targets to compare different imaging devices.ConclusionsVast, realistic, and variable CAD pathologies can be generated to incorporate into computational phantoms for VITs. There they can serve as a known truth from which to optimize and evaluate cardiac imaging technologies quantitatively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Development of physiologically‐informed computational coronary artery plaques for use in virtual imaging trials

Sauer,

Buckler,

Abadi

et al. 2024

Medical Physics

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

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

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.

show abstract

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

Sotoudeh‐Paima,

Segars,

Ghosh

et al. 2024

Medical Physics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 9 publications

References 10 publications

Development of physiologically‐informed computational coronary artery plaques for use in virtual imaging trials

Development of physiologically‐informed computational coronary artery plaques for use in virtual imaging trials

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

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

Contact Info

Product

Resources

About