DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography

Abadi, Ehsan; Harrawood, Brian P.; Sharma, Shobhit; Kapadia, Anuj J.; Segars, W. Paul; Samei, Ehsan

doi:10.1109/tmi.2018.2886530

Cited by 66 publications

(65 citation statements)

References 43 publications

(41 reference statements)

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“…Several groups have utilized VCTs to evaluate their novel CT image reconstruction algorithms. [305][306][307] Abadi et al 141,308 characterized the noise texture across filtered back projection and iterative reconstruction algorithms. In this study, an XCAT phantom 41,55 was imaged 50 times using a validated CT simulator, setup to mimic the parameters and settings of a specific scanner model (Siemens Definition Flash).…”

Section: Ct Imagingmentioning

confidence: 99%

“…They should ideally take place at multiple levels of granularity. They can be applied to a simulation in its subcomponents 41,369,370 (e.g., model of x-ray spectrum), whole component 141,142,262,371,372 (e.g., accuracy in creating realistic simulated images), or multicomponent 26,278 (e.g., accuracy in creating the complete human imaging process from the patient to the output of the imaging task). One approach for these validations has been through the simulation of IEC standard tests.…”

Section: Verification Validations and Inferencementioning

confidence: 99%

“…To include the scatter signal, hybrid approaches in which primary signal (using ray-tracing) is combined with scatter signal using either analytical scatter estimations or MC methods with limited number of histories have been developed. 141 – 143 The other limitation with the ray-tracing methods is that they do not account for the finite size of the focal spot and detector pixels, making the simulated images undersampled and unrealistically sharp. This can be remedied by a subsampling strategy in which each source-to-detector ray is replaced by multiple rays sampling the area of the focal spot and detector pixel.…”

Section: Imaging Simulatorsmentioning

confidence: 99%

“… Simulated full-field digital mammography (top left), digital breast tomosynthesis (top right), 26 and CT images 141 all with embedded abnormalities (microcalcification cluster on the top and spiculated lesion on the bottom). …”

Section: Imaging Simulatorsmentioning

confidence: 99%

“…To date, limited scanner models have been developed and validated. 141 – 143 For comprehensive virtual trials, models of more diverse scanners are needed. Further, as computational phantoms advance, they become more detailed (higher resolution) with more realistic capabilities (e.g., motion and perfusion models).…”

Section: Imaging Simulatorsmentioning

confidence: 99%

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Virtual clinical trials in medical imaging: a review

et al. 2020

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The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

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Section: Ct Imagingmentioning

confidence: 99%

Section: Verification Validations and Inferencementioning

confidence: 99%

Section: Imaging Simulatorsmentioning

confidence: 99%

Section: Imaging Simulatorsmentioning

confidence: 99%

Section: Imaging Simulatorsmentioning

confidence: 99%

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Virtual clinical trials in medical imaging: a review

et al. 2020

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Semiempirical, parameterized spectrum estimation for x‐ray computed tomography

Fitzgerald

Araujo

et al. 2021

Medical Physics

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To develop a tool to produce accurate, well-validated x-ray spectra for standalone use or for use in an open-access x-ray/CT simulation tool. Spectrum models will be developed for tube voltages in the range of 80 kVp through 140 kVp and for anode takeoff angles in the range of 5°to 9°. Methods: Spectra were initialized based on physics models, then refined using empirical measurements, as follows. A new spectrum-parameterization method was developed, including 13 spline knots to represent the bremsstrahlung component and 4 values to represent characteristic lines. Initial spectra at 80, 100, 120, and 140 kVp and at takeoff angles from 5°to 9°were produced using physics-based spectrum estimation tools XSPECT and SpekPy. Empirical experiments were systematically designed with careful selection of attenuator materials and thicknesses, and by reducing measurement contamination from scatter to <1%. Measurements were made on a 64-row CT scanner using the scanner's detector and using multiple layers of polymethylmethacrylate (PMMA), aluminum, titanium, tin, and neodymium. Measurements were made at 80, 100, 120, and 140 kVp and covering the entire 64-row detector (takeoff angles from 5°to 9°); a total of 6,144 unique measurements were made. After accounting for the detector's energy response, parameterized representations of the initial spectra were refined for best agreement with measurements using two proposed optimization schemes: based on modulation and based on gradient descent. X-ray transmission errors were computed for measurements vs calculations using the nonoptimized and optimized spectra. Half-value, tenth-value, and hundredth-value layers for PMMA, Al, and Ti were calculated. Results: Spectra before and after parameterization were in excellent agreement (e.g., R 2 values of 0.995 and 0.997). Empirical measurements produced smoothly varying curves with x-ray transmission covering a range of up to 3.5 orders of magnitude. Spectra from the two optimization schemes, compared with the unoptimized physic-based spectra, each improved agreement with measurements by twofold through tenfold, for both postlog transmission data and for fractional value layers. Conclusion:The resulting well-validated spectra are appropriate for use in the open-access x-ray/CT simulator under development, the x-ray-based Cancer Imaging Toolkit (XCIST), or for standalone use. These spectra can be readily interpolated to produce spectra at arbitrary kVps over the range of 80 to 140 kVp and arbitrary takeoff angles over the range of 5°to 9°. Furthermore, interpolated spectra over these ranges can be obtained by applying the standalone Matlab function available at https:// github.com/xcist/documentation/blob/master/XCISTspectrum.m.

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A dynamic simulation framework for CT perfusion in stroke assessment built from first principles

et al. 2021

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Physicians utilize cerebral perfusion maps (e.g., cerebral blood flow, cerebral blood volume, transit time) to prescribe the plan of care for stroke patients. Variability in scanning techniques and post-processing software can result in differences between these perfusion maps. To determine which techniques are acceptable for clinical care, it is important to validate the accuracy and reproducibility of the perfusion maps. Validation using clinical data is challenging due to the lack of a gold standard to assess cerebral perfusion and the impracticality of scanning patients multiple times with different scanning techniques. In contrast, simulated data from a realistic digital phantom of the cerebral perfusion in acute stroke patients would enable studies to optimize and validate the scanning and post-processing techniques. Methods: We describe a complete framework to simulate CT perfusion studies for stroke assessment. We begin by expanding the XCAT brain phantom to enable spatially varying contrast agent dynamics and incorporate a realistic model of the dynamics in the cerebral vasculature derived from first principles. A dynamic CT simulator utilizes the time-concentration curves to define the contrast agent concentration in the object at each time point and generates CT perfusion images compatible with commercially available post-processing software. We also generate ground truth perfusion maps to which the maps generated by post-processing software can be compared. Results: We demonstrate a dynamic CT perfusion study of a simulated patient with an ischemic stroke and the resulting perfusion maps generated by post-processing software. We include a visual comparison between the computer-generated perfusion maps and the ground truth perfusion maps. The framework is highly tunable; users can modify the perfusion properties (e.g., occlusion location, CBF, CBV, and MTT), scanner specifications (e.g., focal spot size and detector configuration), scanning protocol (e.g., kVp and mAs), and reconstruction parameters (e.g., slice thickness and reconstruction filter). Conclusions: This framework provides realistic test data with the underlying ground truth that enables a robust assessment of CT perfusion techniques and post-processing methods for stroke assessment.

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DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography

Cited by 66 publications

References 43 publications

Virtual clinical trials in medical imaging: a review

Virtual clinical trials in medical imaging: a review

Semiempirical, parameterized spectrum estimation for x‐ray computed tomography

A dynamic simulation framework for CT perfusion in stroke assessment built from first principles

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