Dr. Robert E. Stevens Profile

Dr. Robert E. Stevens

at Johns Hopkins Univ.

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Publications (4)

Proceedings Article | 28 May 2019 Paper

Clinical study of soft-tissue contrast resolution in cone-beam CT of the head using multi-resolution PWLS with multi-motion correction and an electronic noise model

P. Wu, A. Sisniega, J. Stayman, W. Zbijewski, D. Foos, X. Wang, N. Aygun, R. Stevens, J. Siewerdsen

Proceedings Volume 11072, 110720B (2019) https://doi.org/10.1117/12.2534887

KEYWORDS: Head, Computed tomography, Imaging systems, Prototyping, Visualization, Sensors, Motion models, Image quality, Injuries, Brain

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Purpose: Improving soft-tissue contrast resolution beyond the capability of current cone-beam CT (CBCT) systems is essential to a growing range of image guidance and diagnostic imaging scenarios. We present a framework for CBCT model-based image reconstruction (MBIR) combining artifact corrections with multi-resolution reconstruction and multiregion motion compensation and apply the method for the first time in a clinical study of CBCT for high-quality imaging of head injury. Methods: A CBCT prototype was developed for mobile point-of-care imaging in the neuro-critical care unit (NCCU). Projection data were processed via poly-energetic gain correction and an artifacts correction pipeline treating scatter, beam hardening, and motion compensation. The scatter correction was modified to use a penalized weighted least-squares (PWLS) image in the Monte-Carlo (MC) object model for better uniformity in truncated data. The PWLS method included: (1) multi-resolution reconstruction to mitigate lateral truncation from the head-holder; (2) multi-motion compensation allowing separate motion of the head and head-holder; and (3) modified statistical weights to account for electronics noise and fluence modulation by the bowtie filter. Imaging performance was evaluated in simulation and in the first clinical study (N = 54 patients) conducted with the system. Results: Using a PWLS object model in the final iteration of the MC scatter estimate improved image uniformity by 40.4% for truncated datasets. The multi-resolution, multi-motion PWLS method greatly reduced streak artifacts and nonuniformity both in simulation (RMSE reduced by 65.5%) and in the clinical study (visual image quality assessed by a neuroradiologist). Up to 15% reduction in variance was achieved using statistical weights modified according to a model for electronic noise from the detector. Each component was important for improved contrast resolution in the patient data. Conclusion: An integrated pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to a level supporting visualization of low-contrast brain lesions and warranting future studies of diagnostic performance in the NCCU.

Proceedings Article | 1 March 2019 Presentation + Paper

Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise

P. Wu, A. Sisniega, J. W. Stayman, W. Zbijewski, D. Foos, X. Wang, N. Aygun, R. Stevens, J. Siewerdsen

Proceedings Volume 10948, 1094814 (2019) https://doi.org/10.1117/12.2513417

KEYWORDS: Modulation, Imaging systems, Head, Sensors, Modulators, Scanners, Image filtering, Statistical modeling, Signal attenuation, Prototyping

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Purpose: Cone-beam CT (CBCT) systems with a flat-panel detector (FPD) have advanced in a variety of specialty diagnostic imaging scenarios, with fluence modulation and multiple-gain detectors playing important roles in extending dynamic range and improving image quality. We present a penalized weighted least-squares (PWLS) reconstruction approach with a noise model that includes the effects of fluence modulation and electronic readout noise, and we show preliminary results that tests the concept with a CBCT head scanner prototype. Methods: Statistical weights in PWLS were modified using a realistic noise model for the FPD that considers factors such as system blur and spatially varying electronic noise in multiple-gain readout detectors (PWLS^e). A spatially varying gain term was then introduced in the calculation of statistical weights to account for the change in quantum noise due to fluence modulation (e.g. bowtie filter) (PWLS∗). The methods were tested in phantom experiments involving an elliptical phantom specially designed to stress dual-gain readout, and a water phantom and an anthropomorphic head phantom to quantify improvements in noise-resolution characteristics for the new PWLS methods (PWLS𝑒 and PWLS∗, and combined PWLS^∗e). The proposed methods were further tested using a high-quality, low-dose CBCT head scanner prototype in a clinical study involving patients with head injury. Results: Preliminary results show that the PWLS^e method demonstrated superior noise-resolution tradeoffs compared to conventional PWLS, with variance reduced by ~15-25% at matched resolution of 0.65 mm edge-spread-function (ESF) width. Clinical studies confirmed these findings, with variance reduced by ~15% in peripheral regions of the head without loss in spatial resolution, improving visual image quality in detection of peridural hemorrhage. A bowtie filter and polyenergetic gain correction improved image uniformity, and early results demonstrated that the proposed PWLS∗ method showed a ~40% reduction in variance compared to conventional PWLS when used with a bowtie filter. Conclusion: A more accurate noise model incorporated in PWLS statistical weights to account for fluence modulation and electronic readout noise reduces image noise and improves soft-tissue imaging performance in CBCT for clinical applications requiring a high degree of contrast resolution.

Proceedings Article | 9 March 2018 Paper

Image quality, scatter, and dose in compact CBCT systems with flat and curved detectors

A. Sisniega, W. Zbijewski, P. Wu, J. W. Stayman, V. Koliatsos, N. Aygun, R. Stevens, X. Wang, D. Foos, J. Siewerdsen

Proceedings Volume 10573, 105734E (2018) https://doi.org/10.1117/12.2293872

KEYWORDS: X-rays, Imaging systems, Image quality, X-ray detectors, Monte Carlo methods, Signal attenuation, Head, X-ray imaging, Systems modeling

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Purpose: A number of cone-beam CT (CBCT) applications demand increasingly compact system designs for smaller footprint and improved portability. Such compact geometries can be achieved via reduction of air gap and integration of novel, curved detectors; however, the increased x-ray scatter in presents a major challenge to soft-tissue image quality in such compact arrangements. This work investigates pre-patient modulation (bowtie filters) and antiscatter grids to mitigate such effects for compact geometries with curved detectors. Methods: The effects of bowtie filters on dose and x-ray scatter were investigated in a compact geometry (180 mm air gap), for three detector curvatures: Flat, Focused at source, and Compact focused at isocenter. Experiments used bowtie filters of varying curvature combined with antiscatter grids (GR: 8:1, 80 lpmm). Scatter was estimated via GPU-accelerated Monte Carlo simulation in an anthropomorphic head phantom. Primary fluence was estimated with a polychromatic Siddon projector. Realistic Poisson noise (total dose: 20 mGy) was added to the total signal. Scatter magnitude and distribution were evaluated in projection data, and CT image quality was assessed in PWLS reconstructions. Results were validated in physical experiments on an x-ray test-bench for CBCT. Results: Moderate bowties combined with grids reduced average scatter magnitude and SPR, reduced cupping from 90 to 5 HU, and yielded net benefit to CNR despite attenuation of primary fluence. Dose to sensitive organs (eye lens) was reduced by 27%. More aggressive bowties showed further potential for dose reduction (35%) but increased peripheral SPR and increased non-uniformity and artifacts at the periphery of the image. Curved detector geometry exhibited slightly improved uniformity but a slight reduction in CNR compared to conventional flat detector geometry. Conclusion: Highly portable, soft-tissue imaging, CBCT systems with very compact geometry and curved detectors appear feasible, despite elevated x-ray scatter, through combination of moderate pre-patient collimation and antiscatter grids.

Proceedings Article | 9 March 2018 Presentation + Paper

Evaluation of the reconstruction-of-difference (RoD) algorithm for cone-beam CT neuro-angiography

P. Wu, J. Stayman, M. Mow, W. Zbijewski, A. Sisniega, N. Aygun, R. Stevens, D. Foos, X. Wang, J. Siewerdsen

Proceedings Volume 10573, 105731R (2018) https://doi.org/10.1117/12.2293709

KEYWORDS: Reconstruction algorithms, Head, Prototyping, Computed tomography, Angiography, Iodine, Scanners, Brain, Targeting Task Performance metric, Data modeling

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Purpose: Timely detection of neurovascular pathology such as ischemic stroke is essential to effective treatment, and systems for cone-beam CT (CBCT) could provide CT angiography (CTA) assessment in a timely manner close to the point of care. CBCT systems suffer from slow rotation speed and readout speed, which leads to inconsistent or sparse dataset. This work describes a new reconstruction method using a reconstruction of difference (RoD) approach that is robust against such factors.

Methods: Important aspects of CBCT angiography were investigated, weighting tradeoffs among the magnitude of iodine enhancement (peak contrast), the degree of data consistency, and the degree of data sparsity. Simulation studies were performed across a range of CBCT half-scan acquisition speed ranging ~3 – 17 s. Experiments were conducted using a CBCT prototype and an anthropomorphic neurovascular phantom incorporating a vessel with contrast injection with a time-attenuation (TAC) injection giving low data consistency but high peak contrast. Images were reconstructed using filtered back-projection (FBP), penalized likelihood (PL), and the RoD algorithm. Data were evaluated in terms of root mean square error (RMSE) in image enhancement as well as overall image noise and artifact.

Results: Feasibility was demonstrated for 3D angiographic assessment in CBCT images acquired across a range of data consistency and sparsity. Compared to FBP, the RoD method reduced the RMSE in reconstructed images by 50.0% in simulation studies (fixed peak contrast; variable data consistency and sparsity). The improvement in RMSE compared to PL reconstruction was 28.8%. The phantom experiments investigated conditions of low data consistency, RoD provided a 15.6% reduction in RMSE compared to FBP and a 16.3% reduction compared to PL, showing the feasibility of RoD method for slow-rotating CBCT-A system.

Conclusions: Simulations and phantom experiments show the feasibility and improved performance of the RoD approach compared to FBP and PL reconstruction, enabling 3D neuro-angiography on a slowly rotating CBCT system (e.g., 17.1s for a half-scan). The algorithm is relatively robust against data sparsity and is sensitive in detecting low levels of contrast enhancement from the baseline (mask) scan. Tradeoffs among peak contrast, data consistency, and data sparsity are demonstrated clearly in each experiment and help to guide the development of optimal contrast injection protocols for future preclinical and clinical studies.

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