Publications - Advanced Imaging Algorithms and Instrumentation Laboratory

2019

Cao, Qian; Sisniega, Alejandro; Stayman, J. Webster; Yorkston, John; Siewerdsen, Jeffrey H.; Zbijewski, Wojciech

Quantitative cone-beam CT of bone mineral density using model-based reconstruction Proceedings Article

In: Proc. SPIE Medical Imaging, pp. 109480Y-1-6, 2019.

Links | BibTeX | Tags: Artifact Correction, Beam Hardening, CBCT, Extremities, MBIR

2017

Xu, Shiyu; Khanna, A. Jay; Siewerdsen, Jeffrey H.; Stayman, J. Webster

Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra Journal Article

In: Physics in medicine and biology, vol. 62, no. 8, pp. 3352-3374, 2017.

Links | BibTeX | Tags: Artifact Correction, Beam Hardening, Known Components, MBIR, Metal Artifacts

Zhang, Chengzhu; Zbijewski, Wojciech; Zhang, Xiaoxuan; Xu, Shiyu; Stayman, J. Webster

Polyenergetic known-component reconstruction without prior shape models Proceedings Article

In: Flohr, Thomas G.; Lo, Joseph Y.; Schmidt, Taly Gilat (Ed.): SPIE Medical Imaging, pp. 101320O-1–6, 2017.

Links | BibTeX | Tags: Beam Hardening, Known Components, MBIR, Metal Artifacts

Zhang, Xiaoxuan; Tilley, Steven; Xu, Shiyu; Mathews, Aswin; McVeigh, Elliot; Stayman, J. Webster

Deformable Known Component Model-Based Reconstruction for Coronary CT Angiography Proceedings Article

In: Flohr, Thomas G.; Lo, Joseph Y.; Schmidt, Taly Gilat (Ed.): SPIE Medical Imaging, pp. 1013213-1–6, 2017.

Links | BibTeX | Tags: Beam Hardening, Cardiac, Image Registration, Known Components, MBIR, Metal Artifacts

Xu, Shiyu; Khanna, A. Jay; Siewerdsen, Jeffrey H.; Stayman, J. Webster

Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra Journal Article

In: Physics in Medicine and Biology, vol. 62, no. 8, pp. 3352-3374, 2017.

Links | BibTeX | Tags: Artifact Correction, Beam Hardening, Known Components, Metal Artifacts

2015

Dang, Hao; Stayman, J. Webster; Sisniega, Alejandro; Xu, Jennifer; Zbijewski, Wojciech; Yorkston, John; Aygun, Nafi; Koliatsos, Vassilis; Siewerdsen, Jeffrey H.

Cone-Beam CT of Traumatic Brain Injury Using Statistical Reconstruction with a Post-Artifact-Correction Noise Model. Honorable Mention Conference

vol. 9412, 2015, ISSN: 0277-786X, (Wagner Award Finalist and 3rd Place Best Student Paper ).

Abstract | Links | BibTeX | Tags: -Awards-, Artifact Correction, Beam Hardening, Head/Neck, High-Fidelity Modeling, MBIR, Scatter Estimation

@conference{Dang2015a,

title = {Cone-Beam CT of Traumatic Brain Injury Using Statistical Reconstruction with a Post-Artifact-Correction Noise Model.},

author = {Hao Dang and J. Webster Stayman and Alejandro Sisniega and Jennifer Xu and Wojciech Zbijewski and John Yorkston and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen },

editor = {Christoph Hoeschen and Despina Kontos and Thomas G. Flohr },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4539953},

doi = {10.1117/12.2082075},

issn = {0277-786X},

year  = {2015},

date = {2015-02-01},

urldate = {2015-02-01},

journal = {Proceedings of SPIE--the International Society for Optical Engineering},

volume = {9412},

pages = {941207},

abstract = {Traumatic brain injury (TBI) is a major cause of death and disability. The current front-line imaging modality for TBI detection is CT, which reliably detects intracranial hemorrhage (fresh blood contrast 30-50 HU, size down to 1 mm) in non-contrast-enhanced exams. Compared to CT, flat-panel detector (FPD) cone-beam CT (CBCT) systems offer lower cost, greater portability, and smaller footprint suitable for point-of-care deployment. We are developing FPD-CBCT to facilitate TBI detection at the point-of-care such as in emergent, ambulance, sports, and military applications. However, current FPD-CBCT systems generally face challenges in low-contrast, soft-tissue imaging. Model-based reconstruction can improve image quality in soft-tissue imaging compared to conventional filtered backprojection (FBP) by leveraging high-fidelity forward model and sophisticated regularization. In FPD-CBCT TBI imaging, measurement noise characteristics undergo substantial change following artifact correction, resulting in non-negligible noise amplification. In this work, we extend the penalized weighted least-squares (PWLS) image reconstruction to include the two dominant artifact corrections (scatter and beam hardening) in FPD-CBCT TBI imaging by correctly modeling the variance change following each correction. Experiments were performed on a CBCT test-bench using an anthropomorphic phantom emulating intra-parenchymal hemorrhage in acute TBI, and the proposed method demonstrated an improvement in blood-brain contrast-to-noise ratio (CNR = 14.2) compared to FBP (CNR = 9.6) and PWLS using conventional weights (CNR = 11.6) at fixed spatial resolution (1 mm edge-spread width at the target contrast). The results support the hypothesis that FPD-CBCT can fulfill the image quality requirements for reliable TBI detection, using high-fidelity artifact correction and statistical reconstruction with accurate post-artifact-correction noise models.},

note = {Wagner Award Finalist and 3rd Place Best Student Paper },

keywords = {-Awards-, Artifact Correction, Beam Hardening, Head/Neck, High-Fidelity Modeling, MBIR, Scatter Estimation},

pubstate = {published},

tppubtype = {conference}

}

Sisniega, Alejandro; Zbijewski, Wojciech; Xu, Jennifer; Dang, Hao; Stayman, J. Webster; Yorkston, John; Aygun, Nafi; Koliatsos, Vassilis; Siewerdsen, Jeffrey H.

High-fidelity artifact correction for cone-beam CT imaging of the brain. Journal Article

In: Physics in medicine and biology, vol. 60, no. 4, pp. 1415–39, 2015, ISSN: 1361-6560.

Abstract | Links | BibTeX | Tags: Artifact Correction, Beam Hardening, CBCT, Head/Neck, Scatter Estimation

@article{Sisniega2015,

title = {High-fidelity artifact correction for cone-beam CT imaging of the brain.},

author = {Alejandro Sisniega and Wojciech Zbijewski and Jennifer Xu and Hao Dang and J. Webster Stayman and John Yorkston and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen },

url = {http://www.ncbi.nlm.nih.gov/pubmed/25611041},

doi = {10.1088/0031-9155/60/4/1415},

issn = {1361-6560},

year  = {2015},

date = {2015-02-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {4},

pages = {1415--39},

abstract = {CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30-50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening.The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT.Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.},

keywords = {Artifact Correction, Beam Hardening, CBCT, Head/Neck, Scatter Estimation},

pubstate = {published},

tppubtype = {article}

}

CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30-50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening.The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT.Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.

2012

Zbijewski, Wojciech; Stayman, J. Webster; Muhit, Abdullah Al; Yorkston, John; Carrino, John A.; Siewerdsen, Jeffrey H.

CT Reconstruction Using Spectral and Morphological Prior Knowledge: Application to Imaging the Prosthetic Knee Proceedings Article

In: Second Int. Conf. Image Form. X-ray Computed Tomography, pp. 434–438, 2012.

Links | BibTeX | Tags: Artifact Correction, Beam Hardening, Extremities, Known Components, MBIR, Metal Artifacts, Spectral X-ray/CT