Mr. Stephen Z. Liu Profile

Stephen Z. Liu

at Johns Hopkins Univ School of Medicine

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Author

Area of Expertise:

Quantitative Imaging , Deep Learning , Cone-Beam Computed Tomography , Physical Modelling , Optimization Algorithms , Image Reconstruction

Publications (10)

Proceedings Article | 1 April 2024 Presentation + Paper

One-shot estimation of epistemic uncertainty in deep learning image formation with application to high-quality cone-beam CT reconstruction

Stephen Liu, Prasad Vagdargi, Craig Jones, Mark Luciano, William Anderson, Patrick Helm, Ali Uneri, Jeffrey Siewerdsen, Wojciech Zbijewski, Alejandro Sisniega

Proceedings Volume 12925, 129251E (2024) https://doi.org/10.1117/12.3006934

KEYWORDS: Medical image reconstruction, Cone beam computed tomography, Education and training, Deep learning, Brain, Computed tomography, Error analysis, Data modeling, 3D modeling, Monte Carlo methods

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Deep Learning (DL) image synthesis has gained increasing popularity for the reconstruction of CT and cone-beam CT (CBCT) images, especially in combination with physically-principled reconstruction algorithms. However, DL synthesis is challenged by the generalizability of training data and noise in the trained model. Epistemic uncertainty has proven as an efficient way of quantifying erroneous synthesis in the presence of out-of-domain features, but its estimation with Monte Carlo (MC) dropout requires a large number of inference runs, variable as a function of the particular uncertain feature. We propose a single-pass method–the Moment Propagation Model–which approximates the MC dropout by analytically propagating the statistical moments through the network layers, removing the need for multiple inferences and removing errors in estimations from insufficient dropout realizations. The proposed approach jointly computes the change of the expectation and the variance of the input (first two statistical moments) through each network layer, where each moment undergoes a different numerical transformation. The expectation is initialized as the network input; the variance is solely introduced at dropout layers, modeled as a Bernoulli process. The method was evaluated using a 3D Bayesian conditional generative adversarial network (GAN) for synthesis of high-quality head MDCT from low-quality intraoperative CBCT reconstructions. 20 pairs of measured MDCT volumes (120kV, 400 to 550mAs) depicting normal head anatomy, and simulated CBCT volumes (100 to120kV, 32 to 200mAs) were used for training. Scatter, beam-hardening, detector lag and glare were added to the simulated CBCT and were corrected (assuming unknown) prior to reconstruction. Epistemic uncertainty was estimated for 30 heads (outside of the training set) containing simulated brain lesions using the proposed single-pass propagation model, and results were compared to the standard 200-pass dropout approach. Image quality and quantitative accuracy of the estimated uncertainty of lesions and other anatomical sites were further evaluated. The proposed propagation model captured >2HU increase in epistemic uncertainty caused by various hyper- and hypo-density lesions, with <0.31HU error over the brain compared to the reference MC dropout result at 200 inferences and <0.1HU difference to a converged MC dropout estimate at 100 inference passes. These findings indicate a potential 100-fold increase in computational efficiency of neural network uncertainty estimation. The proposed moment propagation model is able to achieve accurate quantification of epistemic uncertainty in a single network pass and is an efficient alternative to conventional MC dropout.

Proceedings Article | 1 April 2024 Poster + Paper

Investigation of correction and decomposition algorithms in bedside x-ray imaging simulating a multi-layer flat panel detector

Jamin Schaefer, Stephen Liu, Steffen Kappler, Ferdinand Lueck, Ludwig Ritschl, Thomas Weber, Wojciech Zbijewski, Georg Rose

Proceedings Volume 12925, 1292536 (2024) https://doi.org/10.1117/12.3006758

KEYWORDS: Bone, Monte Carlo methods, Adipose tissue, Sensors, Detection and tracking algorithms, Computer simulations, Attenuation, X-ray imaging, Computed tomography, X-rays

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Proceedings Article | 29 March 2024 Presentation + Paper

End-to-end 3D neuroendoscopic video reconstruction for robot-assisted ventriculostomy

P. Vagdargi, A. Uneri, S. Liu, C. Jones, A. Sisniega, J. Lee, P. Helm, W. Anderson, M. Luciano, G. Hager, J. Siewerdsen

Proceedings Volume 12928, 129280M (2024) https://doi.org/10.1117/12.3008758

KEYWORDS: Video, Education and training, Deformation, 3D acquisition, Voxels, 3D image processing, Imaging systems, Endoscopy, 3D image reconstruction, Visualization

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Proceedings Article | 7 April 2023 Presentation + Paper

Quantitative dual-energy imaging of bone marrow edema using multi-source cone-beam CT with model-based decomposition

Stephen Liu, Huanyi Zhou, Greg Osgood, Shadpour Demehri, J. Webster Stayman, Wojciech Zbijewski

Proceedings Volume 12463, 1246315 (2023) https://doi.org/10.1117/12.2654449

KEYWORDS: Bone, Cone beam computed tomography, Model-based design, X-ray computed tomography, X-ray sources, Scanners, Physics, Medical imaging, Dual energy imaging, X-ray medical imaging, Computed tomography, Orthopedic imaging

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Purpose: We investigated the feasibility of dual-energy (DE) detection of bone marrow edema (BME) using a dedicated extremity cone-beam CT (CBCT) with a unique three-source x-ray unit. The sources can be operated at different energies to enable single-scan DE acquisitions. However, they are arranged parallel to the axis of rotation, resulting in incomplete sampling and precluding the application of DE projection-domain decompositions (PDD) for beam-hardening reduction. Therefore, we propose a novel combination of a model-based “one-step” DE two-material decomposition followed by a constrained image-domain change-of-basis to obtain virtual non-calcium (VNCa) images for BME detection.

Methods: DE projections were obtained using an “alternating-kV” protocol by operating the peripheral two sources of the CBCT system at low-energy (60 kV, 0.105 mAs/frame) and the central source at high-energy (100 kV, 0.028 mAs/frame), for a total of 600 frames over 216° of gantry rotation. Projections were processed with detector lag, glare and fast Monte Carlo (MC)-based iterative scatter corrections. Model-based material decomposition (MBMD) was then implemented to obtain aluminum (Al) and polyethylene (PE) volume fraction images with minimal beam-hardening. Statistical ray weights in MBMD were modified to account for regions with highly oblique sampling by the peripheral sources. To generate the VNCa maps, image-domain decomposition (IDD) constrained by the volume conservation principle (VCP) was performed to convert the Al and PE MBMD images into volume fractions of water, fat and cortical bone. Accuracy of BME detection was evaluated using physical phantom data acquired on the multi-source extremity CBCT scanner.

Results: The proposed framework estimated the volume of BME with ~10% error. The MC-based scatter corrections and the modified MBMD ray weights were essential to achieve such performance – the error without MC scatter corrections was <30%, whereas the uniformity of estimated VNCa images was 3x improved using the modified weights compared to the conventional weights.

Conclusions: The proposed DE decomposition framework was able to overcome challenges of high scatter and incomplete sampling to achieve BME detection on a CBCT system with axially-distributed x-ray sources.

Proceedings Article | 18 October 2022 Open Access

Dual-energy cone-beam CT with three-material decomposition for bone marrow edema imaging

Stephen Liu, Magdalena Herbst, Thomas Weber, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Jeffrey Siewerdsen, Wojciech Zbijewski

Proceedings Volume 12304, 123040Z (2022) https://doi.org/10.1117/12.2646391

KEYWORDS: Medical image reconstruction, Bone, X-ray computed tomography, Sensors, X-rays, Medical imaging, Aluminum, Physics, Photons, Signal attenuation, Monte Carlo methods

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Proceedings Article | 4 April 2022 Presentation + Paper

Feasibility of dual-energy cone-beam CT of bone marrow edema using dual-layer flat panel detectors

Stephen Liu, Chumin Zhao, Magdalena Herbst, Thomas Weber, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Jeffrey Siewerdsen, Wojciech Zbijewski

Proceedings Volume 12031, 120311J (2022) https://doi.org/10.1117/12.2613211

KEYWORDS: Bone, Collimation, Sensors, Copper, X-ray computed tomography, Medical imaging, Error analysis, Spectral calibration, Physics, Monte Carlo methods

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Purpose: We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition. Methods: A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm² dual-layer FPD (top layer: 0.20 mm CsI:Tl, bottom layer: 0.55 mm CsI:Tl; a 1 mm Cu filter between the layers to improve spectral separation). Tube settings were 120 kV (+2 mm Al, +0.2 mm Cu) and 10 mAs per exposure. The digital phantom consisted of a 160 mm water cylinder with inserts containing mixtures of water (volume fraction ranging 0.18 to 0.46) - fat (0.5 to 0.7) - Ca (0.04 to 0.12); decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material DE decomposition was applied to DE CBCT projections: first, projection-domain decomposition (PDD) into fat-aluminum basis, followed by CBCT reconstruction of intermediate base images, followed by image-domain change of basis into fat, water and bone. Sensitivity to scatter was evaluated by i) adjusting source collimation (12 to 400 mm width) and ii) subtracting various fractions of the true scatter from the projections at 400 mm collimation. The impact of spectral calibration was studied by shifting the effective beam energy (± 2 keV) when creating the PDD lookup table. We further simulated a realistic BME imaging framework, where the scatter was estimated using a fast Monte Carlo (MC) simulation from a preliminary decomposition of the object; the object was a realistic wrist phantom with an 0.85 mL BME stimulus in the radius. Results: The decomposition is sensitive to scatter: approx. <20 mm collimation width or <10% error of scatter correction in a full field-of-view setting is needed to resolve BME. A mismatch in PDD decomposition calibration of ± 1 keV results in ~25% error in fat fraction estimates. In the wrist phantom study with MC scatter corrections, we were able to achieve ~0.79 mL true positive and ~0.06 mL false positive BME detection (compared to 0.85 mL true BME volume). Conclusions: Detection of BME using DE CBCT with dual-layer FPD is feasible, but requires scatter mitigation, accurate scatter estimation, and robust spectral calibration.

Proceedings Article | 15 February 2021 Presentation + Paper

Quantitative dual-energy imaging in the presence of metal implants using locally constrained model-based decomposition

Stephen Liu, Jeffrey Siewerdsen, J. Webster Stayman, Wojciech Zbijewski

Proceedings Volume 11595, 115951C (2021) https://doi.org/10.1117/12.2582277

KEYWORDS: Metals, Model-based design, Dual energy imaging, Bone, Reconstruction algorithms, Tissues, Orthopedic imaging, Optimization (mathematics), Error analysis

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Purpose: To mitigate effects of metal artifacts in Dual-Energy (DE) CT imaging, we introduce a constrained optimization algorithm to enable simultaneous reconstruction-decomposition of three materials: two tissues-of-interest and the metal. Methods: The volume conservation principle and nonnegativity of volume fractions were incorporated as a pair of linear constraints into the Model-Based Material Decomposition (MBMD) algorithm. This enabled solving for three unknown material concentrations from DE projection data. A primal-dual Ordered Subsets Predictor-Corrector Interior-Point (OSPCIP) algorithm was derived to perform the optimization in the proposed constrained-MBMD (CMBMD). To improve computational efficiency and monotonicity of CMBMD, we investigated an approach where the constraint was applied locally onto a small region containing the metal (identified from a preliminary reconstruction) during initial iterations, followed by final iterations with the constraint applied globally. Validation studies involved simulations and test bench experiments to assess the quantitative accuracy of bone concentration measurements in the presence of fracture fixation hardware. In all studies, DE data was acquired using a kVp-switching protocol with the 60 kVp low-energy beam and the 140 kVp high-energy beam. The system geometry emulated the extremity Cone-Beam CT (CBCT). Simulation studies included: i) a cylindrical phantom (80 mm diameter) with a 30 mm long Ti screw and an insert of varying cortical bone concentrations (3 – 13%), and ii) a realistic tibia phantom created from patient CBCT data with Ti fixation hardware of increasing complexity. The test bench experiment involved a 100 mm diameter water bath containing four Ca inserts (6.5 – 39.1% bone concentration) and a Ti plate. Results: CMBMD substantially reduced artifacts in DE decompositions in the presence of metal. The sequentially localglobal constraint strategy resulted in more monotonic convergence than using a global constraint for all iterations. In the simulation studies, CMBMD achieved quantitative accuracy within ~12% of nominal bone concentration in areas adjacent to metal, and within ~5% in areas further away from the metal, compared to ~80% error for the two-material MBMD. In the test bench study, CMBMD generated ~40% reduction in the error of bone concentration estimates compared to MBMD for nominal insert concentrations of <250 mg/mL, and ~12% reduction for concentrations <250 mg/mL. Conclusion: Proposed CMBMD enables accurate DE decomposition in the presence of metal implants by incorporating the metal as an additional base material. Proposed method will be particularly useful in quantitative orthopedic imaging, which is often challenged by metal fracture fixation and joint replacement hardware.

Proceedings Article | 15 February 2021 Presentation + Paper

Effects of x-ray scatter in quantitative dual-energy imaging using dual-layer flat panel detectors

Chumin Zhao, Stephen Liu, Wenying Wang, Magdalena Herbst, Thomas Weber, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, J. Webster Stayman, Jeffrey Siewerdsen, Wojciech Zbijewski

Proceedings Volume 11595, 115952A (2021) https://doi.org/10.1117/12.2581822

KEYWORDS: Sensors, X-rays, X-ray imaging, X-ray detectors, Dual energy imaging, Error analysis, Scintillators, Minerals, Copper, Cesium

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Purpose: We compare the effects of scatter on the accuracy of areal bone mineral density (BMD) measurements obtained using two flat-panel detector (FPD) dual-energy (DE) imaging configurations: a dual-kV acquisition and a dual-layer detector. Methods: Simulations of DE projection imaging were performed with realistic models of x-ray spectra, scatter, and detector response for dual-kV and dual-layer configurations. A digital body phantom with 4 cm Ca inserts in place of vertebrae (concentrations 50 - 400 mg/mL) was used. The dual-kV configuration involved an 80 kV low-energy (LE) and a 120 kV high-energy (HE) beam and a single-layer, 43x43 cm FPD with a 650 μm cesium iodide (CsI) scintillator. The dual-layer configuration involved a 120 kV beam and an FPD consisting of a 200 μm CsI layer (LE data), followed by a 1 mm Cu filter, and a 550 μm CsI layer (HE data). We investigated the effects of an anti-scatter grid (13:1 ratio) and scatter correction. For the correction, the sensitivity to scatter estimation error (varied ±10% of true scatter distribution) was evaluated. Areal BMD was estimated from projection-domain DE decomposition. Results: In the gridless dual-kV setup, the scatter-to-primary ratio (SPR) was similar for the LE and HE projections, whereas in the gridless dual layer setup, the SPR was ~26% higher in the LE channel (top CsI layer) than in the HE channel (bottom layer). Because of the resulting bias in LE measurements, the conventional projection-domain DE decomposition could not be directly applied to dual-layer data; this challenge persisted even in the presence of a grid. In contrast, DE decomposition of dual-kV data was possible both without and with the grid; the BMD error of the 400 mg/mL insert was -0.4 g/cm² without the grid and +0.3 g/cm² with the grid. The dual-layer FPD configuration required accurate scatter correction for DE decomposition: a -5% scatter estimation error resulted in -0.1 g/cm² BMD error for the 50 mg/mL insert and a -0.5 g/cm² BMD error for the 400 mg/mL with a grid, compared to <0.1 g/cm² for all inserts in a dual-kV setup with the same scatter estimation error. Conclusion: This comparative study of quantitative performance of dual-layer and dual-kV FPD-based DE imaging indicates the need for accurate scatter correction in the dual-layer setup due to increased susceptibility to scatter errors in the LE channel.

Proceedings Article | 2 March 2020 Presentation + Paper

Quantitative assessment of weight-bearing fracture biomechanics using extremity cone-beam CT

S. Liu, Q. Cao, G. Osgood, J. Siewerdsen, J. W. Stayman, W. Zbijewski

Proceedings Volume 11317, 113170I (2020) https://doi.org/10.1117/12.2549768

KEYWORDS: 3D modeling, Clouds, Image registration, Model-based design, Computed tomography, Metals, Scanners, Calcium, Surgery, Bone

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Purpose: We investigate an application of multisource extremity Cone-Beam CT (CBCT) with capability of weight-bearing tomographic imaging to obtain quantitative measurements of load-induced deformation of metal internal fixation hardware (e.g. tibial plate). Such measurements are desirable to improve the detection of delayed fusion or non-union of fractures, potentially facilitating earlier return to weight-bearing activities. Methods: To measure the deformation, we perform a deformable 3D-2D registration of a prior model of the implant to its CBCT projections under load-bearing. This Known-Component Registration (KC-Reg) framework avoids potential errors that emerge when the deformation is estimated directly from 3D reconstructions with metal artifacts. The 3D-2D registration involves a free-form deformable (FFD) point cloud model of the implant and a 3D cubic B-spline representation of the deformation. Gradient correlation is used as the optimization metric for the registration. The proposed approach was tested in experimental studies on the extremity CBCT system. A custom jig was designed to apply controlled axial loads to a fracture model, emulating weight-bearing imaging scenarios. Performance evaluation involved a Sawbone tibia phantom with an ~4 mm fracture gap. The model was fixed with a locking plate and imaged under five loading conditions. To investigate performance in the presence of confounding background gradients, additional experiments were performed with a pre-deformed femoral plate placed in a water bath with Ca bone mineral density inserts. Errors were measured using eight reference BBs for the tibial plate, and surface point distances for the femoral plate, where a prior model of deformed implant was available for comparison. Results: Both in the loaded tibial plate case and for the femoral plate with confounding background gradients, the proposed KC-Reg framework estimated implant deformations with errors of <0.2 mm for the majority of the investigated deformation magnitudes (error range 0.14 - 0.25 mm). The accuracy was comparable between 3D-2D registrations performed from 12 x-ray views and registrations obtained from as few as 3 views. This was likely enabled by the unique three-source x-ray unit on the extremity CBCT scanner, which implements two off-central-plane focal spots that provided oblique views of the field-of-view to aid implant pose estimation. Conclusion: Accurate measurements of fracture hardware deformations under physiological weight-bearing are feasible using an extremity CBCT scanner and FFD 3D-2D registration. The resulting deformed implant models can be incorporated into tomographic reconstructions to reduce metal artifacts and improve quantification of the mineral content of fracture callus in CBCT volumes.

Proceedings Article | 28 May 2019 Paper

Known-component model-based material decomposition for dual energy imaging of bone compositions in the presence of metal implant

S. Liu, S. Tilley, Q. Cao, J. Siewerdsen, J. Stayman, W. Zbijewski

Proceedings Volume 11072, 1107213 (2019) https://doi.org/10.1117/12.2534725

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Dual energy computed tomography (DE CT) is a promising technology for the assessment of bone compositions. One of potential applications involves evaluations of fracture healing using longitudinal measurements of callus mineralization. However, imaging of fractures is often challenged by the presence of metal fixation hardware. In this work, we report on a new simultaneous DE reconstruction-decomposition algorithm that integrates the previously introduced Model-Based Material Decomposition (MBMD) with a Known-Component (KC) framework to mitigate metal artifacts. The algorithm was applied to the DE data obtained on a dedicated extremity cone-beam CT (CBCT) with capability for weight-bearing imaging. To acquire DE projections in a single gantry rotation, we exploited a unique multisource design of the system, where three X-ray sources were mounted parallel to the axis of rotation. The central source provided high energy (HE) data at 120 kVp, while the two remaining sources were operated at a low energy (LE) of 60 kVp. This novel acquisition trajectory further motivates the use of MBMD to accommodate this complex DE sampling pattern. The algorithm was validated in a simulation study using a digital extremity phantom. The phantom consisted of a water background with an insert containing varying concentrations of calcium (50 – 175 mg/mL). Two configurations of titanium implants were considered: a fixation plate and an intramedullary nail. The accuracy of calcium-water decompositions obtained with the proposed KC-MBMD algorithm was compared to MBMD without metal component model. Metal artifacts were almost completely removed by KC-MBMD. Relative absolute errors of calcium concentration in the vicinity of metal were 6% - 31% for KC-MBMD (depending on the calcium insert and implant configuration), compared favorably to 48% - 273% for MBMD. Moreover, accuracy of concentration estimates for KC-MBMD in the presence of metal implant approached that of MBMD in a configuration without implant (6%-23%). The proposed algorithm achieved accurate DE material decomposition in the presence of metal implants using a non-conventional, axial multisource DE acquisition pattern.

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