Model-based material decomposition with system blur modeling

Wang, Wenying; Tivnan, Matthew; Gang, Grace J.; Yiqun, Ma; Cao, Qiong; Lu, Minghui; Star‐Lack, Josh; Colbeth, Richard E.; Zbijewski, Wojciech; Stayman, J. Webster

doi:10.1117/12.2549549

Cited by 6 publications

(9 citation statements)

References 3 publications

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“…In summary, Theorem 1 states that the learned iterative scheme in (8) converges to a neighbourhood of the reconstruction obtained with the accurate scheme in (4), provided that the eigenvalues of the matrices Q k used for the reconstruction are uniformly bounded from both above and below, and that the forward-adjoint corrections are sufficiently well trained for all iterates x 0 , x 1 , . .…”

Section: E Choice Of Loss Functionsmentioning

confidence: 96%

“…It is only recently, however, that deblurring methods have been applied to spectral CT. The most recent developments, such as [7] and [8], model the detector blur as a convolution over the detector with a kernel that is independent of the energy. Such a model is comparatively cheap to evaluate.…”

Section: B the Role Of The Forward Operator In Reconstructionmentioning

confidence: 99%

“…Assumption 2. As the step size in the iterative scheme (8) tends to zero, the iterations converge to a twice differentiable curve t → x(t) in X that solves the following ODE:…”

Section: Convergence Of Learned Iterative Reconstructionmentioning

confidence: 99%

“…Assume we have a set of 'supervised' training data {(x n 0 , y n )} N n=1 ⊂ X × Y where y n is a noisy measurement and x n 0 ∈ X is an initial guess for the learned scheme in (8). It is now natural to train the forward and adjoint corrections by minimising…”

Section: F Recursive Trainingmentioning

confidence: 99%

“…However, Proposition 1 relies on a consistently well trained operator correction -throughout the iterations generated by (8). It is therefore important to not only train on the initial guesses x n 0 but also iterations x n 1 , x n 2 , .…”

Section: F Recursive Trainingmentioning

confidence: 99%

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Photon-Counting CT Reconstruction With a Learned Forward Operator

Ström

Persson

Eguizabal

et al. 2022

IEEE Trans. Comput. Imaging

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Photon-Counting CT is an emerging imaging technology that promises higher spatial resolution and the possibility for material decomposition in the reconstruction. A major difficulty in Photon-Counting CT is to efficiently model cross-talk between detectors. In this work, we accelerate image reconstruction tasks for Photon-Counting CT by modelling the cross-talk with an appropriately trained deep convolutional neural network. The main result relates to proving convergence when using such a learned cross-talk model in the context of second-order optimisation methods for spectral CT. Another is to evaluate the method through numerical experiments on small-scale CT acquisitions generated using a realistic physics model. Using the reconstruction with a full cross-talk model as ground truth, the learned cross-talk model results in a 20 dB increase in peaksignal-to noise ratio compared to ignoring cross-talk altogether. At the same time, it effectively cuts the computation time of the full cross-talk model in half. Furthermore, the learned crosstalk model generalises well to both unseen data and unseen detector settings. Our results indicate that such a partially learned forward operator is a suitable way of modelling data generation in Photon-Counting CT with a computational benefit that becomes more noticeable for realistic problem sizes.

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Section: E Choice Of Loss Functionsmentioning

confidence: 96%

Section: B the Role Of The Forward Operator In Reconstructionmentioning

confidence: 99%

“…Assumption 2. As the step size in the iterative scheme (8) tends to zero, the iterations converge to a twice differentiable curve t → x(t) in X that solves the following ODE:…”

Section: Convergence Of Learned Iterative Reconstructionmentioning

confidence: 99%

Section: F Recursive Trainingmentioning

confidence: 99%

Section: F Recursive Trainingmentioning

confidence: 99%

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Photon-Counting CT Reconstruction With a Learned Forward Operator

Ström

Persson

Eguizabal

et al. 2022

IEEE Trans. Comput. Imaging

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High‐resolution model‐based material decomposition in dual‐layer flat‐panel CBCT

et al. 2021

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Purpose: Spectral CT uses energy-dependent measurements that enable material discrimination in addition to reconstruction of structural information. Flat-panel detectors (FPDs) have been widely used in dedicated and interventional systems to deliver high spatial resolution, volumetric cone-beam CT (CBCT) in compact and OR-friendly designs. In this work, we derive a model-based method that facilitates high-resolution material decomposition in a spectral CBCT system equipped with a prototype dual-layer FPD. Through high-fidelity modeling of multilayer detector, we seek to avoid resolution loss that is present in more traditional processing and decomposition approaches. Method: A physical model for spectral measurements in dual-layer flat-panel CBCT is developed including layer-dependent differences in system geometry, spectral sensitivities, and detector blur (e.g., due to varied scintillator thicknesses). This forward model is integrated into a model-based material decomposition (MBMD) method based on minimization of a penalized weighted leastsquared (PWLS) objective function. The noise and resolution performance of this approach was compared with traditional projection-domain decomposition (PDD) and image-domain decomposition (IDD) approaches as well as one-step MBMD with lower-fidelity models that use approximated geometry, projection interpolation, or an idealized system geometry without system blur model. Physical studies using high-resolution three-dimensional (3D)-printed water-iodine phantoms were conducted to demonstrate the high-resolution imaging performance of the compared decomposition methods in iodine basis images and synthetic monoenergetic images. Results: Physical experiments demonstrate that the MBMD methods incorporating an accurate geometry model can yield higher spatial resolution iodine basis images and synthetic monoenergetic images than PDD and IDD results at the same noise level. MBMD with blur modeling can further improve the spatial-resolution compared with the decomposition results obtained with IDD, PDD, and MBMD methods with lower-fidelity models. Using the MBMD without or with blur model can increase the absolute modulation at 1.75 lp/mm by 10% and 22% compared with IDD at the same noise level. Conclusion:The proposed model-based material decomposition method for a dual-layer flat-panel CBCT system has demonstrated an ability to extend high-resolution performance through sophisticated detector modeling including the layer-dependent blur. The proposed work has the potential to not only facilitate high-resolution spectral CT in interventional and dedicated CBCT systems, but may also provide the opportunity to evaluate different flat-panel design trade-offs including multilayer FPDs with mismatched geometries, scintillator thicknesses, and spectral sensitivities.

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A prototype spatial–spectral CT system for material decomposition with energy‐integrating detectors

2021

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Purpose: Spectral CT has great potential for a variety of clinical applications due to improved tissue and material discrimination over conventional single-energy CT. Many clinical and preclinical spectral CT systems have two spectral channels enabling dual-energy CT. Strategies include split filtration, dual-layer detectors, photon-counting detectors, and kVp switching. The motivation for this work is the development of an x-ray source spectral modulation device with three or more spectral channels to enable high-sensitivity multi-material decomposition with energy-integrating detectors. Materials and Methods: We present spatial-spectral filters which are a new x-ray source modulation technology with the potential for additional channel diversity. The filtering device consists of an array of K-edge materials which divide the x-ray beam into spectrally varied beamlets. This design allows for an arbitrary number of spectral channels-trading off spatial and spectral information. We use a one-step model-based material decomposition (MBMD) algorithm to iteratively estimate material density images directly from the spatial-spectral CT data. In this work, we present a prototype spatial-spectral filter integrated with an x-ray CT test bench. The filter is composed of an array of tin, erbium, tantalum, and lead filter tiles which spatially modulate the system spectral sensitivity pattern. In a simulation study, we investigate the particular problem of mis-calibration between the data acquisition and the reconstruction model. With an understanding of the required model accuracy, we present a spectral calibration method to estimate the critical model parameters. To demonstrate feasibility of the spatial-spectral filter with a calibrated beamlet model, we collected a spatial-spectral CT scan of a multicontrast-enhanced phantom containing water, iodine, and gadolinium solutions. Results: With simulations, we show that material decomposition is possible with spatial-spectralfiltered CT data, and we demonstrate the importance of a well-calibrated physical model. We find a 50% increase in error for focal spot model mismatch of 0.27mm and gap width model mismatch of 16 mμ. With physical results, we demonstrate that the calibrated system model is in close agreement with the measured data, and that the reconstructed material density images match the ground truth concentrations for the multicontrast phantom. Empirical results indicate gadolinium density estimation had an error of 17-58% mostly due to a systematic constant bias of 0.30-0.60 mg/ml. Water density estimates are within 1% and iodine estimates are within 10% of ground truth. Conclusion: These preliminary results demonstrate the potential of spatial-spectral filters to enable multicontrast imaging. Moreover, this device is compatible with energy-integrating detectors and so provides a feasible modification to enable spectral CT imaging with existing single-energy systems.

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Model-based material decomposition with system blur modeling

Cited by 6 publications

References 3 publications

Photon-Counting CT Reconstruction With a Learned Forward Operator

Photon-Counting CT Reconstruction With a Learned Forward Operator

High‐resolution model‐based material decomposition in dual‐layer flat‐panel CBCT

A prototype spatial–spectral CT system for material decomposition with energy‐integrating detectors

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