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Personalized, scout-based dose estimation for prospective optimization of CT tube current modulation
Photon counting detectors (PCDs) with energy discrimination capabilities provide numerous benefits over conventional energy integrating detectors (EIDs). PCDs enable higher spatial resolution, improved contrast-to-noise ratio, reduced beam hardening artifact, and quantitative material-selective imaging. However, the increased spatial resolution and multiple energy bins (e.g., 8 bins for a prototype photon counting CT using silicon-based PCDs) greatly increase the amount of data generated. As a consequence, projection data transmission from the detector to the processing computer becomes more challenging due to the limited bandwidth of the slip ring. In this work, we compare the performance of four projection-domain energy bin compression strategies: conventional bins, summed bins, binary weights, and continuous weights, using the raw projection data from a prototype photon counting CT using silicon-based PCDs.
We reduce the 8 energy bins of the projection data from the prototype silicon-based PCDs to 2 or 3 virtual measurements using the phantom independent and globally applicable bin compression strategies, which were each optimized by minimizing the Cramér–Rao lower bound (CRLB) of the virtual measurements using only material decomposition calibration data over a predefined material space, and no other a priori knowledge. We evaluate the performance of the above 4 bin compression strategies in reconstructed images of a Catphan700 phantom. The results show that the 2 measurements generated with continuous weights can provide comparable material decomposition (MD) and virtual monoenergetic images (VMI) that exhibit low bias- and near zero variance-penalty, to that of the original binned counts, with a data reduction of 75%. On the other hand, neither conventional bins, summed bins, nor binary weights can provide comparable results versus continuous weights, even if we use 3 virtual measurements. We also conclude that to achieve low bias and variance in MD and VMI images with only two measurements, it is necessary to first measure as many energy bins as possible, then use continuous weights to compress the binned data.
Previously we developed a single-shot quantitative x-ray imaging (SSQI) method to perform material decomposition in x-ray imaging by combining the use of a primary modulator (PM) and dual-layer (DL) detector, where the PM removes scatter from DL images while the DL provides dual energy (DE) images for material decomposition (MD), which further removes beam hardening resulting from the partially attenuated regions of the PM. We have demonstrated the concept of SSQI using simulation and further tested its efficacy on chest phantom studies performed on our tabletop system.
In this work, we further explored the clinical value of SSQI in interventional guidance, specifically focusing on investigating its potential in real-time x-ray image guidance. We integrated the SSQI on a C-arm system and designed two studies to evaluate its performance for iodine quantification in both static and dynamic imaging using anthropomorphic phantoms. Compared to direct MD without scatter correction, the RMSE in material-specific images was reduced by 38-64% with SSQI when compared to ground truth. For the dynamic study, the SSQI estimated iodine mass was in close agreement with the amount from a ground truth acquisition. The results in this work further expand the potential of SSQI for real-time image guidance.
Deep learning-aided CBCT image reconstruction of interventional material from four x-ray projections
In this work we develop a novel deep learning-based approach to reconstruct interventional tools from only four x-ray projections. We train and test this deep tool reconstruction (DTR) network on simulated data. Only small deviations from the ground truth (GT) reconstruction of the tools were observed, both quantitatively and qualitatively, showing that deep learning-based four-dimensional interventional guidance has the potential to overcome the drawbacks of conventional interventional guidance in the future.
Methods. Using fast, robust 3D-2D registration in combination with 3D models of known components (surgical devices), the 3D pose determination was solved to relate known components to 2D projection images and 3D preoperative CT in near-real-time. Exact and parametric models of the components were used as input to the algorithm to evaluate the effects of model fidelity. The proposed algorithm employs the covariance matrix adaptation evolution strategy (CMA-ES) to maximize gradient correlation (GC) between measured projections and simulated forward projections of components. Geometric accuracy was evaluated in a spine phantom in terms of target registration error at the tool tip (TREx), and angular deviation (TREΦ) from planned trajectory.
Results. Transpedicle surgical devices (probe tool and spine screws) were successfully guided with TREx<2 mm and TREΦ <0.5° given projection views separated by at least >30° (easily accommodated on a mobile C-arm). QA of the surgical product based on 3D-2D registration demonstrated the detection of pedicle screw breach with TREx<1 mm, demonstrating a trend of improved accuracy correlated to the fidelity of the component model employed.
Conclusions. 3D-2D registration combined with 3D models of known surgical components provides a novel method for near-real-time guidance and quality assurance using a mobile C-arm without external trackers or fiducial markers. Ongoing work includes determination of optimal views based on component shape and trajectory, improved robustness to anatomical deformation, and expanded preclinical testing in spine and intracranial surgeries.
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