CTL: modular open-source C++-library for CT-simulations

Pfeiffer, Tim; Frysch, Robert; Bismark, Richard; Rose, Georg

doi:10.1117/12.2534517

Cited by 4 publications

(1 citation statement)

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“…In our studies, the optimization was performed using the derivative-free BOBYQA algorithm [31] of NLOPT opensource library [32] and the consistency function values were computed using the CT simulation and reconstruction library CTL (https://gitlab.com/tpfeiffe/ctl) [33].…”

Section: B Polynomial Optimizationmentioning

confidence: 99%

Comparative Evaluation of Cone-Beam Consistency Conditions for Mono-Material Beam Hardening Correction in Flat Detector Computed Tomography

Abdurahman

Frysch

Pfeiffer

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

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Consistency conditions have been successfully utilized for data-driven artifact reductions in cone-beam computed tomography systems equipped with a large area flat-panel detector. Recently, many formulations and applications of pair-wise cone-beam consistency conditions have been published, including Grangeat, Smith, and fan-beam consistency conditions. Previous works demonstrated that the polynomial coefficients for beam hardening correction could be directly computed from cone-beam raw data by enforcing consistency conditions on projection pairs. This paper compares the effectiveness of pair-wise consistency conditions for mono-material beam hardening correction using a second-degree polynomial. The results from our studies show that similar corrections could be achieved for ideal polychromatic projections. We also investigated the effectiveness of corrections after perturbing the projections with an increasing degree of errors other than those caused by beam hardening. The studies indicate the superior robustness of fan-beam consistency conditions towards Poisson noise, axial truncation, detector shift, and scatter, while Grangeat consistency conditions were less vulnerable to projection intensity errors. The optimal choice of consistency conditions depends on the CBCT system geometry, physical phenomena other than beam hardening, and the availability and accuracy of pre-processing and artifact corrections algorithms before beam hardening correction.

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Section: B Polynomial Optimizationmentioning

confidence: 99%

Comparative Evaluation of Cone-Beam Consistency Conditions for Mono-Material Beam Hardening Correction in Flat Detector Computed Tomography

Abdurahman

Frysch

Pfeiffer

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

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CT‐augmented digital tomosynthesis image reconstruction in image‐guided bronchoscopy interventions

Saad,

Frysch,

Saalfeld

et al. 2024

Medical Physics

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BackgroundTransbronchial needle biopsy is crucial for diagnosing lung cancer, yet its efficacy depends on accurately localizing the target lesion and biopsy needle. Digital tomosynthesis (DTS) is considered a promising imaging modality for guiding bronchoscopy procedures due to its low radiation dose and small footprint relative to cone‐beam computed tomography (CBCT). However, the image quality of DTS is still not sufficient for an accurate guidance, mainly due to its limited‐angle acquisition.PurposePreoperative computed tomography (CT) scans are often performed prior to bronchoscopy interventions for diagnosis or to plan the procedure. The CT images are of high quality and are characterized by a high spatial resolution compared to intraoperative DTS images. These patient‐specific prior CT images and the intraoperative DTS images share a fair amount of anatomical information. The main differences only stem from patient positioning and respiratory motion. When these differences are addressed properly, prior CT images augment intraoperative DTS image reconstruction with strong prior knowledge and potentially enhance DTS image quality.MethodsWe propose in this work a prior‐aided DTS image reconstruction technique leveraging prior CT images to improve DTS image quality. This technique is based on a recently published deformable CT‐to‐DTS image registration algorithm which is customized for bronchoscopy interventions. The main idea is to register a prior CT image to an intermediate DTS image reconstructed using the standard iterative algebraic reconstruction technique (ART), then to re‐reconstruct the DTS image using ART and the registered prior CT image as a first estimation.ResultsThe proposed prior‐aided reconstruction method was tested on a physical phantom and six patient bronchoscopy datasets. Real DTS data acquired with a pseudo‐linear (PL) scan geometry and simulated DTS data generated according to a spherical ellipse (SE) scan geometry were considered. Results evaluated qualitatively by visual inspection and quantitatively by computing Pearson's correlation (PC) with respect to the reference CBCT images suggest significant improvements in image quality using the prior‐aided DTS reconstruction compared to the standard zero‐initialized ART reconstruction. PC coefficients of the six patient datasets were on average and using a zero‐initialized ART reconstruction with SE data and PL data, respectively, and and using the proposed prior‐aided reconstruction with SE data and PL data, respectively.ConclusionsWhile the initial estimation in iterative reconstruction algorithms is often overlooked, we proved that initial estimation is of critical importance in DTS image reconstruction and we have demonstrated the profound advantages of integrating prior CT images in intraoperative DTS image reconstruction. CT‐augmented DTS offers a viable alternative to CBCT in guiding bronchoscopy interventions at a fraction of the radiation dose. Further clinical studies are needed to validate improved diagnostic yield.

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