Deep scatter estimation for coarse anti-scatter grids as used in photon-counting CT

Erath, Julien; Magonov, Jan; Maier, Joscha; Fournié, Éric; Petersilka, Martin; Stierstorfer, Karl; Kachelrieß, Marc

doi:10.1117/12.2646580

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 However, the use of smaller detector pixels also affects the structure of the detectors. 6 In EIDs, each individual detector pixel is usually surrounded by the lamellae of an anti scatter grid (ASG) to minimize scattered radiation and thus avoid image artifacts. The centers of the detector pixels are consequently arranged regularly.…”

Section: Introductionmentioning

confidence: 99%

Deep grid inpainting for photon-counting CT detectors

Magonov,

Maier,

Fournié

et al. 2024

Medical Imaging 2024: Physics of Medical Imaging

View full text Add to dashboard Cite

Computed tomography systems with very small detector pixels, such as they also occur in photon-counting computed tomography (PCCT), may have dead detector rows and columns whether due to the manufacturing process, the anti-scatter grid (ASG) that blocks the primary radiation behind the ASG lamellae, or other reasons. Such situations require a sophisticated inpainting algorithm to avoid possible artifacts in reconstructed images. To meet these requirements, we developed grid inpainting with deep learning (GRIDL), a neural network-based algorithm to allow the inpainting of arbitrary gaps in column and row direction. In our experimental setup, we corrupt detector images from spiral CT scans with a regular pattern of gaps in a comparable way as the ASG would be arranged in a PCCT system and perform an inpainting using GRIDL. Our approach yields reconstructed images with image quality comparable to the gapless ground truth. In comparison to a simple inpainting method utilizing linear interpolation or a more sophisticated diffusion-based inpainting, GRIDL demonstrates a reduction in aliasing artifacts and the root mean square error (RMSE) in reconstructed images.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep grid inpainting for photon-counting CT detectors

Magonov,

Maier,

Fournié

et al. 2024

Medical Imaging 2024: Physics of Medical Imaging

View full text Add to dashboard Cite

show abstract

A rotating beam‐blocker method for cone beam CT scatter correction

Cui,

Zhan,

Yuan

et al. 2024

Medical Physics

View full text Add to dashboard Cite

BackgroundCone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam‐blocker methods have been used for CBCT scatter correction and proved their high cost‐effectiveness.PurposeA rotating beam‐blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full‐ and half‐fan scan scenarios.MethodsThe RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp‐Davis‐Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast‐to‐noise ratio (CNR).ResultsFor the head phantom, the CT number error was decreased to <5 after scatter correction from >200 HU before correction when scanned without a bowtie filter, and to <4 from >160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to <12 after scatter correction from >250 HU before correction when scanned without a bowtie filter, and to <10 from >190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of >79% decrease and CNR of >2 times increase, respectively.ConclusionsHigh‐quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion‐induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging.

show abstract

Systematic Review on Learning-Based Spectral CT

Bousse,

Kandarpa,

Rit

et al. 2024

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

show abstract

Deep scatter estimation for coarse anti-scatter grids as used in photon-counting CT

Cited by 3 publications

References 13 publications

Deep grid inpainting for photon-counting CT detectors

Deep grid inpainting for photon-counting CT detectors

A rotating beam‐blocker method for cone beam CT scatter correction

Systematic Review on Learning-Based Spectral CT

Contact Info

Product

Resources

About