Motion artifacts assessment and correction using optical tracking in synchrotron radiation breast CT

Brombal, Luca; Peña, L.M. Arana; Arfelli, Fulvia; Longo, Renata; Brun, Francesco; Contillo, Adriano; Lillo, Francesca Di; Tromba, Giuliana; Trapani, Vittorio Di; Donato, Sandro; Menk, R. H.; Rigon, Luigi

doi:10.1002/mp.15084

Cited by 9 publications

(6 citation statements)

References 56 publications

(105 reference statements)

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“…For instance, flow of a contrast agent into the imaged volume, or imaging a fixed slice of a 3D time-varying object under compression with cross-slice motion may violate either of these assumptions. As a hardware-based, problem-specific approach, optical tracking of fiducial marks has been used for motion correction [84].…”

Section: B Previous Workmentioning

confidence: 99%

RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging

Iskender,

Klasky,

Bresler

2024

IEEE Trans. Comput. Imaging

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Dynamic imaging addresses the recovery of a timevarying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed. We propose an approach, RED-PSM, which combines for the first time two powerful techniques to address this challenging imaging problem. The first, are non-parametric factorized low rank models, also known as partially separable models (PSMs), which have been used to efficiently introduce a low-rank prior for the spatio-temporal object. The second is the recent Regularization by Denoising (RED), which provides a flexible framework to exploit the impressive performance of state-of-the-art image denoising algorithms, for various inverse problems. We propose a partially separable objective with RED and a computationally efficient and scalable optimization scheme with variable splitting and ADMM. Theoretical analysis proves the convergence of our objective to a value corresponding to a stationary point satisfying the first-order optimality conditions. Convergence is accelerated by a particular projection-domain-based initialization. We demonstrate the performance and computational improvements of our proposed RED-PSM with a learned image denoiser by comparing it to a recent deep-prior-based method known as TD-DIP. Although the main focus is on dynamic tomography, we also show performance advantages of RED-PSM in a cardiac dynamic MRI setting.

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Section: B Previous Workmentioning

confidence: 99%

RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging

Iskender,

Klasky,

Bresler

2024

IEEE Trans. Comput. Imaging

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“…Common iterative reconstruction algorithms make use of gradient-based optimisation to reconstruct the specimen x , which is the solution to the system of equations governing the model [ 5 ]. These approaches are powerful but can be problematic [ 6 , 7 ]: if a sophisticated method can simulate realistic behaviours, adding complexity results in error-prone expressions to be derived manually; this is especially true when the parameters must also be refined (e.g., in tomography [ 8 , 9 ]). If the object unknowns lie in a transform domain (e.g., wavelets), the complexity is also further increased.…”

Section: Introductionmentioning

confidence: 99%

“…In cryo-nanotomography [ 75 , 80 ], the aforementioned problems appear at the same time and are also exacerbated by unwanted and unknown movements in the sample stage [ 9 , 81 , 82 ]. A simple correction model employs serial cross-correlation between projections [ 83 ], also with cosine-stretching, reducing the risk of potentially propagating drifts in the correction [ 84 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy

Guzzi

Gianoncelli

Billè

et al. 2023

Life

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Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced setups, acquisition modalities or where uncertainty is high; the need for complex computational methods clashes with rapid design and execution. In all these cases, Automatic Differentiation, one of the subtopics of Artificial Intelligence, may offer a functional solution, but only if a GPU implementation is available. In this paper, we show how a framework built to solve just one optimisation problem can be employed for many different X-ray imaging inverse problems.

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“…As recently demonstrated, the image quality of PhC bCT outperforms clinical bCT systems, providing a higher spatial resolution, signal-to-noise ratio, and a finer granularity (Brombal et al 2019, Pacilè et al 2019. With the goal of setting up a clinical study, the SYRMA-3D collaboration has been working in the last years to evaluate, quantify and optimize the main parameters of the PhC bCT imaging technique in terms of x-ray energy (Delogu et al 2019, Oliva et al 2020, sample-to-detector distance (Brombal et al 2018b, Brombal 2020b), detector's operating mode, strategies for CT scans and reconstruction workflow (Longo et al 2019, Brombal et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

Donato¹,

Brombal²,

Peña³

et al. 2022

Phys. Med. Biol.

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Objective: To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach: Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images where obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic X-ray beam in the free-space propagation mode. Three independent algorithm’s parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results: Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image “patchiness”, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance: The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either towards diagnostic evaluation or towards image segmentation.

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Motion artifacts assessment and correction using optical tracking in synchrotron radiation breast CT

Cited by 9 publications

References 56 publications

RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging

RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging

Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

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