Intensity-based iterative reconstruction with cross-channel regularization for grating interferometry breast CT

This work demonstrates the successful reconstruction of phase contrast images under challenging acquisition conditions in grating interferometry breast CT (GI-BCT) with an algorithm that adds a novel regularization functional to the existing iterative-based intensity reconstruction (IBIR) algorithm. The addition of a cross-channel regularizer allows to leverage the absorption channel’s convergence to promote that of the phase channel, which otherwise struggles to converge. We demonstrate convergence of phase contrast images on both simulations and real data. This work sets a step towards a clinically compatible reconstruction procedure using cross-channel regularization for the generation of standalone phase-contrast images of breasts

Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior

Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. Our algorithm combines the L-BFGS optimization scheme with a data-driven prior parameterized by a deep neural network. Importantly, we propose a novel regularization strategy to ensure that the trained network is non-expansive, which is critical for the convergence and stability analysis we provide. We empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements

Increased dose efficiency of breast CT with grating interferometry

Refraction-based x-ray imaging can overcome the fundamental contrast limit of computed tomography (CT), particularly in soft tissue, but so far has been constrained to high-dose ex vivo applications or required highly coherent x-ray sources, such as synchrotrons. Here we demonstrate that grating interferometry (GI) is more dose efficient than conventional CT in imaging of human breast under close-to-clinical conditions. Our system, based on a conventional source and commercial gratings, outperformed conventional CT for spatial resolutions better than 263 µm and absorbed dose of 16 mGy. The sensitivity of GI is constrained by grating fabrication, and further progress will lead to significant improvements of clinical CT

Analiza synału FID i nowy system DAQ w eksperymencie nEDM

Eksperyment nEDM przeprowadzany w Instytucie Paula Scherrera, w Villigen w Szwaj-carii mierzy elektryczny moment dipolowy neutronu. Ważnym elementem układu ekspe-rymentalnego jest kohabitujący magnetometr oparty na atomach 199 Hg. Przedmiotempierwszej części niniejszej pracy są rozważania na temat analizy sygnału FID pochodzą-cego z magnetometru. Zaproponowane jest nowe podejście do jego analizy oraz metodyszacowania w niej niepewności statystycznych i systematycznych. Krok po kroku, przed-stawiona jest analiza dwóch przykładowych zestawów danych. Ponadto, zaprezentowanejest oprogramowanie służące zarówno do przeprowadzania tej analizy, jak i do badaniaposzczególnych jej kroków. Druga część pracy prezentuje projekt nowego rozwiązaniado kontroli i akwizycji danych w eksperymencie nEDM. Przedstawione są wyniki ilościo-wych testów dotyczących możliwości czasowych, ADC i DAC, które dowodzą gotowościsystemu do wdrożenia w układ eksperymentalny.The nEDM experiment, carried out in the Paul Scherrer Institute in Villigen, Switzer-land, measures the electric dipole moment of the neutron. An important part of theset–up is the 199Hg cohabiting magnetometer. A thorough research on the magnetome-ter FID signal analysis is presented in the first part of this thesis. A new approachto the analysis is proposed, together with routines to assess statistical and systematicuncertainties. A full, step–by–step analysis of two example data sets is demonstrated.Additionally, a software to investigate and perform the analysis is published. The secondpart of this thesis presents a design of a new control and data acquisition system forthe nEDM experiment. Results of quantitative tests of the system’s ADC, DAC andtiming capabilities are presented, proving that the system is ready to be deployed in theexperimental set–up

Wybór częstotliwości rf-pulsu neutronów w eksperymencie nEDM

W Instytucie Paula Sherrera w Villgen, Szwajcaria przeprowadzany jest eksperyment który ma celu zmierzyć elektyczny moment dipolowy neutronu wykorzystując metodę Ramsey'a: dwa pulsy oscylującego pola magnetycznego przykładane są do spolaryzowanych ultra-zimnych neutronów. Pomiędzy nimi neutrony precesują swobodnie w stałym polu magnetycznym i elektrycznym. Stworzony został prosty model komputerowy eksperymentu w celu zbadania metod wyboru częstotliwości tych pulsów. Wybrana została najlepsza metoda, chociaż wszystkie okazały się mieć równy wpływ na niepewność wyniku eksperymentu.In the Paul Sherrer Institute in Villigen, Switzerland an experiment aims to measure neutron's electric dipole moment with use of Ramsey method of separated oscillating fields: two magnetic pulses are applied to polarized Ultra-Cold Neutrons with time gap in between to allow them to precess freely in constant magnetic and electric fields. A simple computer model of the experiment was created in order to investigate several methods of choosing pulses' frequency. The best method was determined, althogh all that were proposed showed equal efect on experiment's result uncertainty

The choice of an autocorrelation length in dark-field lung imaging.

Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs together with Polymethylmethacrylat (PMMA) microspheres (in air) of diameter sizes between 20 and 500 [Formula: see text] over an autocorrelation range of 0.8-5.2 [Formula: see text]. The results indicate that the dark-field extinction coefficient of porcine lungs is similar to that of densely-packed PMMA spheres with diameter of [Formula: see text], which is approximately the mean alveolar structure size. We evaluated that, in our case, the autocorrelation length would have to be limited to [Formula: see text] in order to image [Formula: see text]-thick lung tissue without critical visibility reduction (signal saturation). We identify the autocorrelation length to be the critical parameter of an interferometer that allows to avoid signal saturation in clinical lung dark-field imaging

The choice of an autocorrelation length in dark-field lung imaging

Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs together with Polymethylmethacrylat (PMMA) microspheres (in air) of diameter sizes between 20 and 500 [Formula: see text] over an autocorrelation range of 0.8-5.2 [Formula: see text]. The results indicate that the dark-field extinction coefficient of porcine lungs is similar to that of densely-packed PMMA spheres with diameter of [Formula: see text], which is approximately the mean alveolar structure size. We evaluated that, in our case, the autocorrelation length would have to be limited to [Formula: see text] in order to image [Formula: see text]-thick lung tissue without critical visibility reduction (signal saturation). We identify the autocorrelation length to be the critical parameter of an interferometer that allows to avoid signal saturation in clinical lung dark-field imaging.ISSN:2045-232

INSIDEnet: Interpretable NonexpanSIve Data-Efficient network for denoising in grating interferometry breast CT.

Funder: Promedica Stiftung; Id: http://dx.doi.org/10.13039/501100008307Funder: Swisslos Lottery Fund of Kanton AargauPURPOSE: Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft-tissue contrast. Grating interferometry breast computed tomography (GI-BCT) is a promising X-ray phase contrast modality that could overcome these limitations by offering high soft-tissue contrast and excellent three-dimensional resolution. To enable the transition of this technology to clinical practice, dedicated data-processing algorithms must be developed in order to effectively retrieve the signals of interest from the measured raw data. METHODS: This article proposes a novel denoising algorithm that can cope with the high-noise amplitudes and heteroscedasticity which arise in GI-BCT when operated in a low-dose regime to effectively regularize the ill-conditioned GI-BCT inverse problem. We present a data-driven algorithm called INSIDEnet, which combines different ideas such as multiscale image processing, transform-domain filtering, transform learning, and explicit orthogonality to build an Interpretable NonexpanSIve Data-Efficient network (INSIDEnet). RESULTS: We apply the method to simulated breast phantom datasets and to real data acquired on a GI-BCT prototype and show that the proposed algorithm outperforms traditional state-of-the-art filters and is competitive with deep neural networks. The strong inductive bias given by the proposed model's architecture allows to reliably train the algorithm with very limited data while providing high model interpretability, thus offering a great advantage over classical convolutional neural networks (CNNs). CONCLUSIONS: The proposed INSIDEnet is highly data-efficient, interpretable, and outperforms state-of-the-art CNNs when trained on very limited training data. We expect the proposed method to become an important tool as part of a dedicated plug-and-play GI-BCT reconstruction framework, needed to translate this promising technology to the clinics

Data-driven gradient regularization for quasi-Newton optimization in iterative grating interferometry CT reconstruction

Grating interferometry CT (GI-CT) is a promising technology that could play an important role in future breast cancer imaging. Thanks to its sensitivity to refraction and small-angle scattering, GI-CT could augment the diagnostic content of conventional absorption-based CT. However, reconstructing GI-CT tomographies is a complex task because of ill problem conditioning and high noise amplitudes. It has previously been shown that combining data-driven regularization with iterative reconstruction is promising for tackling challenging inverse problems in medical imaging. In this work, we present an algorithm that allows seamless combination of data-driven regularization with quasi-Newton solvers, which can better deal with ill-conditioned problems compared to gradient descent-based optimization algorithms. Contrary to most available algorithms, our method applies regularization in the gradient domain rather than in the image domain. This comes with a crucial advantage when applied in conjunction with quasi-Newton solvers: the Hessian is approximated solely based on denoised data. We apply the proposed method, which we call GradReg, to both conventional breast CT and GI-CT and show that both significantly benefit from our approach in terms of dose efficiency. Moreover, our results suggest that thanks to its sharper gradients that carry more high spatial-frequency content, GI-CT can benefit more from GradReg compared to conventional breast CT. Crucially, GradReg can be applied to any image reconstruction task which relies on gradient-based updates.ISSN:0278-0062ISSN:1558-254