Stefano van Gogh

Last Name

van Gogh

First Name

Stefano

ORCID

0000-0001-5856-1124

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03817 - Stampanoni, Marco F.M. / Stampanoni, Marco F.M.

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Publications1 - 9 of 9

Towards virtual histology with X-ray grating interferometry
Polikarpov, Maxim; Vila-Comamala, Joan; Wang, Zhentian; et al. (2023)
Scientific Reports
Breast cancer is the most common type of cancer worldwide. Diagnosing breast cancer relies on clinical examination, imaging and biopsy. A core-needle biopsy enables a morphological and biochemical characterization of the cancer and is considered the gold standard for breast cancer diagnosis. A histopathological examination uses high-resolution microscopes with outstanding contrast in the 2D plane, but the spatial resolution in the third, Z-direction, is reduced. In the present paper, we propose two high-resolution table-top systems for phase-contrast X-ray tomography of soft-tissue samples. The first system implements a classical Talbot–Lau interferometer and allows to perform ex-vivo imaging of human breast samples with a voxel size of 5.57 μm. The second system with a comparable voxel size relies on a Sigray MAAST X-ray source with structured anode. For the first time, we demonstrate the applicability of the latter to perform X-ray imaging of human breast specimens with ductal carcinoma in-situ. We assessed image quality of both setups and compared it to histology. We showed that both setups made it possible to target internal features of breast specimens with better resolution and contrast than previously achieved, demonstrating that grating-based phase-contrast X-ray CT could be a complementary tool for clinical histopathology.
Hybrid tomographic reconstruction in grating interferometry breast CT: combining physics-based likelihoods with data-driven priors
van Gogh, Stefano (2023)
Breast cancer is the most prevalent malignancy among the female population. To fight this public health burden, early detection is crucial. Unfortunately, currently available breast imaging technologies suffer from limitations ranging from insufficient resolution to low soft-tissue contrast, from costly and long scans to painful breast compression. This all negatively impacts the sensitivity and specificity of these methods, thus leading to overdiagnosis and missed tumors. X-ray phase contrast CT has the potential to overcome these limitations and enable painless fast scans with high three-dimensional resolution and high soft-tissue contrast. The method has been successfully used on synchrotron facilities for two decades but its translation into clinical practice has not happened yet. Grating Interferometry CT (GI-CT) is arguably the phase-contrast technique which has the highest chances of making this transition. Thanks to its relatively high mechanical robustness, its modest requirements in terms of spatial and temporal beam coherence and the possibility to operate with a large field-of-view (FOV), the method is in fact compatible with clinical X-ray sources and human-sized subjects. In an attempt to translate Grating Interferometry Breast CT (GI-BCT) to the clinics, our group has developed prototype scanners to test the compatibility of the technology with clinical requirements (large FOV and low-dose) and to investigate the added value phase contrast can offer to breast CT. Owing to the peculiar signal acquisition scheme in GI-CT and the current limitations in hardware fabrication, it is an arduous task to reconstruct high-quality tomographies under clinically compatible conditions. This dissertation aimed at solving this problem. It first discusses the main challenges in GI-CT reconstruction, namely high and heterogeneous noise and high problem ill-conditioning. To tackle these problems, the combination of state-of-the-art classical iterative reconstruction algorithms with data-driven regularization was then investigated. After evaluating many approaches, a hybrid tomographic reconstruction framework is presented which combines a physics-based likelihood with a data-driven prior. Specifically, the high problem ill-conditioning was addressed by developing better conditioned forward operators and by imposing a relation between the absorption and phase-contrast channels which allows to iteratively fuse the two and leverage the strengths of each channel. The noise problem was tackled by deep learning algorithms which remove the unwanted noise and artefacts introduced by the physics-based likelihood, thus allowing for nearly noise-free reconstructions. Thanks to the developed reconstruction method, first ex-vivo studies suggest that GI-BCT could outperform attenuation-based breast CT in a clinical-dose regime, thus bringing the technology a step closer to first in-vivo studies. In particular, the results in this dissertation indicate that with GI-BCT it might be possible to reduce the dose and/or to increase the spatial resolution compared to clinical breast CT. The added value that phase contrast can bring to the clinics thus lies in its higher contrast for high-frequency details compared to absorption.
Intensity-based iterative reconstruction for helical grating interferometry breast CT with static grating configuration
Xu, Jinqiu; Wang, Zhentian; van Gogh, Stefano; et al. (2022)
Optics Express
Grating interferometry breast computed tomography (GI-BCT) has the potential to provide enhanced soft tissue contrast and to improve visualization of cancerous lesions for breast imaging. However, with a conventional scanning protocol, a GI-BCT scan requires longer scanning time and higher operation complexity compared to conventional attenuation-based CT. This is mainly due to multiple grating movements at every projection angle, so-called phase stepping, which is used to retrieve attenuation, phase, and scattering (dark-field) signals. To reduce the measurement time and complexity and extend the field of view, we have adopted a helical GI-CT setup and present here the corresponding tomographic reconstruction algorithm. This method allows simultaneous reconstruction of attenuation, phase contrast, and scattering images while avoiding grating movements. Experiments on simulated phantom and real initial intensity, visibility and phase maps are provided to validate our method.
Data-driven gradient regularization for quasi-Newton optimization in iterative grating interferometry CT reconstruction
van Gogh, Stefano; Mukherjee, Subhadip; Rawlik, Michał; et al. (2024)
IEEE Transactions on Medical Imaging
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.
Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior
van Gogh, Stefano; Mukherjee, Subhadip; Xu, Jinqiu; et al. (2022)
PLoS ONE
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.
Intensity-based cross-channel regularization for grating interferometry breast CT
Montemayor, Natalia Pato; van Gogh, Stefano; Rawlik, Michal Mateusz; et al. (2024)
Optics Continuum
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.
Towards clinical-dose grating interferometry breast CT with fused intensity-based iterative reconstruction
van Gogh, Stefano; Rawlik, Michał; Pereira, Alexandre; et al. (2023)
Optics Express
X-ray grating interferometry CT (GI-CT) is an emerging imaging modality which provides three complementary contrasts that could increase the diagnostic content of clinical breast CT: absorption, phase, and dark-field. Yet, reconstructing the three image channels under clinically compatible conditions is challenging because of severe ill-conditioning of the tomographic reconstruction problem. In this work we propose to solve this problem with a novel reconstruction algorithm that assumes a fixed relation between the absorption and the phase-contrast channel to reconstruct a single image by automatically fusing the absorption and phase channels. The results on both simulations and real data show that, enabled by the proposed algorithm, GI-CT outperforms conventional CT at a clinical dose.
Increased dose efficiency of breast CT with grating interferometry
Rawlik, Michal Mateusz; Pereira, Alexandre; Spindler, Simon; et al. (2023)
Optica
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 clinicalCT.
INSIDEnet: Interpretable NonexpanSIve Data-Efficient network for denoising in grating interferometry breast CT
van Gogh, Stefano; Wang, Zhentian; Rawlik, Michal Mateusz; et al. (2022)
Medical Physics
Purpose 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.

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Stefano van Gogh

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