Tatiana Alessandra Bubba

Tatiana Alessandra Bubba
University of Bath | UB · Department of Mathematical Sciences

PhD

About

27
Publications
3,954
Reads
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224
Citations
Citations since 2017
24 Research Items
224 Citations
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20172018201920202021202220230102030405060
20172018201920202021202220230102030405060
20172018201920202021202220230102030405060
Additional affiliations
July 2021 - December 2021
University of Cambridge
Position
  • Fellow
November 2016 - June 2021
University of Helsinki
Position
  • PostDoc Position
February 2016 - November 2016
Università degli Studi di Modena e Reggio Emilia
Position
  • PostDoc Position
Education
January 2013 - December 2015
University of Ferrara
Field of study
  • Numerical analysis

Publications

Publications (27)
Article
Full-text available
Reliable non-destructive methods for verifying spent nuclear fuel are essential to draw credible nuclear safeguards conclusions from spent fuel. In Finland, spent fuel items are verified prior to the soon starting disposal in a geological repository with Passive Gamma Emission Tomography (PGET), a uniquely accurate method capable of rod-level detec...
Chapter
Gamma rays emitted from within an object can reveal information about that object in a non-destructive way, i.e. without physically opening the object and looking inside. This makes gamma ray emission imaging very useful in widely varying applications. In these notes, we highlight its application to the medical field, where we discuss molecular ima...
Article
Statistical inverse learning theory, a field that lies at the intersection of inverse problems and statistical learning, has lately gained more and more attention. In an effort to steer this interplay more towards the variational regularization framework, convergence rates have recently been proved for a class of convex, p -homogeneous regularizers...
Preprint
Full-text available
Digital breast tomosynthesis is an ill posed inverse problem. In this paper, we provide a try to overcome the problem of stretching artefacts of DBT with the help of learning from the microlocal priors.
Preprint
Full-text available
Statistical inverse learning theory, a field that lies at the intersection of inverse problems and statistical learning, has lately gained more and more attention. In an effort to steer this interplay more towards the variational regularization framework, convergence rates have recently been proved for a class of convex, $p$-homogeneous regularizer...
Preprint
Full-text available
Efficient representations of multivariate functions are critical for the design of state-of-the-art methods of data restoration and feature extraction. In this work, we consider the representation of spatio-temporal data such as temporal sequences (videos) of 2- and 3-dimensional images, where conventional separable representations are usually very...
Article
We propose a novel convolutional neural network (CNN), called \PsiDONet, designed for learning pseudodifferential operators (\PsiDOs) in the context of linear inverse problems. Our starting point is the iterative soft thresholding algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather...
Preprint
Full-text available
The dual-tree complex wavelet transform (DT-$\mathbb{C}$WT) is extended to the 4D setting. Key properties of 4D DT-$\mathbb{C}$WT, such as directional sensitivity and shift-invariance, are discussed and illustrated in a tomographic application. The inverse problem of reconstructing a dynamic three-dimensional target from X-ray projection measuremen...
Preprint
Full-text available
We consider a statistical inverse learning problem, where the task is to estimate a function $f$ based on noisy point evaluations of $Af$, where $A$ is a linear operator. The function $Af$ is evaluated at i.i.d. random design points $u_n$, $n=1,...,N$ generated by an unknown general probability distribution. We consider Tikhonov regularization with...
Preprint
Safeguarding the disposal of spent nuclear fuel in a geological repository needs an effective, efficient, reliable and robust non-destructive assay (NDA) system to ensure the integrity of the fuel prior to disposal. In the context of the Finnish geological repository, Passive Gamma Emission Tomography (PGET) will be a part of such an NDA system. We...
Article
In this paper we propose a motion-aware variational approach to reconstruct moving objects from sparse dynamic data. The motivation of this work stems from x-ray imaging of plants perfused with a liquid contrast agent, aimed at increasing the contrast of the images and studying the phloem transport in plants over time. The key idea of our approach...
Preprint
We propose a novel convolutional neural network (CNN), called $\Psi$DONet, designed for learning pseudodifferential operators ($\Psi$DOs) in the context of linear inverse problems. Our starting point is the Iterative Soft Thresholding Algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rat...
Preprint
Full-text available
In this paper we propose a motion-aware variational approach to reconstruct moving objects from sparse dynamic data. The motivation of this work stems from X-ray imaging of plants perfused with a liquid contrast agent, aimed at increasing the contrast of the images and studying the phloem transport in plants over time. The key idea of our approach...
Preprint
The International Atomic Energy Agency (IAEA) has recently approved passive gamma emission tomography (PGET) as a method for inspecting spent nuclear fuel assemblies (SFAs), an important aspect of international nuclear safeguards which aim at preventing the proliferation of nuclear weapons. The PGET instrument is essentially a single photon emissio...
Article
Full-text available
The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same time, we witness an exceptional success of data-based methodologies such as deep learning. However, in the context of inverse problems, deep neural networks mostly act as...
Preprint
Full-text available
The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same time, we witness an exceptional success of data-based methodologies such as deep learning. However, in the context of inverse problems, deep neural networks mostly act as...
Article
Full-text available
Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruct...
Article
Due to its potential to lower exposure to X-ray radiation and reduce the scanning time, region-of-interest (ROI) computed tomography (CT) is particularly appealing for a wide range of biomedical applications. To overcome the severe ill-posedness caused by the truncation of projection measurements, ad hoc strategies are required, since traditional C...
Article
Full-text available
This is the documentation of the tomographic X-ray data of a carved cheese slice. Data are available at www.fips.fi/dataset.php, and can be freely used for scientific purposes with appropriate references to them, and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram of a single 2D slice of the cheese slice with...
Article
Tomographic reconstruction is an ill-posed inverse problem that calls for regularization. One possibility is to require sparsity of the unknown in an orthonormal wavelet basis. This in turn can be achieved by variational regularization where the penalty term is the sum of absolute values of wavelet coefficients. Daubechies, Defrise and De Mol (Comm...
Article
Full-text available
The possibility to significantly reduce the X-ray radiation dose and shorten the scanning time is particularly appealing, especially for the medical imaging community. Region- of-interest Computed Tomography (ROI CT) has this potential and, for this reason, is currently receiving increasing attention. Due to the truncation of projection images, ROI...
Article
Full-text available
When it comes to computed tomography (CT), the possibility to reconstruct a small region-of-interest (ROI) using truncated projection data is particularly appealing due to its potential to lower radiation exposure and reduce the scanning time. However, ROI reconstruction from truncated projections is an ill-posed inverse problem, with the ill-posed...
Conference Paper
Full-text available
Region-of-interest (ROI) reconstruction in computed tomography (CT) is a problem receiving increasing attention in the medical imaging community, due to its potential to lower exposure to X-ray radiation and to reduce the scanning time. Since the ROI reconstruction problem requires to deal with truncated projection images, classical CT reconstructi...

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