arXiv research

Deep learning reduces artifacts in limited angle X-ray microscopy.

New algorithm improves sparse-view tomography without needing ground-truth data.

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object …

Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180$^\circ$ view of the object. This is impractical in a limited angle scenario, when the viewi…

On a compact Riemannian manifold with boundary, the absolute and relative cohomology groups appear as certain subspaces of harmonic forms. DeTurck and Gluck showed that these concrete realizations of the cohomology groups decompose into orthogonal subspaces corresponding to cohomology coming from the interior and bound…

Study uses machine learning to solve photoacoustic tomography's inverse problem.

Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especially challenging to train the reconstruction networks for three-dimensional comp…

Study assesses CNN model robustness to noise in low-cost CT scans.

Proposes a new method for posterior sampling using MMD with negative distance kernel.

New method uses MRI data to improve PET tomography uncertainty quantification.

We prove that capillary surfaces converge to a specific energy density as the angle approaches zero.

Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.

Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the…

This work tackles uncertainty quantification in tomography reconstruction.

Summary of tensor tomography proofs on manifolds with boundaries.

Characterizes stable minimal capillary surfaces with specific angles.

Paper addresses travel time tomography stability and statistical inversion.

Study gradient flow of phase transitions with fixed contact angle.

Study shows flow convergence to smooth K-Ricci outside a divisor with cusp singularity.

New method for sensing non-planar surfaces using ERT.

In Lorentzian geometry, limited definition of angles restricts the use of angle bisectors in study of triangles. This paper redefines angle bisectors so that they can be used to study attributes of triangles. Using the new definition, this paper investigates the existence of the incenter and the isogonal conjugate of a…

This work uses GANs to improve CT image reconstruction from limited angles.

Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spacing $d_0$ is a known constant limiting their application to the study of stresses generated by manufacturing and loading methods that do not…

This is the second of a series of three papers which provide proofs of results announced in arXiv:1210.7494. In this paper we consider the Gromov-Hausdorff limits of metrics with cone singularities in the case when the limiting cone angle is less than 2π. We show that these are in a natrual way projective algebraic var…

These are lecture notes for the course "Analysis and X-ray tomography". The course is a broad overview of various tools in analysis that can be used to study X-ray tomography. The focus is on tools and ideas, not so much on technical details and minimal assumptions. Only very basic functional analysis is assumed as bac…

Sketch Tomography improves quantum state estimation accuracy.

Minimal surfaces with dihedral symmetry are studied as angles converge to zero.

We determine the factorial growth rate of the number of finite index subgroups of right-angled Artin groups as a function of the index. This turns out to depend solely on the independence number of the defining graph. We also make a conjecture for right-angled Coxeter groups and prove that it holds in a limited setting…

Deep network improves electrical tomography across multiple frequencies.

We give a new notion of angle in general metric spaces; more precisely, given a triple a points $p,x,q$ in a metric space $(X,d)$, we introduce the notion of angle cone ${\angle_{pxq}}$ as being an interval ${\angle_{pxq}}:=[\angle^-_{pxq},\angle^+_{pxq}]$, where the quantities $\angle^\pm_{pxq}$ are defined in terms o…

The study examines conical Kähler-Einstein metrics on specific Fano manifolds and their behavior at the limit angle.

Compact Ricci solitons on surfaces have at most two cone points, and are known as Hamilton's footballs. In this note we completely describe the degenerations of these footballs as one or both of the cone angles approaches zero. In particular, we show that Hamilton's famous non-compact cigar soliton is the Gromov--Hausd…

This is the third and final paper in a series which establish results announced in arXiv:1210.7494. In this paper we consider the Gromov-Hausdorff limits of metrics with cone singularities in the case when the limiting cone angle approaches 2π. We also put all our technical results together to complete the proof of the…

We survey the role of right-angled Artin groups in the theory of diffeomorphism groups of low dimensional manifolds. We first describe some of the subgroup structure of right-angled Artin groups. We then discuss the interplay between algebraic structure, compactness, and regularity for group actions on one--dimensional…

Noise2Filter improves 3D tomography reconstruction efficiency and accuracy.

The paper sets new limits on hyperbolic polyhedra volumes.

Threshold found for hyperbolicity in random Coxeter groups.

Method learns topological states from randomized measurements.

Study Kähler-Einstein edge metrics on Hirzebruch surfaces, verifying a conjecture and finding a rigid singularity.

A new method speeds up quantum state estimation.

Improved computed tomography reconstruction with deep learning and deep image prior.

NF-ULA combines Langevin Monte Carlo with normalizing flows for imaging inverse problems.

New spatiotemporal Besov process improves CT image reconstruction and other inverse problems.

We prove that every right-angled Artin group embeds into the $C^{\infty}$ diffeomorphism group of the real line. As a corollary, we show every limit group, and more generally every countable residually RAAG group, embeds into the $C^{\infty}$ diffeomorphism group of the real line.

The paper studies Kähler-Einstein metrics with singularities and their limits.

With the advent of Deep Learning (DL) techniques, especially Generative Adversarial Networks (GANs), data augmentation and generation are quickly evolving domains that have raised much interest recently. However, the DL techniques are data demanding and since, medical data is not easily accessible, they suffer from dat…

We develop a 2D travel time tomography method which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We propose to use dictionary learning during the inversion to adapt dictionaries to specific slowness…

Sharp stability estimate for tensor tomography in non-positive curvature.