Search Results for author: Felix Lucka

Found 17 papers, 6 papers with code

Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging

1 code implementation22 Apr 2024 Ryan A. L. Schoop, Gijs Hendriks, Tristan van Leeuwen, Chris L. de Korte, Felix Lucka

We acquired a data collection designed for benchmarking data-driven plane wave imaging approaches using a realistic breast mimicking phantom and an ultrasound calibration phantom.


Single-shot Tomography of Discrete Dynamic Objects

1 code implementation9 Nov 2023 Ajinkya Kadu, Felix Lucka, Kees Joost Batenburg

This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time.

Computed Tomography (CT) Dynamic Reconstruction +2

Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning

1 code implementation12 Jul 2023 Tianyuan Wang, Felix Lucka, Tristan van Leeuwen

The approach learns efficient non-greedy policies to solve a given class of OED problems through extensive offline training rather than solving a given OED problem directly via numerical optimization.

3D Reconstruction Computed Tomography (CT) +2

2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

2 code implementations9 Jun 2023 Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, Felix Lucka

We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks.

Computed Tomography (CT) Image Denoising +3

SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography

no code implementations21 Jan 2022 Poulami Somanya Ganguly, Felix Lucka, Holger Kohr, Erik Franken, Hermen Jan Hupkes, K Joost Batenburg

The state-of-the-art approach for deformation estimation uses (semi-)manually labelled marker locations in projection data to fit the parameters of a polynomial deformation model.

Electron Tomography Super-Resolution

Single Plane-Wave Imaging using Physics-Based Deep Learning

no code implementations8 Sep 2021 Georgios Pilikos, Chris L. de Korte, Tristan van Leeuwen, Felix Lucka

We compare our proposed data-to-image network with an image-to-image network in simulated data experiments, mimicking a medical ultrasound application.

Deep Learning for Multi-View Ultrasonic Image Fusion

no code implementations8 Sep 2021 Georgios Pilikos, Lars Horchens, Tristan van Leeuwen, Felix Lucka

These different modes give rise to multiple DAS images reflecting different geometric information about the scatterers and the challenge is to either fuse them into one image or to directly extract higher-level information regarding the materials of the medium, e. g., a segmentation map.


Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image versus Data Domain

1 code implementation26 Nov 2020 Bolin Pan, Simon R. Arridge, Felix Lucka, Ben T. Cox, Nam Huynh, Paul C. Beard, Edward Z. Zhang, Marta M. Betcke

We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame.

Image Reconstruction

Fast ultrasonic imaging using end-to-end deep learning

no code implementations4 Sep 2020 Georgios Pilikos, Lars Horchens, Kees Joost Batenburg, Tristan van Leeuwen, Felix Lucka

Ultrasonic imaging algorithms used in many clinical and industrial applications consist of three steps: A data pre-processing, an image formation and an image post-processing step.

Deep data compression for approximate ultrasonic image formation

no code implementations4 Sep 2020 Georgios Pilikos, Lars Horchens, Kees Joost Batenburg, Tristan van Leeuwen, Felix Lucka

This demonstrates the great potential of deep ultrasonic data compression tailored for a specific image formation method.

Data Compression Decoder +1

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning

no code implementations15 May 2020 Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek

This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.

Image Registration

A Cone-Beam X-Ray CT Data Collection designed for Machine Learning

2 code implementations12 May 2019 Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg

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.

BIG-bench Machine Learning Computed Tomography (CT) +2

A Multi-channel DART Algorithm

no code implementations28 Aug 2018 Mathé Zeegers, Felix Lucka, Kees Joost Batenburg

Discrete tomography is concerned with objects that consist of a small number of materials, which makes it possible to compute accurate reconstructions from highly limited projection data.

Approximate k-space models and Deep Learning for fast photoacoustic reconstruction

no code implementations9 Jul 2018 Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard, Simon Arridge

We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography.

Never look back - A modified EnKF method and its application to the training of neural networks without back propagation

no code implementations21 May 2018 Eldad Haber, Felix Lucka, Lars Ruthotto

Further, we provide numerical examples that demonstrate the potential of our method for training deep neural networks.

Model based learning for accelerated, limited-view 3D photoacoustic tomography

no code implementations31 Aug 2017 Andreas Hauptmann, Felix Lucka, Marta Betcke, Nam Huynh, Jonas Adler, Ben Cox, Paul Beard, Sebastien Ourselin, Simon Arridge

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up.

Tomographic Reconstructions

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