1 code implementation • 22 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.
1 code implementation • 9 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.
1 code implementation • 12 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.
2 code implementations • 9 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.
no code implementations • 21 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.
no code implementations • 8 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.
no code implementations • 8 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.
1 code implementation • 26 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.
no code implementations • 4 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.
no code implementations • 4 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.
no code implementations • 15 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.
2 code implementations • 12 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.
no code implementations • 28 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.
no code implementations • 9 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.
no code implementations • 21 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.
no code implementations • 14 Mar 2018 • Andreas Hauptmann, Simon Arridge, Felix Lucka, Vivek Muthurangu, Jennifer A. Steeden
In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN.
no code implementations • 31 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.