347 citations · 1.1k across the 34 of their papers we have counts for
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Towards robust quantitative photoacoustic tomography via learned iterative methods
Anssi Manninen, Janek Gröhl, Felix Lucka +1
Photoacoustic tomography (PAT) is a medical imaging modality that can provide high-resolution tissue images based on the optical absorption. Classical reconstruction methods for qu…
Benchmarking learned algorithms for computed tomography image reconstruction tasks
Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov +4
Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image recons…
Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Models
Ezgi Demircan-Tureyen, Felix Lucka, Tristan van Leeuwen
Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited from…
Learned denoising with simulated and experimental low-dose CT data
Maximilian B. Kiss, Ander Biguri, Carola-Bibiane Schönlieb +2
Like in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To impro…
Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging
Ryan A. L. Schoop, Gijs Hendriks, Tristan van Leeuwen +2
Ultrafast ultrasound imaging insonifies a medium with one or a combination of a few plane waves at different beam-steered angles instead of many focused waves. It can achieve much…
Single-shot Tomography of Discrete Dynamic Objects
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 boundarie…
Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning
Tianyuan Wang, Felix Lucka, Tristan van Leeuwen
In X-ray Computed Tomography (CT), projections from many angles are acquired and used for 3D reconstruction. To make CT suitable for in-line quality control, reducing the number of…
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg +2
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consi…
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography
Poulami Somanya Ganguly, Felix Lucka, Holger Kohr +3
Tilt-series alignment is crucial to obtaining high-resolution reconstructions in cryo-electron tomography. Beam-induced local deformation of the sample is hard to estimate from the…
Single Plane-Wave Imaging using Physics-Based Deep Learning
Georgios Pilikos, Chris L. de Korte, Tristan van Leeuwen +1
In plane-wave imaging, multiple unfocused ultrasound waves are transmitted into a medium of interest from different angles and an image is formed from the recorded reflections. The…
Deep Learning for Multi-View Ultrasonic Image Fusion
Georgios Pilikos, Lars Horchens, Tristan van Leeuwen +1
Ultrasonic imaging is being used to obtain information about the acoustic properties of a medium by emitting waves into it and recording their interaction using ultrasonic transduc…
Deep data compression for approximate ultrasonic image formation
Georgios Pilikos, Lars Horchens, Kees Joost Batenburg +2
In many ultrasonic imaging systems, data acquisition and image formation are performed on separate computing devices. Data transmission is becoming a bottleneck, thus, efficient da…
Fast ultrasonic imaging using end-to-end deep learning
Georgios Pilikos, Lars Horchens, Kees Joost Batenburg +2
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.…
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg +7
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients wit…
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten +3
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…