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X-ray tomography for fully-3D time-resolved reconstruction of bubbling fluidized beds

Graas, Adriaan B.M. (author), Wagner, E.C. (author), van Leeuwen, Tristan (author), van Ommen, J.R. (author), Batenburg, K. Joost (author), Lucka, Felix (author), Portela, L. (author)

A new X-ray computed tomography technique for the purpose of imaging fluidized beds is presented. It consists of an experimental set-up with three stationary X-ray source and flat panel detector pairs, a geometric calibration and data processing workflow, and an image reconstruction algorithm. The technique enables sparse-angular tomographic...

journal article 2024

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Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction

Zhao, Y. (author), Zhang, Y. (author), Tao, Q. (author)

Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as the regularization term. In quantitative MRI, the physical model of nuclear magnetic resonance...

conference paper 2024

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Joint Reconstruction-Segmentation on Graphs

Budd, Jeremy M. (author), van Gennip, Y. (author), Latz, Jonas (author), Parisotto, Simone (author), Schonlieb, Carola Bibiane (author)

Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations. A recent approach for solving such tasks is to perform this reconstruction jointly with the segmentation, using each to guide the other. However, this work has so far employed relatively simple segmentation...

journal article 2023

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Robust Lane Detection Through Self Pre-Training With Masked Sequential Autoencoders and Fine-Tuning With Customized PolyLoss

Li, Ruohan (author), Dong, Y. (author)

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane...

journal article 2023

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3D Carotid Artery Flow Imaging Using Compressive Sensing with a Spatial Coding Mask: A Simulation Study

Hu, Yuyang (author), Doğan, D. (author), Brown, Michael (author), Bulot, Mahé (author), Ferin, Guillaume (author), Leus, G.J.T. (author), Kruizinga, P. (author), Steen, Antonius F.W. (author), Bosch, Johannes G. (author)

It has been previously demonstrated that applying an aberrating mask for 2D compressive imaging using a low number of sensors (elements) can significantly improve image resolution, as evaluated via the point spread function. Here we investigate the potential to apply a similar approach for 3D flow monitoring. We conducted a 3D k-Wave simulation...

conference paper 2023

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Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning

Stölzle, Maximilian (author), Miki, Takahiro (author), Gerdes, Levin (author), Azkarate, Martin (author), Hutter, Marco (author)

Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion planning or the missing...

journal article 2022

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Cardiac MR: From Theory to Practice

Ismail, Tevfik F. (author), Strugnell, Wendy (author), Coletti, C. (author), Bozic, M. (author), Weingärtner, S.D. (author), Hammernik, Kerstin (author), Correia, Teresa (author), Küstner, Thomas (author)

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically...

review 2022

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Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Karkalousos, D. (author), Noteboom, S. (author), Hulst, H. E. (author), Vos, F.M. (author), Caan, M.W.A. (author)

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity,...

journal article 2022

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Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Demirel, Omer Burak (author), Yaman, Burhaneddin (author), Shenoy, Chetan (author), Moeller, Steen (author), Weingärtner, S.D. (author), Akçakaya, Mehmet (author)

Purpose: To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). Methods: First-pass perfusion CMR acquires highly-accelerated images with dynamically varying...

journal article 2022

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Refraction-Corrected Transcranial Ultrasound Imaging through the Human Temporal Window using a Single Probe

Mozaffarzadeh, M. (author), Verschuur, D.J. (author), Verweij, M.D. (author), Daeichin, V. (author), de Jong, N. (author), Renaud, G.G.J. (author)

Transcranial ultrasound imaging (TUI) is a diagnostic modality with numerous applications, but unfortunately, it is hindered by phase aberration caused by the skull. In this article, we propose to reconstruct a transcranial B-mode image with a refraction-corrected synthetic aperture imaging (SAI) scheme. First, the compressional sound...

journal article 2022

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Compressive Imaging with Spatial Coding Masks on Low Number of Elements: An Emulation Study

Hu, Yuyang (author), Brown, Michael (author), Doğan, D. (author), Leus, G.J.T. (author), Kruizinga, P. (author), Van Der Steen, Antonius F.W. (author), Bosch, Johannes G. (author)

We intend to develop an ultrasound compressive imaging device to perform carotid artery (CA) function and flow monitoring/imaging by using just a few single element transducers equipped with spatial coding masks. The spatially unique impulse responses can be exploited in compressive reconstructions. To explore the potential of different...

conference paper 2022

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Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography

Schaart, D.R. (author), Schramm, Georg (author), Nuyts, Johan (author), Surti, Suleman (author)

The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The...

review 2021

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Learning a preconditioner to accelerate compressed sensing reconstructions in MRI

Koolstra, Kirsten (author), Remis, R.F. (author)

Purpose: To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods: A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images....

journal article 2021

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A survey on deep learning in medical image reconstruction

Ahishakiye, Emmanuel (author), van Gijzen, M.B. (author), Tumwiine, Julius (author), Wario, Ruth (author), Obungoloch, Johnes (author)

Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained...

review 2021

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Low-field magnetic resonance imaging using multiplicative regularization

de Leeuw den Bouter, M.L. (author), van Gijzen, M.B. (author), Remis, R.F. (author)

In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set...

journal article 2021

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Inversion of incomplete spectral data using support information with an application to magnetic resonance imaging

de Leeuw den Bouter, M.L. (author), van den Berg, P.M. (author), Remis, R.F. (author)

In this paper we discuss an imaging method when the object has known support and its spatial Fourier transform is only known on a certain k-space undersampled pattern. The simple conjugate gradient least squares algorithm applied to the corresponding truncated Fourier transform equation produces reconstructions that are basically of a similar...

journal article 2021

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A Dictionary Learning Approach for Joint Reconstruction and Denoising in Low Field Magnetic Resonance Imaging

Ahishakiye, Emmanuel (author), van Gijzen, M.B. (author), Shan, X. (author), Tumwiine, Julius (author), Obungoloch, Johnes (author)

Currently, many children with hydrocephalus in East Africa and other resource-constrained countries do not have access to Magnetic Resonance Imaging (MRI) scanners, the preferred imaging tool during the disease administration and treatment. Conventional MRI scanners are costly to buy and manage, which limits their utilization in low-income...

conference paper 2021

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Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

Zhao, Wenzhao (author), Wang, Hongjian (author), Gemmeke, Hartmut (author), van Dongen, K.W.A. (author), Hopp, Torsten (author), Hesser, Jürgen (author)

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction...

journal article 2020

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An adaptive intelligence algorithm for undersampled knee MRI reconstruction

Pezzotti, Nicola (author), Yousefi, Sahar (author), Elmahdy, Mohamed S. (author), van Gemert, Jeroen Hendrikus Fransiscus (author), Schuelke, Christophe (author), Doneva, Mariya (author), Nielsen, Tim (author), Lelieveldt, B.P.F. (author), Staring, M. (author)

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is...

journal article 2020

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Shadow-free motorized capsule enables accurate beam positioning and sectorized OCT imaging of the esophagus

López-Marín, Antonio (author), Springeling, Geert (author), Beurskens, Robert (author), Van Beusekom, Heleen (author), van der Steen, A.F.W. (author), Koch, Arjun D. (author), Bouma, Brett E. (author), Huber, Robert A. (author), Van Soest, Gijs (author), Wang, Tianshi (author)

In this study, we demonstrate a 12x36 mm motorized capsule for OCT imaging of the esophagus. The capsule produces unobstructed images by using a distal reflector design, thus avoiding shadow caused by the motor wires. The motor synchronous control enables three working modes: circumferential imaging, angular sector imaging and accurate beam...

conference paper 2020