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De Santi B, Kim L, Bulthuis RFG, Lucka F, Manohar S. Automated three-dimensional image registration for longitudinal photoacoustic imaging. J Biomed Opt 2024; 29:S11515. [PMID: 38223681 PMCID: PMC10787589 DOI: 10.1117/1.jbo.29.s1.s11515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/05/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024]
Abstract
Significance Photoacoustic tomography (PAT) has great potential in monitoring disease progression and treatment response in breast cancer. However, due to variations in breast repositioning, there is a chance of geometric misalignment between images. Further, poor repositioning can affect light fluence distribution and imaging field-of-view, making images different from one another. The net effect is that it becomes challenging to distinguish between image changes due to repositioning effects and those due to true biological variations. Aim The aim is to develop a three-dimensional image registration framework for geometrically aligning repeated PAT volumetric images, which are potentially affected by repositioning effects such as misalignment, changed radiant exposure conditions, and different fields-of-view. Approach The proposed framework involves the use of a coordinate-based neural network to represent the displacement field between pairs of PAT volumetric images. A loss function based on normalized cross correlation and Frangi vesselness feature extraction at multiple scales was implemented. We refer to our image registration framework as MUVINN-reg, which stands for multiscale vesselness-based image registration using neural networks. The approach was tested on a longitudinal dataset of healthy volunteer breast PAT images acquired with the hybrid photoacoustic-ultrasound Photoacoustic Mammoscope 3 imaging system. The registration performance was also tested under unfavorable repositioning conditions such as intentional mispositioning, and variation in breast-supporting cup size between measurements. Results A total of 13 pairs of repeated PAT scans were included in this study. MUVINN-reg showed excellent performance in co-registering each pair of images. The proposed framework was shown to be robust to image intensity shifts and field-of-view changes. Furthermore, MUVINN-reg could align vessels at imaging depths greater than 4 cm. Conclusions The proposed framework will enable the use of PAT for quantitative and reproducible monitoring of disease progression and treatment response.
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Affiliation(s)
- Bruno De Santi
- University of Twente, TechMed Centre, Multi-Modality Medical Imaging Group, Enschede, The Netherlands
| | - Lucia Kim
- University of Twente, TechMed Centre, Multi-Modality Medical Imaging Group, Enschede, The Netherlands
| | - Rianne F. G. Bulthuis
- University of Twente, TechMed Centre, Multi-Modality Medical Imaging Group, Enschede, The Netherlands
- Medisch Spectrum Hospital, Department of Radiology, Enschede, The Netherlands
| | - Felix Lucka
- Centrum Wiskunde en Informatica (CWI), Amsterdam, The Netherlands
| | - Srirang Manohar
- University of Twente, TechMed Centre, Multi-Modality Medical Imaging Group, Enschede, The Netherlands
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Graas A, Coban SB, Batenburg KJ, Lucka F. Just-in-time deep learning for real-time X-ray computed tomography. Sci Rep 2023; 13:20070. [PMID: 37973801 PMCID: PMC10654383 DOI: 10.1038/s41598-023-46028-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments.
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Affiliation(s)
- Adriaan Graas
- Computational Imaging, CWI, 1098 XG, Amsterdam, The Netherlands.
| | - Sophia Bethany Coban
- Computational Imaging, CWI, 1098 XG, Amsterdam, The Netherlands
- Frazer-Nash Consultancy, Leatherhead, KT22 7NL, Surrey, UK
| | - K Joost Batenburg
- Computational Imaging, CWI, 1098 XG, Amsterdam, The Netherlands
- Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Felix Lucka
- Computational Imaging, CWI, 1098 XG, Amsterdam, The Netherlands.
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3
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Kiss MB, Coban SB, Batenburg KJ, van Leeuwen T, Lucka F. 2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning. Sci Data 2023; 10:576. [PMID: 37666897 PMCID: PMC10477177 DOI: 10.1038/s41597-023-02484-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/16/2023] [Indexed: 09/06/2023] Open
Abstract
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. 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. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.
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Affiliation(s)
- Maximilian B Kiss
- Centrum Wiskunde & Informatica, Computational Imaging group, Amsterdam, 1098 XG, The Netherlands.
| | - Sophia B Coban
- Centrum Wiskunde & Informatica, Computational Imaging group, Amsterdam, 1098 XG, The Netherlands
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
| | - K Joost Batenburg
- Centrum Wiskunde & Informatica, Computational Imaging group, Amsterdam, 1098 XG, The Netherlands
- Leiden University, LIACS, Leiden, 2300 RA, The Netherlands
| | - Tristan van Leeuwen
- Centrum Wiskunde & Informatica, Computational Imaging group, Amsterdam, 1098 XG, The Netherlands
- Utrecht University, Mathematical Institute, Utrecht, 3584 CD, The Netherlands
| | - Felix Lucka
- Centrum Wiskunde & Informatica, Computational Imaging group, Amsterdam, 1098 XG, The Netherlands.
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Zhu J, Huynh N, Ogunlade O, Ansari R, Lucka F, Cox B, Beard P. Mitigating the Limited View Problem in Photoacoustic Tomography for a Planar Detection Geometry by Regularized Iterative Reconstruction. IEEE Trans Med Imaging 2023; 42:2603-2615. [PMID: 37115840 DOI: 10.1109/tmi.2023.3271390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The use of a planar detection geometry in photoacoustic tomography results in the so- called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularization were evaluated using in-silico, experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularization parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries.
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van Eijnatten M, Rundo L, Batenburg KJ, Lucka F, Beddowes E, Caldas C, Gallagher FA, Sala E, Schönlieb CB, Woitek R. 3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Comput Methods Programs Biomed 2021; 208:106261. [PMID: 34289437 DOI: 10.1016/j.cmpb.2021.106261] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer. METHODS As a pre-processing step, the abdominopelvic CT images were refined by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of the VoxelMorph framework when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images in the longitudinal dataset. This devised training strategy was compared against training on simulated deformations of a single CT volume. A widely used software toolbox for deformable image registration called NiftyReg was used as a benchmark. The evaluations were performed by calculating the Dice Similarity Coefficient (DSC) between manual vertebrae segmentations and the Structural Similarity Index (SSIM). RESULTS The CT table removal procedure allowed both VoxelMorph and NiftyReg to achieve significantly better registration performance. In a 4-fold cross-validation scheme, the incremental training strategy resulted in better registration performance compared to training on a single volume, with a mean DSC of 0.929±0.037 and 0.883±0.033, and a mean SSIM of 0.984±0.009 and 0.969±0.007, respectively. Although our deformable image registration method did not outperform NiftyReg in terms of DSC (0.988±0.003) or SSIM (0.995±0.002), the registrations were approximately 300 times faster. CONCLUSIONS 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.
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Affiliation(s)
- Maureen van Eijnatten
- Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands.
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom
| | - K Joost Batenburg
- Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Mathematical Institute, Leiden University, 2300 RA Leiden, the Netherlands
| | - Felix Lucka
- Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Centre for Medical Image Computing, University College London, WC1E 6BT London, United Kingdom
| | - Emma Beddowes
- Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, CB2 0QQ Cambridge, United Kingdom
| | - Carlos Caldas
- Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, CB2 0QQ Cambridge, United Kingdom
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, CB3 0WA Cambridge, United Kingdom
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090 Vienna, Austria
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6
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Minnema J, Wolff J, Koivisto J, Lucka F, Batenburg KJ, Forouzanfar T, van Eijnatten M. Comparison of convolutional neural network training strategies for cone-beam CT image segmentation. Comput Methods Programs Biomed 2021; 207:106192. [PMID: 34062493 DOI: 10.1016/j.cmpb.2021.106192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Over the past decade, convolutional neural networks (CNNs) have revolutionized the field of medical image segmentation. Prompted by the developments in computational resources and the availability of large datasets, a wide variety of different two-dimensional (2D) and three-dimensional (3D) CNN training strategies have been proposed. However, a systematic comparison of the impact of these strategies on the image segmentation performance is still lacking. Therefore, this study aimed to compare eight different CNN training strategies, namely 2D (axial, sagittal and coronal slices), 2.5D (3 and 5 adjacent slices), majority voting, randomly oriented 2D cross-sections and 3D patches. METHODS These eight strategies were used to train a U-Net and an MS-D network for the segmentation of simulated cone-beam computed tomography (CBCT) images comprising randomly-placed non-overlapping cylinders and experimental CBCT images of anthropomorphic phantom heads. The resulting segmentation performances were quantitatively compared by calculating Dice similarity coefficients. In addition, all segmented and gold standard experimental CBCT images were converted into virtual 3D models and compared using orientation-based surface comparisons. RESULTS The CNN training strategy that generally resulted in the best performances on both simulated and experimental CBCT images was majority voting. When employing 2D training strategies, the segmentation performance can be optimized by training on image slices that are perpendicular to the predominant orientation of the anatomical structure of interest. Such spatial features should be taken into account when choosing or developing novel CNN training strategies for medical image segmentation. CONCLUSIONS The results of this study will help clinicians and engineers to choose the most-suited CNN training strategy for CBCT image segmentation.
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Affiliation(s)
- Jordi Minnema
- Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovationlab, Amsterdam UMC and Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam 1081 HV, theNetherlands.
| | - Jan Wolff
- Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT, Am Schleusengraben 13, Hamburg 21029, Germany; Department of Oral and Maxillofacial Surgery, Division for Regenerative Orofacial Medicine, University Hospital Hamburg-Eppendorf, Hamburg 20246, Germany; Department of Dentistry and Oral Health, Aarhus University, Vennelyst Boulevard 9, DK-8000 Aarhus C, Denmark
| | - Juha Koivisto
- Department of Physics, University of Helsinki, Helsinki 20560, Finland
| | - Felix Lucka
- Centrum Wiskunde & Informatica (CWI), Amsterdam 1090 GB, the Netherlands; University College London, London WC1E 6BT, United Kingdom
| | | | - Tymour Forouzanfar
- Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovationlab, Amsterdam UMC and Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam 1081 HV, theNetherlands
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7
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Minnema J, van Eijnatten M, der Sarkissian H, Doyle S, Koivisto J, Wolff J, Forouzanfar T, Lucka F, Batenburg KJ. Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction. Phys Med Biol 2021; 66. [PMID: 34107467 DOI: 10.1088/1361-6560/ac09a1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 06/09/2021] [Indexed: 11/11/2022]
Abstract
High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three-dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans. We evaluated this novel approach using a dataset of input CBCT scans affected by HCAAs and high-quality artifact-free target CBCT scans. Two different CNN architectures were employed, namely U-Net and a mixed-scale dense CNN (MS-D Net). The artifact reduction performance of the proposed approach was compared to that of a Cartesian slice-based artifact reduction deep learning approach in which a CNN was trained to remove the HCAAs from Cartesian slices. In addition, all processed CBCT scans were segmented to investigate the impact of HCAAs reduction on the quality of CBCT image segmentation. We demonstrate that the proposed deep learning approach with geometry-aware dimension reduction greatly reduces HCAAs in CBCT scans and outperforms the Cartesian slice-based deep learning approach. Moreover, the proposed artifact reduction approach markedly improves the accuracy of the subsequent segmentation task compared to the Cartesian slice-based workflow.
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Affiliation(s)
- Jordi Minnema
- Amsterdam UMC and Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam, Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovationlab, Amsterdam Movement Sciences, 1081 HV Amsterdam, The Netherlands
| | - Maureen van Eijnatten
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands.,Centrum Wiskunde & Informatica (CWI), 1090 GB Amsterdam, The Netherlands
| | | | - Shannon Doyle
- Centrum Wiskunde & Informatica (CWI), 1090 GB Amsterdam, The Netherlands
| | - Juha Koivisto
- Department of Physics, University of Helsinki, Gustaf Hällsströmin katu 2, FI-00560, Helsinki, Finland
| | - Jan Wolff
- Department of Oral and Maxillofacial Surgery, Division for Regenerative Orofacial Medicine, University Hospital Hamburg-Eppendorf, D-20246 Hamburg, Germany.,Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT, Am Schleusengraben 13, D-21029 Hamburg, Germany.,Department of Dentistry and Oral Health, Aarhus University, Vennelyst Boulevard 9, DK-8000 Aarhus C, Denmark
| | - Tymour Forouzanfar
- Amsterdam UMC and Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam, Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovationlab, Amsterdam Movement Sciences, 1081 HV Amsterdam, The Netherlands
| | - Felix Lucka
- Centrum Wiskunde & Informatica (CWI), 1090 GB Amsterdam, The Netherlands.,Centre for Medical Image Computing, University College London, WC1E 6BT London, United Kingdom
| | - Kees Joost Batenburg
- Centrum Wiskunde & Informatica (CWI), 1090 GB Amsterdam, The Netherlands.,Leiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CA Leiden, The Netherlands
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Viganò N, Lucka F, de La Rochefoucauld O, Coban SB, van Liere R, Fajardo M, Zeitoun P, Batenburg KJ. Emulation of X-ray Light-Field Cameras. J Imaging 2020; 6:138. [PMID: 34460535 PMCID: PMC8321154 DOI: 10.3390/jimaging6120138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/03/2020] [Accepted: 12/07/2020] [Indexed: 11/16/2022] Open
Abstract
X-ray plenoptic cameras acquire multi-view X-ray transmission images in a single exposure (light-field). Their development is challenging: designs have appeared only recently, and they are still affected by important limitations. Concurrently, the lack of available real X-ray light-field data hinders dedicated algorithmic development. Here, we present a physical emulation setup for rapidly exploring the parameter space of both existing and conceptual camera designs. This will assist and accelerate the design of X-ray plenoptic imaging solutions, and provide a tool for generating unlimited real X-ray plenoptic data. We also demonstrate that X-ray light-fields allow for reconstructing sharp spatial structures in three-dimensions (3D) from single-shot data.
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Affiliation(s)
- Nicola Viganò
- ESRF—The European Synchrotron, 38043 Grenoble, France
- Centrum Wiskunde & Informatica (CWI), NWO, 1098 XG Amsterdam, The Netherlands; (F.L.); (S.B.C.); (R.v.L.); (K.J.B.)
| | - Felix Lucka
- Centrum Wiskunde & Informatica (CWI), NWO, 1098 XG Amsterdam, The Netherlands; (F.L.); (S.B.C.); (R.v.L.); (K.J.B.)
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | | | - Sophia Bethany Coban
- Centrum Wiskunde & Informatica (CWI), NWO, 1098 XG Amsterdam, The Netherlands; (F.L.); (S.B.C.); (R.v.L.); (K.J.B.)
| | - Robert van Liere
- Centrum Wiskunde & Informatica (CWI), NWO, 1098 XG Amsterdam, The Netherlands; (F.L.); (S.B.C.); (R.v.L.); (K.J.B.)
- Mathematics and Computer Science, Technical University Eindhoven, 5600 MB Eindhoven, The Netherlands
| | - Marta Fajardo
- Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal;
| | - Philippe Zeitoun
- Laboratoire d’Optique Appliquée, ENSTA, CNRS, Institut Polytechnique Paris, 91762 Palaiseau, France;
| | - Kees Joost Batenburg
- Centrum Wiskunde & Informatica (CWI), NWO, 1098 XG Amsterdam, The Netherlands; (F.L.); (S.B.C.); (R.v.L.); (K.J.B.)
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 CA Leiden, The Netherlands
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Coban SB, Lucka F, Palenstijn WJ, Van Loo D, Batenburg KJ. Explorative Imaging and Its Implementation at the FleX-ray Laboratory. J Imaging 2020; 6:jimaging6040018. [PMID: 34460720 PMCID: PMC8321014 DOI: 10.3390/jimaging6040018] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/12/2020] [Accepted: 03/25/2020] [Indexed: 01/21/2023] Open
Abstract
In tomographic imaging, the traditional process consists of an expert and an operator collecting data, the expert working on the reconstructed slices and drawing conclusions. The quality of reconstructions depends heavily on the quality of the collected data, except that, in the traditional process of imaging, the expert has very little influence over the acquisition parameters, experimental plan or the collected data. It is often the case that the expert has to draw limited conclusions from the reconstructions, or adapt a research question to data available. This method of imaging is static and sequential, and limits the potential of tomography as a research tool. In this paper, we propose a more dynamic process of imaging where experiments are tailored around a sample or the research question; intermediate reconstructions and analysis are available almost instantaneously, and expert has input at any stage of the process (including during acquisition) to improve acquisition or image reconstruction. Through various applications of 2D, 3D and dynamic 3D imaging at the FleX-ray Laboratory, we present the unexpected journey of exploration a research question undergoes, and the surprising benefits it yields.
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Affiliation(s)
- Sophia Bethany Coban
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (F.L.); (W.J.P.); (K.J.B.)
- Correspondence:
| | - Felix Lucka
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (F.L.); (W.J.P.); (K.J.B.)
- Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
| | - Willem Jan Palenstijn
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (F.L.); (W.J.P.); (K.J.B.)
| | - Denis Van Loo
- TESCAN-XRE NV, Bollebergen 2B/bus 1, 9052 Ghent, Belgium;
| | - Kees Joost Batenburg
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (F.L.); (W.J.P.); (K.J.B.)
- Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
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10
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Der Sarkissian H, Lucka F, van Eijnatten M, Colacicco G, Coban SB, Batenburg KJ. A cone-beam X-ray computed tomography data collection designed for machine learning. Sci Data 2019; 6:215. [PMID: 31641152 PMCID: PMC6805882 DOI: 10.1038/s41597-019-0235-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 09/03/2019] [Indexed: 11/15/2022] Open
Abstract
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 but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation.
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Affiliation(s)
- Henri Der Sarkissian
- Centrum Wiskunde en Informatica, Computational Imaging group, Science Park 123, 1098XG, Amsterdam, The Netherlands.
| | - Felix Lucka
- Centrum Wiskunde en Informatica, Computational Imaging group, Science Park 123, 1098XG, Amsterdam, The Netherlands.
- Centre for Medical Image Computing, University College London, WC1E 6BT, London, United Kingdom.
| | - Maureen van Eijnatten
- Centrum Wiskunde en Informatica, Computational Imaging group, Science Park 123, 1098XG, Amsterdam, The Netherlands
| | - Giulia Colacicco
- Centrum Wiskunde en Informatica, Computational Imaging group, Science Park 123, 1098XG, Amsterdam, The Netherlands
| | - Sophia Bethany Coban
- Centrum Wiskunde en Informatica, Computational Imaging group, Science Park 123, 1098XG, Amsterdam, The Netherlands
| | - Kees Joost Batenburg
- Centrum Wiskunde en Informatica, Computational Imaging group, Science Park 123, 1098XG, Amsterdam, The Netherlands
- Leiden University, Department of Mathematics, 2300 RA, Leiden, The Netherlands
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11
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Huynh N, Lucka F, Zhang E, Betcke M, Arridge SR, Beard PC, Cox BT. Single-pixel camera photoacoustic tomography. J Biomed Opt 2019; 24:1-6. [PMID: 31535537 PMCID: PMC7005533 DOI: 10.1117/1.jbo.24.12.121907] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/19/2019] [Indexed: 05/18/2023]
Abstract
Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry–Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor—induced by the photoacoustic waves—are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view.
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Affiliation(s)
- Nam Huynh
- University College London, Department of Medical Physics and Biomedical Engineering, England, United Kingdom
| | - Felix Lucka
- University College London, Department of Computer Science, England, United Kingdom
- Centrum Wiskunde and Informatica, Computational Imaging, Amsterdam, The Netherlands
| | - Edward Zhang
- University College London, Department of Medical Physics and Biomedical Engineering, England, United Kingdom
| | - Marta Betcke
- University College London, Department of Computer Science, England, United Kingdom
| | - Simon R. Arridge
- University College London, Department of Computer Science, England, United Kingdom
| | - Paul C. Beard
- University College London, Department of Medical Physics and Biomedical Engineering, England, United Kingdom
| | - Benjamin T. Cox
- University College London, Department of Medical Physics and Biomedical Engineering, England, United Kingdom
- Address all correspondence to Benjamin T. Cox, E-mail:
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12
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Rimpiläinen V, Koulouri A, Lucka F, Kaipio JP, Wolters CH. Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. Neuroimage 2018; 188:252-260. [PMID: 30529398 DOI: 10.1016/j.neuroimage.2018.11.058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 11/30/2018] [Indexed: 10/27/2022] Open
Abstract
Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
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Affiliation(s)
- Ville Rimpiläinen
- Department of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany.
| | - Alexandra Koulouri
- Laboratory of Mathematics, Tampere University of Technology, P. O. Box 692, 33101, Tampere, Finland; Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, 541 24, Greece
| | - Felix Lucka
- Computational Imaging, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, the Netherlands; Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Jari P Kaipio
- Department of Mathematics, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand; Department of Applied Physics, University of Eastern Finland, FI-90211, Kuopio, Finland
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany
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Treeby B, Lucka F, Martin E, Cox BT. Equivalent-Source Acoustic Holography for Projecting Measured Ultrasound Fields Through Complex Media. IEEE Trans Ultrason Ferroelectr Freq Control 2018; 65:1857-1864. [PMID: 30072321 DOI: 10.1109/tuffc.2018.2861895] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Holographic projections of experimental ultrasound measurements generally use the angular spectrum method or Rayleigh integral, where the measured data are imposed as a Dirichlet boundary condition. In contrast, full-wave models, which can account for more complex wave behavior, often use interior mass or velocity sources to introduce acoustic energy into the simulation. Here, a method to generate an equivalent interior source that reproduces the measurement data is proposed based on gradient-based optimization. The equivalent-source can then be used with full-wave models (for example, the open-source k-Wave toolbox) to compute holographic projections through complex media including nonlinearity and heterogeneous material properties. Numerical and experimental results using both time-domain and continuous-wave sources are used to demonstrate the accuracy of the approach.
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14
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Tick J, Pulkkinen A, Lucka F, Ellwood R, Cox BT, Kaipio JP, Arridge SR, Tarvainen T. Three dimensional photoacoustic tomography in Bayesian framework. J Acoust Soc Am 2018; 144:2061. [PMID: 30404490 DOI: 10.1121/1.5057109] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 09/13/2018] [Indexed: 05/18/2023]
Abstract
The image reconstruction problem (or inverse problem) in photoacoustic tomography is to resolve the initial pressure distribution from detected ultrasound waves generated within an object due to an illumination by a short light pulse. Recently, a Bayesian approach to photoacoustic image reconstruction with uncertainty quantification was proposed and studied with two dimensional numerical simulations. In this paper, the approach is extended to three spatial dimensions and, in addition to numerical simulations, experimental data are considered. The solution of the inverse problem is obtained by computing point estimates, i.e., maximum a posteriori estimate and posterior covariance. These are computed iteratively in a matrix-free form using a biconjugate gradient stabilized method utilizing the adjoint of the acoustic forward operator. The results show that the Bayesian approach can produce accurate estimates of the initial pressure distribution in realistic measurement geometries and that the reliability of these estimates can be assessed.
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Affiliation(s)
- Jenni Tick
- Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
| | - Aki Pulkkinen
- Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
| | - Felix Lucka
- Centrum Wiskunde and Informatica, P.O. Box 94079, 1090 GB Amsterdam, Netherlands
| | - Robert Ellwood
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Ben T Cox
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Jari P Kaipio
- Dodd-Walls Centre, Department of Mathematics, University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand
| | - Simon R Arridge
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Tanja Tarvainen
- Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
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15
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Hauptmann A, Arridge S, Lucka F, Muthurangu V, Steeden JA. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med 2018; 81:1143-1156. [PMID: 30194880 PMCID: PMC6492123 DOI: 10.1002/mrm.27480] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 07/11/2018] [Accepted: 07/15/2018] [Indexed: 12/25/2022]
Abstract
Purpose Real‐time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real‐time data in patients with congenital heart disease (CHD). Methods A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real‐time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real‐time data was also reconstructed with compressed sensing (CS) to compare image quality and reconstruction time. Ventricular volume measurements made using both the CNN and CS reconstructed images were compared to reference standard breath hold data. Results It was feasible to train a CNN to remove artifact from highly undersampled radial real‐time data. The overall reconstruction time with the CNN (including creation of aliased images) was shown to be >5 × faster than the CS reconstruction. In addition, the image quality and accuracy of biventricular volumes measured from the CNN reconstructed images were superior to the CS reconstructions. Conclusion This article has demonstrated the potential for the use of a CNN for reconstruction of real‐time radial data within the clinical setting. Clinical measures of ventricular volumes using real‐time data with CNN reconstruction are not statistically significantly different from gold‐standard, cardiac‐gated, breath‐hold techniques.
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Affiliation(s)
- Andreas Hauptmann
- Department of Computer Science, University College London, London, United Kingdom
| | - Simon Arridge
- Department of Computer Science, University College London, London, United Kingdom
| | - Felix Lucka
- Department of Computer Science, University College London, London, United Kingdom.,Computational Imaging, Centrum Wiskunde and Informatica (CWI), Amsterdam, Netherlands
| | - Vivek Muthurangu
- UCL Centre for Cardiovascular Imaging, University College London, London, United Kingdom
| | - Jennifer A Steeden
- UCL Centre for Cardiovascular Imaging, University College London, London, United Kingdom
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Hauptmann A, Lucka F, Betcke M, Huynh N, Adler J, Cox B, Beard P, Ourselin S, Arridge S. Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography. IEEE Trans Med Imaging 2018; 37:1382-1393. [PMID: 29870367 PMCID: PMC7613684 DOI: 10.1109/tmi.2018.2820382] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.
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Affiliation(s)
- Andreas Hauptmann
- the Department of Computer Science, University College London, London WC1E 6BT, U.K
| | - Felix Lucka
- the Department of Computer Science, University College London, London WC1E 6BT, U.K., and also with the Centrum Wiskunde & Informatica, 1098 XG Amsterdam, The Netherlands
| | - Marta Betcke
- the Department of Computer Science, University College London, London WC1E 6BT, U.K
| | - Nam Huynh
- the Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, U.K
| | - Jonas Adler
- the Department of Mathematics, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden, and also with Elekta, 103 93 Stockholm, Sweden
| | - Ben Cox
- the Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, U.K
| | - Paul Beard
- the Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, U.K
| | - Sebastien Ourselin
- the Department of Computer Science, University College London, London WC1E 6BT, U.K
| | - Simon Arridge
- the Department of Computer Science, University College London, London WC1E 6BT, U.K
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Hauptmann A, Lucka F, Betcke M, Huynh N, Adler J, Cox B, Beard P, Ourselin S, Arridge S. Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography. IEEE Trans Med Imaging 2018; 37:1382-1393. [PMID: 29870367 DOI: 10.1109/tmi.42] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.
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18
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Arridge S, Beard P, Betcke M, Cox B, Huynh N, Lucka F, Ogunlade O, Zhang E. Accelerated high-resolution photoacoustic tomography via compressed sensing. Phys Med Biol 2016; 61:8908-8940. [PMID: 27910824 DOI: 10.1088/1361-6560/61/24/8908] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Pérot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.
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Affiliation(s)
- Simon Arridge
- Department of Computer Science, University College London, WC1E 6BT London, UK
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Fiederer LDJ, Lahr J, Vorwerk J, Lucka F, Aertsen A, Wolters CH, Schulze-Bonhage A, Ball T. Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification With Intracranial EEG and FEM Simulations. IEEE Trans Biomed Eng 2016; 63:2552-2563. [PMID: 27448334 PMCID: PMC5298223 DOI: 10.1109/tbme.2016.2570743] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
OBJECTIVE Electric fields (EF) of approx. 0.2 V/m have been shown to be sufficiently strong to both modulate neuronal activity in the cerebral cortex and have measurable effects on cognitive performance. We hypothesized that the EF caused by the electrical activity of extracranial muscles during natural chewing may reach similar strength in the cerebral cortex and hence might act as an endogenous modality of brain stimulation. Here, we present first steps toward validating this hypothesis. METHODS Using a realistic volume conductor head model of an epilepsy patient having undergone intracranial electrode placement and utilizing simultaneous intracranial and extracranial electrical recordings during chewing, we derive predictions about the chewing-related cortical EF strength to be expected in healthy individuals. RESULTS We find that in the region of the temporal poles, the expected EF strength may reach amplitudes in the order of 0.1-1 V/m. CONCLUSION The cortical EF caused by natural chewing could be large enough to modulate ongoing neural activity in the cerebral cortex and influence cognitive performance. SIGNIFICANCE The present study lends first support for the assumption that extracranial muscle activity might represent an endogenous source of electrical brain stimulation. This offers a new potential explanation for the puzzling effects of gum chewing on cognition, which have been repeatedly reported in the literature.
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20
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Wagner S, Lucka F, Vorwerk J, Herrmann CS, Nolte G, Burger M, Wolters CH. Using reciprocity for relating the simulation of transcranial current stimulation to the EEG forward problem. Neuroimage 2016; 140:163-73. [PMID: 27125841 DOI: 10.1016/j.neuroimage.2016.04.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 03/14/2016] [Accepted: 04/03/2016] [Indexed: 01/12/2023] Open
Abstract
To explore the relationship between transcranial current stimulation (tCS) and the electroencephalography (EEG) forward problem, we investigate and compare accuracy and efficiency of a reciprocal and a direct EEG forward approach for dipolar primary current sources both based on the finite element method (FEM), namely the adjoint approach (AA) and the partial integration approach in conjunction with a transfer matrix concept (PI). By analyzing numerical results, comparing to analytically derived EEG forward potentials and estimating computational complexity in spherical shell models, AA turns out to be essentially identical to PI. It is then proven that AA and PI are also algebraically identical even for general head models. This relation offers a direct link between the EEG forward problem and tCS. We then demonstrate how the quasi-analytical EEG forward solutions in sphere models can be used to validate the numerical accuracies of FEM-based tCS simulation approaches. These approaches differ with respect to the ease with which they can be employed for realistic head modeling based on MRI-derived segmentations. We show that while the accuracy of the most easy to realize approach based on regular hexahedral elements is already quite high, it can be significantly improved if a geometry-adaptation of the elements is employed in conjunction with an isoparametric FEM approach. While the latter approach does not involve any additional difficulties for the user, it reaches the high accuracies of surface-segmentation based tetrahedral FEM, which is considerably more difficult to implement and topologically less flexible in practice. Finally, in a highly realistic head volume conductor model and when compared to the regular alternative, the geometry-adapted hexahedral FEM is shown to result in significant changes in tCS current flow orientation and magnitude up to 45° and a factor of 1.66, respectively.
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Affiliation(s)
- S Wagner
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - F Lucka
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Institute for Computational and Applied Mathematics, University of Münster, Münster, Germany; Centre for Medical Image Computing, University College London, WC1E 6BT London, UK
| | - J Vorwerk
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - C S Herrmann
- Experimental Psychology Lab, Center for Excellence Hearing4all, European Medical School, University of Oldenburg, Oldenburg, Germany
| | - G Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - M Burger
- Institute for Computational and Applied Mathematics, University of Münster, Münster, Germany; Cells in Motion Cluster of Excellence, University of Münster, Münster, Germany
| | - C H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Cells in Motion Cluster of Excellence, University of Münster, Münster, Germany.
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Fiederer LDJ, Vorwerk J, Lucka F, Dannhauer M, Yang S, Dümpelmann M, Schulze-Bonhage A, Aertsen A, Speck O, Wolters CH, Ball T. The role of blood vessels in high-resolution volume conductor head modeling of EEG. Neuroimage 2016; 128:193-208. [PMID: 26747748 PMCID: PMC5225375 DOI: 10.1016/j.neuroimage.2015.12.041] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 11/27/2015] [Accepted: 12/22/2015] [Indexed: 12/18/2022] Open
Abstract
Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.
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Affiliation(s)
- L D J Fiederer
- Intracranial EEG and Brain Imaging Lab, Epilepsy Center, University Hospital Freiburg, Germany; Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Germany; Bernstein Center Freiburg, University of Freiburg, Germany.
| | - J Vorwerk
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany
| | - F Lucka
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany; Institute for Computational and Applied Mathematics, University of Münster, Germany; Department of Computer Science, University College London, WC1E 6BT London, UK
| | - M Dannhauer
- Scientific Computing and Imaging Institute, 72 So. Central Campus Drive, Salt Lake City, Utah 84112, USA; Center for Integrative Biomedical Computing, University of Utah, 72 S. Central Campus Drive, 84112, Salt Lake City, UT, USA
| | - S Yang
- Dept. of Biomedical Magnetic Resonance, Otto-von-Guericke University Magdeburg, Germany
| | - M Dümpelmann
- Intracranial EEG and Brain Imaging Lab, Epilepsy Center, University Hospital Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Germany
| | - A Schulze-Bonhage
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Germany; Bernstein Center Freiburg, University of Freiburg, Germany
| | - A Aertsen
- Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Germany; Bernstein Center Freiburg, University of Freiburg, Germany
| | - O Speck
- Dept. of Biomedical Magnetic Resonance, Otto-von-Guericke University Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Site Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - C H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany
| | - T Ball
- Intracranial EEG and Brain Imaging Lab, Epilepsy Center, University Hospital Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Germany; Bernstein Center Freiburg, University of Freiburg, Germany
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Rampersad SM, Janssen AM, Lucka F, Aydin Ü, Lanfer B, Lew S, Wolters CH, Stegeman DF, Oostendorp TF. Simulating transcranial direct current stimulation with a detailed anisotropic human head model. IEEE Trans Neural Syst Rehabil Eng 2014; 22:441-52. [PMID: 24760939 DOI: 10.1109/tnsre.2014.2308997] [Citation(s) in RCA: 146] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique able to induce long-lasting changes in cortical excitability that can benefit cognitive functioning and clinical treatment. In order to both better understand the mechanisms behind tDCS and possibly improve the technique, finite element models are used to simulate tDCS of the human brain. With the detailed anisotropic head model presented in this study, we provide accurate predictions of tDCS in the human brain for six of the practically most-used setups in clinical and cognitive research, targeting the primary motor cortex, dorsolateral prefrontal cortex, inferior frontal gyrus, occipital cortex, and cerebellum. We present the resulting electric field strengths in the complete brain and introduce new methods to evaluate the effectivity in the target area specifically, where we have analyzed both the strength and direction of the field. For all cerebral targets studied, the currently accepted configurations produced sub-optimal field strengths. The configuration for cerebellum stimulation produced relatively high field strengths in its target area, but it needs higher input currents than cerebral stimulation does. This study suggests that improvements in the effects of transcranial direct current stimulation are achievable.
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He BJ, Nolte G, Nagata K, Takano D, Yamazaki T, Fujimaki Y, Maeda T, Satoh Y, Heckers S, George MS, Lopes da Silva F, de Munck JC, Van Houdt PJ, Verdaasdonk RM, Ossenblok P, Mullinger K, Bowtell R, Bagshaw AP, Keeser D, Karch S, Segmiller F, Hantschk I, Berman A, Padberg F, Pogarell O, Scharnowski F, Karch S, Hümmer S, Keeser D, Paolini M, Kirsch V, Koller G, Rauchmann B, Kupka M, Blautzik J, Pogarell O, Razavi N, Jann K, Koenig T, Kottlow M, Hauf M, Strik W, Dierks T, Gotman J, Vulliemoz S, Lu Y, Zhang H, Yang L, Worrell G, He B, Gruber O, Piguet C, Hubl D, Homan P, Kindler J, Dierks T, Kim K, Steinhoff U, Wakai R, Koenig T, Kottlow M, Melie-García L, Mucci A, Volpe U, Prinster A, Salvatore M, Galderisi S, Linden DEJ, Brandeis D, Schroeder CE, Kayser C, Panzeri S, Kleinschmidt A, Ritter P, Walther S, Haueisen J, Lau S, Flemming L, Sonntag H, Maess B, Knösche TR, Lanfer B, Dannhauer M, Wolters CH, Stenroos M, Haueisen J, Wolters C, Aydin U, Lanfer B, Lew S, Lucka F, Ruthotto L, Vorwerk J, Wagner S, Ramon C, Guan C, Ang KK, Chua SG, Kuah WK, Phua KS, Chew E, Zhou H, Chuang KH, Ang BT, Wang C, Zhang H, Yang H, Chin ZY, Yu H, Pan Y, Collins L, Mainsah B, Colwell K, Morton K, Ryan D, Sellers E, Caves K, Throckmorton S, Kübler A, Holz EM, Zickler C, Sellers E, Ryan D, Brown K, Colwell K, Mainsah B, Caves K, Throckmorton S, Collins L, Wennberg R, Ahlfors SP, Grova C, Chowdhury R, Hedrich T, Heers M, Zelmann R, Hall JA, Lina JM, Kobayashi E, Oostendorp T, van Dam P, Oosterhof P, Linnenbank A, Coronel R, van Dessel P, de Bakker J, Rossion B, Jacques C, Witthoft N, Weiner KS, Foster BL, Miller KJ, Hermes D, Parvizi J, Grill-Spector K, Recanzone GH, Murray MM, Haynes JD, Richiardi J, Greicius M, De Lucia M, Müller KR, Formisano E, Smieskova R, Schmidt A, Bendfeldt K, Walter A, Riecher-Rössler A, Borgwardt S, Fusar-Poli P, Eliez S, Schmidt A, Sekihara K, Nagarajan SS, Schoffelen JM, Guggisberg AG, Nolte G, Balazs S, Kermanshahi K, Kiesenhofer W, Binder H, Rattay F, Antal A, Chaieb L, Paulus W, Bodis-Wollner I, Maurer K, Fein G, Camchong J, Johnstone J, Cardenas-Nicolson V, Fiederer LDJ, Lucka F, Yang S, Vorwerk J, Dümpelmann M, Cosandier-Rimélé D, Schulze-Bonhage A, Aertsen A, Speck O, Wolters CH, Ball T, Fuchs M, Wagner M, Kastner J, Tech R, Dinh C, Haueisen J, Baumgarten D, Hämäläinen MS, Lau S, Vogrin SJ, D'Souza W, Haueisen J, Cook MJ, Custo A, Van De Ville D, Vulliemoz S, Grouiller F, Michel CM, Malmivuo J, Aydin U, Vorwerk J, Küpper P, Heers M, Kugel H, Wellmer J, Kellinghaus C, Scherg M, Rampp S, Wolters C, Storti SF, Boscolo Galazzo I, Del Felice A, Pizzini FB, Arcaro C, Formaggio E, Mai R, Manganotti P, Koessler L, Vignal J, Cecchin T, Colnat-Coulbois S, Vespignani H, Ramantani G, Maillard L, Rektor I, Kuba R, Brázdil M, Chrastina J, Rektorova I, van Mierlo P, Carrette E, Strobbe G, Montes-Restrepo V, Vonck K, Vandenberghe S, Ahmed B, Brodely C, Carlson C, Kuzniecky R, Devinsky O, French J, Thesen T, Bénis D, David O, Lachaux JP, Seigneuret E, Krack P, Fraix V, Chabardès S, Bastin J, Jann K, Gee D, Kilroy E, Cannon T, Wang DJ, Hale JR, Mayhew SD, Przezdzik I, Arvanitis TN, Bagshaw AP, Plomp G, Quairiaux C, Astolfi L, Michel CM, Mayhew SD, Mullinger KJ, Bagshaw AP, Bowtell R, Francis ST, Schouten AC, Campfens SF, van der Kooij H, Koles Z, Lind J, Flor-Henry P, Wirth M, Haase CM, Villeneuve S, Vogel J, Jagust WJ, Kambeitz-Ilankovic L, Simon-Vermot L, Gesierich B, Duering M, Ewers M, Rektorova I, Krajcovicova L, Marecek R, Mikl M, Bracht T, Horn H, Strik W, Federspiel A, Schnell S, Höfle O, Stegmayer K, Wiest R, Dierks T, Müller TJ, Walther S, Surmeli T, Ertem A, Eralp E, Kos IH, Skrandies W, Flüggen S, Klein A, Britz J, Díaz Hernàndez L, Ro T, Michel CM, Lenartowicz A, Lau E, Rodriguez C, Cohen MS, Loo SK, Di Lorenzo G, Pagani M, Monaco L, Daverio A, Giannoudas I, La Porta P, Verardo AR, Niolu C, Fernandez I, Siracusano A, Flor-Henry P, Lind J, Koles Z, Bollmann S, Ghisleni C, O'Gorman R, Poil SS, Klaver P, Michels L, Martin E, Ball J, Eich-Höchli D, Brandeis D, Salisbury DF, Murphy TK, Butera CD, Mathalon DH, Fryer SL, Kiehl KA, Calhoun VC, Pearlson GD, Roach BJ, Ford JM, McGlashan TH, Woods SW, Volpe U, Merlotti E, Vignapiano A, Montefusco V, Plescia GM, Gallo O, Romano P, Mucci A, Galderisi S, Mingoia G, Langbein K, Dietzek M, Wagner G, Smesny, Scherpiet S, Maitra R, Gaser C, Sauer H, Nenadic I, Gonzalez Andino S, Grave de Peralta Menendez R, Grave de Peralta Menendez R, Sanchez Vives M, Rebollo B, Gonzalez Andino S, Frølich L, Andersen TS, Mørup M, Belfiore P, Gargiulo P, Ramon C, Vanhatalo S, Cho JH, Vorwerk J, Wolters CH, Knösche TR, Watanabe T, Kawabata Y, Ukegawa D, Kawabata S, Adachi Y, Sekihara K, Sekihara K, Nagarajan SS, Wagner S, Aydin U, Vorwerk J, Herrmann C, Burger M, Wolters C, Lucka F, Aydin U, Vorwerk J, Burger M, Wolters C, Bauer M, Trahms L, Sander T, Faber PL, Lehmann D, Gianotti LRR, Pascual-Marqui RD, Milz P, Kochi K, Kaneko S, Yamashita S, Yana K, Kalogianni K, Vardy AN, Schouten AC, van der Helm FCT, Sorrentino A, Luria G, Aramini R, Hunold A, Funke M, Eichardt R, Haueisen J, Gómez-Aguilar F, Vázquez-Olvera S, Cordova-Fraga T, Castro-López J, Hernández-Gonzalez MA, Solorio-Meza S, Sosa-Aquino M, Bernal-Alvarado JJ, Vargas-Luna M, Vorwerk J, Magyari L, Ludewig J, Oostenveld R, Wolters CH, Vorwerk J, Engwer C, Ludewig J, Wolters C, Sato K, Nishibe T, Furuya M, Yamashiro K, Yana K, Ono T, Puthanmadam Subramaniyam N, Hyttinen J, Lau S, Güllmar D, Flemming L, Haueisen J, Sonntag H, Vorwerk J, Wolters CH, Grasedyck L, Haueisen J, Maeß B, Freitag S, Graichen U, Fiedler P, Strohmeier D, Haueisen J, Stenroos M, Hauk O, Grigutsch M, Felber M, Maess B, Herrmann B, Strobbe G, van Mierlo P, Vandenberghe S, Strobbe G, Cárdenas-Peña D, Montes-Restrepo V, van Mierlo P, Castellanos-Dominguez G, Vandenberghe S, Lanfer B, Paul-Jordanov I, Scherg M, Wolters CH, Ito Y, Sato D, Kamada K, Kobayashi T, Dalal SS, Rampp S, Willomitzer F, Arold O, Fouladi-Movahed S, Häusler G, Stefan H, Ettl S, Zhang S, Zhang Y, Li H, Kong X, Montes-Restrepo V, Strobbe G, van Mierlo P, Vandenberghe S, Wong DDE, Bidet-Caulet A, Knight RT, Crone NE, Dalal SS, Birot G, Spinelli L, Vulliémoz S, Seeck M, Michel CM, Emory H, Wells C, Mizrahi N, Vogrin SJ, Lau S, Cook MJ, Karahanoglu FI, Grouiller F, Caballero-Gaudes C, Seeck M, Vulliemoz S, Van De Ville D, Spinelli L, Megevand P, Genetti M, Schaller K, Michel C, Vulliemoz S, Seeck M, Genetti M, Tyrand R, Grouiller F, Vulliemoz S, Spinelli L, Seeck M, Schaller K, Michel CM, Grouiller F, Heinzer S, Delattre B, Lazeyras F, Spinelli L, Pittau F, Seeck M, Ratib O, Vargas M, Garibotto V, Vulliemoz S, Vogrin SJ, Bailey CA, Kean M, Warren AE, Davidson A, Seal M, Harvey AS, Archer JS, Papadopoulou M, Leite M, van Mierlo P, Vonck K, Boon P, Friston K, Marinazzo D, Ramon C, Holmes M, Koessler L, Rikir E, Gavaret M, Bartolomei F, Vignal JP, Vespignani H, Maillard L, Centeno M, Perani S, Pier K, Lemieux L, Clayden J, Clark C, Pressler R, Cross H, Carmichael DW, Spring A, Bessemer R, Pittman D, Aghakhani Y, Federico P, Pittau F, Grouiller F, Vulliémoz S, Gotman J, Badier JM, Bénar CG, Bartolomei F, Cruto C, Chauvel P, Gavaret M, Brodbeck V, van Leeuwen T, Tagliazzuchi E, Melloni L, Laufs H, Griskova-Bulanova I, Dapsys K, Klein C, Hänggi J, Jäncke L, Ehinger BV, Fischer P, Gert AL, Kaufhold L, Weber F, Marchante Fernandez M, Pipa G, König P, Sekihara K, Hiyama E, Koga R, Iannilli E, Michel CM, Bartmuss AL, Gupta N, Hummel T, Boecker R, Holz N, Buchmann AF, Blomeyer D, Plichta MM, Wolf I, Baumeister S, Meyer-Lindenberg A, Banaschewski T, Brandeis D, Laucht M, Natahara S, Ueno M, Kobayashi T, Kottlow M, Bänninger A, Koenig T, Schwab S, Koenig T, Federspiel A, Dierks T, Jann K, Natsukawa H, Kobayashi T, Tüshaus L, Koenig T, Kottlow M, Achermann P, Wilson RS, Mayhew SD, Assecondi S, Arvanitis TN, Bagshaw AP, Darque A, Rihs TA, Grouiller F, Lazeyras F, Ha-Vinh Leuchter R, Caballero C, Michel CM, Hüppi PS, Hauser TU, Hunt LT, Iannaccone R, Stämpfli P, Brandeis D, Dolan RJ, Walitza S, Brem S, Graichen U, Eichardt R, Fiedler P, Strohmeier D, Freitag S, Zanow F, Haueisen J, Lordier L, Grouiller F, Van de Ville D, Sancho Rossignol A, Cordero I, Lazeyras F, Ansermet F, Hüppi P, Schläpfer A, Rubia K, Brandeis D, Di Lorenzo G, Pagani M, Monaco L, Daverio A, Giannoudas I, Verardo AR, La Porta P, Niolu C, Fernandez I, Siracusano A, Tamura K, Karube C, Mizuba T, Matsufuji M, Takashima S, Iramina K, Assecondi S, Ostwald D, Bagshaw AP, Marecek R, Brazdil M, Lamos M, Slavícek T, Marecek R, Jan J, Meier NM, Perrig W, Koenig T, Minami T, Noritake Y, Nakauchi S, Azuma K, Minami T, Nakauchi S, Rodriguez C, Lenartowicz A, Cohen MS, Rodriguez C, Lenartowicz A, Cohen MS, Iramina K, Kinoshita H, Tamura K, Karube C, Kaneko M, Ide J, Noguchi Y, Cohen MS, Douglas PK, Rodriguez CM, Xia HJ, Zimmerman EM, Konopka CJ, Epstein PS, Konopka LM, Giezendanner S, Fisler M, Soravia L, Andreotti J, Wiest R, Dierks T, Federspiel A, Razavi N, Federspiel A, Dierks T, Hauf M, Jann K, Kamada K, Sato D, Ito Y, Okano K, Mizutani N, Kobayashi T, Thelen A, Murray M, Pastena L, Formaggio E, Storti SF, Faralli F, Melucci M, Gagliardi R, Ricciardi L, Ruffino G, Coito A, Macku P, Tyrand R, Astolfi L, He B, Wiest R, Seeck M, Michel C, Plomp G, Vulliemoz S, Fischmeister FPS, Glaser J, Schöpf V, Bauer H, Beisteiner R, Deligianni F, Centeno M, Carmichael DW, Clayden J, Mingoia G, Langbein K, Dietzek M, Wagner G, Smesny S, Scherpiet S, Maitra R, Gaser C, Sauer H, Nenadic I, Dürschmid S, Zaehle T, Pannek H, Chang HF, Voges J, Rieger J, Knight RT, Heinze HJ, Hinrichs H, Tsatsishvili V, Cong F, Puoliväli T, Alluri V, Toiviainen P, Nandi AK, Brattico E, Ristaniemi T, Grieder M, Crinelli RM, Jann K, Federspiel A, Wirth M, Koenig T, Stein M, Wahlund LO, Dierks T, Atsumori H, Yamaguchi R, Okano Y, Sato H, Funane T, Sakamoto K, Kiguchi M, Tränkner A, Schindler S, Schmidt F, Strauß M, Trampel R, Hegerl U, Turner R, Geyer S, Schönknecht P, Kebets V, van Assche M, Goldstein R, van der Meulen M, Vuilleumier P, Richiardi J, Van De Ville D, Assal F, Wozniak-Kwasniewska A, Szekely D, Harquel S, Bougerol T, David O, Bracht T, Jones DK, Horn H, Müller TJ, Walther S, Sos P, Klirova M, Novak T, Brunovsky M, Horacek J, Bares M, Hoschl C C, Fellhauer I, Zöllner FG, Schröder J, Kong L, Essig M, Schad LR, Arrubla J, Neuner I, Hahn D, Boers F, Shah NJ, Neuner I, Arrubla J, Hahn D, Boers F, Jon Shah N, Suriya Prakash M, Sharma R, Kawaguchi H, Kobayashi T, Fiedler P, Griebel S, Biller S, Fonseca C, Vaz F, Zentner L, Zanow F, Haueisen J, Rochas V, Rihs T, Thut G, Rosenberg N, Landis T, Michel C, Moliadze V, Schmanke T, Lyzhko E, Bassüner S, Freitag C, Siniatchkin M, Thézé R, Guggisberg AG, Nahum L, Schnider A, Meier L, Friedrich H, Jann K, Landis B, Wiest R, Federspiel A, Strik W, Dierks T, Witte M, Kober SE, Neuper C, Wood G, König R, Matysiak A, Kordecki W, Sieluzycki C, Zacharias N, Heil P, Wyss C, Boers F, Arrubla J, Dammers J, Kawohl W, Neuner I, Shah NJ, Braboszcz C, Cahn RB, Levy J, Fernandez M, Delorme A, Rosas-Martinez L, Milne E, Zheng Y, Urakami Y, Kawamura K, Washizawa Y, Hiyoshi K, Cichocki A, Giroud N, Dellwo V, Meyer M, Rufener KS, Liem F, Dellwo V, Meyer M, Jones-Rounds JD, Raizada R, Staljanssens W, Strobbe G, van Mierlo P, Van Holen R, Vandenberghe S, Pefkou M, Becker R, Michel C, Hervais-Adelman A, He W, Brock J, Johnson B, Ohla K, Hitz K, Heekeren K, Obermann C, Huber T, Juckel G, Kawohl W, Gabriel D, Comte A, Henriques J, Magnin E, Grigoryeva L, Ortega JP, Haffen E, Moulin T, Pazart L, Aubry R, Kukleta M, Baris Turak B, Louvel J, Crespo-Garcia M, Cantero JL, Atienza M, Connell S, Kilborn K, Damborská A, Brázdil M, Rektor I, Kukleta M, Koberda JL, Bienkiewicz A, Koberda I, Koberda P, Moses A, Tomescu M, Rihs T, Britz J, Custo A, Grouiller F, Schneider M, Debbané M, Eliez S, Michel C, Wang GY, Kydd R, Wouldes TA, Jensen M, Russell BR, Dissanayaka N, Au T, Angwin A, O'Sullivan J, Byrne G, Silburn P, Marsh R, Mellic G, Copland D, Bänninger A, Kottlow M, Díaz Hernàndez L, Koenig T, Díaz Hernàndez L, Bänninger A, Koenig T, Hauser TU, Iannaccone R, Mathys C, Ball J, Drechsler R, Brandeis D, Walitza S, Brem S, Boeijinga PH, Pang EW, Valica T, Macdonald MJ, Oh A, Lerch JP, Anagnostou E, Di Lorenzo G, Pagani M, Monaco L, Daverio A, Verardo AR, Giannoudas I, La Porta P, Niolu C, Fernandez I, Siracusano A, Shimada T, Matsuda Y, Monkawa A, Monkawa T, Hashimoto R, Watanabe K, Kawasaki Y, Matsuda Y, Shimada T, Monkawa T, Monkawa A, Watanabe K, Kawasaki Y, Stegmayer K, Horn H, Federspiel A, Razavi N, Bracht T, Laimböck K, Strik W, Dierks T, Wiest R, Müller TJ, Walther S, Koorenhof LJ, Swithenby SJ, Martins-Mourao A, Rihs TA, Tomescu M, Song KW, Custo A, Knebel JF, Murray M, Eliez S, Michel CM, Volpe U, Merlotti E, Vignapiano A, Montefusco V, Plescia GM, Gallo O, Romano P, Mucci A, Galderisi S, Laimboeck K, Jann K, Walther S, Federspiel A, Wiest R, Strik W, Horn H. Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectromagnetism (ISBEM), the International Society for Brain Electromagnetic Topography (ISBET), and the EEG and Clinical Neuroscience Society (ECNS), in Geneva, Switzerland, September 5-8, 2013. Clin EEG Neurosci 2013; 44:1550059413507209. [PMID: 24368763 DOI: 10.1177/1550059413507209] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- B J He
- National Institutes of Health, Bethesda, MD, USA
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Janssen AM, Rampersad SM, Lucka F, Lanfer B, Lew S, Aydin U, Wolters CH, Stegeman DF, Oostendorp TF. The influence of sulcus width on simulated electric fields induced by transcranial magnetic stimulation. Phys Med Biol 2013; 58:4881-96. [PMID: 23787706 DOI: 10.1088/0031-9155/58/14/4881] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Volume conduction models can help in acquiring knowledge about the distribution of the electric field induced by transcranial magnetic stimulation. One aspect of a detailed model is an accurate description of the cortical surface geometry. Since its estimation is difficult, it is important to know how accurate the geometry has to be represented. Previous studies only looked at the differences caused by neglecting the complete boundary between cerebrospinal fluid (CSF) and grey matter (Thielscher et al 2011 NeuroImage 54 234-43, Bijsterbosch et al 2012 Med. Biol. Eng. Comput. 50 671-81), or by resizing the whole brain (Wagner et al 2008 Exp. Brain Res. 186 539-50). However, due to the high conductive properties of the CSF, it can be expected that alterations in sulcus width can already have a significant effect on the distribution of the electric field. To answer this question, the sulcus width of a highly realistic head model, based on T1-, T2- and diffusion-weighted magnetic resonance images, was altered systematically. This study shows that alterations in the sulcus width do not cause large differences in the majority of the electric field values. However, considerable overestimation of sulcus width produces an overestimation of the calculated field strength, also at locations distant from the target location.
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Affiliation(s)
- A M Janssen
- Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Reinier Postlaan 4, 6525 CG Nijmegen, The Netherlands.
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Pursiainen S, Lucka F, Wolters CH. Corrigendum: Complete electrode model in EEG: relationship and differences to the point electrode model. Phys Med Biol 2012. [DOI: 10.1088/0031-9155/58/1/185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Lucka F, Pursiainen S, Burger M, Wolters CH. Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents. Neuroimage 2012; 61:1364-82. [DOI: 10.1016/j.neuroimage.2012.04.017] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2011] [Revised: 03/23/2012] [Accepted: 04/07/2012] [Indexed: 11/25/2022] Open
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Abstract
In electroencephalography (EEG) source analysis, a primary current density generated by the neural activity of the brain is reconstructed from external electrode voltage measurements. This paper focuses on accurate and effective simulations of EEG through the complete electrode model (CEM). The CEM allows for the incorporation of the electrode size, shape and effective contact impedance into the forward simulation. Both neural currents in the brain and shunting currents between the electrodes and the skin can affect the measured voltages in the CEM. The goal of this study was to investigate the CEM by comparing it with the point electrode model (PEM), which is the current standard electrode model for EEG. We used a three-dimensional, realistic and high-resolution finite element head model as the reference computational domain in the comparison. The PEM could be formulated as a limit of the CEM, in which the effective impedance of each electrode goes to infinity and the size tends to zero. Numerical results concerning the forward and inverse errors and electrode voltage strengths with different impedances and electrode sizes are presented. Based on the results obtained, limits for extremely high and low impedance values of the shunting currents are suggested.
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Affiliation(s)
- S Pursiainen
- Aalto University, Mathematics, PO Box 11100, FI-00076 Aalto, Finland.
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Wagner S, Lucka F, Burger M, Grasedyck L, Haueisen J, Wolters CH. Comparison of direct and reciprocal forward modeling approaches in EEG source analysis. BIOMED ENG-BIOMED TE 2012. [DOI: 10.1515/bmt-2012-4069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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