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Magnetic resonance diffusion tensor tractography of a midbrain auditory circuit in Alligator. Neurosci Lett 2020; 738:135251. [PMID: 32679057 DOI: 10.1016/j.neulet.2020.135251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/09/2020] [Accepted: 07/13/2020] [Indexed: 11/23/2022]
Abstract
Knowledge of brain circuitry is critical for understanding the organization, function, and evolution of central nervous systems. Most commonly, brain connections have been elucidated using histological and experimental methods that require animal sacrifice. On the other hand, magnetic resonance diffusion tensor imaging and associated tractography have emerged as a preferred method to noninvasively visualize brain white matter tracts. However, existing studies have primarily examined large, heavily myelinated fiber tracts. Whether tractography can visualize fiber bundles that contain thin and poorly myelinated axons is uncertain. To address this question, the midbrain auditory pathway to the thalamus was investigated in Alligator. This species was chosen because of its evolutionary importance as it is the reptilian group most closely related to birds and because its brain contains many thin and poorly myelinated tracts. Furthermore, this auditory pathway is well documented in other reptiles, including a related crocodilian. Histological observations and experimental determination of anterograde connections confirmed this path in Alligator. Tractography identified these tracts in Alligator and provided a 3-dimensional picture that accurately identified the neural elements of this circuit. In addition, tractography identified one possible unrecognized pathway. These results demonstrate that tractography can visualize circuits containing thin, poorly myelinated fibers. These findings open the door for future studies to examine these types of pathways in other vertebrates.
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Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175909] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist–Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.
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Teh I, McClymont D, Carruth E, Omens J, McCulloch A, Schneider JE. Improved compressed sensing and super-resolution of cardiac diffusion MRI with structure-guided total variation. Magn Reson Med 2020; 84:1868-1880. [PMID: 32125040 PMCID: PMC8629124 DOI: 10.1002/mrm.28245] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 02/12/2020] [Accepted: 02/13/2020] [Indexed: 12/14/2022]
Abstract
Purpose Structure‐guided total variation is a recently introduced prior that allows reconstruction of images using knowledge of the location and orientation of edges in a reference image. In this work, we demonstrate the advantages of a variant of structure‐guided total variation known as directional total variation (DTV), over traditional total variation (TV), in the context of compressed‐sensing reconstruction and super‐resolution. Methods We compared TV and DTV in retrospectively undersampled ex vivo diffusion tensor imaging and diffusion spectrum imaging data from healthy, sham, and hypertrophic rat hearts. Results In compressed sensing at an undersampling factor of 8, the RMS error of mean diffusivity and fractional anisotropy relative to the fully sampled ground truth were 44% and 20% lower in DTV compared with TV. In super‐resolution, these values were 29% and 14%, respectively. Similarly, we observed improvements in helix angle, transverse angle, sheetlet elevation, and sheetlet azimuth. The RMS error of the diffusion kurtosis in the undersampled data relative to the ground truth was uniformly lower (22% on average) with DTV compared to TV. Conclusion Acquiring one fully sampled non‐diffusion‐weighted image and 10 diffusion‐weighted images at 8× undersampling would result in an 80% net reduction in data needed. We demonstrate efficacy of the DTV algorithm over TV in reducing data sampling requirements, which can be translated into higher apparent resolution and potentially shorter scan times. This method would be equally applicable in diffusion MRI applications outside the heart.
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Affiliation(s)
- Irvin Teh
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | | | | | - Jeffrey Omens
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Andrew McCulloch
- Department of Medicine, University of California San Diego, La Jolla, California.,Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Jürgen E Schneider
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
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Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI. Med Image Anal 2019; 54:122-137. [DOI: 10.1016/j.media.2019.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 11/22/2018] [Accepted: 02/26/2019] [Indexed: 11/22/2022]
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Aliotta E, Nourzadeh H, Sanders J, Muller D, Ennis DB. Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks. Med Phys 2019; 46:1581-1591. [PMID: 30677141 DOI: 10.1002/mp.13400] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 12/17/2018] [Accepted: 01/13/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans. MATERIALS AND METHODS This retrospective study was conducted using data acquired between 2013 and 2018 and was approved by the local institutional review board. DTI acquired in healthy volunteers (N = 10) was used to train a neural network, DiffNet, to reconstruct fractional anisotropy (FA) and mean diffusivity (MD) maps from small subsets of acquired DTI data with between 3 and 20 diffusion-encoding directions. FA and MD maps were then reconstructed in volunteers and in patients with glioblastoma multiforme (GBM, N = 12) using both DiffNet and conventional reconstructions. Accuracy and precision were quantified in volunteer scans and compared between reconstructions. The accuracy of tumor delineation was compared between reconstructed patient data by evaluating agreement between DTI-derived tumor volumes and volumes defined by contrast-enhanced T1-weighted MRI. Comparisons were performed using areas under the receiver operating characteristic curves (AUC). RESULTS DiffNet FA reconstructions were more accurate and precise compared with conventional reconstructions for all acceleration factors. DiffNet permitted reconstruction with only three diffusion-encoding directions with significantly lower bias than the conventional method using six directions (0.01 ± 0.01 vs 0.06 ± 0.01, P < 0.001). While MD-based tumor delineation was not substantially different with DiffNet (AUC range: 0.888-0.902), DiffNet FA had higher AUC than conventional reconstructions for fixed scan time and achieved similar performance with shorter scans (conventional, six directions: AUC = 0.926, DiffNet, three directions: AUC = 0.920). CONCLUSION DiffNet improved DTI reconstruction accuracy, precision, and tumor delineation performance in GBM while permitting reconstruction from only three diffusion-encoding directions.&!#6.
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Affiliation(s)
- Eric Aliotta
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Hamidreza Nourzadeh
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jason Sanders
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Donald Muller
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
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Bates J, Teh I, McClymont D, Kohl P, Schneider JE, Grau V. Monte Carlo Simulations of Diffusion Weighted MRI in Myocardium: Validation and Sensitivity Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1316-1325. [PMID: 28328501 DOI: 10.1109/tmi.2017.2679809] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
A model of cardiac microstructure and diffusion MRI is presented, and compared with experimental data from ex vivo rat hearts. The model includes a simplified representation of individual cells, with physiologically correct cell size and orientation, as well as intra- to extracellular volume ratio. Diffusion MRI is simulated using a Monte Carlo model and realistic MRI sequences. The results show good correspondence between the simulated and experimental MRI signals. Similar patterns are observed in the eigenvalues of the diffusion tensor, the mean diffusivity (MD), and the fractional anisotropy (FA). A sensitivity analysis shows that the diffusivity is the dominant influence on all three eigenvalues of the diffusion tensor, the MD, and the FA. The area and aspect ratio of the cell cross-section affect the secondary and tertiary eigenvalues, and hence the FA. Within biological norms, the cell length, volume fraction of cells, and rate of change of helix angle play a relatively small role in influencing tissue diffusion. Results suggest that the model could be used to improve understanding of the relationship between cardiac microstructure and diffusion MRI measurements, as well as in testing and refinement of cardiac diffusion MRI protocols.
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MacGowan GA, Parikh JD, Hollingsworth KG. Diffusion Tensor Magnetic Resonance Imaging of the Heart. J Am Coll Cardiol 2017; 69:677-678. [DOI: 10.1016/j.jacc.2016.10.080] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 10/26/2016] [Indexed: 12/26/2022]
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Zhu Y, Peng X, Wu Y, Wu EX, Ying L, Liu X, Zheng H, Liang D. Direct diffusion tensor estimation using a model‐based method with spatial and parametric constraints. Med Phys 2017; 44:570-580. [DOI: 10.1002/mp.12054] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 11/25/2016] [Accepted: 12/01/2016] [Indexed: 01/04/2023] Open
Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Xi Peng
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Yin Wu
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Ed X. Wu
- Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Hong Kong
| | - Leslie Ying
- Department of Electrical Engineering Department of Biomedical Engineering University at Buffalo The State University of New York Buffalo NY 14260 USA
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen China
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McClymont D, Teh I, Carruth E, Omens J, McCulloch A, Whittington HJ, Kohl P, Grau V, Schneider JE. Evaluation of non-Gaussian diffusion in cardiac MRI. Magn Reson Med 2016; 78:1174-1186. [PMID: 27670633 PMCID: PMC5366286 DOI: 10.1002/mrm.26466] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 08/18/2016] [Accepted: 08/23/2016] [Indexed: 12/20/2022]
Abstract
PURPOSE The diffusion tensor model assumes Gaussian diffusion and is widely applied in cardiac diffusion MRI. However, diffusion in biological tissue deviates from a Gaussian profile as a result of hindrance and restriction from cell and tissue microstructure, and may be quantified better by non-Gaussian modeling. The aim of this study was to investigate non-Gaussian diffusion in healthy and hypertrophic hearts. METHODS Thirteen rat hearts (five healthy, four sham, four hypertrophic) were imaged ex vivo. Diffusion-weighted images were acquired at b-values up to 10,000 s/mm2 . Models of diffusion were fit to the data and ranked based on the Akaike information criterion. RESULTS The diffusion tensor was ranked best at b-values up to 2000 s/mm2 but reflected the signal poorly in the high b-value regime, in which the best model was a non-Gaussian "beta distribution" model. Although there was considerable overlap in apparent diffusivities between the healthy, sham, and hypertrophic hearts, diffusion kurtosis and skewness in the hypertrophic hearts were more than 20% higher in the sheetlet and sheetlet-normal directions. CONCLUSION Non-Gaussian diffusion models have a higher sensitivity for the detection of hypertrophy compared with the Gaussian model. In particular, diffusion kurtosis may serve as a useful biomarker for characterization of disease and remodeling in the heart. Magn Reson Med 78:1174-1186, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Darryl McClymont
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Irvin Teh
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Eric Carruth
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
| | - Jeffrey Omens
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA.,Department of Medicine, University of California-San Diego, La Jolla, California, USA
| | - Andrew McCulloch
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
| | - Hannah J Whittington
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Peter Kohl
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Institute for Experimental Cardiovascular Medicine, University Heart Centre Freiburg, Bad Krozingen, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jürgen E Schneider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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Teh I, McClymont D, Burton RAB, Maguire ML, Whittington HJ, Lygate CA, Kohl P, Schneider JE. Resolving Fine Cardiac Structures in Rats with High-Resolution Diffusion Tensor Imaging. Sci Rep 2016; 6:30573. [PMID: 27466029 PMCID: PMC4964346 DOI: 10.1038/srep30573] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 07/04/2016] [Indexed: 02/03/2023] Open
Abstract
Cardiac architecture is fundamental to cardiac function and can be assessed non-invasively with diffusion tensor imaging (DTI). Here, we aimed to overcome technical challenges in ex vivo DTI in order to extract fine anatomical details and to provide novel insights in the 3D structure of the heart. An integrated set of methods was implemented in ex vivo rat hearts, including dynamic receiver gain adjustment, gradient system scaling calibration, prospective adjustment of diffusion gradients, and interleaving of diffusion-weighted and non-diffusion-weighted scans. Together, these methods enhanced SNR and spatial resolution, minimised orientation bias in diffusion-weighting, and reduced temperature variation, enabling detection of tissue structures such as cell alignment in atria, valves and vessels at an unprecedented level of detail. Improved confidence in eigenvector reproducibility enabled tracking of myolaminar structures as a basis for segmentation of functional groups of cardiomyocytes. Ex vivo DTI facilitates acquisition of high quality structural data that complements readily available in vivo cardiac functional and anatomical MRI. The improvements presented here will facilitate next generation virtual models integrating micro-structural and electro-mechanical properties of the heart.
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Affiliation(s)
- Irvin Teh
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Darryl McClymont
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Rebecca A. B. Burton
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, United Kingdom
| | - Mahon L. Maguire
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Hannah J. Whittington
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Craig A. Lygate
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Peter Kohl
- National Heart and Lung Institute, Imperial College London, London, SW3 6NP, United Kingdom
- Institute for Experimental Cardiovascular Medicine, University Heart Centre Freiburg · Bad Krozingen, Medical School of the University of Freiburg, Freiburg, 79110, Germany
| | - Jürgen E. Schneider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
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Teh I, Burton RAB, McClymont D, Capel RA, Aston D, Kohl P, Schneider JE. Mapping cardiac microstructure of rabbit heart in different mechanical states by high resolution diffusion tensor imaging: A proof-of-principle study. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2016; 121:85-96. [PMID: 27320383 PMCID: PMC4959513 DOI: 10.1016/j.pbiomolbio.2016.06.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 06/13/2016] [Indexed: 01/27/2023]
Abstract
Myocardial microstructure and its macroscopic materialisation are fundamental to the function of the heart. Despite this importance, characterisation of cellular features at the organ level remains challenging, and a unifying description of the structure of the heart is still outstanding. Here, we optimised diffusion tensor imaging data to acquire high quality data in ex vivo rabbit hearts in slack and contractured states, approximating diastolic and systolic conditions. The data were analysed with a suite of methods that focused on different aspects of the myocardium. In the slack heart, we observed a similar transmural gradient in helix angle of the primary eigenvector of up to 23.6°/mm in the left ventricle and 24.2°/mm in the right ventricle. In the contractured heart, the same transmural gradient remained largely linear, but was offset by up to +49.9° in the left ventricle. In the right ventricle, there was an increase in the transmural gradient to 31.2°/mm and an offset of up to +39.0°. The application of tractography based on each eigenvector enabled visualisation of streamlines that depict cardiomyocyte and sheetlet organisation over large distances. We observed multiple V- and N-shaped sheetlet arrangements throughout the myocardium, and insertion of sheetlets at the intersection of the left and right ventricle. This study integrates several complementary techniques to visualise and quantify the heart's microstructure, projecting parameter representations across different length scales. This represents a step towards a more comprehensive characterisation of myocardial microstructure at the whole organ level.
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Affiliation(s)
- Irvin Teh
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Rebecca A B Burton
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Darryl McClymont
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Rebecca A Capel
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Daniel Aston
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Peter Kohl
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Institute for Experimental Cardiovascular Medicine, University Heart Centre Freiburg - Bad Krozingen, Medical School of the University of Freiburg, Germany
| | - Jürgen E Schneider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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