1
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Cam RM, Villa U, Anastasio MA. Learning a stable approximation of an existing but unknown inverse mapping: application to the half-time circular Radon transform. INVERSE PROBLEMS 2024; 40:085002. [PMID: 38933410 PMCID: PMC11197394 DOI: 10.1088/1361-6420/ad4f0a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/05/2024] [Accepted: 05/22/2024] [Indexed: 06/28/2024]
Abstract
Supervised deep learning-based methods have inspired a new wave of image reconstruction methods that implicitly learn effective regularization strategies from a set of training data. While they hold potential for improving image quality, they have also raised concerns regarding their robustness. Instabilities can manifest when learned methods are applied to find approximate solutions to ill-posed image reconstruction problems for which a unique and stable inverse mapping does not exist, which is a typical use case. In this study, we investigate the performance of supervised deep learning-based image reconstruction in an alternate use case in which a stable inverse mapping is known to exist but is not yet analytically available in closed form. For such problems, a deep learning-based method can learn a stable approximation of the unknown inverse mapping that generalizes well to data that differ significantly from the training set. The learned approximation of the inverse mapping eliminates the need to employ an implicit (optimization-based) reconstruction method and can potentially yield insights into the unknown analytic inverse formula. The specific problem addressed is image reconstruction from a particular case of radially truncated circular Radon transform (CRT) data, referred to as 'half-time' measurement data. For the half-time image reconstruction problem, we develop and investigate a learned filtered backprojection method that employs a convolutional neural network to approximate the unknown filtering operation. We demonstrate that this method behaves stably and readily generalizes to data that differ significantly from training data. The developed method may find application to wave-based imaging modalities that include photoacoustic computed tomography.
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Affiliation(s)
- Refik Mert Cam
- Department of Electrical and Computer Engineering, University of Illinois Urbana–Champaign, Urbana, IL 61801, United States of America
| | - Umberto Villa
- Oden Institute for Computational Engineering & Sciences, The University of Texas at Austin, Austin, TX 78712, United States of America
| | - Mark A Anastasio
- Department of Electrical and Computer Engineering, University of Illinois Urbana–Champaign, Urbana, IL 61801, United States of America
- Department of Bioengineering, University of Illinois Urbana–Champaign, Urbana, IL 61801, United States of America
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2
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Yin W, Li X, Cao Q, Wang H, Zhang B. Bioluminescence tomography reconstruction in conjunction with an organ probability map as an anatomical reference. BIOMEDICAL OPTICS EXPRESS 2022; 13:1275-1291. [PMID: 35414991 PMCID: PMC8973175 DOI: 10.1364/boe.448862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/15/2022] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
To alleviate the ill-posedness of bioluminescence tomography (BLT) reconstruction, anatomical information from computed tomography (CT) or magnetic resonance imaging (MRI) is usually adopted to improve the reconstruction quality. With the anatomical information, different organs could be segmented and assigned with appropriate optical parameters, and the reconstruction could be confined into certain organs. However, image segmentation is a time-consuming and challenging work, especially for the low-contrast organs. In this paper, we present a BLT reconstruction method in conjunction with an organ probability map to effectively incorporate the anatomical information. Instead of using a segmentation with a fixed organ map, an organ probability map is established by registering the CT image of the mouse to the statistical mouse atlas with the constraints of the mouse surface and high-contrast organs (bone and lung). Then the organ probability map of the low-contrast organs, such as the liver and kidney, is determined automatically. After discretization of the mouse torso, a heterogeneous model is established as the input for reconstruction, in which the optical parameter of each node is calculated according to the organ probability map. To take the advantage of the sparse Bayesian Learning (SBL) method in recovering block sparse signals in inverse problems, which is common in BLT applications where the target distribution has the characteristic of sparsity and block structure, a two-step method in conjunction with the organ probability map is presented. In the first step, a fast sparse algorithm, L1-LS, is used to reveal the source distribution on the organ level. In the second step, the bioluminescent source is reconstructed on the pixel level based on the SBL method. Both simulation and in vivo experiments are conducted, and the results demonstrate that the organ probability map in conjunction with the proposed two-step BLT reconstruction method is feasible to accurately reconstruct the localization of the bioluminescent light source.
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Affiliation(s)
- Wanzhou Yin
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
- Contributed equally
| | - Xiang Li
- Department of Radiology, the Second Hospital of Dalian Medial University, Dalian, Liaoning 116023, China
- Contributed equally
| | - Qian Cao
- Department of Radiology, the Second Hospital of Dalian Medial University, Dalian, Liaoning 116023, China
- Contributed equally
| | - Hongkai Wang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Bin Zhang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
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3
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Malimban J, Lathouwers D, Qian H, Verhaegen F, Wiedemann J, Brandenburg S, Staring M. Deep learning-based segmentation of the thorax in mouse micro-CT scans. Sci Rep 2022; 12:1822. [PMID: 35110676 PMCID: PMC8810936 DOI: 10.1038/s41598-022-05868-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 01/18/2022] [Indexed: 12/18/2022] Open
Abstract
For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ([Formula: see text]) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.
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Affiliation(s)
- Justin Malimban
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands.
| | - Danny Lathouwers
- Department of Radiation Science and Technology, Faculty of Applied Sciences, Delft University of Technology, 2629 JB, Delft, The Netherlands
| | - Haibin Qian
- Department of Medical Biology, Amsterdam University Medical Centers (Location AMC) and Cancer Center Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6229 ER, Maastricht, The Netherlands
| | - Julia Wiedemann
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands
- Department of Biomedical Sciences of Cells and Systems-Section Molecular Cell Biology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands
| | - Sytze Brandenburg
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands
| | - Marius Staring
- Department of Radiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
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4
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A preclinical micro-computed tomography database including 3D whole body organ segmentations. Sci Data 2018; 5:180294. [PMID: 30561432 PMCID: PMC6298256 DOI: 10.1038/sdata.2018.294] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 10/31/2018] [Indexed: 12/13/2022] Open
Abstract
The gold-standard of preclinical micro-computed tomography (μCT) data processing is still manual delineation of complete organs or regions by specialists. However, this method is time-consuming, error-prone, has limited reproducibility, and therefore is not suitable for large-scale data analysis. Unfortunately, robust and accurate automated whole body segmentation algorithms are still missing. In this publication, we introduce a database containing 225 murine 3D whole body μCT scans along with manual organ segmentation of most important organs including heart, liver, lung, trachea, spleen, kidneys, stomach, intestine, bladder, thigh muscle, bone, as well as subcutaneous tumors. The database includes native and contrast-enhanced, regarding spleen and liver, μCT data. All scans along with organ segmentation are freely accessible at the online repository Figshare. We encourage researchers to reuse the provided data to evaluate and improve methods and algorithms for accurate automated organ segmentation which may reduce manual segmentation effort, increase reproducibility, and even reduce the number of required laboratory animals by reducing a source of variability and having access to a reliable reference group.
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5
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Klose AD, Paragas N. Automated quantification of bioluminescence images. Nat Commun 2018; 9:4262. [PMID: 30323260 PMCID: PMC6189049 DOI: 10.1038/s41467-018-06288-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 08/24/2018] [Indexed: 02/03/2023] Open
Abstract
We developed a computer-aided analysis tool for quantitatively determining bioluminescent reporter distributions inside small animals. The core innovations are a body-fitting animal shuttle and a statistical mouse atlas, both of which are spatially aligned and scaled according to the animal’s weight, and hence provide data congruency across animals of varying size and pose. In conjunction with a multispectral bioluminescence tomography technique capitalizing on the spatial framework of the shuttle, the in vivo biodistribution of luminescent reporters can rapidly be calculated and, thus, enables operator-independent and computer-driven data analysis. We demonstrate its functionality by quantitatively monitoring a bacterial infection, where the bacterial organ burden was determined and validated with the established serial-plating method. In addition, the statistical mouse atlas was validated and compared to existing techniques providing an anatomical reference. The proposed data analysis tool promises to increase data throughput and data reproducibility and accelerate human disease modeling in mice. Analysis of bioluminescence images of bacterial distributions in living animals is mostly manual and semiquantitative. Here, the authors present an analysis platform featuring an animal mold, a probabilistic organ atlas, and a mirror gantry to perform automatic in vivo bioluminescence quantification.
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Affiliation(s)
| | - Neal Paragas
- InVivo Analytics, New York, NY, USA. .,University of Washington, Seattle, WA, USA.
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6
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Zhang B, Yin W, Liu H, Cao X, Wang H. Bioluminescence tomography with structural information estimated via statistical mouse atlas registration. BIOMEDICAL OPTICS EXPRESS 2018; 9:3544-3558. [PMID: 30338139 PMCID: PMC6191626 DOI: 10.1364/boe.9.003544] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 06/27/2018] [Accepted: 07/02/2018] [Indexed: 05/10/2023]
Abstract
Due to an ill-posed and underestimated characteristic of bioluminescence tomography (BLT) reconstruction, a priori anatomical information obtained from computed tomography (CT) or magnetic resonance imaging (MRI), is usually incorporated to improve the reconstruction accuracy. The organs need to be segmented, which is time-consuming and challenging, especially for the low-contrast CT images. In this paper, we present a BLT reconstruction method based on a statistical mouse atlas to improve the efficiency of heterogeneous model generation and the accuracy of target localization. The low-contrast CT image of the mouse was first registered to the statistical mouse atlas model with the constraints of mouse surface and high-contrast organs (bone and lung). Then the other organs, such as the liver and kidney, were determined automatically through the statistical mouse atlas model. The estimated organs were then discretized into tetrahedral meshes for BLT reconstruction. The linearized Bregman method was used to solve the sparse inverse problem of BLT by minimizing the regularization function (L1 norm plus L2 norm with smooth factor). Both numerical simulations and in vivo experiments were conducted, and the results demonstrate that even though the localization of the estimated organs may not be exactly accurate, the proposed method is feasible to reconstruct the bioluminescent source effectively and accurately with the estimated organs. This method would greatly benefit the bioluminescent light source localization for hybrid BLT/CT systems.
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Affiliation(s)
- Bin Zhang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Wanzhou Yin
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Hao Liu
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Xu Cao
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education & School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Hongkai Wang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
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7
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Wang H, Sun X, Wu T, Li C, Chen Z, Liao M, Li M, Yan W, Huang H, Yang J, Tan Z, Hui L, Liu Y, Pan H, Qu Y, Chen Z, Tan L, Yu L, Shi H, Huo L, Zhang Y, Tang X, Zhang S, Liu C. Deformable torso phantoms of Chinese adults for personalized anatomy modelling. J Anat 2018; 233:121-134. [PMID: 29663370 PMCID: PMC5987821 DOI: 10.1111/joa.12815] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2018] [Indexed: 11/26/2022] Open
Abstract
In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RCvlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.
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Affiliation(s)
- Hongkai Wang
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Xiaobang Sun
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
- Department of Information TechnologyUniversity of JyväskyläJyväskyläFinland
| | - Tongning Wu
- China Academy of Industry and Communications TechnologyBeijingChina
| | - Congsheng Li
- China Academy of Industry and Communications TechnologyBeijingChina
| | - Zhonghua Chen
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Meiying Liao
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Mengci Li
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Wen Yan
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Hui Huang
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Jia Yang
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Ziyu Tan
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Libo Hui
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Yue Liu
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Hang Pan
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Yue Qu
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Zhaofeng Chen
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Liwen Tan
- Institute of Digital MedicineThird Military Medical UniversityChongqingChina
| | - Lijuan Yu
- The Affiliated Cancer Hospital of Hainan Medical CollegeHaikouHainanChina
| | - Hongcheng Shi
- Department of Nuclear MedicineZhongshan HospitalFudan UniversityShanghaiChina
| | - Li Huo
- Department of Nuclear MedicinePeking Union Medical College HospitalBeijingChina
| | - Yanjun Zhang
- Department of Nuclear Medicinethe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoningChina
| | - Xin Tang
- Trauma Department of Orthopaedicsthe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoningChina
| | - Shaoxiang Zhang
- Institute of Digital MedicineThird Military Medical UniversityChongqingChina
| | - Changjian Liu
- Trauma Department of Orthopaedicsthe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoningChina
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8
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Singh R, Pervin S, Lee SJ, Kuo A, Grijalva V, David J, Vergnes L, Reddy ST. Metabolic profiling of follistatin overexpression: a novel therapeutic strategy for metabolic diseases. Diabetes Metab Syndr Obes 2018; 11:65-84. [PMID: 29618935 PMCID: PMC5875402 DOI: 10.2147/dmso.s159315] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Follistatin (Fst) promotes brown adipocyte characteristics in adipose tissues. METHODS Abdominal fat volume (CT scan), glucose clearance (GTT test), and metabolomics analysis (mass spectrometry) of adipose tissues from Fst transgenic (Fst-Tg) and wild type (WT) control mice were analyzed. Oxygen consumption (Seahorse Analyzer) and lipidomics (gas chromatography) was analyzed in 3T3-L1 cells. RESULTS Fst-Tg mice show significant decrease in abdominal fat content, increased glucose clearance, improved plasma lipid profiles and significant changes in several conventional metabolites compared to the WT mice. Furthermore, overexpression of Fst in 3T3-L1 cells resulted in up regulation of key brown/beige markers and changes in lipidomics profiles. CONCLUSION Fst modulates key factors involved in promoting metabolic syndrome and could be used for therapeutic intervention.
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Affiliation(s)
- Rajan Singh
- Department of Obstetrics and Gynecology, UCLA School of Medicine, Los Angeles, CA, USA
- Division of Endocrinology and Metabolism, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA
- Correspondence: Rajan Singh, Division of Endocrinology and Metabolism, Charles R. Drew University of Medicine and Science, 3084 Hawkins Building, 1731 East 120 Street, Los Angeles, CA 90059, USA, Tel +1 323 563 5828, Email
| | - Shehla Pervin
- Department of Obstetrics and Gynecology, UCLA School of Medicine, Los Angeles, CA, USA
- Division of Endocrinology and Metabolism, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA
| | - Se-Jin Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut School of Medicine, CT, USA
| | - Alan Kuo
- Department of Biology, California State University Dominguez Hills, CA, USA
| | - Victor Grijalva
- Department of Molecular and Medical Pharmacology, UCLA School of Medicine, Los Angeles, CA, USA
| | - John David
- Department of Comparative Medicine, Pfizer Inc, San Diego, CA, USA
| | - Laurent Vergnes
- Department of Human Genetics, UCLA School of Medicine, Los Angeles, CA, USA
| | - Srinivasa T Reddy
- Department of Obstetrics and Gynecology, UCLA School of Medicine, Los Angeles, CA, USA
- Department of Molecular and Medical Pharmacology, UCLA School of Medicine, Los Angeles, CA, USA
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9
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Dillenseger JP, Goetz C, Sayeh A, Healy C, Duluc I, Freund JN, Constantinesco A, Aubertin-Kirch G, Choquet P. Estimation of subject coregistration errors during multimodal preclinical imaging using separate instruments: origins and avoidance of artifacts. J Med Imaging (Bellingham) 2017; 4:035503. [PMID: 28840171 DOI: 10.1117/1.jmi.4.3.035503] [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: 04/27/2017] [Accepted: 07/24/2017] [Indexed: 11/14/2022] Open
Abstract
We use high-resolution [Formula: see text] data in multiple experiments to estimate the sources of error during coregistration of images acquired on separate preclinical instruments. In combination with experiments with phantoms, we completed in vivo imaging on mice, aimed at identifying the possible sources of registration errors, caused either by transport of the animal, movement of the animal itself, or methods of coregistration. The same imaging cell was used as a holder for phantoms and animals. For all procedures, rigid coregistration was carried out using a common landmark coregistration system, placed inside the imaging cell. We used the fiducial registration error and the target registration error to analyze the coregistration accuracy. We found that moving an imaging cell between two preclinical devices during a multimodal procedure gives an error of about [Formula: see text] at most. Therefore, it could not be considered a source of coregistration errors. Errors linked to spontaneous movements of the animal increased with time, to nearly 1 mm at most, excepted for body parts that were properly restrained. This work highlights the importance of animal intrinsic movements during a multiacquisition procedure and demonstrates a simple method to identify and quantify the sources of error during coregistration.
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Affiliation(s)
- Jean-Philippe Dillenseger
- Hôpitaux Universitaires de Strasbourg, Imagerie Préclinique-UF6237, Pôle d'imagerie, Hôpital de Hautepierre, Strasbourg Cedex, France.,Université de Strasbourg, Icube, équipe MMB, CNRS, Strasbourg, France.,Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Faculté de Médecine, Strasbourg, France
| | - Christian Goetz
- Hôpitaux Universitaires de Strasbourg, Imagerie Préclinique-UF6237, Pôle d'imagerie, Hôpital de Hautepierre, Strasbourg Cedex, France.,Universitätsklinikum, Klinik für Nuklear Medizin, Freiburg, Germany
| | - Amira Sayeh
- Hôpitaux Universitaires de Strasbourg, Imagerie Préclinique-UF6237, Pôle d'imagerie, Hôpital de Hautepierre, Strasbourg Cedex, France
| | - Chris Healy
- King's College London, Department of Craniofacial Development and Stem Cell Biology, Guy's Hospital, London, United Kingdom
| | - Isabelle Duluc
- Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Faculté de Médecine, Strasbourg, France.,Université de Strasbourg, Inserm, France
| | - Jean-Noël Freund
- Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Faculté de Médecine, Strasbourg, France.,Université de Strasbourg, Inserm, France
| | - André Constantinesco
- Hôpitaux Universitaires de Strasbourg, Imagerie Préclinique-UF6237, Pôle d'imagerie, Hôpital de Hautepierre, Strasbourg Cedex, France
| | - Gaëlle Aubertin-Kirch
- Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Faculté de Médecine, Strasbourg, France.,Université de Strasbourg, Laboratoire de Neurobiologie et Pharmacologie Cardiovasculaire, Faculté de Médecine, France
| | - Philippe Choquet
- Hôpitaux Universitaires de Strasbourg, Imagerie Préclinique-UF6237, Pôle d'imagerie, Hôpital de Hautepierre, Strasbourg Cedex, France.,Université de Strasbourg, Icube, équipe MMB, CNRS, Strasbourg, France.,Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Faculté de Médecine, Strasbourg, France
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10
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Klyuzhin IS, Sossi V. PET Image Reconstruction and Deformable Motion Correction Using Unorganized Point Clouds. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1263-1275. [PMID: 28287962 DOI: 10.1109/tmi.2017.2675989] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Quantitative positron emission tomography imaging often requires correcting the image data for deformable motion. With cyclic motion, this is traditionally achieved by separating the coincidence data into a relatively small number of gates, and incorporating the inter-gate image transformation matrices into the reconstruction algorithm. In the presence of non-cyclic deformable motion, this approach may be impractical due to a large number of required gates. In this paper, we propose an alternative approach to iterative image reconstruction with correction for deformable motion, wherein unorganized point clouds are used to model the imaged objects in the image space, and motion is corrected for explicitly by introducing a time-dependence into the point coordinates. The image function is represented using constant basis functions with finite support determined by the boundaries of the Voronoi cells in the point cloud. We validate the quantitative accuracy and stability of the proposed approach by reconstructing noise-free and noisy projection data from digital and physical phantoms. The point-cloud-based maximum likelihood expectation maximization (MLEM) and one-pass list-mode ordered-subset expectation maximization (OSEM) algorithms are validated. The results demonstrate that images reconstructed using the proposed method are quantitatively stable, with noise and convergence properties comparable to image reconstruction based on the use of rectangular and radially-symmetric basis functions.
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11
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Rawashdeh WA, Zuo S, Melle A, Appold L, Koletnik S, Tsvetkova Y, Beztsinna N, Pich A, Lammers T, Kiessling F, Gremse F. Noninvasive Assessment of Elimination and Retention using CT-FMT and Kinetic Whole-body Modeling. Am J Cancer Res 2017; 7:1499-1510. [PMID: 28529633 PMCID: PMC5436509 DOI: 10.7150/thno.17263] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 01/16/2017] [Indexed: 11/25/2022] Open
Abstract
Fluorescence-mediated tomography (FMT) is a quantitative three-dimensional imaging technique for preclinical research applications. The combination with micro-computed tomography (µCT) enables improved reconstruction and analysis. The aim of this study is to assess the potential of µCT-FMT and kinetic modeling to determine elimination and retention of typical model drugs and drug delivery systems. We selected four fluorescent probes with different but well-known biodistribution and elimination routes: Indocyanine green (ICG), hydroxyapatite-binding OsteoSense (OS), biodegradable nanogels (NG) and microbubbles (MB). µCT-FMT scans were performed in twenty BALB/c nude mice (5 per group) at 0.25, 2, 4, 8, 24, 48 and 72 h after intravenous injection. Longitudinal organ curves were determined using interactive organ segmentation software and a pharmacokinetic whole-body model was implemented and applied to compute physiological parameters describing elimination and retention. ICG demonstrated high initial hepatic uptake which decreased rapidly while intestinal accumulation appeared for around 8 hours which is in line with the known direct uptake by hepatocytes followed by hepatobiliary elimination. Complete clearance from the body was observed at 48 h. NG showed similar but slower hepatobiliary elimination because these nanoparticles require degradation before elimination can take place. OS was strongly located in the bones in addition to high signal in the bladder at 0.25 h indicating fast renal excretion. MB showed longest retention in liver and spleen and low signal in the kidneys likely caused by renal elimination or retention of fragments. Furthermore, probe retention was found in liver (MB, NG and OS), spleen (MB) and kidneys (MB and NG) at 72 h which was confirmed by ex vivo data. The kinetic model enabled robust extraction of physiological parameters from the organ curves. In summary, µCT-FMT and kinetic modeling enable differentiation of hepatobiliary and renal elimination routes and allow for the noninvasive assessment of retention sites in relevant organs including liver, kidney, bone and spleen.
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A Novel Mouse Segmentation Method Based on Dynamic Contrast Enhanced Micro-CT Images. PLoS One 2017; 12:e0169424. [PMID: 28060917 PMCID: PMC5217965 DOI: 10.1371/journal.pone.0169424] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 12/17/2016] [Indexed: 11/22/2022] Open
Abstract
With the development of hybrid imaging scanners, micro-CT is widely used in locating abnormalities, studying drug metabolism, and providing structural priors to aid image reconstruction in functional imaging. Due to the low contrast of soft tissues, segmentation of soft tissue organs from mouse micro-CT images is a challenging problem. In this paper, we propose a mouse segmentation scheme based on dynamic contrast enhanced micro-CT images. With a homemade fast scanning micro-CT scanner, dynamic contrast enhanced images were acquired before and after injection of non-ionic iodinated contrast agents (iohexol). Then the feature vector of each voxel was extracted from the signal intensities at different time points. Based on these features, the heart, liver, spleen, lung, and kidney could be classified into different categories and extracted from separate categories by morphological processing. The bone structure was segmented using a thresholding method. Our method was validated on seven BALB/c mice using two different classifiers: a support vector machine classifier with a radial basis function kernel and a random forest classifier. The results were compared to manual segmentation, and the performance was assessed using the Dice similarity coefficient, false positive ratio, and false negative ratio. The results showed high accuracy with the Dice similarity coefficient ranging from 0.709 ± 0.078 for the spleen to 0.929 ± 0.006 for the kidney.
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Xie T, Zaidi H. Development of computational small animal models and their applications in preclinical imaging and therapy research. Med Phys 2016; 43:111. [PMID: 26745904 DOI: 10.1118/1.4937598] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.
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Affiliation(s)
- Tianwu Xie
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland; Geneva Neuroscience Center, Geneva University, Geneva CH-1205, Switzerland; and Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
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