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Author |
Debora Gil; Oriol Rodriguez; Josepa Mauri; Petia Radeva |

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Title |
Statistical descriptors of the Myocardial perfusion in angiographic images |
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Conference Article |
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2006 |
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Proc. Computers in Cardiology |
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677-680 |
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Anisotropic processing; intravascular ultrasound (IVUS); vessel border segmentation; vessel structure classification. |
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Abstract |
Restoration of coronary flow after primary percutaneous coronary intervention in acute myocardial infarction does not always correlate with adequate myocardial perfusion. Recently, coronary angiography has been used to assess microcirculation integrity (Myocardial BlushAnalysis, MBA). Although MBA correlates with patient prognosis there are few image processing methods addressing objective perfusion quantification. The goal of this work is to develop statistical descriptors of the myocardial dyeing pattern allowing objective assessment of myocardial perfusion. Experiments on healthy right coronary arteries show that our approach allows reliable measurements without any specific image acquisition protocol. |
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IAM;MILAB |
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IAM @ iam @ GRR2006 |
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1528 |
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Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |


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Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
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Conference Article |
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Year |
2018 |
Publication |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
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11041 |
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Keywords  |
Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
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Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
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Granada; September 2018 |
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MICCAIW |
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IAM; 600.096; 600.075; 601.323; 600.145 |
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Call Number |
Admin @ si @ RSB2018b |
Serial |
3137 |
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Author |
Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell |

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Title |
Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches |
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Conference Article |
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2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops |
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9401 |
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62-70 |
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Keywords  |
Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy |
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Abstract |
Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface. |
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Quebec; Canada; September 2016 |
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MICCAIW |
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IAM; MV; 600.060; 600.075 |
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no |
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Call Number |
Admin @ si @ SGB2016 |
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2885 |
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Author |
Debora Gil; Jaume Garcia; Ruth Aris; Guillaume Houzeaux; Manuel Vazquez |

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Title |
A Riemmanian approach to cardiac fiber architecture modelling |
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Conference Article |
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Year |
2009 |
Publication |
1st International Conference on Mathematical & Computational Biomedical Engineering |
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59-62 |
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cardiac fiber architecture; diffusion tensor magnetic resonance imaging; differential (Rie- mannian) geometry. |
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There is general consensus that myocardial fiber architecture should be modelled in order to fully understand the electromechanical properties of the Left Ventricle (LV). Diffusion Tensor magnetic resonance Imaging (DTI) is the reference image modality for rapid measurement of fiber orientations by means of the tensor principal eigenvectors. In this work, we present a mathematical framework for across subject comparison of the local geometry of the LV anatomy including the fiber architecture from the statistical analysis of DTI studies. We use concepts of differential geometry for defining a parametric domain suitable for statistical analysis of a low number of samples. We use Riemannian metrics to define a consistent computation of DTI principal eigenvector modes of variation. Our framework has been applied to build an atlas of the LV fiber architecture from 7 DTI normal canine hearts. |
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Swansea (UK) |
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Nithiarasu, R.L.R.V.L. |
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CMBE |
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IAM |
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no |
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Call Number |
IAM @ iam @ FGA2009 |
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1520 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |

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Title |
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging |
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Conference Article |
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2005 |
Publication |
Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
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67-74 |
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Keywords  |
classification; vessel border modelling; IVUS |
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IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability. |
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IOS Press |
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Amsterdam, The Netherlands |
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IAM;MILAB |
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IAM @ iam @ HGR2005c |
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1549 |
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Author |
Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil |

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Title |
Mental Workload Detection Based on EEG Analysis |
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Conference Article |
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2021 |
Publication |
Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. |
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339 |
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268-277 |
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Cognitive states; Mental workload; EEG analysis; Neural Networks. |
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The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation. |
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Virtual; October 20-22 2021 |
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CCIA |
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IAM; 600.139; 600.118; 600.145 |
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no |
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Admin @ si @ |
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3723 |
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Author |
Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester |


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Title |
Anatomical parameterization for volumetric meshing of the liver |
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Conference Article |
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2014 |
Publication |
SPIE – Medical Imaging |
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9036 |
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Coordinate System; Anatomy Modeling; Parameterization |
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A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values
at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites
of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the
volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient’s liver, and allows comparing livers from several patients in a common framework of reference. |
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Amsterdam; September 2014 |
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SPIE-MI |
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IAM; 600.075 |
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no |
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Admin @ si @ VGG2014 |
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2456 |
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Author |
Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez |


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Title |
Data Augmentation from Sketch |
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Conference Article |
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2019 |
Publication |
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging |
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11840 |
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155-162 |
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Data augmentation; cycleGANs; Multi-objective optimization |
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State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.
We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives. |
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Shenzhen; China; October 2019 |
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CLIP |
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IAM; 600.145; 601.337; 600.139; 600.145 |
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no |
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Admin @ si @ GES2019 |
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3359 |
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Author |
David Roche; Debora Gil; Jesus Giraldo |


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Title |
Detecting loss of diversity for an efficient termination of EAs |
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Conference Article |
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Year |
2013 |
Publication |
15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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561 - 566 |
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EA termination; EA population diversity; EA steady state |
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Abstract |
Termination of Evolutionary Algorithms (EA) at its steady state so that useless iterations are not performed is a main point for its efficient application to black-box problems. Many EA algorithms evolve while there is still diversity in their population and, thus, they could be terminated by analyzing the behavior some measures of EA population diversity. This paper presents a numeric approximation to steady states that can be used to detect the moment EA population has lost its diversity for EA termination. Our condition has been applied to 3 EA paradigms based on diversity and a selection of functions
covering the properties most relevant for EA convergence.
Experiments show that our condition works regardless of the search space dimension and function landscape. |
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Timisoara; Rumania; |
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978-1-4799-3035-7 |
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SYNASC |
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IAM; 600.044; 600.060; 605.203 |
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Admin @ si @ RGG2013c |
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2299 |
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Author |
Debora Gil; Aura Hernandez-Sabate; Antoni Carol; Oriol Rodriguez; Petia Radeva |

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Title |
A Deterministic-Statistic Adventitia Detection in IVUS Images |
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Conference Article |
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2005 |
Publication |
ESC Congress |
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Keywords  |
Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation |
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Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles. |
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Stockholm; Sweden; September 2005 |
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,Sweden (EU) |
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ESC |
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IAM;MILAB |
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Call Number |
IAM @ iam @ RMF2005a |
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1523 |
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