Publications
2025
Caleme, Eduardo Duarte; Cevidanes, Lucia; Mattos, Claudia Trindade; Miranda, Felicia; Gurgel, Marcela; Barone, Selene; Gaydamour, Alban; Tulissi, Enzo; Claret, Jeanne; Leroux, Gaelle; Moro, Alexandre; Gonçalves, João; Ruellas, Antônio; Zuperlari, Marina Morettin; Gonçalves, Paulo Zupelari; Hsu, Nina; Wolford, Larry; Prieto, Juan; Bianchi, Jonas
Aligning MRI and CBCT for advanced TMJ diagnostics: Case series using AI-powered registration in dentistry and orthodontics Bachelor Thesis
2025, ISSN: 1073-8746.
Abstract | Links | BibTeX | Tags: CBCT, diagnosis, MRI, orthodontics, TMJ complex visualization
@bachelorthesis{nokey,
title = {Aligning MRI and CBCT for advanced TMJ diagnostics: Case series using AI-powered registration in dentistry and orthodontics},
author = {Eduardo Duarte Caleme and Lucia Cevidanes and Claudia Trindade Mattos and Felicia Miranda and Marcela Gurgel and Selene Barone and Alban Gaydamour and Enzo Tulissi and Jeanne Claret and Gaelle Leroux and Alexandre Moro and João Gonçalves and Antônio Ruellas and Marina Morettin Zuperlari and Paulo Zupelari Gonçalves and Nina Hsu and Larry Wolford and Juan Prieto and Jonas Bianchi},
url = {https://www.sciencedirect.com/science/article/pii/S1073874625000611},
doi = {https://doi.org/10.1053/j.sodo.2025.07.001},
issn = {1073-8746},
year = {2025},
date = {2025-07-11},
journal = {Seminars in Orthodontics},
abstract = {This study demonstrates the functionality and clinical value of magnetic resonance imaging (MRI) to cone-beam computed tomography (CBCT) registration using a new open-source artificial intelligence (AI) model called MR2CBCT. We present five clinical cases in which the AI-based method was used to register CBCT and MRI images. For comparison, manual registration was also performed. Qualitative inspection revealed that manual alignment often showed errors that could compromise diagnostic accuracy. In contrast, the AI-based approach consistently corrected these discrepancies, producing more anatomically coherent fused images to better support clinical decision-making. Our findings highlight MR2CBCT as a reliable and accessible tool for multimodal integration in temporomandibular joint (TMJ) assessment in orthodontics and general dentistry.},
keywords = {CBCT, diagnosis, MRI, orthodontics, TMJ complex visualization},
pubstate = {published},
tppubtype = {bachelorthesis}
}
This study demonstrates the functionality and clinical value of magnetic resonance imaging (MRI) to cone-beam computed tomography (CBCT) registration using a new open-source artificial intelligence (AI) model called MR2CBCT. We present five clinical cases in which the AI-based method was used to register CBCT and MRI images. For comparison, manual registration was also performed. Qualitative inspection revealed that manual alignment often showed errors that could compromise diagnostic accuracy. In contrast, the AI-based approach consistently corrected these discrepancies, producing more anatomically coherent fused images to better support clinical decision-making. Our findings highlight MR2CBCT as a reliable and accessible tool for multimodal integration in temporomandibular joint (TMJ) assessment in orthodontics and general dentistry.
2024
Leroux, Gaelle; Mattos, Claudia; Claret, Jeanne; Caleme, Eduardo; Barone, Selene; Gurgel, Marcela; Miranda, Felicia; Goncalves, Joao; Goncalves, Paulo Zupelari; marina Morettin Zupelari,; Wolford, Larry; Hsu, Nina; Ruellas, Antonio; Bianchi, Jonas; Prieto, Juan; Cevidanes, Lucia
Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease Journal Article
In: Clinical Image-Based Procedures. CLIP, vol. 15196, pp. 63-72, 2024, ISBN: 978-3-031-73083-2.
Abstract | Links | BibTeX | Tags: 3D Slicer, CBCT, MRI, TMJ complex visualization
@article{Leroux2024,
title = {Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease},
author = {Gaelle Leroux and Claudia Mattos and Jeanne Claret and Eduardo Caleme and Selene Barone and Marcela Gurgel and Felicia Miranda and Joao Goncalves and Paulo Zupelari Goncalves and marina Morettin Zupelari and Larry Wolford and Nina Hsu and Antonio Ruellas and Jonas Bianchi and Juan Prieto and Lucia Cevidanes},
url = {https://doi.org/10.1007/978-3-031-73083-2_7},
doi = {10.1007/978-3-031-73083-2_7},
isbn = {978-3-031-73083-2},
year = {2024},
date = {2024-09-29},
urldate = {2024-09-29},
journal = {Clinical Image-Based Procedures. CLIP},
volume = {15196},
pages = {63-72},
abstract = {Temporomandibular Degenerative Joint Disease (TM DJD) is characterized by chronic and progressive degeneration of the joint, leading to functional limitations. Converging on enhancing TM DJD diagnosis, prognosis, and personalized patient care, multimodal Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) registration aims to allow comprehensive understanding of the articular disc and subchondral bone alterations towards elucidating the onset, advancement, and progression of TM DJDs. This study proposes a novel multimodal image registration (fusion) approach that combines image processing techniques with mutual information to register MRI to CBCT images, enhancing TMJ complex visualization and analysis. The algorithm leverages automated image orientation, resampling, MRI inversion, normalization and rigid mutual information registration methods to align and overlay multimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information with CBCT bony details, this novel open-source tool available in the 3D Slicer platform provides a more comprehensive patient-specific TM DJD model. The current 98.75% success rate, with mean absolute rotation differences of 1.53 degrees ± 1.75 degrees, 1.69 degrees ± 1.54 degrees, and 2.70 degrees ± 2.89 degrees in Pitch, Roll and Yaw respectively; and translation differences of 0.92mm ± 1.64mm, 0.98mm ± 0.85mm, and 0.5mm ± 0.43mm in respectively the Left-Right, Antero-Posterior and Supero-Inferior axes. The proposed approach has potential to enhance care for TM DJD and other conditions requiring multimodal images.
},
keywords = {3D Slicer, CBCT, MRI, TMJ complex visualization},
pubstate = {published},
tppubtype = {article}
}
Temporomandibular Degenerative Joint Disease (TM DJD) is characterized by chronic and progressive degeneration of the joint, leading to functional limitations. Converging on enhancing TM DJD diagnosis, prognosis, and personalized patient care, multimodal Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) registration aims to allow comprehensive understanding of the articular disc and subchondral bone alterations towards elucidating the onset, advancement, and progression of TM DJDs. This study proposes a novel multimodal image registration (fusion) approach that combines image processing techniques with mutual information to register MRI to CBCT images, enhancing TMJ complex visualization and analysis. The algorithm leverages automated image orientation, resampling, MRI inversion, normalization and rigid mutual information registration methods to align and overlay multimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information with CBCT bony details, this novel open-source tool available in the 3D Slicer platform provides a more comprehensive patient-specific TM DJD model. The current 98.75% success rate, with mean absolute rotation differences of 1.53 degrees ± 1.75 degrees, 1.69 degrees ± 1.54 degrees, and 2.70 degrees ± 2.89 degrees in Pitch, Roll and Yaw respectively; and translation differences of 0.92mm ± 1.64mm, 0.98mm ± 0.85mm, and 0.5mm ± 0.43mm in respectively the Left-Right, Antero-Posterior and Supero-Inferior axes. The proposed approach has potential to enhance care for TM DJD and other conditions requiring multimodal images.
Caleme, Eduardo Duarte; Cevidanes, Lucia; Mattos, Claudia Trindade; Miranda, Felicia; Gurgel, Marcela; Barone, Selene; Gaydamour, Alban; Tulissi, Enzo; Claret, Jeanne; Leroux, Gaelle; Moro, Alexandre; Gonçalves, João; Ruellas, Antônio; Zuperlari, Marina Morettin; Gonçalves, Paulo Zupelari; Hsu, Nina; Wolford, Larry; Prieto, Juan; Bianchi, Jonas
Aligning MRI and CBCT for advanced TMJ diagnostics: Case series using AI-powered registration in dentistry and orthodontics Bachelor Thesis
2025, ISSN: 1073-8746.
@bachelorthesis{nokey,
title = {Aligning MRI and CBCT for advanced TMJ diagnostics: Case series using AI-powered registration in dentistry and orthodontics},
author = {Eduardo Duarte Caleme and Lucia Cevidanes and Claudia Trindade Mattos and Felicia Miranda and Marcela Gurgel and Selene Barone and Alban Gaydamour and Enzo Tulissi and Jeanne Claret and Gaelle Leroux and Alexandre Moro and João Gonçalves and Antônio Ruellas and Marina Morettin Zuperlari and Paulo Zupelari Gonçalves and Nina Hsu and Larry Wolford and Juan Prieto and Jonas Bianchi},
url = {https://www.sciencedirect.com/science/article/pii/S1073874625000611},
doi = {https://doi.org/10.1053/j.sodo.2025.07.001},
issn = {1073-8746},
year = {2025},
date = {2025-07-11},
journal = {Seminars in Orthodontics},
abstract = {This study demonstrates the functionality and clinical value of magnetic resonance imaging (MRI) to cone-beam computed tomography (CBCT) registration using a new open-source artificial intelligence (AI) model called MR2CBCT. We present five clinical cases in which the AI-based method was used to register CBCT and MRI images. For comparison, manual registration was also performed. Qualitative inspection revealed that manual alignment often showed errors that could compromise diagnostic accuracy. In contrast, the AI-based approach consistently corrected these discrepancies, producing more anatomically coherent fused images to better support clinical decision-making. Our findings highlight MR2CBCT as a reliable and accessible tool for multimodal integration in temporomandibular joint (TMJ) assessment in orthodontics and general dentistry.},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
This study demonstrates the functionality and clinical value of magnetic resonance imaging (MRI) to cone-beam computed tomography (CBCT) registration using a new open-source artificial intelligence (AI) model called MR2CBCT. We present five clinical cases in which the AI-based method was used to register CBCT and MRI images. For comparison, manual registration was also performed. Qualitative inspection revealed that manual alignment often showed errors that could compromise diagnostic accuracy. In contrast, the AI-based approach consistently corrected these discrepancies, producing more anatomically coherent fused images to better support clinical decision-making. Our findings highlight MR2CBCT as a reliable and accessible tool for multimodal integration in temporomandibular joint (TMJ) assessment in orthodontics and general dentistry.
Leroux, Gaelle; Mattos, Claudia; Claret, Jeanne; Caleme, Eduardo; Barone, Selene; Gurgel, Marcela; Miranda, Felicia; Goncalves, Joao; Goncalves, Paulo Zupelari; marina Morettin Zupelari,; Wolford, Larry; Hsu, Nina; Ruellas, Antonio; Bianchi, Jonas; Prieto, Juan; Cevidanes, Lucia
Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease Journal Article
In: Clinical Image-Based Procedures. CLIP, vol. 15196, pp. 63-72, 2024, ISBN: 978-3-031-73083-2.
@article{Leroux2024,
title = {Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease},
author = {Gaelle Leroux and Claudia Mattos and Jeanne Claret and Eduardo Caleme and Selene Barone and Marcela Gurgel and Felicia Miranda and Joao Goncalves and Paulo Zupelari Goncalves and marina Morettin Zupelari and Larry Wolford and Nina Hsu and Antonio Ruellas and Jonas Bianchi and Juan Prieto and Lucia Cevidanes},
url = {https://doi.org/10.1007/978-3-031-73083-2_7},
doi = {10.1007/978-3-031-73083-2_7},
isbn = {978-3-031-73083-2},
year = {2024},
date = {2024-09-29},
urldate = {2024-09-29},
journal = {Clinical Image-Based Procedures. CLIP},
volume = {15196},
pages = {63-72},
abstract = {Temporomandibular Degenerative Joint Disease (TM DJD) is characterized by chronic and progressive degeneration of the joint, leading to functional limitations. Converging on enhancing TM DJD diagnosis, prognosis, and personalized patient care, multimodal Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) registration aims to allow comprehensive understanding of the articular disc and subchondral bone alterations towards elucidating the onset, advancement, and progression of TM DJDs. This study proposes a novel multimodal image registration (fusion) approach that combines image processing techniques with mutual information to register MRI to CBCT images, enhancing TMJ complex visualization and analysis. The algorithm leverages automated image orientation, resampling, MRI inversion, normalization and rigid mutual information registration methods to align and overlay multimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information with CBCT bony details, this novel open-source tool available in the 3D Slicer platform provides a more comprehensive patient-specific TM DJD model. The current 98.75% success rate, with mean absolute rotation differences of 1.53 degrees ± 1.75 degrees, 1.69 degrees ± 1.54 degrees, and 2.70 degrees ± 2.89 degrees in Pitch, Roll and Yaw respectively; and translation differences of 0.92mm ± 1.64mm, 0.98mm ± 0.85mm, and 0.5mm ± 0.43mm in respectively the Left-Right, Antero-Posterior and Supero-Inferior axes. The proposed approach has potential to enhance care for TM DJD and other conditions requiring multimodal images.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Temporomandibular Degenerative Joint Disease (TM DJD) is characterized by chronic and progressive degeneration of the joint, leading to functional limitations. Converging on enhancing TM DJD diagnosis, prognosis, and personalized patient care, multimodal Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) registration aims to allow comprehensive understanding of the articular disc and subchondral bone alterations towards elucidating the onset, advancement, and progression of TM DJDs. This study proposes a novel multimodal image registration (fusion) approach that combines image processing techniques with mutual information to register MRI to CBCT images, enhancing TMJ complex visualization and analysis. The algorithm leverages automated image orientation, resampling, MRI inversion, normalization and rigid mutual information registration methods to align and overlay multimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information with CBCT bony details, this novel open-source tool available in the 3D Slicer platform provides a more comprehensive patient-specific TM DJD model. The current 98.75% success rate, with mean absolute rotation differences of 1.53 degrees ± 1.75 degrees, 1.69 degrees ± 1.54 degrees, and 2.70 degrees ± 2.89 degrees in Pitch, Roll and Yaw respectively; and translation differences of 0.92mm ± 1.64mm, 0.98mm ± 0.85mm, and 0.5mm ± 0.43mm in respectively the Left-Right, Antero-Posterior and Supero-Inferior axes. The proposed approach has potential to enhance care for TM DJD and other conditions requiring multimodal images.
2025 |
Caleme, Eduardo Duarte; Cevidanes, Lucia; Mattos, Claudia Trindade; Miranda, Felicia; Gurgel, Marcela; Barone, Selene; Gaydamour, Alban; Tulissi, Enzo; Claret, Jeanne; Leroux, Gaelle; Moro, Alexandre; Gonçalves, João; Ruellas, Antônio; Zuperlari, Marina Morettin; Gonçalves, Paulo Zupelari; Hsu, Nina; Wolford, Larry; Prieto, Juan; Bianchi, Jonas: Aligning MRI and CBCT for advanced TMJ diagnostics: Case series using AI-powered registration in dentistry and orthodontics. 2025, ISSN: 1073-8746. (Type: Bachelor Thesis | Abstract | Links | BibTeX | Tags: CBCT, diagnosis, MRI, orthodontics, TMJ complex visualization)@bachelorthesis{nokey,This study demonstrates the functionality and clinical value of magnetic resonance imaging (MRI) to cone-beam computed tomography (CBCT) registration using a new open-source artificial intelligence (AI) model called MR2CBCT. We present five clinical cases in which the AI-based method was used to register CBCT and MRI images. For comparison, manual registration was also performed. Qualitative inspection revealed that manual alignment often showed errors that could compromise diagnostic accuracy. In contrast, the AI-based approach consistently corrected these discrepancies, producing more anatomically coherent fused images to better support clinical decision-making. Our findings highlight MR2CBCT as a reliable and accessible tool for multimodal integration in temporomandibular joint (TMJ) assessment in orthodontics and general dentistry. |
2024 |
Leroux, Gaelle; Mattos, Claudia; Claret, Jeanne; Caleme, Eduardo; Barone, Selene; Gurgel, Marcela; Miranda, Felicia; Goncalves, Joao; Goncalves, Paulo Zupelari; marina Morettin Zupelari,; Wolford, Larry; Hsu, Nina; Ruellas, Antonio; Bianchi, Jonas; Prieto, Juan; Cevidanes, Lucia: Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease. In: Clinical Image-Based Procedures. CLIP, vol. 15196, pp. 63-72, 2024, ISBN: 978-3-031-73083-2. (Type: Journal Article | Abstract | Links | BibTeX | Tags: 3D Slicer, CBCT, MRI, TMJ complex visualization)@article{Leroux2024,Temporomandibular Degenerative Joint Disease (TM DJD) is characterized by chronic and progressive degeneration of the joint, leading to functional limitations. Converging on enhancing TM DJD diagnosis, prognosis, and personalized patient care, multimodal Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) registration aims to allow comprehensive understanding of the articular disc and subchondral bone alterations towards elucidating the onset, advancement, and progression of TM DJDs. This study proposes a novel multimodal image registration (fusion) approach that combines image processing techniques with mutual information to register MRI to CBCT images, enhancing TMJ complex visualization and analysis. The algorithm leverages automated image orientation, resampling, MRI inversion, normalization and rigid mutual information registration methods to align and overlay multimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information with CBCT bony details, this novel open-source tool available in the 3D Slicer platform provides a more comprehensive patient-specific TM DJD model. The current 98.75% success rate, with mean absolute rotation differences of 1.53 degrees ± 1.75 degrees, 1.69 degrees ± 1.54 degrees, and 2.70 degrees ± 2.89 degrees in Pitch, Roll and Yaw respectively; and translation differences of 0.92mm ± 1.64mm, 0.98mm ± 0.85mm, and 0.5mm ± 0.43mm in respectively the Left-Right, Antero-Posterior and Supero-Inferior axes. The proposed approach has potential to enhance care for TM DJD and other conditions requiring multimodal images. |