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1. Remote intensive management to improve antiplatelet adherence in acute myocardial infarction: a secondary analysis of the randomized controlled IMMACULATE trial

Author

Sim, Hui Wen, Koh, Karen W. L., Poh, Sock-Cheng, Chan, Siew Pang, Marchesseau, Stephanie, Singh, Devinder, Han, Yiying, Ng, Faclin, Lim, Eleanor, Prabath, Joseph F., Lee, Chi-Hang, Chen, Ruth, Carvalho, Leonardo, Tan, Sock-Hwee, Loh, Joshua P. Y., Tan, Jack W. C., Kuwelker, Karishma, Amanullah, R. M., Chin, Chee-Tang, Yip, James W. L., Lee, Choy-Yee, Gan, Juvena, Lo, Chew-Yong, Ho, Hee-Hwa, Hausenloy, Derek J., Tai, Bee-Choo, Richards, A. Mark, and Chan, Mark Y.

Published
2024
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2. Training CNN Classifiers for Semantic Segmentation using Partially Annotated Images: with Application on Human Thigh and Calf MRI

Author

Wong, Chun Kit, Marchesseau, Stephanie, Kalimeri, Maria, Yap, Tiang Siew, Teo, Serena S. H., Krishna, Lingaraj, Franco-Obregón, Alfredo, Tay, Stacey K. H., Khoo, Chin Meng, Lee, Philip T. H., Leow, Melvin K. S., Totman, John J., and Stephenson, Mary C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Objective: Medical image datasets with pixel-level labels tend to have a limited number of organ or tissue label classes annotated, even when the images have wide anatomical coverage. With supervised learning, multiple classifiers are usually needed given these partially annotated datasets. In this work, we propose a set of strategies to train one single classifier in segmenting all label classes that are heterogeneously annotated across multiple datasets without moving into semi-supervised learning. Methods: Masks were first created from each label image through a process we termed presence masking. Three presence masking modes were evaluated, differing mainly in weightage assigned to the annotated and unannotated classes. These masks were then applied to the loss function during training to remove the influence of unannotated classes. Results: Evaluation against publicly available CT datasets shows that presence masking is a viable method for training class-generic classifiers. Our class-generic classifier can perform as well as multiple class-specific classifiers combined, while the training duration is similar to that required for one class-specific classifier. Furthermore, the class-generic classifier can outperform the class-specific classifiers when trained on smaller datasets. Finally, consistent results are observed from evaluations against human thigh and calf MRI datasets collected in-house. Conclusion: The evaluation outcomes show that presence masking is capable of significantly improving both training and inference efficiency across imaging modalities and anatomical regions. Improved performance may even be observed on small datasets. Significance: Presence masking strategies can reduce the computational resources and costs involved in manual medical image annotations. All codes are publicly available at https://github.com/wong-ck/DeepSegment., Comment: Submitted to IEEE Transactions on Medical Imaging (Special Issue on Annotation-Efficient Deep Learning for Medical Imaging)

Published
2020

3. Deep Learning Segmentation of the Left Ventricle in Structural CMR: Towards a Fully Automatic Multi-scan Analysis

Author

Fadil, Hakim, Totman, John J., Marchesseau, Stephanie, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pop, Mihaela, editor, Sermesant, Maxime, editor, Zhao, Jichao, editor, Li, Shuo, editor, McLeod, Kristin, editor, Young, Alistair, editor, Rhode, Kawal, editor, and Mansi, Tommaso, editor

Published
2019
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4. Effect of Ticagrelor on Left Ventricular Remodeling in Patients With ST-Segment Elevation Myocardial Infarction (HEALING-AMI)

Author

Park, Yongwhi, Kwak, Choong Hwan, Jeong, Young-Hoon, Koh, Jin-Sin, Hwang, Seok-Jae, Hwang, Jin-Yong, Park, Hyun-Woong, Kang, Min Gyu, Park, Jeong Rang, Kim, Kyehwan, Kim, Jin Hyun, Park, Jae-Hyeong, Seong, In-Whan, Choi, Si-Wan, Lee, Jae-Hwan, Shin, Eun Seok, Ann, Soe Hee, Kim, Shin Jae, Oh, Ju Hyeon, Chun, Woojung, Kang, Gu Hyun, Lee, Mi Rae, Jang, Woo Jin, Park, Yong Hwan, Lee, Sang Yeub, Bae, Jang-Whan, Kim, Sang Min, Lee, Chung Suk, Kim, Jeongsu, Kim, June Hong, Chun, Kook Jin, Chon, Min Gu, Lee, Sang Hyun, Kim, Weon, Kim, Woo-Shik, Kim, Jin-Bae, Woo, Jong-Shin, Suh, Jung-Won, Chae, In-Ho, Youn, Tae-Jin, Cho, Young Seok, Yoon, Chang-Hwan, Park, Jin Joo, Kang, Si-Hyuck, Lee, Euijae, Kim, Donghoon, Kang, Jeehoon, Yang, Dong Heon, Ki, Nam Kyun, Lee, Jang Hoon, Hong, Young Joon, Cho, Jaeyeong, Kim, Min Chul, Sim, Doo Sun, Chan, Mark Y., Marchesseau, Stephanie, Koh, Jin Sin, Shin, Eun-Seok, Kim, Jeong Su, Hong, Young-Joon, Ahn, Jong-Hwa, and Choi, Si Wan

Published
2020
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8. Segmentation and Registration Coupling from Short-Axis Cine MRI: Application to Infarct Diagnosis

Author

Marchesseau, Stephanie, Duchateau, Nicolas, Delingette, Hervé, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mansi, Tommaso, editor, McLeod, Kristin, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2017
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9. 4D Automatic Centre Detection of the Right and Left Ventricles from Cine Short-Axis MRI

Author

Fadil, Hakim, Totman, John J., Marchesseau, Stephanie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mansi, Tommaso, editor, McLeod, Kristin, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2017
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10. Basal Slice Detection Using Long-Axis Segmentation for Cardiac Analysis

Author

Paknezhad, Mahsa, Brown, Michael S., Marchesseau, Stephanie, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ourselin, Sebastien, editor, Joskowicz, Leo, editor, Sabuncu, Mert R., editor, Unal, Gozde, editor, and Wells, William, editor

Published
2016
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12. Evaluation of Personalised Canine Electromechanical Models

Author

Giffard-Roisin, Sophie, Marchesseau, Stéphanie, Le Folgoc, Loïc, Delingette, Hervé, Sermesant, Maxime, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2015
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14. Computational and Physical Phantom Setups for the Second Cardiac Motion Analysis Challenge (cMAC2)

Author

De Craene, Mathieu, Allain, Pascal, Gao, Hang, Prakosa, Adityo, Marchesseau, Stephanie, Somphone, Oudom, Hilpert, Loic, Manrique, Alain, Delingette, Hervé, Makram-Ebeid, Sherif, Villain, Nicolas, D’hooge, Jan, Sermesant, Maxime, Saloux, Eric, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2013
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16. Cardiac Mechanical Parameter Calibration Based on the Unscented Transform

Author

Marchesseau, Stéphanie, Delingette, Hervé, Sermesant, Maxime, Rhode, Kawal, Duckett, Simon G., Rinaldi, C. Aldo, Razavi, Reza, Ayache, Nicholas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ayache, Nicholas, editor, Delingette, Hervé, editor, Golland, Polina, editor, and Mori, Kensaku, editor

Published
2012
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17. Multiplicative Jacobian Energy Decomposition Method for Fast Porous Visco-Hyperelastic Soft Tissue Model

Author

Marchesseau, Stéphanie, Heimann, Tobias, Chatelin, Simon, Willinger, Rémy, Delingette, Hervé, Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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20. Enhanced Thrombin Generation Is Associated with Worse Left Ventricular Scarring after ST-Segment Elevation Myocardial Infarction: A Cohort Study

Author

Sia, Ching-Hui, primary, Tan, Sock-Hwee, additional, Chan, Siew-Pang, additional, Marchesseau, Stephanie, additional, Sim, Hui-Wen, additional, Carvalho, Leonardo, additional, Chen, Ruth, additional, Amin, Nor Hanim Mohd, additional, Fong, Alan Yean-Yip, additional, Richards, Arthur Mark, additional, Yip, Christina, additional, and Chan, Mark Y., additional

Published
2022
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21. PREDICTIVE VALUE OF INDEX OF MICROVASCULAR RESISTANCE MEASURED AFTER PRIMARY PERCUTANEOUS CORONARY INTERVENTION ON MYOCARDIAL SCARRING ON CARDIAC MAGNETIC RESONANCE AND LONG-TERM OUTCOMES IN PATIENTS WITH ST-ELEVATION MYOCARDIAL INFARCTION

Author

Djohan, Andie Hartanto, primary, Chai, Ping, additional, Marchesseau, Stephanie, additional, Ling, Lieng Hsi, additional, Tan, Li-Ling, additional, Richards, Mark, additional, Chan, Siew Pang, additional, Chan, Mark Y.Y., additional, and Low, Adrian, additional

Published
2022
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24. Computational and Physical Phantom Setups for the Second Cardiac Motion Analysis Challenge (cMAC2)

Author

De Craene, Mathieu, primary, Allain, Pascal, additional, Gao, Hang, additional, Prakosa, Adityo, additional, Marchesseau, Stephanie, additional, Somphone, Oudom, additional, Hilpert, Loic, additional, Manrique, Alain, additional, Delingette, Hervé, additional, Makram-Ebeid, Sherif, additional, Villain, Nicolas, additional, D’hooge, Jan, additional, Sermesant, Maxime, additional, and Saloux, Eric, additional

Published
2013
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26. Remote Postdischarge Treatment of Patients With Acute Myocardial Infarction by Allied Health Care Practitioners vs Standard Care: The IMMACULATE Randomized Clinical Trial.

Author

Chan, Mark Y., Koh, Karen W. L., Poh, Sock-Cheng, Marchesseau, Stephanie, Singh, Devinder, Han, Yiying, Ng, Faclin, Lim, Eleanor, Prabath, Joseph F., Lee, Chi-Hang, Sim, Hui-Wen, Chen, Ruth, Carvalho, Leonardo, Tan, Sock-Hwee, Loh, Joshua P. Y., Tan, Jack W. C., Kuwelker, Karishma, Amanullah, R. M., Chin, Chee-Tang, and Yip, James W. L.

Published
2021
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35. Simulation de modèles personnalisés du coeur pour la prédiction de thérapies cardiaques

Author

Marchesseau, Stephanie, Centre de Mathématiques Appliquées (CMA), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ecole Nationale Supérieure des Mines de Paris, Hervé Delingette, Nicholas Ayache, and MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
Imagerie Médicale, Cardiac mechanics, [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Computer model, Etude de Spécificité, Patient-specific, Modélisation Cardiaque, Mécanique Cardiaque, Medical imaging, Cardiac modeling, Personnalisation, Specificity analysis, Modèle Informatique
Abstract

The clinical understanding and treatment of cardiovascular diseases is highly complex. For each patient, cardiologists face issues in determining the pathology, choosing a therapy or selecting suitable patients for the therapy. In order to provide additional guidance to cardiologists, many research groups are investigating the possibility to plan such therapies based on biophysical models of the heart. The hypothesis is that one may combine anatomical and functional data to build patient-specific cardiac models that could have the potential to predict the benefits of different therapies. Cardiac electromechanical simulations are based on computational models that can represent the heart geometry, motion and electrophysiology patterns during a cardiac cycle with sufficient accuracy. Integration of anatomical, mechanical and electrophysiological information for a given subject is essential to build such models.In this thesis, we first introduce the geometry, kinematics and electrophysiology personalizations that are necessary inputs to mechanical modeling. We propose to use the Bestel-Cl'ement-Sorine electromechanical model of the heart, which is sufficiently accurate without being over-parametrized for the available data. We start by presenting a new implementation of this model in an efficient opensource framework for interactive medical simulation and we analyze the resulting simulations through a complete sensitivity analysis.In a second step, the goal is to personalize the mechanical parameters from medical images (MRI data). To this end, we first propose an automatic calibration algorithm that estimates global mechanical parameters from volume and pressure curves. This technique was tested on 7 volunteers and 2 heart failure cases and allowed to perform a preliminary specificity study that intends to determine the relevant parameters able to differentiate the pathological cases from the control cases.Once initialized with the calibrated values, the parameters are then locally personalized with a more complex optimization algorithm. Reduced Order Unscented Kalman Filtering is used to estimate the contractilities on all of the AHA zones of the Left Ventricle, matching the regional volumes extracted from cine MRI data. This personalization strategy was validated and tested on several pathological and healthy cases. These contributions have led to promising results through this thesis and some are already used for various research studies.; La compréhension clinique et le traitement des maladies cardiovasculaires est extrêmement complexe. Pour chaque patient, les cardiologues sont confrontés à des difficultés pour déterminer la pathologie, choisir une thérapie ou encore sélectionner les patients susceptibles de bien répondre à un traitement donné. Afin de fournir une aide additionnelle aux cardiologues, de nombreuses équipes de recherche étudient la possibilité de planifier de telles thérapies grâce à des modèles biophysiques du cœur. Ils formulent l'hypothèse que l'on peut combiner les données fonctionnelles et anatomiques afin de créer des modèles cardiaques personnalisés à chaque patient qui auraient le potentiel de prédire les bénéfices des différentes thérapies. Les simulations électromécaniques du cœur sont basées sur des modèles informatiques qui peuvent représenter la géométrie, le mouvement et la propagation d'ondes électriques pendant un cycle cardiaque avec suffisamment de précision. L'intégration d'information anatomique, mécanique et électrophysiologique pour un patient donné est essentielle pour construire ce type de modèle.Dans cette thèse, nous présentons tout d'abord les méthodes de personnalisations géométriques, cinématiques et électrophysiologiques nécessaires à toutes modélisations mécaniques. Nous utilisons ensuite le modèle électromécanique de Bestel-Clément-Sorine qui a déjà prouvé avoir un bon réalisme sans être trop complexe au vu des données disponibles. Nous commençons par détailler la nouvelle implémentation de ce modèle dans une plateforme efficace de simulation médicale ayant l'avantage d'être libre et interactive, puis nous analysons les résultats de la simulation par une étude de sensibilité complète.Dans un deuxième temps, nous étudions la possibilité de personnaliser les paramètres mécaniques de ce modèle à partir d'images médicales (IRM). Pour cela, nous proposons en premier lieu une méthode automatique de calibration qui estime les paramètres mécaniques globaux à partir de courbes de pressions et volumes. Cette technique testée sur 6 volontaires et 2 cas pathologiques nous a permis de faire une étude de spécificité qui consiste à déterminer les paramètres pertinents capables de différencier les cas pathologiques des cas sains.Une fois initialisés à ces valeurs calibrées, les paramètres sont personnalisés localement avec un algorithme d'optimisation plus complexe. Le « Reduced Order Unscented Kalman Filtering » est utilisé pour estimer les contractilités de toutes les zones AHA du ventricule gauche à partir des volumes régionaux extraits des séquences d'images IRM. Cette stratégie de personnalisation a été validée et testée sur plusieurs cas pathologiques et volontaires. Ces différentes contributions ont montré des résultats prometteurs tout au long de cette thèse et certains sont déjà utilisés pour quelques études de recherche.

Published
2013

36. 18F-FDG PET-MRI with T1 MOLLI mapping to detect systemic sclerosis bowel inflammation and fibrosis.

Author

Marchesseau, Stephanie, Ng, Sue-Ann, Wang, Yu Tien, Xie, Wanying, Ng, David Chee-Eng, Totman, John James, and Low, Andrea Hsiu Ling

Subjects
*SYSTEMIC scleroderma, *POSITRON emission tomography, *MAGNETIC resonance imaging, *INFLAMMATORY bowel disease diagnosis, *FIBROSIS, *DIAGNOSIS
Abstract

Background: Systemic sclerosis-associated gastrointestinal tract involvement (SSc-GIT) is an independent predictor of 2-year mortality in early SSc. Availability of non-invasive investigations will facilitate early diagnosis and monitoring.Hypothesis: We investigate the role of 18F-FDG-PET-MRI in SSc-GIT, hypothesizing that i) higher bowel FDG-PET uptake, a surrogate biomarker for inflammation, distinguishes healthy bowel from inflamed SSc-GIT; ii) MRI T1-MOLLI mapping, a surrogate biomarker for cardiac fibrosis, distinguishes healthy bowel from fibrotic SSc-GIT.Methods: In this prospective study, 16 SSc patients and 15 healthy controls were recruited. All SSc patients and 5 controls underwent PET-MRI (with T1-MOLLI mapping) on a Siemens 3T mMR; 10 controls underwent MRI without PET. Manual segmentation of the large and small bowels was performed jointly by two trained analysts in order to report T1 and PET values. Control dataset was used to assess normal healthy range. Mean T1 values, mean Tissue-to-Background (TBR) PET values, as well as amount of supposedly abnormal bowel (measured using the healthy ranges) was compared using Student's t-test and Cohen's d effect size.Results: Mean T1 values in large (1113 ± 182 ms vs 856 ± 176 ms; p-value < 0.001) and small bowel (1331 ± 239 ms vs 1169 ± 118 ms; p = 0.02) were higher in SSc patients than controls. 87.5% of the SSc patients' bowel had at least a grade 3 segmental FDG-PET uptake, while no controls showed more than a grade 2 segmental uptake. Patients had higher large bowel mean PET TBR (1.12 ± 0.22) than controls (0.82 ± 0.20, p = 0.02). Using PET and T1 thresholds defined using the control PET-MR data, the percentage of supposedly healthy (non-fibrotic and non-inflamed) tissue was significantly lower in SSc patients (81.1 ± 13.1%) than controls (95.7 ± 3.1%, p = 0.03) for the large bowel.Conclusion: Our novel study of FDG-PET-MRI in SSc-GIT demonstrated promising results in non-invasively evaluating concurrently bowel inflammation and fibrosis. [ABSTRACT FROM AUTHOR]

Published
2018
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38. Contributors

Author

Adam, Clayton, Ambrosi, Davide, Anderson, Peter, Avril, Stéphane, Babarenda Gamage, Thiranja P., Badel, Pierre, Bellini, Chiara, Blemker, Silvia S., Boubaker, Mohamed Bader, Bovendeerd, Peter H.M., Bucki, Marek, Calvo, Begonã, Cannard, Francis, Chabanas, Matthieu, Chagnon, Grégory, Chatelin, Simon, Courtecuisse, Hadrien, Curt, Nicolas, Cyron, Christian J., Delhaas, Tammo, Delingette, Hervé, Di Martino, Elena S., Dumas, Raphaël, Dupuis, Olivier, Favier, Denis, Fels, Sidney, Fereidoonnezhad, Behrooz, Finet, Gérard, Flynn, Cormac, Frauziols, Fanny, Ganghoffer, Jean-François, Garcia, Alberto, Gasser, Thomas C., Gharib, Ahmed M., Glass, Paul, Gregersen, Hans, Harandi, Negar M., Hermant, Nicolas, Hernández-Gascón, Belén, Ho, Andrew, Holzapfel, Gerhard A., Humphrey, Jay D., Lafon, Yoann, Laporte, Sébastien, Liao, Donghua, Luboz, Vincent, Malvè, Mauro, Marchesseau, Stéphanie, Martiel, Jean-Louis, Moisik, Scott, Molimard, Jérôme, Morin, Fanny, Nash, Martyn P., Navarro, Laurent, Nazari, Mohammad Ali, Nielsen, Poul M.F., Ohayon, Jacques, Oomens, Cees W.J., Payan, Yohan, Peña, Estefanía, Perrier, Pascal, Perrier, Antoine, Peters, Gerrit W.M., Pettigrew, Roderic I., Pioletti, Dominique P., Pluijmert, Marieke, Rakotomanana, Lalao, Rohan, Pierre-Yves, Rouch, Philippe, Sánchez, C. Antonio, Skalli, Wafa, Stavness, Ian, Stelletta, Julien, Tang, Keyi, Van den Abbeele, Maxim, Vijven, Marc van, Vogel, Arne, Vuillerme, Nicolas, Wilson, John S., Yazdani, Saami K., Zara, Florence, and Zhao, Jingbo

Published
2017
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40. 18 F-FDG PET-MRI with T1 MOLLI mapping to detect systemic sclerosis bowel inflammation and fibrosis.

Author

Marchesseau S, Ng SA, Wang YT, Xie W, Ng DC, Totman JJ, and Low AHL

Subjects
Computer Simulation, Cross-Over Studies, Female, Fibrosis pathology, Humans, Image Processing, Computer-Assisted, Inflammation pathology, Intestinal Diseases pathology, Male, Middle Aged, Pilot Projects, Reproducibility of Results, Scleroderma, Systemic pathology, Fibrosis diagnostic imaging, Fluorodeoxyglucose F18, Inflammation diagnostic imaging, Intestinal Diseases diagnostic imaging, Magnetic Resonance Imaging, Positron-Emission Tomography methods, Radiopharmaceuticals, Scleroderma, Systemic diagnostic imaging
Abstract

Background: Systemic sclerosis-associated gastrointestinal tract involvement (SSc-GIT) is an independent predictor of 2-year mortality in early SSc. Availability of non-invasive investigations will facilitate early diagnosis and monitoring., Hypothesis: We investigate the role of 18 F-FDG-PET-MRI in SSc-GIT, hypothesizing that i) higher bowel FDG-PET uptake, a surrogate biomarker for inflammation, distinguishes healthy bowel from inflamed SSc-GIT; ii) MRI T1-MOLLI mapping, a surrogate biomarker for cardiac fibrosis, distinguishes healthy bowel from fibrotic SSc-GIT., Methods: In this prospective study, 16 SSc patients and 15 healthy controls were recruited. All SSc patients and 5 controls underwent PET-MRI (with T1-MOLLI mapping) on a Siemens 3T mMR; 10 controls underwent MRI without PET. Manual segmentation of the large and small bowels was performed jointly by two trained analysts in order to report T1 and PET values. Control dataset was used to assess normal healthy range. Mean T1 values, mean Tissue-to-Background (TBR) PET values, as well as amount of supposedly abnormal bowel (measured using the healthy ranges) was compared using Student's t-test and Cohen's d effect size., Results: Mean T1 values in large (1113 ± 182 ms vs 856 ± 176 ms; p-value < 0.001) and small bowel (1331 ± 239 ms vs 1169 ± 118 ms; p = 0.02) were higher in SSc patients than controls. 87.5% of the SSc patients' bowel had at least a grade 3 segmental FDG-PET uptake, while no controls showed more than a grade 2 segmental uptake. Patients had higher large bowel mean PET TBR (1.12 ± 0.22) than controls (0.82 ± 0.20, p = 0.02). Using PET and T1 thresholds defined using the control PET-MR data, the percentage of supposedly healthy (non-fibrotic and non-inflamed) tissue was significantly lower in SSc patients (81.1 ± 13.1%) than controls (95.7 ± 3.1%, p = 0.03) for the large bowel., Conclusion: Our novel study of FDG-PET-MRI in SSc-GIT demonstrated promising results in non-invasively evaluating concurrently bowel inflammation and fibrosis., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Published
2018
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