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Littérature scientifique sur le sujet « Inter-modal Registration »

Auteur : Grafiati
Publié le 1 mars 2025

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Articles de revues sur le sujet "Inter-modal Registration"

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Wang, Chengjia, Guang Yang et Giorgos Papanastasiou. « Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis ». Sensors 22, no 6 (9 mars 2022) : 2125. http://dx.doi.org/10.3390/s22062125.

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Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an unsupervised deep learning registration methodology that can accurately model affine and non-rigid transformations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent representations, and involves two factorised transformation networks (one per each encoder-decoder channel) and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named “FIRE”) shows improved performances against the reference standard baseline method (i.e., Symmetric Normalization implemented using the ANTs toolbox) on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments. We focus on explaining model-data components to enhance model explainability in medical image registration. On computational time experiments, we show that the FIRE model performs on a memory-saving mode, as it can inherently learn topology-preserving image registration directly in the training phase. We therefore demonstrate an efficient and versatile registration technique that can have merit in multi-modal image registrations in the clinical setting.
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Copson, Bridget, Sudanthi Wijewickrema, Christopher Slinger, Daniel Youssef, Jean-Marc Gerard et Stephen O’Leary. « Definition of a coordinate system for multi-modal images of the temporal bone and inner ear ». PLOS ONE 19, no 10 (7 octobre 2024) : e0294828. http://dx.doi.org/10.1371/journal.pone.0294828.

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Purpose The position and orientation of the head is maintained to be relatively similar during the CT / MR imaging process. However, the position / orientation dissimilarities present in the resulting images between patients, or between different scans of the same patient, do not allow for direct comparison of the images themselves or features / metrics extracted from them. This paper introduces a method of defining a coordinate system which is consistent between patients and modalities (CT and MR) for images of the temporal bone, using easily identifiable landmarks within the semicircular canals. Methods Cone Beam CT and high resolution MRI (T2) images of the temporal bone from 20 patients with no cochlear or temporal bone pathology in either modality were obtained. Four landmarks within the semicircular canals were defined that can be identified in both modalities. A coordinate system was defined using these landmarks. Reproducibility of landmark selection was assessed using intra- and inter-rater reliability (for three expert raters and two repeats of the landmark selection). Accuracy of the coordinate system was determined by comparing the coordinates of two additional landmarks in CT and MR images after their conversion to the proposed coordinate system. Results Intraclass Correlation Coefficients at a 95% level of confidence showed significant agreement within and between raters as well as between modalities. The differences between selections, raters, and modalities (as measured using mean, standard deviation, and maximum) were low and acceptable for clinical applications. Conclusion The proposed coordinate system is suited for use in images of the temporal bone and inner ear. Its multi-modal nature enables the coordinate system to be used in tasks such as image co-registration.
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West, Malcolm, Andrew Bates, Chloe Grimmett, Cait Allen, Richard Green, Lesley Hawkins, Helen Moyses et al. « The Wessex Fit-4-Cancer Surgery Trial (WesFit) : a protocol for a factorial-design, pragmatic randomised-controlled trial investigating the effects of a multi-modal prehabilitation programme in patients undergoing elective major intra–cavity cancer surgery ». F1000Research 10 (2 août 2022) : 952. http://dx.doi.org/10.12688/f1000research.55324.2.

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Background: Surgical resection remains the primary curative treatment for intra-cavity cancer. Low physical fitness and psychological factors such as depression are predictive of post–operative morbidity, mortality and length of hospital stay. Prolonged post-operative morbidity is associated with persistently elevated risk of premature death. We aim to investigate whether a structured, responsive exercise training programme, a psychological support programme or combined exercise and psychological support, delivered between treatment decision and major intra-cavity surgery for cancer, can reduce length of hospital stay, compared with standard care. Methods: WesFit is a pragmatic, 2x2 factorial-design, multi-centre, randomised-controlled trial, with planned recruitment of N=1560. Participants will be randomised to one of four groups. Group 1 (control) will receive usual pre-operative care, Group 2 (exercise) patients will undergo 2/3 aerobic, high-intensity interval training sessions per week supervised by personal trainers. Group 3 (psychological support) patients are offered 1 session per week at a local cancer support centre. Group 4 will receive both exercise and psychological support. All patients undergo baseline and pre-operative cardiopulmonary exercise testing, complete self-report questionnaires and will be followed up at 30 days, 12 weeks and 12 months post-operatively. Primary outcome is post-operative length-of-stay. Secondary outcomes include disability-adjusted survival at 1-year postoperatively, post-operative morbidity, and health-related quality of life. Exploratory investigations include objectively measured changes in physical fitness assessed by cardiopulmonary exercise test, disease-free and overall mortality at 1-year postoperatively, longer-term physical activity behaviour change, pre-operative radiological tumour regression, pathological tumour regression, pre and post-operative body composition analysis, health economics analysis and nutritional characterisation and its relationship to post-operative outcome. Conclusions: The WesFit trial will be a randomised controlled study investigating whether a high-intensity exercise training programme +/- psychological intervention results in improvements in clinical and patient reported outcomes in patients undergoing major inter-cavity resection of cancer. ClinicalTrials.gov registration: NCT03509428 (26/04/2018)
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West, Malcolm, Andrew Bates, Chloe Grimmett, Cait Allen, Richard Green, Lesley Hawkins, Helen Moyses et al. « The Wessex Fit-4-Cancer Surgery Trial (WesFit) : a protocol for a factorial-design, pragmatic randomised-controlled trial investigating the effects of a multi-modal prehabilitation programme in patients undergoing elective major intra–cavity cancer surgery ». F1000Research 10 (21 septembre 2021) : 952. http://dx.doi.org/10.12688/f1000research.55324.1.

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Background: Surgical resection remains the primary curative treatment for intra-cavity cancer. Low physical fitness and psychological factors such as depression are predictive of post–operative morbidity, mortality and length of hospital stay. Prolonged post-operative morbidity is associated with persistently elevated risk of premature death. We aim to investigate whether a structured, responsive exercise training programme, a psychological support programme or combined exercise and psychological support, delivered between treatment decision and major intra-cavity surgery for cancer, can reduce length of hospital stay, compared with standard care. Methods: WesFit is a pragmatic, 2x2 factorial-design, multi-centre, randomised-controlled trial, with planned recruitment of N=1560. Participants will be randomised to one of four groups. Group 1 (control) will receive usual pre-operative care, Group 2 (exercise) patients will undergo 2/3 aerobic, high-intensity interval training sessions per week supervised by personal trainers. Group 3 (psychological support) patients are offered 1 session per week at a local cancer support centre. Group 4 will receive both exercise and psychological support. All patients undergo baseline and pre-operative cardiopulmonary exercise testing, complete self-report questionnaires and will be followed up at 30 days, 12 weeks and 12 months post-operatively. Primary outcome is post-operative length-of-stay. Secondary outcomes include disability-adjusted survival at 1-year postoperatively, post-operative morbidity, and health-related quality of life. Exploratory investigations include objectively measured changes in physical fitness assessed by cardiopulmonary exercise test, disease-free and overall mortality at 1-year postoperatively, longer-term physical activity behaviour change, pre-operative radiological tumour regression, pathological tumour regression, pre and post-operative body composition analysis, health economics analysis and nutritional characterisation and its relationship to post-operative outcome. Conclusions: The WesFit trial will be the first randomised controlled study investigating whether an exercise training programme +/- psychological intervention results in improvements in clinical and patient reported outcomes in patients undergoing major inter-cavity resection of cancer. ClinicalTrials.gov registration: NCT03509428 (26/04/2018)
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Masoumi, Nima, Hassan Rivaz, M. Omair Ahmad et Yiming Xiao. « DiffeoRaptor : diffeomorphic inter-modal image registration using RaPTOR ». International Journal of Computer Assisted Radiology and Surgery, 29 septembre 2022. http://dx.doi.org/10.1007/s11548-022-02749-2.

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Iommi, David, Alejandra Valladares, Michael Figl, Marko Grahovac, Gabor Fichtinger et Johann Hummel. « 3D ultrasound guided navigation system with hybrid image fusion ». Scientific Reports 11, no 1 (23 avril 2021). http://dx.doi.org/10.1038/s41598-021-86848-1.

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AbstractA prototype of a navigation system to fuse two image modalities is presented. The standard inter-modality registration is replaced with a tracker-based image registration of calibrated imaging devices. Intra-procedure transrectal US (TRUS) images were merged with pre-procedure magnetic resonance (MR) images for prostate biopsy. The registration between MR and TRUS images was performed by an additional abdominal 3D-US (ab-3D-US), which enables replacing the inter-modal MR/TRUS registration by an intra-modal ab-3D-US/3D-TRUS registration. Calibration procedures were carried out using an optical tracking system (OTS) for the pre-procedure image fusion of the ab-3D-US with the MR. Inter-modal ab-3D-US/MR image fusion was evaluated using a multi-cone phantom for the target registration error (TRE) and a prostate phantom for the Dice score and the Hausdorff distance of lesions . Finally, the pre-procedure ab- 3D-US was registered with the TRUS images and the errors for the transformation from the MR to the TRUS were determined. The TRE of the ab-3D-US/MR image registration was 1.81 mm. The Dice-score and the Hausdorff distance for ab-3D-US and MR were found to be 0.67 and 3.19 mm. The Dice score and the Hausdorff distance for TRUS and MR were 0.67 and 3.18 mm. The hybrid navigation system showed sufficient accuracy for fusion guided biopsy procedures with prostate phantoms. The system might provide intra-procedure fusion for most US-guided biopsy and ablation interventions.
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Hu, Xin, Yan Wu, Xingyu Liu, Zhikang Li, Zhifei Yang et Ming Li. « Intra- and Inter-Modal Graph Attention Network and Contrastive Learning for SAR and Optical Image Registration ». IEEE Transactions on Geoscience and Remote Sensing, 2023, 1. http://dx.doi.org/10.1109/tgrs.2023.3328368.

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Alley, Stephanie, Edward Jackson, Damien Olivié, Uulke A. van der Heide, Cynthia Ménard et Samuel Kadoury. « Effect of magnetic resonance imaging pre-processing on the performance of model-based prostate tumor probability mapping ». Physics in Medicine & ; Biology, 12 octobre 2022. http://dx.doi.org/10.1088/1361-6560/ac99b4.

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Abstract Objective: Multi-parametric magnetic resonance imaging (mpMRI) has become an important tool for the detection of clinically significant prostate cancer in the past two decades. Despite the high sensitivity of MRI for tissue characterization, it often suffers from a lack of specificity. Several well-established pre-processing tools are publicly available for improving image quality and removing both intra- and inter-patient variability in order to increase the diagnostic accuracy of prostate cancer. To date, most of these pre-processing tools have largely been assessed individually. In this study we present a systematic evaluation of a multi-step mpMRI pre-processing pipeline to automate tumor localization within the prostate using a previously trained model. Approach: The study was conducted on 31 treatment-naïve prostate cancer patients with a PI-RADS-compliant mpMRI examination. Multiple methods were compared for each pre-processing step: (1) bias field correction, (2) normalization, and (3) deformable multi-modal registration. Optimal parameter values were estimated for each step on the basis of relevant individual metrics. Tumor localization was then carried out via a model-based approach that takes both mpMRI and prior clinical knowledge features as input. A sequential optimization approach was adopted for determining the optimal parameters and techniques in each step of the pipeline. Main results: The application of bias field correction alone increased the accuracy of tumor localization (area under the curve (AUC)=0.77) over unprocessed data (AUC=0.74). Adding normalization to the pre-processing pipeline further improved diagnostic accuracy of the model to an AUC of 0.85. Multi-modal registration of ADC images to T2-weighted images improved the alignment of tumor locations in all but one patient, resulting in a slight decrease in accuracy (AUC=0.84). Significance: Overall, our findings suggest that the combined effect of multiple pre-processing steps with optimal values has the ability to improve the quantitative classification of prostate cancer using mpMRI. Clinical trials: NCT03378856 and NCT033677025
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Thèses sur le sujet "Inter-modal Registration"

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Ndzimbong, William Brice. « Recalage automatique des images echographiques tridimensionnelles et tomodensitométriques du rein ». Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD047.

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Le recalage automatique des images échographiques abdominales et des images tomodensitométriques (scanner) est essentiel pour améliorer le guidage interventionnel en chirurgie rénale. Toutefois, il représente encore un défi de recherche majeur. Une limitation importante est le manque de jeu de données publiques contenant des images des deux modalités pour un même patient (jeu de données appariées). Cette absence freine les avancées méthodologiques et empêche une comparaison systématique des méthodes de pointe. Une autre limitation importante est qu’il existe peu de méthodes robustes capables de réaliser un recalage sans intervention manuelle, notamment en ce qui concerne les méthodes dites « globales ». Cette thèse vise à surmonter ces obstacles par plusieurs contributions de recherche. La première est la création d’un nouveau jeu de données, composé d’images échographiques tridimensionnelles transabdominales et scanner appariées, provenant de reins de 48 patients humains. Ce jeu de données inclut des annotations de segmentation et de points de repère anatomiques, validées par deux radiographes expérimentés. En plus de fournir ces données, la concordance des annotations est analysée, et la valeur du jeu de données est démontrée en évaluant des méthodes qui traitent deux tâches fondamentales : la segmentation automatique des reins et le recalage d’images intermodales. Les résultats montrent que ces deux défis restent ouverts, et ce jeu de données constitue une ressource importante pour faire progresser ces domaines. La seconde contribution principale est une méthode automatique de recalage global pour les images échographiques tridimensionnelles et scanner des reins. Cette méthode surmonte l’ambiguïté de recalage due à la symétrie naturelle de l’organe et, combinée à un algorithme d’affinement, permet un recalage précis, robuste et sans initialisation manuelle. Cette méthode a également d’autres applications importantes, telles que la conversion inter-modale d’images, la synthèse d’images, et le transfert d’annotations entre modalités
Automatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance in kidney surgery. However, it remains an open research challenge. One striking limitation is the lack of public datasets that comprise images of the same patient in both modalities (paired datasets). This has hindered methodological progress, as well as prevented a systematic comparison of state-of-the-art methods. Another limitation is the lack of robust methods capable of solving registration without manual initialization (’global’ methods). This thesis aims to overcome these challenges with several research contributions. The first contribution is a novel dataset with paired transabdominal 3D US and CT kidney images from 48 human patients that includes segmentation and anatomical landmark annotations from two expert radiographers. In addition to the dataset, annotation consistency is analyzed, and its value assessed by benchmarking methods that tackle two fundamental tasks : automatic kidney segmentation and inter-modal image registration. The findings show that both challenges are still open, and the dataset should serve as an important resource for advancing both topics. As a second main contribution, an automatic method for global registration of kidneys in 3D US and CT images is proposed. This method handles registration ambiguity caused by the organ’s natural symmetry. Combined with a registration refinement algorithm, it achieves robust and accurate kidney registration while avoiding manual initialization. The method has several other important applications, including inter-modal image translation and image synthesis, as well as label transfer between modalities
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Chapitres de livres sur le sujet "Inter-modal Registration"

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Salari, Soorena, Amirhossein Rasoulian, Hassan Rivaz et Yiming Xiao. « FocalErrorNet : Uncertainty-Aware Focal Modulation Network for Inter-modal Registration Error Estimation in Ultrasound-Guided Neurosurgery ». Dans Lecture Notes in Computer Science, 689–98. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43996-4_66.

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Actes de conférences sur le sujet "Inter-modal Registration"

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Li, Xia, Thomas E. Yankeelov, Glenn Rosen, John C. Gore et Benoit M. Dawant. « Multi-modal inter-subject registration of mouse brain images ». Dans Medical Imaging, sous la direction de Joseph M. Reinhardt et Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.652407.

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Chappelow, Jonathan, Anant Madabhushi, Mark Rosen, John Tomaszeweski et Michael Feldman. « A COMBINED FEATURE ENSEMBLE BASED MUTUAL INFORMATION SCHEME FOR ROBUST INTER-MODAL, INTER-PROTOCOL IMAGE REGISTRATION ». Dans 2007 4th IEEE International Symposium on Biomedical Imaging : From Nano to Macro. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.356934.

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Bashkanov, Oleksii, Anneke Meyer, Daniel Schindele, Martin Schostak, Klaus-Dietz Tonnies, Christian Hansen et Marko Rak. « Learning Multi-Modal Volumetric Prostate Registration With Weak Inter-Subject Spatial Correspondence ». Dans 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433848.

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Papp, L., N. Zsoter, G. Szabo, C. Bejan, E. Szimjanovszki et M. Zuhayra. « Parallel registration of multi-modal medical image triples having unknown inter-image geometry ». Dans 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5335168.

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