EIBIR 2 - EIBIR Stage bonus session 2

Author Block: Y. Wang, Y. Xiao; Shanghai/CN
Purpose: This prospective study evaluated the feasibility of chemical exchange saturation transfer (CEST) MRI for assessing disease activity in thyroid-associated ophthalmopathy (TAO).
Methods or Background: A total of 88 patients with active TAO, 76 with inactive TAO, and 30 healthy controls were enrolled. CEST MRI-derived MTR and MTRasym at 1ppm, 2ppm, and 3.5ppm were calculated. Clinical data, MTR and MTRasym of extraocular muscles were compared across groups using the Wilcoxon signed rank test. Spearman's correlations were used to examine the associations between imaging parameters and the CAS. Logistic regression analysis was carried out to identify independent predictors, and ROC analysis (DeLong's test) was used to evaluate diagnostic performance for active TAO.
Results or Findings: Patients with active TAO showed lower MTR (P<0.001) and higher MTRasym_1ppm, MTRasym_2ppmand MTRasym_3.5ppm ( all P<0.001). MTR was negatively correlated with CAS (R=-0.402; P<0.001), while MTRasym_1ppm, MTRasym_2ppm and MTRasym_3.5ppm (R=0.369; R=0.350; R=0.349; all P<0.001) were positively correlated. Both MTR and MTRasym_1ppm were independent predictors of TAO activity. The AUC for MTR and MTRasym_1ppm in discriminating active from inactive TAO were 0.772 and 0.730, respectively. Combining MTR with MTRasym_1ppm significantly improved diagnostic performance, achieving an AUC of 0.805 ( P<0.05).
Conclusion: Our results showed MTR and MTRasym_1ppm independently distinguished active from inactive TAO. Their combination further enhanced diagnostic accuracy. These findings suggested that MTR and MTRasym_1ppm could serve as quantitative imaging biomarkers to guide treatments in patients with TAO.
Limitations: Firstly, it is a prospective single-center study. Secondly, the complex structure of the orbit, and the high risk associated with invasive surgery precluded the collection of histopathological samples, preventing direct confirmation of the relationship between GAGs/collagen content and MTRasym_1ppm of CEST in TAO patients
Funding for this study: This study has received funding from the National Natural Science Foundation of China [No. 82271994]; the Military Commission health care special project [NO. 22BJZ07]; the Shanghai Hospital Development Center [NO. SHDC22025311-A]; The Eastern Elite Program Leading Project of Shanghai Municipality [NO.LJ2023094] Navy Medical University teaching achievement cultivation project [No. JPY2022B15]; Shanghai Changzheng Hospital teaching achievement cultivation project [No. JXPY2021B10];
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: No.82170858

Author Block: A. Holden¹, R. Bradley¹, K. Rhode²; ¹Oxford/UK, ²London/UK
Purpose: This project aims to establish a standardised testing procedure for the emerging technology of contrast-to-noise driven exposure controls (CEC) as seen on the Siemens ARTIS Icono. Additionally, the project will compare the performance of ARTIS Icono with that of its predecessor, namely the Siemens ARTIS Zee.
Methods or Background: The Siemens ARTIS Icono is an advanced fluoroscopy system used in cardiac catheterisation laboratories, where high-quality imaging is essential for precise interventions, while ensuring patient dose is kept as low as reasonably practicable. Unlike conventional dose-driven automatic exposure control (DEC) systems that maintain a constant detector dose, the ARTIS Icono employs OPTIQ©, a CEC where the system selects imaging parameters based on a predefined target image quality. Additionally, Structure Scout enables material-specific optimisation.
Results or Findings: A phantom has been designed to test the systems capability, containing a range of reference materials, including iron, platinum, and iodine. Using this phantom we can establish if the system maintains the target CNR with varying parameters. Additionally, a figure of merit has been established to compare a metric of visibility to one of dose. By conducting measurements on both CEC and DEC systems we will be able to assess the success of CEC. Preliminary results suggest an increase in image quality at comparable doses.
Conclusion: CEC presents an opportunity to improve image quality in fluoroscopic procedures while minimising patient dose. This could lead to a major breakthrough in optimising patient dose. However, there is currently no established method available for conducting quality assurance on the system. This project demonstrates a successful possible testing method for sites using the ARTIS Icono
Limitations: Static phantom study. No measurements of effect of movement.
Funding for this study: King's College London, School of Biomedical Engineering and Imaging Sciences
Has your study been approved by an ethics committee? Not applicable
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Author Block: F. Orzan¹, L. Dioșan², Z. Bálint¹; ¹Cluj Napoca/RO, ²Cluj-Napoca/RO
Purpose: We aimed to build an AI-based decision support system for automatic segmentation and characterization of MS lesions from 3D MRI, with the long-term goal of personalized treatment. This study focuses on preprocessing and segmentation, providing the basis for future characterization and clinical integration.
Methods or Background: Our study evaluated a 3D U-Net segmentation model with four encoding-decoding levels, integrating attention gates and skip connections to improve lesion localization. The architecture employs BatchNorm3d, ReLU activations, and Dropout for regularization. Evaluation was conducted on 40 cases from the MICCAI 2021 dataset. Preprocessing included intensity normalization, isotropic resampling, N4 bias field correction, skull stripping, and rigid registration. Training used Adam optimization with BCE loss over 50 epochs, with early stopping, 5-fold cross-validation, and an 80/20 train-validation split.
Results or Findings: This study focuses exclusively on image preprocessing, model development, and lesion segmentation. Textural analysis, lesion classification, and the development of a user interface for fine-tuning will be addressed in future work.
The model showed a steady decrease in both training and validation loss, with stabilization after epoch 40. The close alignment between losses suggests minimal overfitting and good generalization - an essential property for clinical applications. The Dice score increased consistently, reaching 0.78 on training and 0.72 on validation data, confirming the model’s ability to accurately segment MS lesions across diverse samples.
Conclusion: Our U-Net model with attention gates and skip connections shows promising performance for automated MS detection on 3D MRI. Future work will add textural analysis, lesion classification, user interface development, and validation on multi-vendor datasets for robustness and scalability.
Limitations: A key limitation of MS detection algorithms, including our study, is the scarcity of large imaging datasets, which limits performance relative to human experts.
Funding for this study: Not applicable
Has your study been approved by an ethics committee? Not applicable
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Author Block: C. Verde, L. Tarotto, S. Stilo, F. Fiore; Naples/IT
Purpose: Our purpose is to describe clinical use of Artificial Intelligence (AI) in the current and future practice of Interventional Radiology (IR).
Methods or Background: AI has demonstrated great potential in a wide variety of applications in IR.
Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging have all been investigated.
Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy.
The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. The full range of possibilities is yet to be explored.
A systematic review of the bibliography from the earliest possible date through March 2024 was performed.
Results or Findings: Perspectives in AI differ and are more complex for IR than for diagnostic radiology because IR encompasses diagnostic imaging, imaging guidance, and early imaging evaluation as well as therapeutic tools.
The integration of artificial intelligence in IR is an emerging field with transformative potential, aiming to make a great contribution to the health domain.
Based on the overview, the integration of AI in IR presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion.
Conclusion: The integration of these technologies makes interventional radiology more personalized, patient-centered and safe.
Limitations: No limitations were identified in this study.
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Not applicable
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