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PD Dr. phil. Francesco Santini

Department of Biomedical Engineering
Profiles & Affiliations

Research Interests

  • MR Method development
  • Quantitative Muscle MRI
  • Open and Reproducible Science

Selected Publications

Francesco Santini, Jakob Wasserthal, Abramo Agosti, Xeni Deligianni, Kevin R. Keene, Hermien E. Kan, Stefan Sommer, Christoph Stuprich, Fengdan Wang, Claudia Weidensteiner, Giulia Manco, Matteo Paoletti, Valentina Mazzoli, Arjun Desai, & Anna Pichiecchio. (2023). Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation. https://doi.org/10.48550/arxiv.2302.06352

URLs
URLs

Deligianni X, Santini F, Paoletti M, Solazzo F, Bergsland N, Savini G, Faggioli A, Germani G, Monforte M, Ricci E, Tasca G, & Pichiecchio A. (2022). Dynamic magnetic resonance imaging of muscle contraction in facioscapulohumeral muscular dystrophy. Scientific Reports, 12(1), 7250. https://doi.org/10.1038/s41598-022-11147-2

URLs
URLs

Santini F, Deligianni X, Paoletti M., Solazzo F, Weigel M, de Sousa P.L., Bieri O., Monforte M, Ricci E., Tasca G., Pichiecchio A., & Bergsland N. (2021). Fast Open-Source Toolkit for Water T2 Mapping in the Presence of Fat From Multi-Echo Spin-Echo Acquisitions for Muscle MRI. Frontiers in Neurology, 12. https://doi.org/10.3389/fneur.2021.630387

URLs
URLs

Deligianni X, Pansini M, Garcia M, Hirschmann A, Schmidt-Trucksäss A, Bieri O, & Santini F. (2017). Synchronous MRI of muscle motion induced by electrical stimulation. Magnetic Resonance in Medicine, 77(2), 664–672. https://doi.org/10.1002/mrm.26154

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URLs

Selected Projects & Collaborations

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Self-Improving Collaborative Segmentation Platform for Magnetic Resonance Images

Research Project  | 1 Project Members

Muscular dystrophies and in general neuromuscular diseases are serious illnesses that mostly affect children at a young age, often with fatal outcomes. The development of effective therapies has also been challenging because of the lack of objective biomarkers for the evaluation of the natural course of the disease and the efficacy of the therapy. While MR imaging is a good candidate to provide such biomarkers, an accurate segmentation of the single muscle groups is an important step for the correct image analysis and evaluation.The segmentation of medical images is a task that has been optimally tackled by deep learning methods in the recent years. However, the accuracy of a deep learning model heavily depends on the type, the quantity and the quality of the data used for the training, and the relative rarity of these diseases often prevents a single site or clinic from having sufficient data for the task, and often from even having the appropriate software tools for an accurate and effective segmentation.In this project, I propose to develop a collaborative segmentation platform that would be accessible to users around the globe, who can use it as a tool to segment their own images, and by doing so, they would contribute to the improvement of the accuracy of the platform itself. Thanks to the principles of federated and incremental learning, the users would not need to share their data, thus preserving privacy and anonymity. The platform will be developed in a modular way, in order to be extensible to multiple clinical questions and input image modalities and contrasts.

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Development and Clinical Validation of Magnetic Resonance Methods for the Functional Imaging and Spectroscopy of Skeletal Muscles by Means of Synchronized Electrical Muscle Stimulation

Research Project  | 2 Project Members

Assessing the functionality of skeletal muscle fibers is essential in the progress monitoring of both pathological (neuro- and musculodegenerative) and physiological processes (training and rehabilitation). While morphological information is mostly important in the longitudinal follow-up of a single patient, it is not an absolute marker of organ health. In order to obtain an absolute indication of muscle status, it is necessary to follow a functional approach, which would monitor the ability of the muscle to contract and to gather supplies of nutrients and oxygen from the blood. This approach can follow two paths: the first is the studying of the muscular kinematics using gated or real-time sequences during muscle movement; the second is metabolic assessment, which can either be measured directly (through 31P spectroscopy of muscle metabolites) or indirectly (for example by T2- or T2*-weighted imaging).In the last two years, our group has been developing a novel approach to acquire high-temporal-resolution images of the contraction of the skeletal muscle by inducing a reproducible movement by means of electrical muscle stimulation (EMS). With this method, contraction speed, strain, and strain rate were measured in a consistent and controlled way.This approach revealed itself promising for the purpose of observing the functionality of the muscles. However, so far it has been only developed for the study of kinematics, thus missing the metabolic information, and while further studies are ongoing, a clinical validation of the method is still needed. In addition, quantitative evaluation of the safety of the hardware setup connected to the subject has so far limited the acquisition to low-power sequences.Ideally, one would extend this approach to a more general "toolbox" consisting of methods that can give a broader view on the muscle functionality, coupling contractility and metabolism in the same kind of setup.In the first part of this project, we propose to continue the development of the synchronized imaging method, also by objectively evaluating its safety limits, and to extend it to spectroscopy, for which our group has access to the appropriate hardware, exploiting the same characteristics of repeatability and controllability of the electrical muscle stimulation. The velocity imaging sequence will be optimized by including the modern concept of simultaneous multislice imaging, in order to obtain volumetric coverage in reasonable scan time, and relaxometry methods currently used for the heart will be adapted to work in the muscle, in order to monitor changes in T1 and T2 during exercise.Once these steps are completed, we will focus on the development of interleaved imaging/spectroscopy methods, a concept which has been recently proposed, which would fuse the two main instruments of muscle functional imaging into a single acquisition.The second part of the project will be dedicated to the validation of the methods in a clinical setting on two different subject populations: on the one side, they will be used to objectively monitor the training of athletes (in collaboration with the Department of Sport, Exercise and Health (DSBG), Division Sports and Exercise Medicine of the University of Basel) and on the other side, they will be applied to the diagnosis and to the severity assessment of patients suffering from Becker muscular dystrophy (in collaboration with the Department of Neurology of the University Children's Hospital of Basel).In addition to the immediate scientific output, the outcome of this project will be a set of imaging and spectroscopy tools to be used in a clinical setting for the evaluation of the condition of muscles in different contexts, both pathological and physiological. The attractiveness of this setup is its ease of deployment and its low cost, which would make it implementable in a large number of clinics and hospitals equipped with a standard clinical MRI system.