469 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of BernCH73 papers
- University of ZurichCH14 papers
- University of FribourgCH10 papers
- InsermFR8 papers
- King's College LondonGB8 papers
- University of Electronic Science and Technology of ChinaCN8 papers
- ETH ZurichCH7 papers
- German Cancer Research CenterDE7 papers
- Heidelberg UniversityDE7 papers
- Imperial College LondonGB7 papers
- Technical University of MunichDE7 papers
- University Hospital HeidelbergDE7 papers
90 papers
VQ-Wave: A physics-driven spatio-temporal deep learning approach for non-contrast-enhanced lung ventilation and perfusion MRI
Grzegorz Bauman, Pavlos Panos, Philipp Latzin +1
Purpose: To develop a robust deep learning framework for non-contrast-enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiolo…
ORGAN: Object-Centric Representation Learning using Cycle Consistent Generative Adversarial Networks
Joël Küchler, Ellen van Maren, Vaiva Vasiliauskaitė +3
Although data generation is often straightforward, extracting information from data is more difficult. Object-centric representation learning can extract information from images in…
A Composable Channel-Adaptive Architecture for Seizure Classification
Francesco Carzaniga, Michael Hersche, Kaspar Schindler +1
Objective: We develop a channel-adaptive (CA) architecture that seamlessly processes multi-variate time-series with an arbitrary number of channels, and in particular intracranial…
First Positronium Lifetime Imaging using Mn and Co with a plastic-based PET scanner
Manish Das, Sushil Sharma, Ermias Yitayew Beyene +45
Positronium Lifetime Imaging (PLI) extends positron emission tomography by using the lifetime of positronium atoms as a probe of tissue molecular architecture. In this work, we rep…
A Brief History of Digital Twin Technology
Yunqi Zhang, Kuangyu Shi, Biao Li
Emerging from NASA's spacecraft simulations in the 1960s, digital twin technology has advanced through industrial adoption to spark a healthcare transformation. A digital twin is a…
Mathematical and Computational Nuclear Oncology: Toward Optimized Radiopharmaceutical Therapy via Digital Twins
Marc Ryhiner, Yangmeihui Song, Babak Saboury +3
This article presents the general framework of theranostic digital twins (TDTs) in computational nuclear medicine, designed to support clinical decision-making and improve cancer p…
Beam Energy Measurement using a Bayesian Approach with the Stacked Foil Method
Alexander Gottstein, Lorenzo Mercolli, Eva Kasanda +7
We present a practical method to measure the energy of proton beams at a medical cyclotron using the stacked foil technique in combination with a Bayesian inference method. By meas…
Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging
Fereshteh Yousefirizi, Movindu Dassanayake, Alejandro Lopez +6
MTV is increasingly recognized as an accurate estimate of disease burden, which has prognostic value, but its implementation has been hindered by the time-consuming need for manual…
Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities
Movindu Dassanayake, Alejandro Lopez, Andrew Reader +6
LAFOV PET/CT has the potential to unlock new applications such as ultra-low dose PET/CT imaging, multiplexed imaging, for biomarker development and for faster AI-driven reconstruct…
First positronium imaging using Sc with the J-PET scanner: a case study on the NEMA-Image Quality phantom
Manish Das, Sushil Sharma, Aleksander Bilewicz +41
Positronium Lifetime Imaging (PLI), an emerging extension of conventional positron emission tomography (PET) imaging, offers a novel window for probing the submolecular properties…
Subgroup Performance Analysis in Hidden Stratifications
Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann +3
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level…
Positronium Imaging: History, Current Status, and Future Perspectives
Paweł Moskal, Aleksander Bilewicz, Manish Das +11
Positronium imaging was recently proposed to image the properties of positronium atoms in the patient body. Positronium properties depend on the size of intramolecular voids and ox…
Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner
Baptiste Laurent, Alexandre Bousse, Thibaut Merlin +3
Objective: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a…
Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations
Daan Kuppens, Sebastiano Barbieri, Daisy van den Berg +4
Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learn…
Optimal switching strategies in multi-drug therapies for chronic diseases
Juan Magalang, Javier Aguilar, Jose Perico Esguerra +2
Antimicrobial resistance is a threat to public health with millions of deaths linked to drug resistant infections every year. To mitigate resistance, common strategies that are use…
Remote Analysis of Femoroacetabular Impingement by a triade of label-free optical spectroscopy techniques
Martin Hohmann, Lucas Kreiss, Faramarz Dehghani +5
This paper introduces the combination of Laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy (RS) and diffuse reflectance spectroscopy (DRS) in the field of biomedical…
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions…
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)
Amirhosein Toosi, Isaac Shiri, Habib Zaidi +1
We introduce an innovative, simple, effective segmentation-free approach for outcome prediction in head \& neck cancer (HNC) patients. By harnessing deep learning-based feature ext…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
An automated pipeline for computation and analysis of functional ventilation and perfusion lung MRI with matrix pencil decomposition: TrueLung
Orso Pusterla, Corin Willers, Robin Sandkühler +6
Purpose: To introduce and evaluate TrueLung, an automated pipeline for computation and analysis of free-breathing and contrast-agent free pulmonary functional MRI. Material and Met…
Characterization of Polarimetric Properties in Various Brain Tumor Types Using Wide-Field Imaging Mueller Polarimetry
Romane Gros, Omar Rodriguez-Nunez, Leonard Felger +8
Neuro-oncological surgery is the primary brain cancer treatment, yet it faces challenges with gliomas due to their invasiveness and the need to preserve neurological function. Henc…
Near-Real-Time Mueller Polarimetric Image Processing for Neurosurgical Intervention
Stefano Moriconi, Omar Rodriguez-Nunez, Romane Gros +7
Wide-field imaging Mueller polarimetry is a revolutionary, label-free, and non-invasive modality for computer-aided intervention: in neurosurgery it aims to provide visual feedback…
A Unified Generation-Registration Framework for Improved MR-based CT Synthesis in Proton Therapy
Xia Li, Renato Bellotti, Barbara Bachtiary +5
Background: In MR-guided proton therapy planning, aligning MR and CT images is key for MR-based CT synthesis, especially in mobile regions like the head-and-neck. Misalignments her…
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches
Alexandre Bousse, Venkata Sai Sundar Kandarpa, Kuangyu Shi +4
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the…