Season 2 of the MR motion correction virtual seminars is upon us! First episode will air Nov 17th at 16h30 UTC+1, when Niek Huttinga will discuss his work on MR-MOTUS. Check out bit.ly/moco-sg-semina… for more info🙃

Thanks to #SCMR23 for the invitation to talk about the potentials of machine learning in the CMR workflow. #whycmr MIDAS.lab

After exciting session on next gen black hole imaging, “our“ session on medical image recon and analysis - fab intro from ⁦⁦Kerstin Hammernik⁩ , great talks and posters from Andreas Hauptmann, Thomas Küstner, Danny Alexander, Ender Konukoglu, Reinhard Heckel… #BASPFrontiers

Happy to introduce Thomas Kuestner as newly appointed juniorprofessor for Data Engineering and Advanced Processing for Medical Imaging Modalities! Find out more on his research: midaslab.org #ai #imaging #research

Happy to talk today about the potential and challenges of AI in radiology at #ECR23. Join us at 14:00 in room K2 for RC 1506. MIDAS.lab

Join us at #ECR23 today (16:00, Room G2) for the summary of the autoPET whole-body PET/CT lesion segmentation challenge and a discussion for the future of AI in hybrid imaging. MIDAS.lab Universität München ESHIᴹᵀ

📢 Participate in the autoPET-II challenge hosted at #MICCAI2023 Generalization for automated PET/CT lesion segmentation: autopet-ii.grand-challenge.org 💰Compete for the prize money of 11000 €💶 MIDAS.lab ESHIᴹᵀ Universität München Uniklinikum Essen MICCAI Society

Come study #MachineLearning in Tübingen: 10 days left to apply to our International MSc Program! Tübingen boasts an amazing research environment #AI/#ML, with several world-class institutions Cyber Valley Universität Tübingen Tuebingen AI @ml4science Intelligent Systems, uni-tuebingen.de/de/140323

Join " The Chest Pain Chess Game" on Monday 8:15am♟❤ ISMRM @SMRT_ISMRM 🙋🏻‍♀️ I'll be talking about Quantitative perfusion CMR 🧲🔢 ...& check out the CMR Rodeo 🤠🐴 on Wed 2:45pm. Thomas Kuestner and I will be handing out some 🎁 #whyCMR #ISMRM_Iberian #ISMRM23 CCMAR - Centro de Ciências do Mar

Thanks #ISMRM23 for the invitation to talk in the educational session about reconstruction solutions for CMR MIDAS.lab

📢 Please join me today at #ISMRM23 for my digital poster #1818 (1:45pm, computer 1) about robust motion detection in multiple imaging cohorts. MIDAS.lab

📷 We will be presenting our research on 'Avoiding shortcut learning by Mutual Information Minimization in deep learning-based MRI processing' at #ISMRM23! Join us at 13:45 for insights on causal learning in medical imaging! Abstract#0154 MIDAS.lab

🧲❤ cardiac MRI Rodeo: taiming AI 🤖 for clinical practice👩🏻‍⚕️ tutorial ISMRM @SMRT_ISMRM Join the rodeo 🤠 in a panel discussion and win 🎁 Get your ropes ready! 🐎 More info 👇 🌐 ismrm-mit-cmr.github.io #whyCMR #CardioTwitter #DeepLearning #ISMRM_Iberian #ISMRM23 #ISMRT23

🏆Congratulations to Aya Ghoul for making the 1st place🥇in the #ISMRM2023 motion correction study group on her work about deep learning image registration for undersampled cardiac CINE MR MIDAS.lab

.. ⚡ ISMRM Flash interviews by RRSG are back 🤩 MRM Highlights #MRITogether #ISMRMtogether #ChallengeISMRM 👉 Click the video to hear Thomas Kuestner research group at #ISMRM23 ! Pls check Aya Ghoul Abstracts 3168 👉 Code available at: github.com/lab-midas/lapn…

📢 Excited to present at #ISMRM23 on "Contrastive Self-Supervised Learning for Cardiac MR Image Reconstruction"! Join me at 15:45 for an innovative approach to learn from undersampled cardiac Cine MR images. Abstract ID: #0709 #WhyCMR MIDAS.lab AIMLabTUM

In case you missed it, we presented at #ISMRM23 on "Attention Mechanisms for Sharing Low-Rank, Image, and k-Space Information during MR Image Reconstruction"! #0819: Top 100 Abstracts, Magna Cum Laude Merit Award. MIDAS.lab AIMLabTUM

📷 Check out our new deep learning-based framework for non-rigid registration of accelerated cine images for different sampling trajectories and accelerations of up to x16 at #ismrm23. If curious, drop by today at abstract #1368 MIDAS.lab AIMLabTUM

Early Access Paper Just Posted: Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation: a more accurate, efficient and unified solution. Read the paper: ieeexplore.ieee.org/abstract/docum…

📢 Participate in the autoPET-III challenge hosted at #MICCAI2024 Multi-site and multi-tracer generalization for automated PET/CT lesion segmentation: autopet-iii.grand-challenge.org 📷 ESHIᴹᵀ MICCAI Society Universität München MIDAS.lab