MIDAS.lab

[MIDAS.lab @lab_midas]

📷 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 @lab_midas @aimlab_tum

[MIDAS.lab @lab_midas]

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.
@lab_midas @aimlab_tum

[MIDAS.lab @lab_midas]

📢 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 @lab_midas @aimlab_tum

[MIDAS.lab @lab_midas]

📷 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 @lab_midas

[Thomas Kuestner @KuestnerThomas]

Our whole-body FDG-PET/CT lesion dataset is now on @TCIA_News
📢https://t.co/eIutDl5j8b
With this dataset we address the limited public availability of annotated high-quality PET/CT data for #AI research🔬
@lab_midas

[Thomas Kuestner @KuestnerThomas]

📢Participate in the autoPET challenge hosted at #MICCAI
https://t.co/W07kCmfuKl
A machine learning-based automated tumor lesion segmentation on 1000 whole-body FDG-PET/CT datasets🔬
💰Compete for the prize money of 15000 Euro💶
@lab_midas @MICCAI_Society @ESHI_Society

[Teresa Correia @TeresaM2Correia]

A wonderful and fun collaboration that started with a tutorial @ISMRM with @KuestnerThomas, @khammernik, @WendyStrugnell, T Ismail & S Weingärtner led to this ❤🧲 review 📃https://t.co/RFRkAaYmJA

🤷🏻‍♀️Maybe you will find it useful #whyCMR #CardioTwitter
🙋🏻‍♀️More to come at #ISMRM22

[MRM Highlights @mrm_highlights]

November MRM Editor's Pick #3: "Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute", by Thomas Küstner (@KuestnerThomas) et al. @lab_midas @uni_tue. https://t.co/6d13VE4cNR

[Radiology: Artificial Intelligence @Radiology_AI]

#DeepLearning model measures geometric hip joint parameters https://t.co/YYWwyR9U9z @lab_midas @Uni_Stuttgart #HipPain #MRI #MachineLearning

[Radiology: Artificial Intelligence @Radiology_AI]

Automatic extraction of hip morphometry from MRI using deep learning https://t.co/YYWwyR9U9z @lab_midas @Uni_Stuttgart #DeepLearning #arthritis #MRI

[Radiology: Artificial Intelligence @Radiology_AI]

Automatic extraction of hip morphometry from MRI using deep learning https://t.co/YYWwyR9U9z @lab_midas @Uni_Stuttgart #DeepLearning #arthritis #ML

[Teresa Correia @TeresaM2Correia]

Join the Cardiac MR 👩🏼‍🏫 tutorial and 👨🏻‍💻 challenges 🏆 @ISMRM @SMRT_ISMRM if you're curious 🤔 about #whyCMR or need to refresh or deepen your understanding on ❤🧲 clinical applications, protocols, physics and reconstruction methods.

🌐 https://t.co/hHepjupWBF

#ISMRM21

[Thomas Kuestner @KuestnerThomas]

Join the Cardiac MR tutorial @ISMRM @SMRT_ISMRM to learn from our distinguished speakers about #whyCMR: why we need it, how to run a CMR protocol, the physics behind it, and how we can analyse the data with machine learning

https://t.co/HgsgBGOvnU

#ismrm21

[MIDAS.lab @lab_midas]

Congratulations

[Thomas Kuestner @KuestnerThomas]

Grateful to receive the best paper award at APSIPA 2020 for our work on DL-based motion-compensated reconstruction via non-rigid registration in k-space
@lab_midas @KCL_CardiacMR @BioMedIAICL @RMIT @CUHKofficial

[Radiology: Artificial Intelligence @Radiology_AI]

"[A]dipose tissue distribution in the body has been shown to be a key indicator of the pathogenesis of insulin resistance and type 2 #diabetes mellitus" https://t.co/ir5LUPEoSx @KuestnerThomas @lab_midas #AI #RadAI #MachineLearning

[Thomas Kuestner @KuestnerThomas]

Grateful to receive the ESMRMB Magna Cum Laude Award for our work on DL super resolution to enable fast sub-millimeter coronary MRA. If you are @ESMRMB please join me at my lightning talk: https://t.co/PPGR3kdVp5 #ESMRMB2020 @KCL_CardiacMR @KingsImaging @SmartHeartUK @rbotnar

[Thomas Kuestner @KuestnerThomas]

Check out our recent work on free-running 3D isotropic Cartesian cardiac CINE in less than 2 minutes: https://t.co/NcDP2KPkTH #KCL @KCL_CardiacMR @KingsImaging @SmartHeartUK @rbotnar

[Thomas Kuestner @KuestnerThomas]

Check out our recent work in Nature Scientific Reports on a 4D recon network to enable 3D cardiac CINE with isotropic left ventricle coverage
in a single breath-hold of <12s: https://t.co/v4CnGpEzOU #KCL @KCL_CardiacMR @KingsImaging @BioMedIAICL @SmartHeartUK @rbotnar @lab_midas

[KCL Cardiac MRI @KCL_CardiacMR]

Join us today at the #ISMRM20 Myocardial Perfusion & Function Q&A at 15.50 UTC⌚️. @KuestnerThomas will be answering questions on our work on Free-Running 3D Whole-Heart CINE (#1312,👉 https://t.co/ZqHTfpX8Gb ). #KCL #ISMRM @KingsImaging @rbotnar @SmartHeartUK

[KCL Cardiac MRI @KCL_CardiacMR]

Check out the work of @khammernik “On the influence of Prior Knowledge in Learning Non-Cartesian 2D CINE Image Reconstruction" at #ISMRM20 (#602, https://t.co/i9OQlPoFD0). #ISMRM #KCL @KingsImaging @rbotnar @SmartHeart @DanielRueckert @BioMedIAICL