Alexis Poitrasson-Rivière

Trying out this professional twitter thingy. This will mostly be posts about papers from my group, maybe about other papers if I get in a groove. And maybe some pointers on using Corridor4DM if anyone needs it. All delivered with a positive upbeat attitude!

Automated segmentation of cardiac inflammatory #ThinkPET studies using #AI significantly improves clinical workflow @alexispvpr @venkmurthy @umichmedicine. Read now👉bit.ly/407S7hh #cvnuc

FDG PET is now a widely used tool for identification and quantification of inflammation and infection in the heart, including in patients with: * known or suspected cardiac sarcoidosis * known or suspected endocarditis (particularly prosthetic valve, CIED, VAD) Accurate

**New Paradigm in Cardiac Perfusion Evaluation** Quantitative measures of blood flow from PET have been a huge advance and are driving PET adoption. Until recently, these were generally computed globally or across large regions (e.g. vascular territories). Our group has now

Poster time! #ASNC2024 @MyASNC

RT @venkmurthy: Latest preprint from our group using DL to process cardiac sarcoid PET to improve visualization and quantification We did…

#NewPaperAlert: New Preprint on medRxiv about automated segmentation of cardiac inflammatory #ThinkPET studies using a 3D-UNET (medrxiv.org/content/10.110…). A promising application of DL segmentation to improve clinical workflow. @venkmurthy @almallahmo @rlweinberg @PanithayaC

Coronary/myocardial flow reserve is powerful measure of CAD & essential for dx of ANOCA/INOCA but is difficult to obtain, requiring invasive studies or PET Now possible using stress EKG data! Deep learning derived in 3887 pts w/ PET. Validated in 963 PET pts & 5102 SPECT pts medrxiv.org/content/10.110… @umichCVC @UMichRadiology @UMichResearch @MyASNC @almallahmo @alexispvpr

CC: @MyASNC @CVC_SNMMI @ACCinTouch @DekempRob @iansarmstrong @mdicarli @Piotr_JSlomka @edwardmillermd @RupaSanghaniMD

#NewPaperAlert(twofer edition): Two new papers in @EANM_NucMed EJNMMI on a novel method for looking at #ThinkPET MBF and MFR. Check out the full papers here: rdcu.be/dnFA5 rdcu.be/dn9Le @venkmurthy @almallahmo @rlweinberg @bnallamo

Professor: what a journey! The days were long but the years short Could measure the journey in papers/R01s but I would rather measure it by all I learned along the way & the colleagues who became friends 🙏 to all the friends/family whose support at every step made it possible

Spline fitting reconstruction improves accuracy and test/re-test reproducibility of MFR quantification with 99mTc-sestamibi CZT SPECT ahajournals.org/doi/abs/10.116… #AHAJournals @BWHCVImaging @anachsouza @mdicarli @premsoman123 @VTaqMD @alexispvpr @AllisonGHaysMD @cshenoy3

#NewPaperAlert: Deep-Learning can replicate the performance of CT-Attenuation-Corrected polar maps: link.springer.com/article/10.100… @venkmurthy @almallahmo @edwardmillermd @RupaSanghaniMD

#NewPaperAlert: Cool brief report on residual subtraction for #ThinkPET MBF with 18F-FPZ and 13N-NH3: link.springer.com/article/10.100… Spoiler alert: residual subtraction is necessary for accurate #CardiacPET quantifications with interscan delays under 4 half-lives (same-dose injections)

#JournalNC What is the impact of number of iterations and TOF on 82Rb #ThinkPET perfusion and MBF distributions? Learn more here👉bit.ly/3l7J4XW #CVNuc @MyASNC @umichCVC @UMichRadiology @venkmurthy

#JournalNC How precise is MFR estimation using CZT #SPECT and 99mTc-tracers? Learn more here👉bit.ly/3Aos9GQ #CVNuc @MyASNC