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We're thrilled to announce that our data scientists, Guillaume Gatineau and Michele de Gruttola, will be presenting at the 24th International Workshop on Quantitative Musculoskeletal Imaging (QMSKI) in Australia on November 7th at 3 PM! If you are attending the QMSKI, join them as they share groundbreaking research on: 1️⃣ Artificial Intelligence-Based, Automatic and Explainable Bone Fragility Detection from X-ray: A Multi-National Cohort Study 2️⃣ Deep Learning-Enhanced Identification and Segmentation of Lumbar Vertebrae from Multi-view X-ray Images Don’t miss the opportunity to gain insights into how AI is revolutionizing healthcare and enhancing diagnostic processes! #radiology #AI hashtag #StepUpForBoneHealth #osteopenia #TrabecularBoneScore #DXA #BMD #BoneHealth #osteoporosis #WorldOsteoporosisDay #TBS #EarlyDetection #OsteoporosisAwareness #FRAX #FragilityFractures #QMSKI #X-Ray #bonefragility #deeplearning

🚀Final Raptor Conference Abstract of Francesco Russo.    🔬Francesco is an Early-Stage Researcher in the RAPTOR project at Medphoton, a Brainlab Company, Salzburg. 🔍Learn more: https://shorturl.at/LhwKZ 💬In his own words: "Shading correction enhances CBCT uniformity and HU accuracy while preserving anatomical fidelity and mitigating the risk of AI hallucinations, bringing AI closer to safe clinical adoption."     In particular, medPhoton has developed and is constantly improving a prototype CBCT system currently adopted in several ion beam therapy facilities.      💡If you are interested to learn more about enhancing X-ray cone-beam CT imaging for ion beam therapy to improve image quality and patient treatment outcomes visit: 🌐(bit.ly/3ZEldU4) / (Newsletter RAPTOR n*6, December 2024). #IonBeamTherapy #CBCT #MedicalImaging #RadiationTherapy #ProtonTherapy #ParticleTherapy #MedPhoton #AdaptiveTherapy #RadiationOncology 

Eagerly looking forward to this workshop. Academia and industry together! We will discuss about the role of digital technologies in cardiovascular imaging.

Last call for the webinar! 📯 Last call for the webinar! 📯 It's just in 2 hours, I'm sure can find some time to hear more from the expert panel!

Are you joining ISMRM 2024, too ? We are presenting our research and are happy to discuss with you: #0294: Ilicak et al. Investigating Phase-Cycled bSSFP Imaging for Functional Lung Imaging in 2-Year-Olds After Congenital Diaphragmatic Hernia Repair #0924: Licht et al. Dynamic Mode Decomposition reveals 23Na Multi-Quantum Coherences and allows incomplete RF Phase-Cycling #0925: Licht et al. Low-rank reconstruction for simultaneous Double-Half-Echo 23Na and undersampled 23Na Multi-Quantum Coherences MRI #1257: Božić-Iven et al. Double Inversion Recovery in myocardial Arterial Spin Labeling (ASL) for reduced physiological noise  #1980 : Strittmatter et al. Deep Learning-Based Multistep Deformable Medical Image Registration for Multimodal Minimal-Invasive Image-Guided Intervention #2911: Ilicak et al. Investigating SMS in dynamic MR for Free-Breathing Functional Lung Imaging #3030: Reichert et al. A Novel Method to Estimate 23Na Triple Quantum (TQ) Signal: Spin Echo Sequence, Impact of Noise and Proof-of-Concept Imaging #4258: Zehender et al. Harnessing sparsity: Feasibility of undersampled fixed evolution time ²³Na TQTPPI FID reconstruction for optimal ²³Na TQ/SQ-signal estimation

Day 11/12 - highlighting achievement and innovation in #medicalimaging Heuron Let's start with the team, who are full of life and passionate about this business. I had the pleasure of speaking to Jamie Chong at #RSNA24 about all things Heuron, the future and recruitment, 2025 is a big one for them! Mentions to ChanIck Park, Donghoon Shin, Myungjin Shin, Soohwa Song, Eunsil Kang and many others. 🎉 Heuron's Breakthrough in Parkinson's Diagnostics 🧠 Heuron achieved a major milestone with its solutions, Heuron IPD and Heuron NI, in accurately diagnosing Parkinson’s disease. Highlights from the study published in the American Journal of #Neuroradiology (AJNR): - 𝐇𝐞𝐮𝐫𝐨𝐧 𝐈𝐏𝐃: Sensitivity 97%, Specificity 95.7%, Accuracy 96.6% - 𝐇𝐞𝐮𝐫𝐨𝐧 𝐍𝐈: Sensitivity 96.4%, Accuracy 89.9% The tools also quantify nigrosome volume changes linked to disease progression using the Hoehn and Yahr Scale (H&Y). #medicalimaging #radiology #AI

Today we are excited to announce the release of a machine learning-enabled image interpolation algorithm designed to significantly reduce scan times by approximately 50%. This advancement is expected to: 1) enhance patient comfort by decreasing time spent on the scanner, and 2) improve operational efficiency by increasing the number of patients scanned within a given timeframe. “By integrating machine learning into our imaging systems, we ensure its application is to create meaningful improvements in patient experience by reducing discomfort and scan times, while simultaneously streamlining workflows to enhance efficiency for healthcare providers ” - Bilal Malik, PhD Read more here: https://lnkd.in/gvp4iBwt Raluca Dinu Avi Katz Stas Budagov Bilal Malik, PhD James Wiskin, PhD, MS Nasser Pirshafiey Yvonne Karney MD Jon Stoa Ross Taylor Steve Choate Satrajit Misra Sam A. Hill Brent Ness Helen Messier Kristine Burke, MD, ABFM, ABoIM, IFMCP Danielle F. Brian Casey NXC Imaging Fountain Life Qlarity Breast Imaging CL PerfeQTion Imaging Breastlink Sunnybrook Montclair Breast Center #Mammography #Radiology #BreastHealth #Healthcare #Innovation #MedicalInnovation #MedicalImaging #WomensHealth #PublicHealth #BreastCancer #HealthcareInnovation #MedicalTechnology #Ultrasound #machinelearning #AI #mammography

Our team has unveiled a groundbreaking ML-enabled image interpolation algorithm designed to drastically cut scan times. This innovation represents a significant leap forward in enhancing imaging efficiency while maintaining diagnostic precision.

Excited to share our latest breakthrough in MR-only radiotherapy! 🌟 We developed a transformer-powered cycle-GAN that tackles the complexities of abdominal imaging, like motion from breathing and digestion, with remarkable precision. The results? Sharper synthetic CTs (MAE 79.52 HU, PSNR 27.05 dB) and higher dosimetric accuracy (gamma passing rate 97.1% @ 2%/2 mm). Proud to push the boundaries of AI for precision medicine and deliver solutions that truly make a difference! Read our paper! https://lnkd.in/gG-y2puF

Day 2 Highlights: Medical Imaging Workshop on Segmentation Today’s session was packed with exciting advancements in medical imaging! Here’s what we covered: Brain Tumor Segmentation with U-Net: -Demonstrated how U-Net architecture can be effectively applied for precise brain tumor segmentation. -Explained its role in identifying critical regions in medical scans. Introduction to U-Net+: -Presented the enhanced U-Net+ model with additional capabilities for improved segmentation performance. -Highlighted the differences and advantages over the standard U-Net architecture. Vision Transformers (ViT) for Brain Tumor Classification: -Introduced ViT, a revolutionary approach leveraging transformer models for image analysis. -Applied ViT to classify brain tumors, demonstrating its cutting-edge accuracy and efficiency. The energy and engagement from participants were truly inspiring! Looking forward to diving deeper into more advanced topics in the upcoming sessions. Stay tuned as we continue to explore how AI is transforming the future of healthcare! #MedicalImaging #BrainTumorSegmentation #VisionTransformers #UNet #DeepLearning #AIinHealthcare #Workshop