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Thick-tissue spatial transcriptomics w/ 3DEEP-HybISS in skin enabled pseudotemporal molecular mapping of hair follicle organogenesis via asynchrony of follicle development: Explore the data for urself: Congrats to Reza Kalhor +team

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Dr. Jean Fan

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12,703 просмотров • 2 месяцев назад •via X (Twitter)

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Thanks to the authors for sharing all components of this mouse embryo spatial transcriptomics data from cell gene counts to per-molecule coordinates: 🥳 So I vibe coded an app to explore the 3D subcellular transcript organization:
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Thanks to the authors for sharing all components of this mouse embryo spatial transcriptomics data from cell gene counts to per-molecule coordinates: 🥳 So I vibe coded an app to explore the 3D subcellular transcript organization:

Dr. Jean Fan

50,647 просмотров • 7 месяцев назад

The office of Davao City 1st District Rep. Paolo Duterte released a copy of his hair follicle drug test, which showed negative results. Duterte challenged his opponent in the 2025 polls to undergo the same test. (🎥: Office of Rep. Paolo Duterte) | via Vivienne Gulla
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The office of Davao City 1st District Rep. Paolo Duterte released a copy of his hair follicle drug test, which showed negative results. Duterte challenged his opponent in the 2025 polls to undergo the same test. (🎥: Office of Rep. Paolo Duterte) | via Vivienne Gulla

ABS-CBN News

138,906 просмотров • 1 год назад

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:
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PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:

evo-devo

13,429 просмотров • 4 месяцев назад

DISCO-MS allows molecular analysis via proteomics and AI after tissue clearing. Now we are in the game to discover the molecular underpinnings of diseases. To this end, we are expanding our proteomics team. Please apply at (distribute😊):
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DISCO-MS allows molecular analysis via proteomics and AI after tissue clearing. Now we are in the game to discover the molecular underpinnings of diseases. To this end, we are expanding our proteomics team. Please apply at (distribute😊):

Ali Max Erturk

31,979 просмотров • 3 лет назад

Today in nature, we report a spatial single-cell atlas of human cortical development, revealing surprisingly early specification of human cortical layers and areas. We built an interactive browser to explore the spatial data: Paper link below👇
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Today in nature, we report a spatial single-cell atlas of human cortical development, revealing surprisingly early specification of human cortical layers and areas. We built an interactive browser to explore the spatial data: Paper link below👇

Xuyu Qian

53,373 просмотров • 1 год назад

⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.
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⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.

Faisal Mahmood

18,047 просмотров • 1 год назад

🔬 Training Transformers to solve 95% failure rate of Cancer Trials the AI for Science pod is back with Ron Alfa, CEO of NOETIK, and Daniel Bear, VP Research at Noetik, explaining exactly how their team of top AI x Bio researchers and engineers (shoutout owl) will use AI to cure cancer, by focusing on key bottlenecks like patient selection, and training large cancer foundation models like TARIO-2, an autoregressive transformer trained on one of the largest sets of tumor spatial transcriptomics datasets in the world... which first required years of blind faith in collecting good data to even get going:
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🔬 Training Transformers to solve 95% failure rate of Cancer Trials the AI for Science pod is back with Ron Alfa, CEO of NOETIK, and Daniel Bear, VP Research at Noetik, explaining exactly how their team of top AI x Bio researchers and engineers (shoutout owl) will use AI to cure cancer, by focusing on key bottlenecks like patient selection, and training large cancer foundation models like TARIO-2, an autoregressive transformer trained on one of the largest sets of tumor spatial transcriptomics datasets in the world... which first required years of blind faith in collecting good data to even get going:

Latent.Space

18,323 просмотров • 4 месяцев назад

We were thrilled to have Niantic Spatial 🌎's Project Jade demo on Spectacles at Lens Fest 2025 to show the bleeding edge of what spatial, location-based experiences can be. Voices of VR host Kent Bye (Voices of VR) sat down with the team to talk how it came together, the role Snap’s tech stack plays in development, and what’s next for XR.
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We were thrilled to have Niantic Spatial 🌎's Project Jade demo on Spectacles at Lens Fest 2025 to show the bleeding edge of what spatial, location-based experiences can be. Voices of VR host Kent Bye (Voices of VR) sat down with the team to talk how it came together, the role Snap’s tech stack plays in development, and what’s next for XR.

Spectacles

15,259 просмотров • 10 месяцев назад

Agents are finally starting to work in biology. We’ve partnered with Anthropic and major biotech vendors - Vizgen, AtlasXOmics, Takara, 10x Genomics - to build a tool that allows scientists to steer their own analysis with natural language. Raw spatial data to publication quality figures. Our team believes this will soon be the standard way biologists interact with data. Spatial biology agents look a bit different from coding products: - tailored to the molecular details of each kit type - run in sandboxes on very large machines - orchestrate data infra, eg. bioinformatics workflows, with tool calls - build graphical analysis notebooks to communicate results Detailed breakdown of engineering decisions, product philosophy and concrete flows follows:
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Agents are finally starting to work in biology. We’ve partnered with Anthropic and major biotech vendors - Vizgen, AtlasXOmics, Takara, 10x Genomics - to build a tool that allows scientists to steer their own analysis with natural language. Raw spatial data to publication quality figures. Our team believes this will soon be the standard way biologists interact with data. Spatial biology agents look a bit different from coding products: - tailored to the molecular details of each kit type - run in sandboxes on very large machines - orchestrate data infra, eg. bioinformatics workflows, with tool calls - build graphical analysis notebooks to communicate results Detailed breakdown of engineering decisions, product philosophy and concrete flows follows:

Kenny Workman

114,480 просмотров • 10 месяцев назад

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels
2:15

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Mo Lotfollahi

50,959 просмотров • 1 месяц назад

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:
1:09

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:

Mo Lotfollahi

11,832 просмотров • 5 месяцев назад

If the DDS cult is obsessed what’s in PBBM’s hair follicle, it’s only fair we find out what’s inside Sara Duterte’s head. It is our right to demand Sara’s long-overdue psychiatric evaluation. Between the epic public meltdowns and the casual "unalive" threats against the nation's top leaders, it’s clear Sara is not just bastos at balahura — we are dealing with a textbook case of clinical instability and a dangerous propensity for violence that belongs in a ward, not a public office.
0:55

If the DDS cult is obsessed what’s in PBBM’s hair follicle, it’s only fair we find out what’s inside Sara Duterte’s head. It is our right to demand Sara’s long-overdue psychiatric evaluation. Between the epic public meltdowns and the casual "unalive" threats against the nation's top leaders, it’s clear Sara is not just bastos at balahura — we are dealing with a textbook case of clinical instability and a dangerous propensity for violence that belongs in a ward, not a public office.

Thea Tan

11,258 просмотров • 4 месяцев назад

A step closer towards #ViksitKerala.. At the invitation of PM Shri Narendra Modi, a 115-member delegation is arriving in Delhi to explore fresh development opportunities for Kerala, driving the dream of #ViksitKerala. The team will take part in the Special Governance Skills Development Camp (Delhi Yatra) from 9–13 February, strengthening governance skills and bringing back valuable insights to accelerate Kerala’s growth journey. Watch. 🔽
0:55

A step closer towards #ViksitKerala.. At the invitation of PM Shri Narendra Modi, a 115-member delegation is arriving in Delhi to explore fresh development opportunities for Kerala, driving the dream of #ViksitKerala. The team will take part in the Special Governance Skills Development Camp (Delhi Yatra) from 9–13 February, strengthening governance skills and bringing back valuable insights to accelerate Kerala’s growth journey. Watch. 🔽

BJP

14,821 просмотров • 7 месяцев назад

Lions QB Jared Goff on Eagles Fans “they’re well known for being the meanest fans. I’ll have to have some thick skin, when I'm in there so none of their insults can really get to me” 📸 Via 97.1 The Ticket
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Lions QB Jared Goff on Eagles Fans “they’re well known for being the meanest fans. I’ll have to have some thick skin, when I'm in there so none of their insults can really get to me” 📸 Via 97.1 The Ticket

Jeff Skversky

176,853 просмотров • 10 месяцев назад

For KPop Demon Hunters’ concert scene, Sony Pictures Imageworks were able to animate hundreds of lights and easily export them with a custom plugin via USD into Katana. The team were able to preserve the animation form UE for the lights in Katana, removing the need for thousands of extra frames and a surplus of data. Dive into how the team used UE in our spotlight here: And stream Kpop Demon Hunters now on Netflix!
0:53

For KPop Demon Hunters’ concert scene, Sony Pictures Imageworks were able to animate hundreds of lights and easily export them with a custom plugin via USD into Katana. The team were able to preserve the animation form UE for the lights in Katana, removing the need for thousands of extra frames and a surplus of data. Dive into how the team used UE in our spotlight here: And stream Kpop Demon Hunters now on Netflix!

Unreal Engine

32,467 просмотров • 6 месяцев назад

Bundle Tracker - Now LIVE 🎯 After months of development and extensive testing, we're launching the most sophisticated bundle tracking system in the space. What's inside: • instant notifications when team moves tokens • unlimited wallet tracking across entire eth chain • real-time pnl calculation on team trading • precise supply retention monitoring • automated wallet relationship mapping • complete trading pattern detection How to use: /track - monitor any token /balance - check team holdings /pnl - analyze trading activity /list - manage tracked tokens Beta testers are already calling this a game-changing addition to our suite of tools. Because in a market full of noise, clear data wins. Time to put those team wallet moves under the microscope.
1:24

Bundle Tracker - Now LIVE 🎯 After months of development and extensive testing, we're launching the most sophisticated bundle tracking system in the space. What's inside: • instant notifications when team moves tokens • unlimited wallet tracking across entire eth chain • real-time pnl calculation on team trading • precise supply retention monitoring • automated wallet relationship mapping • complete trading pattern detection How to use: /track - monitor any token /balance - check team holdings /pnl - analyze trading activity /list - manage tracked tokens Beta testers are already calling this a game-changing addition to our suite of tools. Because in a market full of noise, clear data wins. Time to put those team wallet moves under the microscope.

BribeAI

108,743 просмотров • 1 год назад

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution
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PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution

Dr. Filippo Cademartiri

11,849 просмотров • 1 год назад

I need you help, team. But first off… Congrats everyone! We made it to 1 of 5 nominations for ‘live creator of the year’ I’m blown away and it’s thanks to every single person who joins the live sessions and helps this channel spread a happy spark. P.S You can vote once a day via the link in the bio ❤️
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I need you help, team. But first off… Congrats everyone! We made it to 1 of 5 nominations for ‘live creator of the year’ I’m blown away and it’s thanks to every single person who joins the live sessions and helps this channel spread a happy spark. P.S You can vote once a day via the link in the bio ❤️

Seb Gower

14,086 просмотров • 1 год назад

Introducing WorldView 3D, a first-of-its-kind satellite tasking product that delivers up-to-date 3D ground truth anywhere on Earth. From GPS-denied autonomy and intelligence analysis to consumer-grade mapping and disaster response, the world's most important missions increasingly depend on accurate, up-to-date 3D terrain. But for years, keeping 3D maps current at global scale hasn't been possible. WorldView 3D changes that. Built on Vantor’s trusted spatial foundation, our new tasking capability empowers customers to task and receive updated 3D data within 24 hours of image collection. For missions that require greater fidelity, they can also produce high-definition 3D maps anywhere in the world. 3D is the future of spatial intelligence, and Vantor is leading the change. Our trusted spatial foundation already includes 100 million sq km of the world mapped inhighly accurate 3D. Now customers can update the locations that matter most with the speed, scale, and fidelity required for critical missions. Read the full announcement:
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Introducing WorldView 3D, a first-of-its-kind satellite tasking product that delivers up-to-date 3D ground truth anywhere on Earth. From GPS-denied autonomy and intelligence analysis to consumer-grade mapping and disaster response, the world's most important missions increasingly depend on accurate, up-to-date 3D terrain. But for years, keeping 3D maps current at global scale hasn't been possible. WorldView 3D changes that. Built on Vantor’s trusted spatial foundation, our new tasking capability empowers customers to task and receive updated 3D data within 24 hours of image collection. For missions that require greater fidelity, they can also produce high-definition 3D maps anywhere in the world. 3D is the future of spatial intelligence, and Vantor is leading the change. Our trusted spatial foundation already includes 100 million sq km of the world mapped inhighly accurate 3D. Now customers can update the locations that matter most with the speed, scale, and fidelity required for critical missions. Read the full announcement:

Vantor

16,030 просмотров • 2 месяцев назад

Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.
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Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.

AK

40,485 просмотров • 2 лет назад