Advanced Imaging Techniques in Medical Diagnostics

📌 Open-Source Medical Imaging AI Models (2024–2025) This curated list highlights the latest open-source AI models transforming medical imaging, from generalist vision-language foundations to specialized tools for segmentation, diagnosis, and report generation. Explore models across radiology, oncology, and multimodal analysis. Full links and details below. 👇 📌 Foundation & Multimodal Models • Rad-DINO – Self-supervised ViT trained on 1M+ chest X-rays • RayDINO – Large-scale DINO-based transformer for multi-task chest X-ray learning • Med-Gemini – Gemini-based model fine-tuned for multi-task chest X-ray applications • Merlin – Large 3D vision–language model for CT interpretation and reporting • RadFound – Radiology-wide VLM for report generation and question answering • LLaVA-Rad – Vision–language model for chest X-ray finding generation 📌 Segmentation Models • MedSAM2 – Promptable 3D segmentation model extending Segment Anything to medical imaging • FluoroSAM – SAM variant trained from scratch on synthetic X-ray/fluoro images • ONCOPILOT – Interactive model for CT-based 3D tumor segmentation in oncology 📌 Task-Specific / Tuned Models • MAIRA-2 – Enhanced CXR report generator with finding localization • CheXagent – Instruction-tuned multimodal model for chest X-ray tasks • RadVLM – Dialogue assistant for chest X-ray interpretation and reporting • Mammo-CLIP – CLIP-based model for mammogram classification and BI-RADS prediction • CheXFound – ViT model using GLoRI architecture for disease localization in X-rays Know a model that got missed? Drop it in the comments, let’s build this resource list together. 🤔 _________________________________________________ #ai #imaging #radiology #oncology #machinelearning

Why do hospitals race against time every day to receive fresh shipments of a rapidly decaying radioactive element? Over 30 million medical imaging procedures every year depend on technetium-99m. This isotope, critical in nuclear medicine, is an essential tool for diagnosing heart disease, bone disorders, and cancer. Its story is one of precision, ingenuity, and purpose. The "99m" in technetium-99m indicates a metastable nuclear state. This means the isotope's nucleus holds extra energy, which it releases as gamma rays while stabilizing. These gamma rays, invisible to the eye, are detected by imaging systems to create detailed pictures of the body's internal functions. But where does this lifesaving isotope come from? Technetium-99m is derived from molybdenum-99, a parent isotope produced in nuclear reactors. Molybdenum-99 decays into technetium-99m, which is then extracted in specialized facilities and distributed to hospitals in radiopharmaceutical kits. The logistics are incredibly time-sensitive, as molybdenum-99 itself has a short half-life of about 66 hours. This means the reactors, processing facilities, and distribution networks must operate seamlessly to ensure a steady supply. Once it arrives at a medical facility, technetium-99m is rarely used on its own. It's chemically bound to pharmaceutical compounds designed to target specific tissues. For instance, technetium-99m diphosphonates bind to bone tissue to reveal fractures or metastases, while other compounds focus on cardiac or renal function. This versatility makes technetium-99m indispensable in nuclear medicine. One of the most common imaging technologies using technetium-99m is SPECT, or Single-Photon Emission Computed Tomography. SPECT employs gamma cameras to detect the isotope's gamma rays and transform them into 3D images. These cameras use collimators to filter out scattered radiation, allowing only rays traveling in the correct direction to strike a scintillation crystal, typically sodium iodide doped with thallium. The crystal converts the gamma rays into light, which is amplified by photomultiplier tubes and processed into diagnostic images. Advancements in this technology have been pivotal. Improved collimator designs enhance image resolution, while modern scintillation crystals and detectors increase sensitivity. These innovations ensure sharper images, faster scans, and more reliable diagnostics, ultimately improving patient care. The brilliance of technetium-99m lies not only in its ability to illuminate the invisible but also in the collaborative effort behind its production and use. From the physics driving its decay to the precision engineering of imaging systems and the global logistics keeping hospitals supplied, each and every step is a picture of human ingenuity and determination.

🧪 New Machine Learning Research: Enhancing 3D Medical Image Annotation with SAM Integration Zafer Yildiz and his colleagues from Duke University have introduced a new extension for 3D Slicer, integrating the Segment Anything Model (SAM) and SAM 2 to improve 3D medical image annotation. - Research goal: To enhance the efficiency and accuracy of 3D medical image annotation by adapting SAM and SAM 2 models within the 3D Slicer software. - Research methodology: The team integrated SAM and SAM 2 into 3D Slicer, enabling both single-directional and bi-directional propagation of segmentation masks across 3D volumes. The extension allows users to interactively place point prompts on 2D slices, which are then used to generate and propagate annotations throughout the entire 3D volume. - Key findings: The adapted SAM 2 model demonstrated effective segmentation across different medical imaging modalities, including CT and MRI. For example, it achieved a 33% increase in segmentation accuracy when using bi-directional propagation compared to traditional methods. - Practical implications: This extension reduces the time required for medical image annotation by up to 50%, providing a valuable tool for radiologists and medical professionals in applications like tumor detection, organ segmentation, and pre-surgical planning. Stay tuned for more updates on the latest advancements in ML and data science! #LabelYourData #MachineLearning #Innovation #AIResearch #MLResearch #MedicalImaging #DataScience #MedicalAI

How Do Different Imaging Techniques Visualize the Lungs? 🫁 Imaging modalities are vital for diagnosing lung diseases, each offering unique insights into lung structure and function. Polarean’s Xenon MRI stands out by providing detailed ventilation imaging in seconds—without ionizing radiation. Here’s a breakdown of key techniques used to assess lung health: * Chest X-Ray (CXR) Definition: A 2D imaging technique using ionizing radiation to visualize the lung structure. Principle: X-rays penetrate the body; denser structures (e.g., bones, fluid, infections) absorb more radiation and appear white. Use Examples: Identifies pneumonia, lung nodules, pleural effusion, and pneumothorax (lung collapse). Approximate Scan Time: Less than 1 minute (typically a few seconds). *Computed Tomography (CT) Definition: A 3D imaging method using ionizing radiation and computer reconstruction for detailed cross-sectional structural views. Principle: Rotating X-ray beams create high-resolution images of lung anatomy. Use Examples: Evaluates lung nodules (benign or malignant), pulmonary embolism, emphysema, and interstitial lung disease. Approximate Scan Time: A few seconds (prep may extend total time). *Scintigraphy (Ventilation/Perfusion Scan - V/Q Scan) Definition: A 2D nuclear medicine scan assessing ventilation (airflow) and perfusion (blood flow). Principle: Patients inhale radioactive Xenon-133 gas for ventilation and receive radioactive Technetium-99m injections for perfusion; a gamma camera captures regional function. Use Examples: Detects pulmonary embolism and perfusion defects. Approximate Scan Time: 30-60 minutes (each phase takes ~15 minutes). *Single Photon Emission Computed Tomography (SPECT) Definition: A 3D nuclear medicine scan mapping ventilation and perfusion. Principle: Patients inhale a radioactive Technetium-99m-labeled aerosol for ventilation and are injected with radioactive Technetium-99m for perfusion, enabling 3D imaging of lung function. Use Examples: Evaluates chronic lung diseases, pulmonary hypertension, and perfusion abnormalities. Approximate Scan Time: 30-40 minutes (includes image acquisition). *Xenon MRI Definition: A cutting-edge 3D technique using Xenon gas in an MRI to map ventilation. Principle: Patients inhale non-radioactive Xenon-129 gas, and MRI detects gas distribution for high-resolution ventilation imaging. Researchers continue to explore gas exchange imaging with Xenon-129. Use Examples: Evaluates COPD, asthma, cystic fibrosis, lung/bone marrow transplants, cancer, and post-COVID lung disease. Approximate Scan Time: ~10 seconds (single breath-hold). #LungHealth #MedicalImaging #Xray #CT #NuclearMedicine #XenonMRI #LungMRI

Light can travel through the head, guided by cerebrospinal fluid & tissue geometry. Optical modalities for noninvasive imaging of the human 🧠 hold promise to fill the technology gap between cheap and portable devices such as electroencephalography (EEG) and expensive high-resolution instruments such as functional magnetic resonance imaging (fMRI). A widespread of optical brain reading devices in clinics and neuroscience studies is the low number of photons emerging from deep layers of the brain that restrict the sensitivity of these methods. But, it has been shown that photons experiencing similar scattering length scales as the diameter of the human head can carry imaging information. In a recent study, a wide variety of light propagation pathways were found that were largely determined by the presence of and guided by cerebrospinal fluid. Different source positions on the head can then selectively isolate and probe deep regions of the brain. This suggests that optical techniques could be used to monitor activity in the sulci, midbrain, and deep regions of the cerebellum, which are currently inaccessible with functional near-infrared spectroscopy. Overall, these findings uncover the potential to extend noninvasive light based on brain imaging technologies to the tomography of critical biomarkers deep in the head. Which could be a major assistance for healthcare applications such as sensing or imaging #strokes and #tumors at point-of-care. Learn more: https://lnkd.in/gTRnzemK One love #brain #scan #invisible #disability

𝗪𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗦𝗸𝗶𝗽 𝗮 𝗠𝗮𝗺𝗺𝗼𝗴𝗿𝗮𝗺 𝗶𝗳 𝗧𝗵𝗲𝗿𝗲 𝗪𝗮𝘀 𝗔 𝗣𝗮𝗶𝗻𝗹𝗲𝘀𝘀, 𝗥𝗮𝗱𝗶𝗮𝘁𝗶𝗼𝗻-𝗙𝗿𝗲𝗲 𝗔𝗟𝗧𝗘𝗥𝗡𝗔𝗧𝗜𝗩𝗘? Most women avoid mammograms because of one reason: pain. But what if AI could offer a safer, faster, and completely painless way to detect breast cancer without compression or X-rays? That’s exactly what Caltech just introduced with their new imaging breakthrough: PACT (Photoacoustic Computed Tomography). And it’s not science fiction. It’s already working on real patients. 𝗪𝗵𝗮𝘁 𝗦𝗲𝘁𝘀 𝗣𝗔𝗖𝗧 𝗔𝗽𝗮𝗿𝘁? → No Pain. No Radiation. Just gentle infrared light + sound. AI reconstructs it into crystal-clear tumor images. → Fast & Precise Only 15 seconds per scan. Spotted tumors as small as 0.25mm even in dense tissue. → AI-Powered Detection ML analyzes blood vessel growth (angiogenesis), often missed in traditional scans. → Clinically Proven Outperformed mammograms in early trials. Zero discomfort. No sedation. No stress. → Future-Friendly Design Allergy-safe materials, lower cost, and portable enough to go beyond hospitals. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗖𝗼𝘂𝗹𝗱 𝗠𝗲𝗮𝗻? ✓ No more fear around painful screenings ✓ More frequent and inclusive early detection ✓ A better experience for patients and providers alike The real innovation? Not just the tech but how patient-first thinking is finally shaping diagnostic AI. 𝗬𝗢𝗨𝗥 𝗧𝗔𝗞𝗘? → Would you trust AI imaging over traditional methods? → What would it take to bring this into public clinics or underserved areas? 👇 Drop your thoughts. This could be the future of diagnostic care and not just for breast cancer. #HealthTech #BreastCancer #PACT #MedicalAI #WomenInHealth #PatientExperience #DigitalHealth #HealthcareInnovation #RadiologyAI #Caltech #AIinHealthcare #CancerScreening #EarlyDetection #MedTech #FutureOfHealth

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗵𝗼𝘁𝗼𝗮𝗰𝗼𝘂𝘀𝘁𝗶𝗰 𝗜𝗺𝗮𝗴𝗶𝗻𝗴? Photoacoustic imaging, an emerging technique in the past decade, holds promise for both research and clinical diagnoses due to its versatility and radiation-free nature. This imaging modality operates on the photoacoustic effect, wherein sound waves are generated following light absorption in a material. In this method, technicians use a non-ionizing laser to illuminate the target tissue with short-pulse light at close range. Chromophores in the tissue absorb specific wavelengths' photon energy, inducing molecular vibrations that lead to tissue expansion. This expansion generates acoustic waves in the ultrasound range, capable of propagating through thin tissue layers with minimal scattering. These waves are then detected by a tomographic array. Advanced algorithms in image-reconstructing software convert these signals into 2D or 3D images, offering anatomical and pathological insights to researchers and physicians. Different chromophores like deoxygenated and oxygenated hemoglobin exhibit unique absorption profiles, responding with varying strength to multi-spectral laser pulses. Besides hemoglobin, this imaging method identifies melanin, lipids, collagen, water, and contrast agents tailored to locate diverse biomarkers. Video and more: https://lnkd.in/gQJy-Wmk