Biomedical Engineering Research Topics

🌀 PET vs. SPECT: What Sets Them Apart? In nuclear medicine, two powerful imaging systems take center stage — PET and SPECT. Both visualize physiological processes using radiotracers, but they differ in how they work, what they detect, and their clinical focus. Here's a clear and concise breakdown: 🔹 1. PET (Positron Emission Tomography) What it is: A scanner that detects positron-emitting tracers (e.g., ¹⁸F-FDG) to generate high-resolution metabolic images. How it works: The tracer accumulates in metabolically active tissues (like tumors). The scanner detects pairs of gamma photons from positron annihilation. Clinical uses: Cancer staging, brain imaging (e.g., dementia, epilepsy), and cardiac perfusion/metabolism studies. 🔹 2. SPECT (Single Photon Emission Computed Tomography) What it is: A rotating gamma camera that captures multiple 2D images and reconstructs 3D distributions of single-photon emitting tracers (e.g., Tc‑99m). How it works: The tracer emits gamma rays that are detected from multiple angles, producing cross-sectional images of tracer distribution. Clinical uses: Myocardial perfusion, bone scans, thyroid imaging, renal function studies, and infection localization. 🔍 Note: Most modern PET and SPECT systems are integrated with CT scanners (PET/CT, SPECT/CT), allowing both functional and anatomical imaging in a single exam — enhancing accuracy, localization, and attenuation correction.

Researchers have developed a new bioluminescent technology that allows neurons to emit their own light, enabling continuous, high-resolution monitoring of brain activity without lasers, invasive optics, or tissue damage. This represents a fundamental shift for neuroscience. For the first time, we can observe living neural circuits firing in real time, at single cell precision, across extended time periods. The implications are significant: Better models of learning and memory. Clearer insights into neurodegenerative diseases. And a new window into psychiatric disorders where circuit-level changes are key. What makes this especially promising is its scalability, this technique could eventually allow whole brain activity mapping in ways that were impossible even a year ago. As we enter 2026, breakthroughs like this will redefine how we map, understand, and eventually repair the human brain. #Neuroscience #Biotechnology #BrainResearch #MedicalInnovation #Neurotechnology

🔬 A new era of biology is coming—not just single-cell, but cell–cell. When flow cytometry first became widely adopted in the 1980s, it revolutionized immunology. Suddenly, we could dissect the immune system one cell at a time, revealing T cell subsets, memory phenotypes, activation states, and more. Entire fields flourished because we could see and sort what was previously invisible. Now imagine doing that—not with one-dimensional fluorescence signals—but with full images of each cell as it's flowing by at thousands per second. And not just of single cells, but of cell pairs, clusters, and interactions. That’s the promise of image-activated cell sorting (IACS). Our recent review in Nature Bioengineering explores how IACS is poised to drive a new biological revolution: 📄 https://lnkd.in/guMkSxqJ At its core, IACS combines high-throughput microscopy, real-time image processing, and precision microfluidic sorting, opening the door to analyze and isolate cells based on morphology, subcellular localization, cell-cell contact, cell secretions and more. 💡 At UCLA Henry Samueli School of Engineering and Applied Science, I’ve had the privilege of watching and contributing to many of these advances emerge—from our collaborations with Keisuke Goda, Bahram Jalali and Kevin Tsia on STEAM to the early FIRE imaging system (Eric Diebold, Ph.D.) that now powers BD’s FACSDiscover CellView, to participating in the "Serendipiter" developed by Keisuke Goda's ImPACT program, to Deepcell (founded by my former PhD student Maddison Masaeli), and now through our work on nanovials (Joe de Rutte, Partillion Bioscience), which serve as test tubes for probing cell-cell communication. We are no longer limited to what a cell expresses in isolation, but can now ask how it behaves, who it talks to, and how it responds. Just as early flow cytometry revealed the immune system's complexity, these tools will help uncover the dynamic networks that govern multicellular biology, development, and disease. Providing the massive data needed to fuel predictive AI models that link cells to tissues to organisms—and perturbations that transform health to disease. 🔁 The future is moving beyond single-cell to interaction-level biology. And the tools are finally here. #CellBiology #SingleCell #ImageActivatedCellSorting #Nanovials #microfluidics #FlowCytometry #IACS #UCLA #Bioengineering #NatureBioengineering

🇨🇭 Switzerland Built a Medical Imaging Device That Sees Without Radiation Swiss physicists have created a quantum-enhanced MRI alternative that images soft tissue using ultra-low magnetic fields — eliminating the need for high-energy radiation or massive superconducting magnets. By exploiting quantum coherence in atomic vapors, the system detects biological signals once thought impossible to measure at room temperature. It’s portable, silent, and dramatically safer for repeated use. This could transform diagnostics in remote regions, emergency zones, and long-term monitoring of brain and heart disorders — where imaging is no longer limited by infrastructure.

Imagine we could map every cell in the human body, revealing its precise location and molecular identity. This tantalizing possibility is at the heart of our latest perspective piece published in Nature Methods, where we explore a groundbreaking approach to understanding biological systems at unprecedented depth and scale: Deep 3D Histology. In this perspective article, we discuss three key pillars of this emerging field: Advanced Tissue Clearing and Imaging: -Cutting-edge tissue clearing techniques for intact specimen visualization -High-resolution light-sheet microscopy pushing the boundaries of 3D imaging -Applications ranging from mouse embryos to entire human organs Spatial Omics Technologies: -Integration of single-cell omics data with 3D spatial context -Creation of comprehensive molecular atlases of entire organisms -Bridging the gap between molecular profiles and tissue architecture Artificial Intelligence in Image Analysis: -Deep learning revolutionizing 3D histology data processing -Automation of tasks from image enhancement to cell segmentation -Unveiling information invisible to the human eye through "virtual staining" The potential impact of combining these technologies is staggering. By accelerating our understanding of diseases and drug discovery, we could compress centuries of insights into just a few years of research. Challenges remain, including improving resolution, increasing imaging speed for large samples, and developing user-friendly AI tools. But as we overcome these hurdles, Deep 3D Histology could become a routine tool in both research and clinical settings. The future of biomedical research is three-dimensional, molecularly detailed, and AI-enhanced. This new era of 3D omics has the potential to revolutionize medicine and our understanding of life itself. You can read the full perspective and join the discussion on this exciting frontier of science: https://rdcu.be/dNBe8 More technical details are here as tweetorial: https://lnkd.in/d48cTXDE #AI #DeepLearning #Clearing #3D #Imaging #Omics #Deep3DHistology

220,000 brain MRIs. 5.6 million sequences. One single model. Meet Prima — the first brain MRI foundation model trained on an entire health system’s clinical data. Just published in Nature Biomedical Engineering. And the results deserve a closer look: → 29,431 MRI studies tested in real-world operations over 12 months → 52 radiologic diagnoses with a mean diagnostic AUC of 92.0% → Explainable differential diagnoses, worklist prioritization, and referral recommendations → Context-aware: integrates imaging with clinical history and the ordering reason But here’s the real takeaway: This isn’t a “clever prompt on GPT-4.” This is a model that learned from the clinical routine of an entire health system. The path to a “generalist imaging copilot” doesn’t run through prompt engineering. It runs through system-wide learning, workflow design, and proof in routine operations. The question is no longer if, but when: At what evidence threshold would you let an AI reprioritize your brain MRI worklist? 📄 Paper: Nature Biomedical Engineering (link in comments) 🏥 Team: University of Michigan / Michigan Medicine #Neuroradiology #MedicalAI #FoundationModels #Radiology #HealthTech

𝗣𝗮𝗽𝗲𝗿 𝗙𝗿𝗶𝗱𝗮𝘆 🎯 𝗦𝗲𝗲𝗶𝗻𝗴 𝗱𝗲𝗲𝗽𝗲𝗿 𝗶𝗻 𝘁𝗵𝗶𝗰𝗸 𝘁𝗶𝘀𝘀𝘂𝗲 with scattering 𝗯𝘆 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗹𝗶𝗴𝗵𝘁 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺 🔬 One of the biggest limitations in biological microscopy is that thick tissue scatters light, quickly washing out contrast and preventing us from seeing meaningful structures beyond the first few hundred microns. ❓ 𝗦𝗼 𝘁𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗻𝗮𝘁𝘂𝗿𝗮𝗹𝗹𝘆 𝗰𝗼𝗺𝗲𝘀: 𝗰𝗮𝗻 𝘄𝗲 𝗯𝗼𝗼𝘀𝘁 𝗱𝗲𝗲𝗽-𝘁𝗶𝘀𝘀𝘂𝗲 𝗰𝗼𝗻𝘁𝗿𝗮𝘀𝘁 𝘂𝘀𝗶𝗻𝗴 𝘀𝗶𝗺𝗽𝗹𝗲 𝗼𝗽𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗹𝗮𝗯𝗲𝗹𝘀? Turns out… You can. Gregory McKay MD PhD, Jerome Mertz and Nicholas Durr propose a surprisingly elegant answer with their new approach: 𝗕𝗮𝗰𝗸-𝗶𝗹𝗹𝘂𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗧𝗼𝗺𝗼𝗴𝗿𝗮𝗽𝗵𝘆 (𝗕𝗜𝗧) ⚙️ 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸: Instead of shining light from above, they use an incoherent LED source that is demagnified and its image placed past the focal plane of the objective. This creates a small source of semi-coherent back-scattered light. 💡That means the light effectively comes from “behind” the focal plane, passes through the focal plane region, and is collected by the objective.  This provides interference contrast to weakly scattering objects at the focal plane. 🎯 Despite using simple optics, they achieve: 🔹 Much higher contrast in millimetre-thick, scattering tissue 🔹 Label-free imaging of deep structures without sectioning 🔹 OCT-like depth sensitivity but with wide-field simplicity What I found most impressive was the in vivo imaging of human ventral tongue vasculature, where blood cells can be clearly seen in the blood flow. Here is the link to the paper, on ArXiv(free access): 🔗 https://lnkd.in/erwm2inW ⚠️ Keep in mind that the paper was therefore not peer-reviewed yet. By rethinking something as fundamental as illumination direction, this work opens new possibilities for accessible deep-tissue imaging, useful for pathology, biomedical diagnostics, and fast label-free screening. Congratulations to the authors for this clever and impactful work #PaperFriday#OpticalMicroscopy #LabelFreeImaging #DeepTissueImaging #Photonics #ComputationalMicroscopy #ImagingInnovation

I'm happy to share our latest publication @MRM introducing RELAX-MORE, a new self-supervised #deeplearning reinforced with #MRI physics modeling and optimization theory to improve quantitative MRI. This new study demonstrated that integrating optimization theory and physics-informed #AI can substantially improve the overall performance, accuracy, robustness, generalizability, and computing timing efficiency in #qMRI reconstruction. RELAX-MORE presents a promising qMRI approach that can benefit many clinical applications in the future. Particularly, we addressed several challenges of deep learning by leveraging: • Subject-specific learning enables the method to use minimal data for qMRI model training. • Adaptive learning mitigates reconstruction bias due to discrepancies between training and testing data. • Bi-level optimization applied unrolled gradient descent algorithm in deep learning, allowing improved reconstruction and feature learning for qMRI. • A general framework enables the modular design of deep neural networks for many qMRI signal models to characterize tissue properties.   The full paper of RELAX-MORE: https://lnkd.in/eHFXaPcA. The original RELAX: https://lnkd.in/eiYR-DwY. The code for implementing the model is now available upon request.   #Research is performed at The MGH/HST Martinos Center for Biomedical Imaging, and sponsored by National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) and National Institute of Biomedical Imaging and Bioengineering (NIBIB) #ismrm #machinelearning #imaging #biomedicalengineering

Bioelectronics now have their own nervous system. In our latest research, we engineer networks of therapeutic microchips with yearlong lifetimes that wirelessly communicate by sending signals through the body's own tissue. BioRxiv Paper: https://lnkd.in/gKSSfq9G Our Smart Wireless Artificial Nervous System (SWANS) is 15-30x more energy efficient than Bluetooth or NFC components. It's also multiple times smaller, allowing it to easily fit inside of a pill or needle and work for 9+ months without recharging. This research has the potential to revolutionize neuromodulation, biosensing, targeted drug delivery, and many other forms of personalized medicine. Imagine a central wearable hub, such as a smart watch, capable of seamlessly controlling, communicating with, and coordinating any internal medical device. Just like how our nervous system induces voltage gradients in nerves to efficiently send signals across the body, when SWANS emits signals, it generates voltage gradients in the surrounding tissue that selectively turn on transistor switches placed in other devices. A transistor will switch on when its gate pins are biased past a certain threshold, and the generated electric field can be tuned to uniquely bias many possible transistor circuits. This allows for bioelectronic wearables and implants to communicate individually or in groups. In rats, SWANS signals can pass from the skin all the way to the center of the digestive tract and across the entire body. Previously, we have also shown that these signals can pass through swine. In our latest research paper, we characterize the SWANS system and demonstrate SWANS’ ability to wirelessly regulate dual hind leg motor control by connecting electronic-skin sensors to implantable neural interfaces via ionic signaling. We show that a motion sensor placed on the left front paw of a rat can signal the left hind paw to move. It works by sending a small electrical pulse ionically through the tissue when triggered, which switches on a nerve cuff attached to the sciatic nerve. Even more exciting, we can add multiple sensors and multiple nerve cuffs. If we place a second sensor on the rat's right front paw and a second nerve cuff on the right hind paw, each sensor can trigger pulses that uniquely stimulate each leg. Left, right, left, right. This work was made possible by a number of amazing scientists, including Ramy Ghanim, Yoon Jae Phillip Lee, and W. Hong Yeo, as well as a number of funding sources, including the NIH and Georgia Institute of Technology's Institute for Matter and Systems. Other co-authors include Garan Byun, Joy Jackson, Julia Ding, Elaine Feller, Eugene Kim, Dilay Aygun, Anika Kaushik, Alaz Cig, Jihoon Park, Sean Healy, Camille Cunin, and Aristide Gumyusenge, Ph.D.. It's also our lab's first research paper!

Two open-source libraries often sit at the heart of our workflow: VTK and SimpleITK. Here's how we use both. VTK (Visualization Toolkit) We use VTK to power all the visualization capabilities of our DICOM viewers. It’s what makes it possible to: - Display MPR (multi-planar reconstruction) views (axial, sagittal, and coronal). - Enable 3D volume rendering for CT or MRI scans. - Support measurements and annotations directly inside the viewer. - Provide interactive tools for zooming, clipping, and exploring medical images in detail. In short, VTK is what brings the medical data to life for clinicians and researchers. SimpleITK We rely on SimpleITK for medical image processing. It’s a key part of how we prepare and transform data behind the scenes. It helps us: - Pre-process DICOM or NIfTI data before AI inference (e.g., resampling, normalization, or intensity clipping). - Post-process AI outputs like segmentation masks for cleaner visualization. - Run custom image processing algorithms asynchronously in the background, such as: - Noise reduction or image denoising - Image registration or alignment between scans - Region growing or threshold-based segmentation - Morphological operations (erosion, dilation, etc.) These operations often happen invisibly to the user but make a huge difference in accuracy and quality. In short, the combination of VTK for visualization and SimpleITK for processing lets us build powerful, end-to-end medical imaging solutions. From loading raw DICOM data to viewing clean 3D reconstructions or AI-powered segmentations in the browser. Both are open-source, reliable, and incredibly flexible, and when used together, they enable the kind of medical imaging platforms that used to require proprietary software.