OCT Technique Improves Accuracy of Deep Brain Stimulation Mapping

Deep brain stimulation (DBS), a surgical procedure that can be used to treat Parkinson’s, obsessive-compulsive disorder, and other neurological disorders, involves implanting electrodes in specific brain regions to regulate abnormal neural activity. The precise placement of these electrodes is crucial for a successful clinical outcome.

Magnetic resonance imaging (MRI), the tool commonly used for DBS mapping, lacks the resolution and contrast needed to accurately pinpoint the small, deep brain nuclei targeted for electrode placement. Consequently, researchers are exploring optical imaging techniques with better contrast, higher resolution, and lower costs than MRI to serve as supplementary tools in intraoperative DBS.

A study by Laval University and Harvard Medical School explores one such tool, polarization-sensitive optical coherence tomography (PS-OCT), and demonstrates its potential as a complementary imaging technique for guiding DBS surgery.

Unlike MRI, which provides mm-scale resolution, PS-OCT can visualize brain structures at the μm level. This enables it to provide detailed information essential to accurately target electrodes used in DBS surgery.

The researchers tested PS-OCT on three primary DBS targets in a postmortem animal. To simulate a DBS procedure, they inserted a PS-OCT probe into the brain along predefined trajectories. As the probe was pulled through the tissue, it collected data and captured high-resolution images of the brain’s internal structure. The researchers matched these images with MRI scans and anatomical references to assess their accuracy.

The PS-OCT system used a rotating catheter with a tiny lens and prism to direct light into the tissue and measure how the light’s polarization changed as it passed through different structures. This change, or birefringence, reflects the alignment and density of fibers in the brain’s white matter. The use of polarized light to detect subtle structural differences in tissue and capture birefringence could enable more accurate identification of white matter fiber tracts — bundles of nerve fibers in the brain that are crucial landmarks for DBS targeting.

The researchers used a simplified segmentation approach to compare the performance of PS-OCT with MRI. They averaged data along the probe path and applied clustering to separate tissue types. This allowed them to create “tissue barcodes” showing transitions between white and gray matter.

The results showed that PS-OCT was able to distinguish between white and gray matter more clearly than MRI. PS-OCT also captured fine fiber structures that MRI missed, such as the internal capsule, a dense bundle of fibers important for DBS planning. In one case, PS-OCT identified highly organized fiber tracts near the external pallidum that were invisible in MRI scans.

Overall, PS-OCT’s polarization-sensitive reconstruction algorithms provided more detailed, accurate information than MRI, while remaining consistent with MRI findings.

PS-OCT could provide surgeons with supplementary intraoperative feedback during DBS procedures, improving accuracy and reducing the risk of electrode misplacement. Moreover, the system’s compact form factor and imaging paradigm could be integrated seamlessly into the surgical workflow.

“Catheter-based PS OCT shows strong promise as a tool complementary to MRI in DBS neurosurgery,” researcher Shadi Masoumi said. “By providing high-resolution structural information and visualizing critical fiber pathways, it could help surgeons target brain regions more precisely.”

Although PS-OCT offers superior resolution, future advancements could further broaden its applicability. It currently measures fiber orientation in 2D only, and the ability to capture fiber orientations in 3D would increase its value as a visualization tool. The PS-OCT probe used in the study was slightly larger than standard DBS electrodes, but smaller probes are now available and could be adapted for clinical use.

Next steps include live testing, integration into surgical workflows, and direct comparisons with diffusion MRI, another technique used to map brain fibers. If successful, PS-OCT could become a valuable addition to the neurosurgical toolkit, improving outcomes for patients undergoing DBS.

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Hypervision and imec Collaborate on Hyperspectral Imaging for Surgery

Hypervision, a spin-out company from King’s College London that aims to advance computer-assisted tissue analysis for improved surgical precision and patient safety, has signed a strategic development agreement with imec. The collaboration targets the co-development of scalable technologies tailored for surgical applications, as the company works to scale its on-chip hyperspectral imaging and real-time AI analytics.

Hypervision's technology delivers tissue-level insights, including on oxygenation, perfusion, and tissue differentiation. Its regulatory-cleared intraoperative imaging platform combines on-chip hyperspectral imaging with real-time AI analytics operating at over 60 fps. Additionally, the technology is designed to integrate into existing surgical vision platforms and workflows. The company's platform is currently under clinical evaluation in U.K. hospitals, with a primary focus on gastrointestinal surgery.

Though hyperspectral imaging is already used in medical applications, previous hyperspectral systems have struggled with integration due to hardware complexity, slow processing speeds, and poor compatibility with surgical workflows. These bottlenecks can restrict the use of these system to research settings or post-operative analysis.

As part of the collaboration, imec is leveraging its expertise in semiconductor fabrication, equipment, and process technology to develop on-chip spectral imaging and to design and manufacture interference-based optical filters at the wafer level. imec's CMOS infrastructure provides compact, clean, and high-yield optical filter integration with scalability to high-volume production at low cost.

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Low-Cost Microphone Listens with Light

Traditional microphones capture tiny vibrations on the surfaces of objects caused by sound waves and turn them into audible signals. A microphone developed by researchers at the Beijing Institute of Technology operates differently: Rather than sound, the microphone listens with light. This light-enabled microphone is able to pick up sounds in situations where traditional microphones are ineffective, such as through a glass window.

According to the researchers, the technique can work on everyday objects such as leaves and paper.

“The new technology could potentially change the way we record and monitor sound, bringing new opportunities to many fields, such as environmental monitoring, security, and industrial diagnostics,” said research team leader Xu-Ri Yao. “For example, it could make it possible to talk to someone stuck in a closed-off space like a room or a vehicle.”

While this technology has seen previous exploration, its earlier iteration has required expensive optical equipment. The current project sought to simplify the process by using single-pixel imaging, which would make this technology more accessible. Single-pixel imaging captures images using just one light detector instead of a traditional camera sensor with millions of pixels.

Rather than recording an image all at once, the scene's light is modulated using time-varying structured patterns by a spatial light modulator (SLM), and the single-pixel detector measures the amount of modulated light for each pattern. A computer then uses these measurements to reconstruct information about the object.

The team used a high-speed SLM to encode light reflected from the vibrating surface. The sound-induced motion causes subtle changes in light intensity that were captured by the single-pixel detector and then decoded into audible sound. Team members then used Fourier-based localization methods to track object vibrations, which enabled efficient and precise measurement of minute variations. Single-pixel detectors record the information in a relatively small amount of data, which means the data can be transferred between devices quickly.

The researchers tested the microphone, using it to reconstruct Chinese and English pronunciations of numbers as well as a segment from Beethoven’s Für Elise. They used a paper card and a leaf as vibration targets, placing them 0.5 m away from the objects while a nearby speaker played the audio. The system was able to reconstruct clear and intelligible audio, with the paper card producing better results than the leaf. Low-frequency sounds (<1 kHz) were accurately recovered, while high-frequency sounds (>1 kHz) showed slight distortion that improved when a signal processing filter was applied. Tests of the system's data rate showed it produced 4?MB/s, a rate sufficiently low to minimize storage demands and allow for long-term recording.

Future plans with this technology are human pulse and heart rate detection, as well as a wider range for long-distance sound detection.

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Hybrid Material Achieves Fast, Stable Phosphorescent Emission for OLEDs

A hybrid material made from organic chromophores and transition metal dichalcogenides (TMDs) can produce stable, fast, phosphorescent light emission for OLED displays. The new hybrid, developed by a team co-led by the University of Michigan (U-M), could replace the heavy metal components currently used to improve efficiency, brightness, and color range in OLED devices.

Organic materials with room-temperature phosphorescence are an appealing alternative to heavy metals because of their tunable luminescent properties, large design window, environmentally-friendly components, and economical production cost.

Phosphorescence is 3 times more energy-efficient than fluorescence, but happens more slowly. To keep pace with modern displays, which operate at 120 frames per second, phosphorescence must occur in microseconds. The metals used in OLEDs, like iridium and platinum, enable phosphorescence to take place in microseconds instead of milliseconds. The large atomic nucleus of the heavy metal generates a magnetic field that causes the excited electrons to emit light faster as they go from the excited to the ground state.

The researchers developed an alternative strategy for developing emitters for phosphorescence by creating heterostructures of organic chromophores and TMDs.

The heterostructures were made of diethyl 2,5-dihydroxy terephthalate (DDT), an organic fluorophore, using various TMDs. A 2D layer of molybdenum (MoS2) and sulfur is positioned near a similarly thin layer of the organic light-emitting material, achieving physical proximity without any chemical bonding. Light emission occurs entirely within the organic material, without the need for weak, metal-organic ligand bonding.

The team observed the TMD-induced photophysical variations in the DDT and found that the DDT on the TMDs emitted microsecond phosphorescence at room temperature. It further found that spin-orbit couplings of the DDT were enhanced by the through-space, spin-orbit proximity effect of the TMDs in the heterostructures.

The hybrid construction increased light emission by 1000 times, achieving speeds fast enough for modern displays. “We found a way to make a phosphorescent organic molecule that can emit light on the microsecond scale, without including heavy metals in the molecular framework,” professor Jinsang Kim said.

Phosphorescent OLEDs that rely on heavy metals also use the metals to help produce color. The weak chemical bonds between the metal and the organic material can break apart when two excited electrons come into contact, dimming the pixel.

Pixel burnout in high-energy blue light has yet to be resolved, but the researchers hope their new design approach will contribute to stable, blue phosphorescent pixels. Currently, OLEDs use phosphorescent red and green pixels and fluorescent blue pixels, avoiding blue pixel burnout at the expense of lowering energy efficiency.

When the researchers analyzed the molecular hybrid system, they made an unusual discovery — the system appeared to break a rule of quantum mechanics.

Paired electrons sharing an orbital seemed to have a combined spin under dark conditions, suggesting a “forbidden” triplet state, when instead their spins should have cancelled one another out. According to a principle of quantum mechanics, the Pauli Exclusion Principle, an electron and its partner in the ground state must spin in opposite directions.

“We don’t yet fully understand what causes this triplet character in the ground state because this violates the Pauli Exclusion Principle,” Kim said. “That’s why we have a lot of questions about what really makes that happen.”

The researchers plan to explore how the hybrid material achieves triplet character ground states, while also pursuing potential spintronics device applications. The team has applied for patent protection with the assistance of U-M Innovation Partnerships and is seeking partners to create devices using the hybrid material.

In addition to the team from U-M, researchers from Inha University; Sungkyunkwan University; the University of California, Berkeley; and Dongguk University contributed to the study.


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Imaging Technique Provides Superresolution and High SNR for Thick Samples

The creation of sharp, detailed images of thick biological samples, such as those of human tissue, are now possible using an optical microscopy method developed by researchers at the Italian Institute of Technology (IIT). The team aimed to achieve the full resolution and signal to noise (SNR) benefits of image scanning microscopy (ISM). At the same time, it wanted to improve optical sectioning for complex, high-density samples.

The resulting imaging technique, which the researchers named superresolution sectioning image scanning microscopy (s2ISM), could help scientists gain insight into the aging process and the origin of certain diseases by studying the biomolecular processes inside living cells.

The technique reconstructs an image with digital and optical superresolution, high SNR, and enhanced optical sectioning from single-plane acquisition. It provides superresolution and optical sectioning simultaneously.

An instrument that acts like a light scalpel penetrates the sample deeply and observes the sample without damaging it. A small array of sensors captures the light at the point where it hits, and captures the various ways the light spreads in the sample. Once this information is recorded, a reconstruction algorithm processes the information, identifying the path of the light through the sample and producing sharper, better-sectioned images, without losing signal quality.

“The optical microscope used is equipped with an array of [single-photon avalanche diode] detectors, capable of detecting the arrival of individual photons with very high spatial and temporal precision,” researcher Alessandro Zunino said. “This characteristic not only improves the resolution and optical sectioning, but also enables advanced techniques such as fluorescence lifetime, which are fundamental to explore molecular dynamics in living tissues and to provide functional as well as structural information.”

Previous approaches to optical microscopy made it difficult to observe thick samples in detail, because the contrast in the image was hindered by the high density of the samples’ structures.

“What we did was rethink the way microscopes measure the light that hits the samples under observation, improving both the spatial resolution and the contrast when studying thick tissues, where background light would normally overpower their structure, creating noise in the images,” researcher Giuseppe Vicidomini, who coordinated the study, said.

The researchers formulated a comprehensive theoretical framework for their approach and validated the approach with images of biological samples captured using a custom setup equipped with a SPAD array detector. They demonstrated the s2ISM technique by exciting fluorescence emission in both linear and nonlinear regimes. Also, they generalized the reconstruction algorithm for fluorescence lifetime imaging.

The s2 microscopy method requires no changes in the optical system and can be extended to any laser scanning microscopy technique.

The new microscopy technique has many potential applications. For example, it could be used to study brain tissue, tumors, organoids, and other biological systems and observe the processes of living cells to better understand disease progression. In the pharmaceutical field, the technique could be used to visualize in real time how drugs interact with living biological tissues, speeding the discovery of new treatments.

The s2 technique is open-access and the code is provided as an open-source Python package; any laboratory can adopt, modify, and apply this technique at no cost and without the need for complex equipment. To simplify the application of s2ISM, the researchers have proposed a rigorous strategy to automatically extract the relevant parameters needed to run the algorithm.

The team hopes that making the software and data available for broad, rapid dissemination will encourage further innovation within the scientific community, especially in the fields of optical microscopy and life sciences.


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‘Self-Driving’ Microscope Predicts Neurodegenerative Disease

The accumulation of misfolded proteins in the brain is central to the progression of neurodegenerative diseases such as Huntington’s, Alzheimer’s, and Parkinson’s. But to the human eye, proteins that are destined to form harmful aggregates are indistinguishable from normal proteins. The formation of such aggregates also tends to happen randomly and relatively rapidly — on the scale of minutes. The ability to identify and characterize protein aggregates is essential for understanding and fighting neurodegenerative diseases.

Now, using deep learning, École Polytechnique Fédérale de Lausanne (EPFL) researchers have developed a “self-driving” imaging system that leverages multiple microscopy methods to track and analyze protein aggregation in real time — and even anticipate it before it begins. In addition to maximizing imaging efficiency, the approach minimizes the use of fluorescent labels, which can alter the biophysical properties of cell samples and impede accurate analysis.

“This is the first time we have been able to accurately foresee the formation of these protein aggregates,” said recent EPFL Ph.D. graduate Khalid Ibrahim. “Because their biomechanical properties are linked to diseases and the disruption of cellular function, understanding how these properties evolve throughout the aggregation process will lead to fundamental understanding essential for developing solutions.”

The project is the result of a longstanding collaboration between the labs of Aleksandra Radenovic, head of the Laboratory of Nanoscale Biology and Hilal Lashuel of the school of sciences. The work unites complementary expertise in neurodegeneration and advanced live-cell imaging technologies.

“This project was born out of a motivation to build methods that reveal new biophysical insights, and it is exciting to see how this vision has now borne fruit,” Radenovic said.

In their first collaborative effort, led by Ibrahim, the team developed a deep learning algorithm that was able to detect mature protein aggregates when presented with unlabeled images of living cells. The new study builds on that work with an image classification version of the algorithm that analyzes such images in real time: when this algorithm detects a mature protein aggregate, it triggers a Brillouin microscope, which analyzes scattered light to characterize the aggregates’ biomechanical properties like elasticity.

Normally, the slow imaging speed of a Brillouin microscope would make it a poor choice for studying rapidly evolving protein aggregates. But thanks to the EPFL team’s AI-driven approach, the Brillouin microscope is only switched on when a protein aggregate is detected, speeding up the entire process while opening new avenues in smart microscopy.

“This is the first publication that shows the impressive potential for self-driving systems to incorporate label-free microscopy methods, which should allow more biologists to adopt rapidly evolving smart microscopy techniques,” Ibrahim said.

Because the image classification algorithm only targets mature protein aggregates, the researchers still needed to go further if they wanted to catch aggregate formation in the act. For this, they developed a second deep learning algorithm and trained it on fluorescently labelled images of proteins in living cells. This “aggregation-onset” detection algorithm can differentiate between near-identical images to correctly identify when aggregation will occur with 91% accuracy. Once this onset is spotted, the self-driving system again switches on Brillouin imaging to provide a never-before-seen window into protein aggregation. For the first time, the biomechanics of this process can be captured dynamically as it occurs.

Lashuel emphasized that in addition to advancing smart microscopy, this work has important implications for drug discovery and precision medicine. “Label-free imaging approaches create entirely new ways to study and target small protein aggregates called toxic oligomers, which are thought to play central causative roles in neurodegeneration,” he said. “We are excited to build on these achievements and pave the way for drug development platforms that will accelerate more effective therapies for neurodegenerative diseases.”

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Novel 3D Laser Scanner Helps Harvest with Purpose

Due to a shortage of skilled workers, researchers around the world are working to develop harvesting robots that could provide support to agricultural businesses. Currently, however, according to Andreas Nüchter, from Julius-Maximilians-Universität (JMU) Würzburg, initial prototypes have yet to reach high levels of functionality for the necessary applications.

In response, researchers at the University of Würzburg have developed a 3D laser scanner system that aims to provide a better understanding of the condition of plants — for example, by reliably measuring the water content of fruits. This knowledge is crucial for determining the right time to harvest, according to Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) researcher Manuela Zude-Sasse, who led the development team.

“For the production of horticultural products, knowledge of the stage of ripeness is very important in order to be able to optimally control cultivation, harvest time and storage,” Zude-Sasse said.

“Against the backdrop of increasingly variable growth factors due to global warming, precise data on fruit development is becoming increasingly important — for scientific modelling as well as for the future use of commercial harvesting robots.”

The development team installed the system on a test site in Potsdam, Germany, and initial tests have been successful, team members said. For testing, the 3D laser scanner was mounted on a sensor conveyor station that circles a plantation of 120 apple trees. Harvesting robots, or the imaging and sensing systems embedded into or onto them, must be able to ‘read’ apple trees and other plants correctly, since no two plants look exactly alike.

The plant scanner is further designed to withstand wind and weather, and to operate in temperatures between 0 °C - 40 °C. The sensor system works on the principle of structured light: It projects three wavelengths: 520 nm, 660 nm, and 830 nm onto the plants. The reflected signals provide precise spatial information about the plants. Because the signals are available separately for each wavelength, they open possibilities for recording physiological properties of the plants, such as water content.

The sensor system will be used continuously on the ATB test site until November to monitor the 120 apple trees. The researchers designed the 3D laser scanner exclusively for experimental use, they said, with the goal of improving the data basis for modelling work and the specifications for future harvesting robots.

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