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China’s neuromorphic chips: Darwin and Tianjic

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I believe that China has more than two neuromorphic chips. The two being featured here are the ones for which I was easily able to find information.

The Darwin chip

The first information (that I stumbled across) about China and a neuromorphic chip (Darwin) was in a December 22, 2015 Science China Press news release on EurekAlert,

Artificial Neural Network (ANN) is a type of information processing system based on mimicking the principles of biological brains, and has been broadly applied in application domains such as pattern recognition, automatic control, signal processing, decision support system and artificial intelligence. Spiking Neural Network (SNN) is a type of biologically-inspired ANN that perform information processing based on discrete-time spikes. It is more biologically realistic than classic ANNs, and can potentially achieve much better performance-power ratio. Recently, researchers from Zhejiang University and Hangzhou Dianzi University in Hangzhou, China successfully developed the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on Spiking Neural Networks, fabricated by standard CMOS technology.

With the rapid development of the Internet-of-Things and intelligent hardware systems, a variety of intelligent devices are pervasive in today’s society, providing many services and convenience to people’s lives, but they also raise challenges of running complex intelligent algorithms on small devices. Sponsored by the college of Computer science of Zhejiang University, the research group led by Dr. De Ma from Hangzhou Dianzi university and Dr. Xiaolei Zhu from Zhejiang university has developed a co-processor named as Darwin.The Darwin NPU aims to provide hardware acceleration of intelligent algorithms, with target application domain of resource-constrained, low-power small embeddeddevices. It has been fabricated by 180nm standard CMOS process, supporting a maximum of 2048 neurons, more than 4 million synapses and 15 different possible synaptic delays. It is highly configurable, supporting reconfiguration of SNN topology and many parameters of neurons and synapses.Figure 1 shows photos of the die and the prototype development board, which supports input/output in the form of neural spike trains via USB port.

The successful development ofDarwin demonstrates the feasibility of real-time execution of Spiking Neural Networks in resource-constrained embedded systems. It supports flexible configuration of a multitude of parameters of the neural network, hence it can be used to implement different functionalities as configured by the user. Its potential applications include intelligent hardware systems, robotics, brain-computer interfaces, and others.Since it uses spikes for information processing and transmission,similar to biological neural networks, it may be suitable for analysis and processing of biological spiking neural signals, and building brain-computer interface systems by interfacing with animal or human brains. As a prototype application in Brain-Computer Interfaces, Figure 2 [not included here] describes an application example ofrecognizingthe user’s motor imagery intention via real-time decoding of EEG signals, i.e., whether he is thinking of left or right, and using it to control the movement direction of a basketball in the virtual environment. Different from conventional EEG signal analysis algorithms, the input and output to Darwin are both neural spikes: the input is spike trains that encode EEG signals; after processing by the neural network, the output neuron with the highest firing rate is chosen as the classification result.

The most recent development for this chip was announced in a September 2, 2019 Zhejiang University press release (Note: Links have been removed),

The second generation of the Darwin Neural Processing Unit (Darwin NPU 2) as well as its corresponding toolchain and micro-operating system was released in Hangzhou recently. This research was led by Zhejiang University, with Hangzhou Dianzi University and Huawei Central Research Institute participating in the development and algorisms of the chip. The Darwin NPU 2 can be primarily applied to smart Internet of Things (IoT). It can support up to 150,000 neurons and has achieved the largest-scale neurons on a nationwide basis.

The Darwin NPU 2 is fabricated by standard 55nm CMOS technology. Every “neuromorphic” chip is made up of 576 kernels, each of which can support 256 neurons. It contains over 10 million synapses which can construct a powerful brain-inspired computing system.

“A brain-inspired chip can work like the neurons inside a human brain and it is remarkably unique in image recognition, visual and audio comprehension and naturalistic language processing,” said MA De, an associate professor at the College of Computer Science and Technology on the research team.

“In comparison with traditional chips, brain-inspired chips are more adept at processing ambiguous data, say, perception tasks. Another prominent advantage is their low energy consumption. In the process of information transmission, only those neurons that receive and process spikes will be activated while other neurons will stay dormant. In this case, energy consumption can be extremely low,” said Dr. ZHU Xiaolei at the School of Microelectronics.

To cater to the demands for voice business, Huawei Central Research Institute designed an efficient spiking neural network algorithm in accordance with the defining feature of the Darwin NPU 2 architecture, thereby increasing computing speeds and improving recognition accuracy tremendously.

Scientists have developed a host of applications, including gesture recognition, image recognition, voice recognition and decoding of electroencephalogram (EEG) signals, on the Darwin NPU 2 and reduced energy consumption by at least two orders of magnitude.

In comparison with the first generation of the Darwin NPU which was developed in 2015, the Darwin NPU 2 has escalated the number of neurons by two orders of magnitude from 2048 neurons and augmented the flexibility and plasticity of the chip configuration, thus expanding the potential for applications appreciably. The improvement in the brain-inspired chip will bring in its wake the revolution of computer technology and artificial intelligence. At present, the brain-inspired chip adopts a relatively simplified neuron model, but neurons in a real brain are far more sophisticated and many biological mechanisms have yet to be explored by neuroscientists and biologists. It is expected that in the not-too-distant future, a fascinating improvement on the Darwin NPU 2 will come over the horizon.

I haven’t been able to find a recent (i.e., post 2017) research paper featuring Darwin but there is another chip and research on that one was published in July 2019. First, the news.

The Tianjic chip

A July 31, 2019 article in the New York Times by Cade Metz describes the research and offers what seems to be a jaundiced perspective about the field of neuromorphic computing (Note: A link has been removed),

As corporate giants like Ford, G.M. and Waymo struggle to get their self-driving cars on the road, a team of researchers in China is rethinking autonomous transportation using a souped-up bicycle.

This bike can roll over a bump on its own, staying perfectly upright. When the man walking just behind it says “left,” it turns left, angling back in the direction it came.

It also has eyes: It can follow someone jogging several yards ahead, turning each time the person turns. And if it encounters an obstacle, it can swerve to the side, keeping its balance and continuing its pursuit.

… Chinese researchers who built the bike believe it demonstrates the future of computer hardware. It navigates the world with help from what is called a neuromorphic chip, modeled after the human brain.

Here’s a video, released by the researchers, demonstrating the chip’s abilities,

Now back to back to Metz’s July 31, 2019 article (Note: A link has been removed),

The short video did not show the limitations of the bicycle (which presumably tips over occasionally), and even the researchers who built the bike admitted in an email to The Times that the skills on display could be duplicated with existing computer hardware. But in handling all these skills with a neuromorphic processor, the project highlighted the wider effort to achieve new levels of artificial intelligence with novel kinds of chips.

This effort spans myriad start-up companies and academic labs, as well as big-name tech companies like Google, Intel and IBM. And as the Nature paper demonstrates, the movement is gaining significant momentum in China, a country with little experience designing its own computer processors, but which has invested heavily in the idea of an “A.I. chip.”

…

If you can get past what seems to be a patronizing attitude, there are some good explanations and cogent criticisms in the piece (Metz’s July 31, 2019 article, Note: Links have been removed),

… it faces significant limitations.

A neural network doesn’t really learn on the fly. Engineers train a neural network for a particular task before sending it out into the real world, and it can’t learn without enormous numbers of examples. OpenAI, a San Francisco artificial intelligence lab, recently built a system that could beat the world’s best players at a complex video game called Dota 2. But the system first spent months playing the game against itself, burning through millions of dollars in computing power.

Researchers aim to build systems that can learn skills in a manner similar to the way people do. And that could require new kinds of computer hardware. Dozens of companies and academic labs are now developing chips specifically for training and operating A.I. systems. The most ambitious projects are the neuromorphic processors, including the Tianjic chip under development at Tsinghua University in China.

Such chips are designed to imitate the network of neurons in the brain, not unlike a neural network but with even greater fidelity, at least in theory.

Neuromorphic chips typically include hundreds of thousands of faux neurons, and rather than just processing 1s and 0s, these neurons operate by trading tiny bursts of electrical signals, “firing” or “spiking” only when input signals reach critical thresholds, as biological neurons do.

…

Tiernan Ray’s August 3, 2019 article about the chip for ZDNet.com offers some thoughtful criticism with a side dish of snark (Note: Links have been removed),

Nature magazine’s cover story [July 31, 2019] is about a Chinese chip [Tianjic chip]that can run traditional deep learning code and also perform “neuromorophic” operations in the same circuitry. The work’s value seems obscured by a lot of hype about “artificial general intelligence” that has no real justification.

The term “artificial general intelligence,” or AGI, doesn’t actually refer to anything, at this point, it is merely a placeholder, a kind of Rorschach Test for people to fill the void with whatever notions they have of what it would mean for a machine to “think” like a person.

Despite that fact, or perhaps because of it, AGI is an ideal marketing term to attach to a lot of efforts in machine learning. Case in point, a research paper featured on the cover of this week’s Nature magazine about a new kind of computer chip developed by researchers at China’s Tsinghua University that could “accelerate the development of AGI,” they claim.

The chip is a strange hybrid of approaches, and is intriguing, but the work leaves unanswered many questions about how it’s made, and how it achieves what researchers claim of it. And some longtime chip observers doubt the impact will be as great as suggested.

“This paper is an example of the good work that China is doing in AI,” says Linley Gwennap, longtime chip-industry observer and principal analyst with chip analysis firm The Linley Group. “But this particular idea isn’t going to take over the world.”

The premise of the paper, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” is that to achieve AGI, computer chips need to change. That’s an idea supported by fervent activity these days in the land of computer chips, with lots of new chip designs being proposed specifically for machine learning.

The Tsinghua authors specifically propose that the mainstream machine learning of today needs to be merged in the same chip with what’s called “neuromorphic computing.” Neuromorphic computing, first conceived by Caltech professor Carver Mead in the early ’80s, has been an obsession for firms including IBM for years, with little practical result.

…

[Missing details about the chip] … For example, the part is said to have “reconfigurable” circuits, but how the circuits are to be reconfigured is never specified. It could be so-called “field programmable gate array,” or FPGA, technology or something else. Code for the project is not provided by the authors as it often is for such research; the authors offer to provide the code “on reasonable request.”

More important is the fact the chip may have a hard time stacking up to a lot of competing chips out there, says analyst Gwennap. …

…

What the paper calls ANN and SNN are two very different means of solving similar problems, kind of like rotating (helicopter) and fixed wing (airplane) are for aviation,” says Gwennap. “Ultimately, I expect ANN [?] and SNN [spiking neural network] to serve different end applications, but I don’t see a need to combine them in a single chip; you just end up with a chip that is OK for two things but not great for anything.”

But you also end up generating a lot of buzz, and given the tension between the U.S. and China over all things tech, and especially A.I., the notion China is stealing a march on the U.S. in artificial general intelligence — whatever that may be — is a summer sizzler of a headline.

ANN could be either artificial neural network or something mentioned earlier in Ray’s article, a shortened version of CANN [continuous attractor neural network].

Shelly Fan’s August 7, 2019 article for the SingularityHub is almost as enthusiastic about the work as the podcasters for Nature magazine  were (a little more about that later),

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

…

Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

…

BTW, Fan is a neuroscientist (from her SingularityHub profile page),

Shelly Xuelai Fan is a neuroscientist-turned-science writer. She completed her PhD in neuroscience at the University of British Columbia, where she developed novel treatments for neurodegeneration. While studying biological brains, she became fascinated with AI and all things biotech. Following graduation, she moved to UCSF [University of California at San Francisco] to study blood-based factors that rejuvenate aged brains. She is the co-founder of Vantastic Media, a media venture that explores science stories through text and video, and runs the award-winning blog NeuroFantastic.com. Her first book, “Will AI Replace Us?” (Thames & Hudson) will be out April 2019.

Onto Nature. Here’s a link to and a citation for the paper,

Towards artificial general intelligence with hybrid Tianjic chip architecture by Jing Pei, Lei Deng, Sen Song, Mingguo Zhao, Youhui Zhang, Shuang Wu, Guanrui Wang, Zhe Zou, Zhenzhi Wu, Wei He, Feng Chen, Ning Deng, Si Wu, Yu Wang, Yujie Wu, Zheyu Yang, Cheng Ma, Guoqi Li, Wentao Han, Huanglong Li, Huaqiang Wu, Rong Zhao, Yuan Xie & Luping Shi. Nature volume 572, pages106–111(2019) DOI: https//doi.org/10.1038/s41586-019-1424-8 Published: 31 July 2019 Issue Date: 01 August 2019

This paper is behind a paywall.

The July 31, 2019 Nature podcast, which includes a segment about the Tianjic chip research from China, which is at the 9 mins. 13 secs. mark (AI hardware) or you can scroll down about 55% of the way to the transcript of the interview with Luke Fleet, the Nature editor who dealt with the paper.

Some thoughts

The pundits put me in mind of my own reaction when I heard about phones that could take pictures. I didn’t see the point but, as it turned out, there was a perfectly good reason for combining what had been two separate activities into one device. It was no longer just a telephone and I had completely missed the point.

This too may be the case with the Tianjic chip. I think it’s too early to say whether or not it represents a new type of chip or if it’s a dead end.

Sugar in your bones might be better for you than you think

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These days sugar is often  viewed as leading to health problems but there is an instance where it may be useful—bone regeneration. From a June 19, 2017 news item on Nanowerk (Note: A link has been removed),

There hasn’t been a gold standard for how orthopaedic spine surgeons promote new bone growth in patients, but now Northwestern University scientists have designed a bioactive nanomaterial that is so good at stimulating bone regeneration it could become the method surgeons prefer.

While studied in an animal model of spinal fusion, the method for promoting new bone growth could translate readily to humans, the researchers say, where an aging but active population in the U.S. is increasingly receiving this surgery to treat pain due to disc degeneration, trauma and other back problems. Many other procedures could benefit from the nanomaterial, ranging from repair of bone trauma to treatment of bone cancer to bone growth for dental implants.

“Regenerative medicine can improve quality of life by offering less invasive and more successful approaches to promoting bone growth,” said Samuel I. Stupp, who developed the new nanomaterial. “Our method is very flexible and could be adapted for the regeneration of other tissues, including muscle, tendons and cartilage.”

Stupp is director of Northwestern’s Simpson Querrey Institute for BioNanotechnology and the Board of Trustees Professor of Materials Science and Engineering, Chemistry, Medicine and Biomedical Engineering.

For the interdisciplinary study, Stupp collaborated with Dr. Wellington K. Hsu, associate professor of orthopaedic surgery, and Erin L. K. Hsu, research assistant professor of orthopaedic surgery, both at Northwestern University Feinberg School of Medicine. The husband-and-wife team is working to improve clinically employed methods of bone regeneration.

Sugar molecules on the surface of the nanomaterial provide its regenerative power. The researchers studied in vivo the effect of the “sugar-coated” nanomaterial on the activity of a clinically used growth factor, called bone morphogenetic protein 2 (BMP-2). They found the amount of protein needed for a successful spinal fusion was reduced to an unprecedented level: 100 times less of BMP-2 was needed. This is very good news, because the growth factor is known to cause dangerous side effects when used in the amounts required to regenerate high-quality bone, and it is expensive as well.

A June 19, 2017 Northwestern University news release by Megan Fellman, which originated the news item, tells the rest of the story,

Stupp’s biodegradable nanomaterial functions as an artificial extracellular matrix, which mimics what cells in the body usually interact with in their surroundings. BMP-2 activates certain types of stem cells and signals them to become bone cells. The Northwestern matrix, which consists of tiny nanoscale filaments, binds the protein by molecular design in the way that natural sugars bind it in our bodies and then slowly releases it when needed, instead of in one early burst, which can contribute to side effects.

To create the nanostructures, the research team led by Stupp synthesized a specific type of sugar that closely resembles those used by nature to activate BMP-2 when cell signaling is necessary for bone growth. Rapidly moving flexible sugar molecules displayed on the surface of the nanostructures “grab” the protein in a specific spot that is precisely the same one used in biological systems when it is time to deploy the signal. This potentiates the bone-growing signals to a surprising level that surpasses even the naturally occurring sugar polymers in our bodies.

In nature, the sugar polymers are known as sulfated polysaccharides, which have super-complex structures impossible to synthesize at the present time with chemical techniques. Hundreds of proteins in biological systems are known to have specific domains to bind these sugar polymers in order to activate signals. Such proteins include those involved in the growth of blood vessels, cell recruitment and cell proliferation, all very important biologically in tissue regeneration. Therefore, the approach of the Stupp team could be extended to other regenerative targets.

Spinal fusion is a common surgical procedure that joins adjacent vertebra together using a bone graft and growth factors to promote new bone growth, which stabilizes the spine. The bone used in the graft can come from the patient’s pelvis — an invasive procedure — or from a bone bank.

“There is a real need for a clinically efficacious, safe and cost-effective way to form bone,” said Wellington Hsu, a spine surgeon. “The success of this nanomaterial makes me excited that every spine surgeon may one day subscribe to this method for bone graft. Right now, if you poll an audience of spine surgeons, you will get 15 to 20 different answers on what they use for bone graft. We need to standardize choice and improve patient outcomes.”

In the in vivo portion of the study, the nanomaterial was delivered to the spine using a collagen sponge. This is the way surgeons currently deliver BMP-2 clinically to promote bone growth.

The Northwestern research team plans to seek approval from the Food and Drug Administration to launch a clinical trial studying the nanomaterial for bone regeneration in humans.

“We surgeons are looking for optimal carriers for growth factors and cells,” Wellington Hsu said. “With its numerous binding sites, the long filaments of this new nanomaterial is more successful than existing carriers in releasing the growth factor when the body is ready. Timing is critical for success in bone regeneration.”

In the new nanomaterial, the sugars are displayed in a scaffold built from self-assembling molecules known as peptide amphiphiles, first developed by Stupp 15 years ago. These synthetic molecules have been essential in his work on regenerative medicine.

“We focused on bone regeneration to demonstrate the power of the sugar nanostructure to provide a big signaling boost,” Stupp said. “With small design changes, the method could be used with other growth factors for the regeneration of all kinds of tissues. One day we may be able to fully do away with the use of growth factors made by recombinant biotechnology and instead empower the natural ones in our bodies.”

Here’s a link to and a citation for the paper,

Sulfated glycopeptide nanostructures for multipotent protein activation by Sungsoo S. Lee, Timmy Fyrner, Feng Chen, Zaida Álvarez, Eduard Sleep, Danielle S. Chun, Joseph A. Weiner, Ralph W. Cook, Ryan D. Freshman, Michael S. Schallmo, Karina M. Katchko, Andrew D. Schneider, Justin T. Smith, Chawon Yun, Gurmit Singh, Sohaib Z. Hashmi, Mark T. McClendon, Zhilin Yu, Stuart R. Stock, Wellington K. Hsu, Erin L. Hsu, & Samuel I. Stupp. Nature Nanotechnology 12, 821–829 (2017) doi:10.1038/nnano.2017.109 Published online 19 June 2017

This paper is behind a paywall.

Better bioimaging accuracy with direct radiolabeling of nanomaterials

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Even I can tell the image is improved when the chelator is omitted,

Courtesy: Wiley

A Feb. 9, 2017 news item on phys.org describes a new, chelator-free technique for increased bioimaging accuracy,

Positron emission tomography (PET) plays a pivotal role for monitoring the distribution and accumulation of radiolabeled nanomaterials in living subjects. The radioactive metals are usually connected to the nanomaterial through an anchor, a so-called chelator, but this chemical binding can be omitted if nanographene is used, as American scientists report in the journal Angewandte Chemie. The replacement of chelator-based labeling by intrinsic labeling significantly enhances the bioimaging accuracy and reduces biases.

A Feb 9, 2017Wiley press release (also on EurekAlert), which originated the news item, provides more detail,

Nanoparticles are very promising substances for biodiagnostics (e.g., detecting cancerous tissue) and biotherapy (e.g., destroying tumors by molecular agents), because they are not as fast [sic] metabolized as normal pharmaceuticals and they particularly enrich [sic] in tumors through an effect called enhanced permeability and retention (EPR). Chelators, which have a macrocyclic structure, are used to anchor the radioactive element (e.g., copper-64) onto the nanoparticles’ surface. The tracers are then detected and localized in the body with the help of a positron emission tomography (PET) scanner. However, the use of a chelator can also be problematic, because it can detach from the nanoparticles or bias the imaging. Therefore, the group of Weibo Cai at University of Wisconsin-Madison, USA, sought for chelator-free solutions—and found it in nanographene, one of the most promising substances in nanotechnology.

Nanographene offers the electronic system to provide special binding electrons for some transition metal ions. “π bonds of nanographene are able to provide the additional electron to stably incorporate the 64Cu2+ acceptor ions onto the surface of graphene,” the authors wrote. Thus, it was possible to directly and stably attach the copper isotope to reduced graphene oxide nanomaterials stabilized by poly(ethylene glycol) (PEG), and this system was used for several bioimaging tests including the detection of tumors in mice.

After injection in the mouse model, the scientists observed long blood circulation and high tumor uptake. “Prolonged blood circulation of 64Cu-RGO-PEG […] induced a prompt and persistent tumor uptake via EPR effect,” they wrote. Moreover, the directly radiolabeled nanographene was readily prepared by simply mixing both components and heating them. This simple chelator-free, intrinsically labeled system may provide an attractive alternative to the chelator-based radiolabeling, which is still the “gold standard” in bioimaging.

Here’s a link to and a citation for the paper,

Chelator-Free Radiolabeling of Nanographene: Breaking the Stereotype of Chelation by Sixiang Shi, Cheng Xu, Dr. Kai Yang, Shreya Goel, Hector F. Valdovinos, Dr. Haiming Luo, Emily B. Ehlerding, Dr. Christopher G. England, Dr. Liang Cheng, Dr. Feng Chen, Prof. Robert J. Nickles, Prof. Zhuang Liu, and Prof. Weibo Cai. Angewandte Chemie International Edition DOI: 10.1002/anie.201610649 Version of Record online: 7 FEB 2017

© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

This paper is behind a paywall.

A nanoparticle for a medical imaging machine that doesn’t exist yet

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Researchers at the University of Buffalo (New York state) have created a nanoparticle that can be detected by six imaging devices according to a Jan. 20, 2015 news item on ScienceDaily,

It’s technology so advanced that the machine capable of using it doesn’t yet exist.

Using two biocompatible parts, University at Buffalo researchers and their colleagues have designed a nanoparticle that can be detected by six medical imaging techniques:

• computed tomography (CT) scanning;

• positron emission tomography (PET) scanning;

• photoacoustic imaging;

• fluorescence imaging;

• upconversion imaging; and

• Cerenkov luminescence imaging.

The advantages are obvious should somebody, somewhere create a hexamodal (aka, multimodal, aka hypmodal) sensing device capable of exploiting the advantages of this nanoparticle as the researchers hope.

A Jan. 20, 2015 University of Buffalo news release (also on EurekAlert) by Charlotte Hsu, which originated the news item, describes the ideas underlying the research,

This kind of “hypermodal” imaging — if it came to fruition — would give doctors a much clearer picture of patients’ organs and tissues than a single method alone could provide. It could help medical professionals diagnose disease and identify the boundaries of tumors.

“This nanoparticle may open the door for new ‘hypermodal’ imaging systems that allow a lot of new information to be obtained using just one contrast agent,” says researcher Jonathan Lovell, PhD, UB assistant professor of biomedical engineering. “Once such systems are developed, a patient could theoretically go in for one scan with one machine instead of multiple scans with multiple machines.”

When Lovell and colleagues used the nanoparticles to examine the lymph nodes of mice, they found that CT and PET scans provided the deepest tissue penetration, while the photoacoustic imaging showed blood vessel details that the first two techniques missed.

Differences like these mean doctors can get a much clearer picture of what’s happening inside the body by merging the results of multiple modalities.

A machine capable of performing all six imaging techniques at once has not yet been invented, to Lovell’s knowledge, but he and his coauthors hope that discoveries like theirs will spur development of such technology.

The news release also offers a description of the nanoparticles,

The researchers designed the nanoparticles from two components: An “upconversion” core that glows blue when struck by near-infrared light, and an outer fabric of porphyrin-phospholipids (PoP) that wraps around the core.

Each part has unique characteristics that make it ideal for certain types of imaging.

The core, initially designed for upconversion imaging, is made from sodium, ytterbium, fluorine, yttrium and thulium. The ytterbium is dense in electrons — a property that facilitates detection by CT scans.

The PoP wrapper has biophotonic qualities that make it a great match for fluorescence and photoacoustic imagining. The PoP layer also is adept at attracting copper, which is used in PET and Cerenkov luminescence imaging.

“Combining these two biocompatible components into a single nanoparticle could give tomorrow’s doctors a powerful, new tool for medical imaging,” says Prasad, also a SUNY Distinguished Professor of chemistry, physics, medicine and electrical engineering at UB. “More studies would have to be done to determine whether the nanoparticle is safe to use for such purposes, but it does not contain toxic metals such as cadmium that are known to pose potential risks and found in some other nanoparticles.”

“Another advantage of this core/shell imaging contrast agent is that it could enable biomedical imaging at multiple scales, from single-molecule to cell imaging, as well as from vascular and organ imaging to whole-body bioimaging,” Chen adds. “These broad, potential capabilities are due to a plurality of optical, photoacoustic and radionuclide imaging abilities that the agent possesses.”

Lovell says the next step in the research is to explore additional uses for the technology.

For example, it might be possible to attach a targeting molecule to the PoP surface that would enable cancer cells to take up the particles, something that photoacoustic and fluorescence imaging can detect due to the properties of the smart PoP coating. This would enable doctors to better see where tumors begin and end, Lovell says.

The researchers have provided two images,

This transmission electron microscopy image shows the nanoparticles, which consist of a core that glows blue when struck by near-infrared light, and an outer fabric of porphyrin-phospholipids (PoP) that wraps around the core. Credit: Jonathan Lovell

This transmission electron microscopy image shows the nanoparticles, which consist of a core that glows blue when struck by near-infrared light, and an outer fabric of porphyrin-phospholipids (PoP) that wraps around the core.
Credit: Jonathan Lovell

University at Buffalo researchers and colleagues have designed a nanoparticle detectable by six medical imaging techniques. This illustration depicts the particles as they are struck by beams of energy and emit signals that can be detected by the six methods: CT and PET scanning, along with photoacoustic, fluorescence, upconversion and Cerenkov luminescence imaging. Credit: Jonathan Lovell

University at Buffalo researchers and colleagues have designed a nanoparticle detectable by six medical imaging techniques. This illustration depicts the particles as they are struck by beams of energy and emit signals that can be detected by the six methods: CT and PET scanning, along with photoacoustic, fluorescence, upconversion and Cerenkov luminescence imaging.
Credit: Jonathan Lovell

Here’s a link to and a citation for the paper,

Hexamodal Imaging with Porphyrin-Phospholipid-Coated Upconversion Nanoparticles by James Rieffel, Feng Chen, Jeesu Kim, Guanying Chen, Wei Shao, Shuai Shao, Upendra Chitgupi, Reinier Hernandez, Stephen A. Graves, Robert J. Nickles, Paras N. Prasad, Chulhong Kim, Weibo Cai, and Jonathan F. Lovell. Advanced Materials DOI: 10.1002/adma.201404739 Article first published online: 14 JAN 2015

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This article is behind a paywall.