BitFlow Frame Grabber Eliminates Data Bottlenecks in New Optical Mapping Platform

New approach opens up dual mapping technology to broader cardiovascular research community

WOBURN, MA, OCTOBER 19, 2020 — Optical mapping is an imaging technique that measures fluorescence signals across a cardiac preparation with high spatiotemporal resolution. Optical mapping of transmembrane voltage and intracellular calcium is a powerful tool for investigating cardiac physiology and pathophysiology.

Researchers at the Sheikh Zayed Institute for Pediatric and Surgical Innovation, Washington, DC, recently introduced a novel, easy-to-use approach to optical mapping that requires only a path splitter, a single camera, a frame grabber and an excitation light to simultaneously acquire voltage and calcium signals from whole heart preparations.1 This cost-effective yet highly reliable system eliminates the need for multiple cameras, excitation light patterning, or frame interleaving, therefore aiding in the adoption of dual mapping technology by the broader cardiovascular research community, and decreasing the barrier of entry into panoramic heart imaging.

At the heart of the new system is a BitFlow four-channel frame grabber. It is used for imaging control and acquisition from an Andor Zyla 4.2 PLUS Scientific CMOS (sCMOS) camera acquiring images at 4.2 megapixels. A 10-tap CameraLink™ connection with a clock rate of 85 MHz was necessary to achieve the fastest frame rates possible. The researchers selected the BitFlow frame grabber in part because of its “2x” mode that shares DMA responsibility between two DMA engines, effectively doubling the frame grabber’s bandwidth and providing much needed headroom to DMA images from the camera continuously, regardless of system load. Because of the high data rate of acquisition — due to high spatial and temporal resolution and bit depth — an NVMe SSD disk was also essential for reducing data rate bottlenecks.

To achieve optimal results, an image splitting device is positioned in front of the sCMOS camera. A fixed focal length 17 mm/F0.95 lens is attached to the front of the device for experiments with rat hearts, while a wide-angle 6mm f/1.2 lens is used for pig hearts. To guide manual alignment, MetaMorph software from Molecular Devices overlays live images as contrasting colors or as subtractive grey scales to highlight misalignment. With this live feedback, images are quickly aligned using the “long” and “short” control knobs. After alignment, any standard image acquisition software can be used such as MetaMorph, μManager, or Solis. The acquired image includes two fields which can be separated using imaging software that includes automated tools.

The computer consisted of a Xeon CPU E3–1245 v5 3.50 GHz (Intel corporation), 32 GB of RAM, and a non-volatile memory express solid state disk (NVMe SSD, Samsung 960 Pro). Notably, the platform is composed entirely of off-the-shelf components, which will help in the adoption and successful implementation of this setup by other laboratories.

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1. Jaimes, R., McCullough, D., Siegel, B. et al. Lights, camera, path splitter: a new approach for truly simultaneous dual optical mapping of the heart with a single camera. BMC biomed eng 1, 25 (2019).

Photo caption: A Optical system configuration with image splitting device positioned in front of a sCMOS camera. B Emission of each complementary probe (Vm, Ca) is separated by wavelength using an image splitting device. C Dichroic cube setup with the two emission filters and a dichroic mirror. 

BitFlow Turbo-Charges Single Link Frame Grabber with Latest CoaXPress Standard

Claxon CXP1 designed for new generation of single link CXP-12 cameras

WOBURN, MA, SEPTEMBER 18, 2020 – BitFlow has expanded its Claxon™ series of high-performance CoaXPress CXP-12 frame grabbers with a new single link version that transfers image data from a CXP camera to the host memory at speeds up to 12.5 Gb/S, or twice the acquisition bandwidth of the previous generation of the CoaXPress standard. The BitFlow Claxon CXP1 provides developers of smaller-scale, yet complex vision systems with a deterministic, zero-latency pipeline ideal for applications that include aerospace, AOI, science and robotics, as well as high-speed linescan inspection of printed materials or textiles.

Like the Claxon CXP4 quad link model from BitFlow, the new frame grabber takes full advantage of a half-size PCI Express expansion bus and StreamSync™ DMA to deliver the sustained bandwidth needed to support acquisition from one of the new generation single-link CXP-12 cameras. Cameras are “plug-and-play” with automatic link speed and camera parameter detection.

In addition to transmitting bitrates up to 12.5 Gb/S, the Claxon CXP1 has an uplink interface of up to 41.6 Mbps, and further simplifies integration by supplying 13 watts of Safe Power through PoCXP — all on a single Coaxial cable using reliable micro-BNC (HD-BNC) connectors. Unlike USB3, Camera Link or other interfaces that rely on passive cable lengths of a few meters or less, the Claxon CXP1 frame grabber supports a 40-meter maximum cable length without the use of a repeater that could jeopardize signal integrity. Fanless passive cooling ensures extended use of the frame grabber without maintenance.

Claxon CXP1 frame grabbers are supported by BitFlow’s software development kit (SDK) to help developers in the configuration of vision systems. The SDK contains fully developed applications, and a variety of utilities and libraries, and supports both 32-bit and 64-bit Windows and Linux. Drivers for third party applications are also available, such as LabView, VisionPro and HALCON.

BitFlow Joins with OneBoxVision in Developing Vision Systems for Plastic Film Extruder

WOBURN, MA, AUGUST 18, 2020 — BitFlow recently assisted OneBoxVision, an integrator located in Ireland, in developing two web-based machine vision systems for a North American film extruder. The systems ensure consistent, defect-free film for plastic bags used for storage of powered milk. Dry milk products are highly sensitive to environmental conditions, particularly to moisture. This vulnerability makes it critical that plastic bags storing the powder not have holes where water or humidity can enter to spoil the product.

To prevent holes, the extruder required its plastic film be  continuously assessed for the presence of carbon or “black” specks while it was being extruded on a blown film line. Black specks in plastic film or sheet – especially in light-colored or clear plastics used to store foods – can cause holes in the material when it’s later oriented or thermoformed. Black specks also lead to expensive rejects, wasted resin, and production downtime, not to mention client dissatisfaction.

Once the extruded plastic is processed, inspected and approved at the first plant, it is shipped to a second site where an automated line fabricates it into bags during a multi-state production process. Another inspection station was installed there by OneBoxVision to scan the finished bags. OneBoxVision networked the system so that management at the second plant had access to the inspection data from the extrusion plant to compare results.

For the key technology enabler, OneBoxVision relied upon its SurfaceFlow™ software. SurfaceFlow is a complete quality package that can be deployed on plastic film lines to automatically detect holes, carbon, wrinkles, contamination and gels. Hardware included BitFlow Axion CL 2xE CameraLink frame grabbers, along with Chromasens allPIXA pro line scan cameras transmitting color images full-tilt at over 1 Gbytes per second. By adopting an “off-the-shelf” non-proprietaryhardware approach, OneBoxVision enables plastic film extruders, printers and converters, medical packaging manufacturers, and injection molding plants to deploy its high-performance solutions at a far lower cost with reduced risk.

By timing the line-by-line scanning with the transport cycle in the transport direction, a distortion-free image of the bags can be achieved. The particular strength of the line scan cameras emerges when they are used in conjunction with a highly deterministic, low latency frame grabber. In this situation, the BitFlow frame grabber synchronizes the image captures with the incoming triggers, plus provides energy to the cameras with its Power over Camera Link (PoCL).

One of the challenges for the second operation was that the bag inspection system had to be placed after a rotary punch perforated the bags. This meant the new system had to not only inspect for random holes in the plastic that could spoil the milk powder, but check that the manufactured perforations were properly made and in-place. Adding to this challenge is that the roll-feed runs continuously for hours, at high feed rates, and with limited human inspection. Again, SurfaceFlow was up to the job. OneBoxVision developed a suite of tools that perform both perforation and seam analysis while checking for the presence of defects. Simple but effective, the second vision system is made up of a single camera coupled with a BitFlow Axion CL 2xE frame grabber installed on the bag line.

The BitFlow Axion-CL is the most powerful CL frame grabber BitFlow has ever manufactured, featuring a StreamSync Direct Memory Access (DMA) engine and buffer manager to prevent overloading of the CPU with image transfer operations, along with a PCIe Gen 2 expansion bus to deliver high-speed access to host memory.

Google Healthcare Relies on BitFlow CoaXPress Frame Grabber for Augmented Reality Microscope

WOBURN, MA, AUGUST 6, 2020 – BitFlow frame grabber technology has been incorporated into a prototype Augmented Reality Microscope (ARM) platform that researchers at Google AI Healthcare (Mountain View, CA) believe will accelerate the adoption of deep learning tools for pathologists around the world in the critical task of visually examining both biological and physical samples at sub-millimeter scales.

The application driving the ARM platform runs on a standard off-the-shelf computer with a BitFlow Cyton CoaXPress (CXP) 4-channel frame grabber (CYT-PC2-CXP4) connected to an Adimec S25A80 25-megapixel CXP camera for live image capture, along with an NVidia Titan Xp GPU for running deep learning algorithms. Using Artificial Intelligence (AI), the platform enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view.

Importantly, the ARM can be retrofitted into existing light microscopes found in hospitals and clinics around the world using low-cost, readily-available components, such as the BitFlow Cyton frame grabber, and without the need for whole slide digital versions of the tissue being analyzed. This innovation comes as welcome news: despite significant advances in AI research, integration of deep-learning tools into real-world diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions in microscopic analysis. Besides being economical, the ARM platform is application-agnostic and can be utilized in most microscopy applications.

According to Google researchers, opto-mechanical component selection were driven by final performance requirements, specifically for effective cell and gland level feature representation. The Adimec camera’s 5120×5120 pixel color sensor features high sensitivity and global shutter capable of capturing images at up to 80 frames/sec, while the BitFlow Cyton CXP-4 has a universal PCI-E interface to the computer that simplifies set-up. The eMagin SXGA096,1292×1036 pixel microdisplay mounted on the side of the microscope includes an HDMI interface for receiving images from the computer. This opto-mechanical design can be easily retrofitted into most standard bright field microscopes. Including the computer, the overall cost of the ARM system is at least an order of magnitude lower than conventional whole-slide scanners, without incurring the workflow changes and delays associated with digitization.

The basic ARM pipeline consists of a set of threads that continuously grab an image frame from the camera, debayer it to convert the raw sensor output into an RGB color image, prepare the data, run the deep learning algorithm, process the results, and finally display the output.

Google researchers believe that the ARM has potential for a large impact on global health, particularly for the diagnosis of infectious diseases, including tuberculosis and malaria, in developing countries. Furthermore, even in hospitals that will adopt a digital pathology workflow in the near future, ARM could be used in combination with the digital workflow where scanners still face major challenges or where rapid turnaround is required as is the case with cytology, fluorescent imaging, or intra-operative frozen sections.

Since light microscopes have proven useful in many industries other than pathology, the ARM can be adapted for a broad range of applications across healthcare, life sciences research, and material science. Beyond the life science, the ARM can potentially be applied to other microscopy applications such as material characterization in metallurgy 12 and defect detection in electronics manufacturing.