Healthcare demands new computing paradigms to meet the need for personalized medicine, next-generation clinics, enhanced quality of care, and breakthroughs in biomedical research to treat disease. With OSS products combined with NVIDIA® A100 Tensor Core GPUs, healthcare institutions can harness the power of artificial intelligence and high-performance computing (HPC) to define the future of medicine.
Medical Imaging Overview
AI-powered tools can be an extra set of "eyes," helping clinicians to quickly read images, calculate measurements, monitor changes, and identify urgent findings to optimize workflows and enhance patient care. Image Processing serves as a crucial role to the aid of disease prevention, early detection, diagnosis and treatment. Medical imaging Procedures are often computationally demanding due to the large medical datasets required to process clinical applications. GPUs are massively parallel computational engines, ideal for computationally expensive tasks in a wide range of medical applications.
Radiologists carry a lot of responsibility, specializing in an area of medicine that is cruvial in determining an accurate diagnosis. AI-powered tools can help simplify the lives of radiologists, accelerating workflows and providing a second set of eyes with less bias. The top advantages of GPU-powered medical imaging benefit from high throughput computing, high memory bandwidth, supporting 32-bit floating-point arithmetic, excellent price-to-performance ratio, and specialized hardware for interpolation. For this reason, GPUs are well suited for medical image processing and analysis, particularly in the areas of image segmentation, image visualization and image reconstruction.
Segmenting lesions calculating blood flow, detecting nodules, and triaging biopsies are just some of the benefits that AI brings to medical imaging. Image segmentation partitions a target image into distinct regions containing pixels with similar attributes, such as color information, grayscale intensities or texture features that are meaningful for mathematical analysis and quantification. Image segmentation plays an important role in medical image analysis, such as computer-aided diagnosis (CAD), surgical planning and navigation. With the help of high performance computing platforms OSS offers with NVIDIA GPUs, AI model training can be accelerated allowing researchers to work on models to segment and align multiple chest scans to calculate lung disease severity.
Image registration is the process of integrating information from multiple images using different modalities. Since valuable information needed for more accurate diagnosis and treatment planning is contained within more than one image (i.e. acquired at different times or viewpoints), the accurate fusion of the useful information from 2 or more images is very important.
Image visualization uses computers to create 3D images from medical image data sets collected from MR imaging scanners and CT scanners. Almost all surgery and cancer treatment in the developed world relies on it.
Image reconstruction in CT is a mathematical process that generates tomographic images from X-ray projection data acquired at many different angles around the patient. In short, manipulation of digitized information obtained during body imaging into interpretable pictures that represent anatomical details and diseases. Image reconstruction has fundamental impacts on image quality and therefore on radiation dose. For a given radiation dose it is desirable to reconstruct images with the lowest possible noise without sacrificing image accuracy and spatial resolution. Reconstructions that improve image quality can be translated into a reduction of radiation dose because images of the same quality can be reconstructed at lower dose.
Bioinformatics as a Whole
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. Bioinformatics develops and applies computationally intensive techniques to understand biological processes. Without technology, certain aspects of bioinformatics would not be practical or possible. For example, comparing multiple protein sequences by hand is impractical and annotating genomes is impossible because most genomes are too large to annotate by hand. But now that technology is as advanced as it is, bioinformatics can greatly benefit from GPU acceleration and Flash Storage. GPU-BLAST, MUMmerGPU and BarraCUDA are three applications that can benefit from GPU acceleration.
Bioinformatics in Medicine
There are many different applications in which the field of bioinformatics is used, including medicine. Bioinformatics enables advances such as drug discovery, diagnostics and disease management. The methods of bioinformatics can be applied to diagnose cancer sub-types, predict survival time of cancer patients, identify the mode of action of candidate drugs, model protein binding, and model drug target properties. It’s possible, that in the future, doctors will be able to perform a genomic analysis on cancer patients and the results of that analysis will help them determine the drug treatment that will be the most effective for that particular patient. Speeding up bioinformatics with GPUs will hopefully help advance the field for the benefit of all.
One of the branches of simulation technology is medical simulation. It is used in the education and training in various medical fields. Medical simulation was originally intended to be used to train medical professionals so that there is less chance of accidents during surgery, the prescription of medication and medical practice in general. However, it is more widely used now, both to train medical students in anatomy and physiology during medical school, and for professionals in nursing, sonography, pharmaceutics, and physical therapy.
Simulations, especially in a field as complex as the medical industry, require high tech equipment in order to make the simulations as accurate and precise as possible. When it truly is a matter of life and death, precision is incredibly important. To achieve the best performance possible, GPUs are the best way to get high performance without adding a lot of additional hardware. Many servers today don’t have the room, power or cooling to accommodate high end GPUs. One Stop Systems' GPU accelerators can fit anywhere from 1-16 GPUs to accomodate this needed performance.
Medical simulations require large amounts of data to portray an accurate approximation. Considering the complexity of the human body and sheer amount of various surgeries, diseases and reactions to medications that can be simulated, a simulation system would need a lot of storage to store all of the data. The One Stop Systems Flash Storage Array can provide not only large amounts of storage but specifically fast storage.
Our compute accelerators support from one to sixteen double-wide PCIe cards and can be cabled up to four host computers through PCIe x16 Gen3 connections each operating at 128Gb/s. The all-steel construction chassis house power supplies, fans, and a system monitor that monitors the fans, temperature sensors and power voltages. Front panel LEDs signal minor, major or critical alarms. The compute accelerators are transparent and do not require software except for the drivers required by the PCIe add-in cards. Compute accelerators are the best appliance for applications that require a large amount of compute power.Learn More
Our flash storage arrays support from one to thirty-two single-wide PCIe cards and can be cabled up to four host computers through PCIe x16 Gen3 connections each operating at 128Gb/s. The light-weight, compact chassis house power supplies, fans, and a system monitor that monitors the fans, temperature sensors and power voltages. Flash storage arrays are the best appliance for applications that require a large amount of fast, flexible storage.Learn More