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GPU → Use Cases → Medical Imaging
Revolutionizing medical imaging with cloud GPUs
Conventional medical imaging techniques have long been the cornerstone of healthcare diagnostics, providing critical insights into the human body. With the advent of GPU and cloud technology, medical imaging is poised to ascend to an entirely new level of precision, speed, and capability—potentially saving countless lives.
By leveraging the power of a GPU-accelerated cloud, healthcare providers can achieve scalability, flexibility, and efficiency that were previously unattainable. This combination enables medical imaging to move beyond traditional on-premises systems, allowing seamless integration of AI models, real-time diagnostics, and massive data processing from virtually anywhere.
Join us as we explore the integration of GPU technology into medical imaging and how cloud platforms are redefining the future of healthcare diagnostics.
What is a GPU?
A GPU (Graphics Processing Unit) is a specialized circuit (or computer chip) that excels at simultaneous task processing. GPUs offer fast speeds and robust capabilities for technology involving images, videos, or large amounts of data.
Most recently, GPUs have become essential to artificial intelligence (AI) server implementations, scientific simulations, and 3D imaging.
When combined with cloud platforms, GPUs can be deployed at scale, enabling healthcare providers to process vast medical datasets without the need for expensive, high-maintenance on-premises infrastructure.
What is medical imaging technology?
Medical imaging technology, also known as diagnostic imaging systems, refers to the tools, machines, and software used to create detailed pictures of the inside of the body for diagnosing and monitoring medical conditions.
Common forms of medical imaging are:
- CT (Computed Tomography) scans
- Ultrasound scans
- MRIs (Magnetic Resonance Imaging) scans
Medical imaging technology includes both the physical devices (like scanners and X-ray machines) and the software that processes the raw data into clear, interpretable images. These technologies are essential for improving patient care.
With GPU-accelerated cloud platforms, the raw data from imaging devices can now be processed remotely, providing faster, scalable, and highly efficient diagnostic results.
How GPUs work with medical imaging
Modern imaging techniques produce vast amounts of data, and GPUs manage these workloads efficiently while improving image quality through noise reduction and resolution enhancement. When integrated into cloud platforms, GPUs allow healthcare organizations to scale their operations dynamically, adapting to fluctuating demands in processing power.
The GPU chip’s architecture enables advanced parallelism through thousands of specialized cores designed to process pixel-level data. Each GPU core can handle discrete segments of medical imagery independently, creating a distributed processing environment that dramatically accelerates image rendering and analysis.
Cloud-hosted GPUs further enhance this capability by offering on-demand computing resources, enabling remote access to data. This makes it easier for smaller healthcare organizations to benefit from cutting-edge medical imaging technology without owning physical GPUs.
How GPUs and cloud platforms solve medical imaging challenges
Despite advances in imaging technology, processing speed and infrastructure scalability remain major hurdles. Conventional imaging systems often require extensive time and resources to reconstruct high-resolution images from raw data. For emergency situations, such as stroke or trauma cases, delays in processing and diagnostics can significantly impact patient outcomes.
The combination of GPUs and cloud platforms addresses these challenges by offering unparalleled speed and scalability. Cloud-hosted GPUs process massive medical imaging datasets quickly and provide real-time access to results, no matter where healthcare providers are located.
Additionally, cloud platforms eliminate the need for costly on-site servers, allowing hospitals and clinics to access the latest technology without extensive investment in hardware upgrades.
Cloud-enabled GPUs: Key benefits in healthcare
Faster image reconstruction
With GPU architecture in the cloud, medical imaging technology can compute complex three-dimensional reconstruction algorithms in seconds rather than hours. Cloud integration ensures that even high-demand facilities can access the necessary computational resources instantly, enabling faster diagnoses.
Image clarity
Noise, a common issue in scans like low-dose CT or fast MRI, can obscure critical details. GPUs run advanced noise-reduction algorithms to filter out unwanted artifacts while preserving important features. These improvements are amplified on cloud platforms, where powerful GPUs process high-resolution data without latency concerns.
Real-time diagnostics and scalability
Cloud-based GPUs process live data streams from imaging devices to produce high-resolution visuals in real time. This allows doctors to visualize and adjust their approach during surgeries or treatments with precision, guided by immediate feedback from the imaging system. Cloud platforms enable this at scale, supporting multiple facilities and imaging systems simultaneously.
AI models in the cloud
Training and deploying AI models for medical imaging require substantial computational power, which cloud-hosted GPUs provide. By hosting AI models in the cloud, healthcare and SaaS providers can analyze new images in real time, detect abnormalities, and make critical decisions without investing in on-site infrastructure.
Three ways GPUs are transforming medicine
GPU use cases span critical industries on a global scale, like cybersecurity, finance, and gaming. In medicine, the technology has delivered significant advances. Here are three examples.
Human genome mapping
Cloud-hosted GPUs significantly accelerate the computational processes involved in analyzing and assembling genomic data. Genome mapping requires handling enormous datasets and performing complex algorithms to sequence, align, and analyze DNA. GPUs on cloud platforms provide scalable, on-demand power for these tasks.
Example: The Francis Crick Institute employs NVIDIA Parabricks, a GPU-accelerated genomic analysis tool, to process whole-genome sequencing data. This technology has enabled them to achieve a 26-fold speedup in sequencing for their cancer research projects.
Protein structure predictions
Protein structure abnormalities can signal serious conditions. GPUs on cloud platforms enable researchers to run complex simulations to predict 3D protein shapes with remarkable speed and accuracy. The cloud ensures these capabilities are accessible globally without requiring local hardware.
Example: The University of Washington’s Baker Lab created RoseTTAFold, a deep learning model for protein structure prediction. Training RoseTTAFold employed NVIDIA GPUs, facilitating efficient processing of complex protein data.
AI-driven medical diagnostics
GPUs in the cloud power AI models that analyze medical images to detect abnormalities like tumors or fractures with high accuracy. Hosting these models in the cloud allows healthcare providers to access the latest AI tools remotely, enabling consistent and scalable diagnostics across multiple locations.
Example: Invenio Imaging utilizes NVIDIA GPUs to accelerate AI training and inference. Their NIO Laser Imaging System rapidly analyzes fresh tissue biopsies during surgical procedures, aiding in immediate and informed decision-making.
Discover how cloud-based GPUs can improve your medical imaging solution
The integration of GPUs into medical imaging, especially through cloud platforms, has revolutionized the way healthcare professionals diagnose and treat diseases. By enabling faster processing, enhanced image quality, and scalable AI-powered insights, GPUs in the cloud are driving advancements in areas like 3D visualization, genome mapping, and real-time diagnostics.
To fully leverage these capabilities, healthcare organizations need access to robust, flexible hosting infrastructure. GPU server hosting and cloud platforms offer scalable, cost-effective solutions, providing the computational power required for cutting-edge imaging technologies without the need for extensive on-premises investment.
Liquid Web offers a powerful selection of GPU server solutions featuring NVIDIA hardware, cloud-ready AI/ML software stacks, and HIPAA compliant hosting. Shop our selection now, or contact us for more information.
Luke Cavanagh, Strategic Support & Accelerant at Liquid Web, is one of the company’s most seasoned subject matter experts, focusing on web hosting, digital marketing, and ecommerce. He is dedicated to educating readers on the latest trends and advancements in technology and digital infrastructure.
Additional resources
GPUs for Cybersecurity →
Discover the impact of GPUs on modern web security
What is a GPU? →
A beginner’s guide to graphics processing units (GPUs)
10 GPU use cases →
How GPUs are accelerating almost every industry