United Imaging Healthcare Enhanced the Speed and Quality of MR Imaging
United Imaging Healthcare· ChinaNVIDIA data center GPUs · NVIDIA CUDA Toolkit · NVIDIA cuSolver +1
United Imaging Healthcare worked with NVIDIA since 2018 to accelerate its magnetic resonance (MR) imaging products with AI, developing an AI-assisted compressed sensing (ACS) technology to speed up MR image acquisition and reconstruction. Using NVIDIA data center GPUs, the CUDA Toolkit, cuSolver matrix computation library, and NVIDIA AI Enterprise software (with support from NVIDIA DevTech engineers and solution architects), United Imaging Healthcare reduced image reconstruction time by nearly 95%, with NVIDIA's support teams further accelerating algorithms by an additional 3x, on top of an initial 10x increase in computational speed from a tailored algorithm.
Overview
United Imaging Healthcare worked with NVIDIA since 2018 to accelerate its magnetic resonance (MR) imaging products with AI, developing an AI-assisted compressed sensing (ACS) technology to speed up MR image acquisition and reconstruction. Using NVIDIA data center GPUs, the CUDA Toolkit, cuSolver matrix computation library, and NVIDIA AI Enterprise software (with support from NVIDIA DevTech engineers and solution architects), United Imaging Healthcare reduced image reconstruction time by nearly 95%, with NVIDIA's support teams further accelerating algorithms by an additional 3x, on top of an initial 10x increase in computational speed from a tailored algorithm.
The challenge
Reconstructing high-quality MR images requires extremely compute-intensive workloads, and the company needed to leverage more streaming sensor data to achieve higher throughput in less time; even with proprietary algorithms, processing streaming sensor data still took too much time and expense to meet the needs of clinicians.
The solution
Since 2018, United Imaging Healthcare has worked with NVIDIA to accelerate its MR imaging products with AI, developing AI-assisted compressed sensing (ACS) technology to speed up MR image acquisition and reconstruction. The team used NVIDIA data center GPUs, the CUDA Toolkit, the cuSolver matrix computation library, and NVIDIA AI Enterprise software, with support from NVIDIA DevTech engineers and solution architects who helped train models, analyze code, and migrate to GPUs; NVIDIA engineers also tailored an algorithm that increased computational speed by more than 10x.
Reported business value
United Imaging Healthcare reduced image reconstruction time by nearly 95%, with NVIDIA's support teams further accelerating algorithms by an additional 3x, on top of an initial 10x increase in computational speed from the tailored algorithm. Clinical teams can now limit the amount of time patients spend in constrained MR machines, and hospitals can increase access to MR procedures.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other healthcare entries in the register.
Novo Nordisk builds AI drug discovery platform on Azure with Microsoft Research
Novo Nordisk partnered with Microsoft Research to build an AI platform on Azure AI and data stacks spanning regulatory affairs, early research, drug discovery and trial design, using Azure OpenAI Service, Azure Cosmos DB and Azure Kubernetes Service, with Power BI and Power Apps for collaboration. The platform includes a copilot for researchers, shared reasoning-chain templates, and governance/auditing of how data and models are used. The teams published early results on predictive AI models for cardiovascular disease risk detection, including an algorithm that Novo Nordisk says predicts patients' cardiovascular risk better than the best clinical standards, drawing on more than 100 years of insulin research data.
Novo Nordisk· DenmarkAzure OpenAI Service · Azure Cosmos DB · Azure Kubernetes Service +2BAYADA Builds a Unified, AI-Ready Platform for Compassionate Care
BAYADA Home Health Care is consolidating three separate legacy data platforms (multiple practice management systems, an on-prem ODS, and Snowflake) and 65+ enterprise data sources into a single Databricks Lakehouse under its Data Modernization program, using medallion tiers, Lakeflow Jobs, Asset Bundles, and Unity Catalog governance. As part of the migration, BAYADA uses an LLM-powered code converter and Databricks Assistant to automate SQL and stored-procedure translation, and applies a machine learning-based data mastering accelerator to create golden Client, Payor, Candidate, and Referrer records. BAYADA reports roughly 30% better workload performance and cost efficiency versus its prior Snowflake environment, and is laying a governed foundation for AI agents supporting payroll validation, compliance monitoring, and operational insights, separately from its roadmap for AI-assisted chart review and risk prediction.
BAYADA Home Health CareDatabricks SQL · Lakeflow Jobs · Unity Catalog +1
CDPHP modernizes infrastructure and improves medical data extraction with AWS AI/ML
CDPHP, a not-for-profit health plan serving 400,000 members in Upstate New York, used AWS services including Amazon Comprehend Medical, Amazon Textract, and Amazon SageMaker to automate its data processing pipeline for unstructured medical records and health data. The organization processed over seven million records during initial migration and now processes 3,000 electronic health records weekly. CDPHP achieved a 60% improvement in overall efficiency and reduced HEDIS report generation from 4-5 days (three data scientists) to two reports produced daily.
Mayo Clinic deploys NVIDIA DGX SuperPOD to accelerate pathology foundation models
Mayo Clinic deployed an NVIDIA DGX SuperPOD with NVIDIA DGX B200 systems to support foundation model development for pathomics, drug discovery and precision medicine. In partnership with Aignostics, Mayo Clinic built the Atlas pathology foundation model, trained on more than 1.2 million histopathology whole-slide images. The new infrastructure is reducing four weeks of pathology slide analysis work to one week.
Mayo Clinic· United StatesNVIDIA DGX SuperPOD · NVIDIA DGX B200 · NVIDIA BlackwellWas this helpful?
Your feedback helps us improve our use case database