AI service — AI Medical · Radiology
MRI reconstruction and acceleration
Reconstruct diagnostic-quality MR images from undersampled k-space, so a scan takes a quarter of the table time.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Scanner time is the scarcest resource in an MRI department. Reconstruction models rebuild a diagnostic image from undersampled acquisition, which turns a four-fold acceleration into four-fold throughput on the same magnet.
Raw k-space arrives from the scanner and the reconstructed volume returns in the format your reading workstation expects. Because the output is a generated image, it is marked as reconstructed and the acquisition parameters travel with it.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the mri reconstruction and acceleration pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Throughput on existing magnets — Shorten acquisition without replacing hardware.
- Paediatric and motion-prone patients — Reduce time on the table where stillness is hard to maintain.
- Research reconstruction — Compare reconstruction methods on your own k-space data.
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