Medical Imaging Network Upgrade for Multi-Modality PACS Integration

Engineering Case Study

Case Study Industrial Automation

Scenario

A Level I trauma hospital in Boston, MA upgraded its Picture Archiving and Communication System (PACS) backbone to support concurrent DICOM transfers from MRI (500 Mbps sustained), CT (800 Mbps burst), and ultrasound (150 Mbps streaming) modalities to a central storage cluster. The existing 1 GbE infrastructure suffered intermittent frame loss during peak shift handoffs (07:00–09:00). Constraints included: zero downtime during cutover, compatibility with FDA-cleared legacy imaging devices (all 100 Mbps or 1 GbE), and strict HIPAA-aligned QoS policies requiring lossless transmission for diagnostic-grade images.

Given Data

  • Round Trip Time (RTT): 1.2 ms (measured end-to-end across core L3 switch and storage array)
  • Bandwidth: 1000 Mbps (conservative design target for aggregated modality traffic)
  • Number of Packets in Flight: 8
  • Packet Size: 1500 bytes (jumbo frames enabled end-to-end for DICOM bulk transfers)

Calculation

Using the same formula:

  • Bandwidth = 1000 Mbps = 1,000,000,000 bps
  • RTT = 1.2 ms = 0.0012 s
  • Bandwidth × RTT = 1,000,000,000 × 0.0012 = 1,200,000 bits = 150,000 bytes
  • Packets-in-flight contribution = 8 × 1500 = 12,000 bytes
  • Total recommended buffer depth = 150,000 + 12,000 = 162,000 bytes162 KB

Result and Decision

The team procured Arista 7050X3 switches with 256 KB of per-port buffer memory and configured static buffer allocation (192 KB reserved for the PACS VLAN). They paired this with PFC (Priority Flow Control) on priority queue 4 (DICOM) and ECN marking for best-effort traffic—reducing observed loss from 0.32% to 0.00% during 72-hour stress testing with simulated multi-modality bursts.

Lesson

Jumbo frames amplify the packet-size term significantly—but rarely dominate the total buffer requirement when RTT exceeds ~1 ms at gigabit speeds. Always measure actual RTT under load (not ping), as storage stack latency (e.g., NVMe-oF response time) often contributes >60% of observed RTT in medical imaging networks.

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