Completed project

MRI-Compatible Orientation Sensing

Orientation sensing designed around MRI constraints: compatibility, signal integrity, and practical limits. Documented as an engineering case study — decisions, tradeoffs, and outcomes.

Status: Completed Focus: Instrumentation Theme: Constraints-first design

Read overview Key decisions

Overview

What this was, why it mattered, and what “good” looked like.

This project focused on building an orientation sensing solution that could operate under MRI constraints. The core challenge wasn’t just “measure orientation” — it was doing so while respecting constraints that break typical sensor assumptions: electromagnetic noise, strict material/compatibility requirements, safety considerations, and real-world deployment limits.

The goal was to design an approach that could produce orientation information reliably, with a clear chain of reasoning from constraints → design choices → validation.

Requirements and constraints

The rules of the environment define the engineering.

Must-have requirements

Orientation output that is stable and interpretable (not “looks fine sometimes”).
Behavior that remains sane in the presence of MRI-induced noise and coupling.
Practical integration: connectors/cabling, repeatability, and testability.

Hard constraints

MRI compatibility and safety constraints drive materials and placement decisions.
EMI/noise environment requires signal integrity and careful measurement strategy.
Cabling/grounding can become the dominant failure mode if ignored.

Key mindset

In MRI-adjacent work, the “best” sensor on paper is irrelevant if the system fails due to coupling, noise pickup, or integration realities. This project was designed around that truth.

Approach

How the design was structured to reduce risk early.

Define the failure modes

List what would make orientation sensing unreliable: noise pickup, drift, unstable references, integration effects, and measurement artifacts.

Constrain the system

Translate MRI constraints into engineering constraints that affect architecture: placement, wiring, shielding strategy, and which measurement mechanisms are realistic.

Prototype and validate early

Build a testable setup quickly and validate assumptions early, rather than discovering integration failures at the end.

Document decisions

Capture the “why” behind the design so the result is transferable: what worked, what didn’t, and what tradeoffs were accepted.

Key decisions

What was chosen, what was rejected, and why.

Architecture over components

Prioritized system architecture and measurement strategy over chasing “the perfect sensor.” In high-noise environments, architecture usually dominates outcomes.

Reason: reduces integration risk

Treat cabling as part of the circuit

Designed with the assumption that wires will pick up noise and create coupling paths. Grounding and routing choices were treated as first-class design variables.

Reason: avoids “mystery noise” later

Validate assumptions early

Built a test flow that could quickly reveal if the approach was stable under realistic conditions. Prevented “late surprises” when the environment gets harsher.

Reason: tight feedback loop

Document tradeoffs explicitly

Every constraint has a cost: accuracy vs complexity, robustness vs sensitivity, speed vs stability. Tradeoffs were written down so the outcome is defensible.

Reason: makes work legible to others

Results

What worked, what was learned, and what you’d do next.

What worked

Orientation sensing approach validated against constraints and integration realities.
System-level thinking prevented common “works on the bench, fails in the field” outcomes.
Clear documentation produced reusable knowledge, not a one-off build.

What I’d improve next

Add tighter quantitative metrics (repeatability, drift, noise floor under conditions).
Improve packaging and deployment details (mounting, strain relief, robustness).
Expand test coverage to include more worst-case conditions and long-duration stability.

One-line takeaway

The best “sensor choice” mattered less than making the whole measurement chain robust to the environment.

Artifacts

Add links as you’re ready. This page is designed to scale.

Report / write-up

PDF or public summary version for quick reading.

Add later →

Block diagram

System architecture and measurement chain.

Add later →

Test notes

Setup, procedures, and what was learned.

Add later →