Closed-Loop Mobile 3D Scanning for Uncertainty-Aware Volume Measurement
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Solution Overview
Problem
Existing mobile scanning applications lack real-time quality assurance and uncertainty quantification, leading to inconsistent and unreliable volumetric measurements of anatomical structures, which are crucial for conditions requiring repeatable and longitudinal monitoring, such as lymphedema and congestive heart failure.
Innovation Solution
A device-resident, closed-loop system that provides real-time corrective guidance, quantifies uncertainty, and enforces acceptance criteria to ensure reliable volumetric measurements by using a scan-validation layer, probabilistic and analytic uncertainty engines, and operational guardrails on mobile and head-worn devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile scanning applications are used for volumetric measurement, then portability and accessibility are improved, but measurement precision and reliability deteriorate due to user technique variability, coverage gaps, and motion artifacts
Solution Approach 1:
The system implements real-time feedback by computing coverage metrics, quality scores, and uncertainty estimates during acquisition, then providing corrective guidance to the user. This closed-loop feedback mechanism allows novice users to achieve clinically acceptable measurements without training by immediately adjusting their scanning technique based on system guidance.
Solution Approach 2:
The system performs preliminary actions by establishing acceptance criteria and uncertainty thresholds before measurement completion. It pre-computes coverage requirements and quality thresholds that must be satisfied before export, preventing poor quality measurements from being generated in the first place rather than correcting them afterward.
2Reliability
If real-time quality assurance and uncertainty quantification are implemented, then measurement reliability is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The mobile device performs self-service by executing all quality assurance, uncertainty quantification, and acceptance gating computations locally on-device. This eliminates the need for external quality control infrastructure or manual verification, allowing the device to autonomously ensure measurement reliability while managing its own computational resources.
Solution Approach 2:
The system manages complexity by dynamically adjusting computational parameters such as uncertainty sampling intensity and quality metric computation frequency based on acquisition progress and observed data quality. This adaptive parameter adjustment maintains reliability while optimizing computational resource utilization during the scanning process.
3Stability of the object's composition
If acceptance criteria and export gating are enforced, then measurement consistency is improved, but acquisition time and user burden worsen
Solution Approach 1:
The system establishes acceptance criteria and quality thresholds before acquisition begins, allowing real-time evaluation during scanning. This preliminary setup enables the system to provide targeted corrective guidance that efficiently directs user effort toward meeting criteria, rather than requiring complete rescans due to post-acquisition rejection.
Solution Approach 2:
Real-time feedback on coverage progress and quality metric attainment allows users to adjust their scanning behavior dynamically. This feedback mechanism guides users to efficiently satisfy acceptance criteria during the acquisition process itself, minimizing unnecessary scanning time while ensuring consistency requirements are met before export.
4Measurement precision
If uncertainty propagation and probabilistic resampling are performed, then measurement accuracy is improved, but computational load and processing time worsen
Solution Approach 1:
The system adapts uncertainty quantification parameters such as the number of Monte Carlo samples or resampling iterations based on observed data quality, geometry complexity, and confidence levels. This dynamic parameter adjustment maintains volumetric accuracy by performing sufficient uncertainty propagation only when needed, while reducing computational energy consumption when data quality is already high or geometry is simple.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures repeatable, regulator-ready acquisitions with quantified uncertainty, improving clinical reliability and safety by converting failing scans into passing scans and providing auditable records for longitudinal comparisons.
Implementation Method 1
depth sensing, including light detection and ranging
Data Source
AI summary
A system and method for quality-controlled three-dimensional volumetric measurement on mobile or head-worn devices. During acquisition, the device evaluates quantitative scan-quality metrics in real time and provides corrective guidance to address deficient regions. An on-device acceptance gate prevents export until thresholds for coverage, alignment, motion stability, and volumetric uncertainty are satisfied. Following validated capture, the system generates a watertight surface model, computes volume with quantified standard uncertainty, and records the quality context supporting acceptance. Follow-up scans are registered to a baseline so that longitudinal changes are judged against propagated uncertainty, enabling statistically reliable alerts. On devices lacking hardware depth, scale is stabilized by fusing visual-inertial mapping with anthropometric priors. The validated result, including volume, uncertainty, quality indicators, and device provenance, is digitally signed and packaged as an interoperable artifact for integration with electronic health record and remote-monitoring systems.