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Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Published on: August 9, 2024

Quantification of Ventilatory Control in Sleep Apnea: From Physiological Insight to Computable Loop Gain.

Thijs Nassi^1,2, Eline Oppersma², Dirk W Donker^2,3

¹Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

|June 18, 2026

Summary

Ventilatory loop gain (LG) measurement for sleep-disordered breathing is moving from labs to clinics. New methods estimate LG from polysomnography, aiding personalized treatment selection for better patient outcomes.

Keywords:

Endotyping central sleep Apnea loop gain obstructive sleep Apnea polysomnography precision medicine self-similarity ventilatory control instability

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Area of Science:

Sleep medicine
Respiratory physiology
Computational biology

Background:

Sleep-disordered breathing (SDB) involves complex interactions affecting ventilation.
Ventilatory loop gain (LG) quantifies respiratory control stability, with values >1 indicating instability.
Current LG measurement methods are invasive and lab-based, limiting clinical application.

Purpose of the Study:

To review and compare emerging, accessible methods for estimating ventilatory loop gain (LG) from routine sleep studies.
To provide a framework for selecting appropriate LG estimation methods based on clinical needs and feasibility.
To link LG estimation to personalized treatment strategies for SDB.

Main Methods:

Review of indirect LG estimation techniques including breath-hold maneuvers, cardiopulmonary coupling, and self-similarity analysis.
Analysis of data-driven and model-based estimation methods, such as Phenotyping Using Polysomnography (PUP).
Synthesis of perturbation tests, signal-based surrogates, and model-based identification into a decision framework.

Main Results:

Accessible LG estimation methods are emerging from polysomnography and home monitoring signals.
Model-based methods offer individualized LG profiles but require higher signal quality.
A dynamic LG threshold (approx. 0.7) may guide selection between chemorespiratory stabilizers and anatomy-focused therapies.

Conclusions:

Translating LG estimation beyond the research lab is feasible with new computational methods.
Method selection for LG assessment should consider the trade-off between fidelity and feasibility.
A pragmatic framework can guide the clinical deployment of LG estimation for SDB management.