Noninvasive phenotyping of respiratory function in people with muscular dystrophies using electrical impedance tomography

The National Heart, Lung, and Blood Institute awarded Colorado State University $1.892 million on September 15, 2025, under the Cardiovascular Diseases Research program (CFDA 93.837) to develop and validate electrical impedance tomography (EIT) as a noninvasive functional imaging technique for assessing respiratory function in patients with muscular dystrophy, with performance through June 30, 2030, at Fort Collins, Colorado.

The award funds longitudinal study of EIT effectiveness as a measure of lung function and as a surrogate for ventilation metrics obtained during sleep laboratory studies in muscular dystrophy patients. The work will compare EIT measures across different dystrophy types to determine whether outcomes have consistent meaning across disease variants. Current clinical decision-making regarding initiation of non-invasive ventilatory support relies on pulmonary function tests and sleep studies, both of which present practical limitations—pulmonary function testing cannot always be performed due to cognitive or physical disability or age, and sleep studies are conducted in artificial environments where patients may not tolerate equipment or achieve normal sleep. EIT offers advantages as a real-time, noninvasive, non-ionizing imaging technique with no harmful side effects, suitable for patients of any age.

The Research Foundation For The State University Of New York (University at Albany) is developing and building an ACT 5 electrical impedance tomography system during year one, producing a near-copy of an existing 32-electrode system with minor packaging modifications and replacement circuit boards, then providing hardware and software support and repairs through the remainder of the performance period. GE Medical Systems Information Technologies, Inc. (GE Healthcare Technology & Innovation Center) is developing disposable C-shape electrode arrays, performing hardware optimization including cable and connection solutions and performance and safety testing, and supporting physiology parameters development and data analysis including EIT ventilation and perfusion image generation and derived flow-volume curve analysis, with participation in manuscript preparation during years four and five.

Abstract

The muscular dystrophies (MDS) are rare disorders that consist of over 30 genetic diseases that cause progressive muscle weakening and deterioration with duchenne muscular dystrophy as the most common and most debilitating form, affecting about 1 on 3,500 live male births per year. Respiratory problems are a common symptom in persons with MDS due to weakness of inspiratory and expiratory muscles. However, the onset and pattern of respiratory symptoms varies according to the specific muscular dystrophy and stage of progression. Across all of the dystrophies, the most common cause of death is respiratory failure. To support respiration as the disease progresses, patients eventually receive non-invasive ventilatory support (NIV). However, the decision as to when NIV should start is subjective and is informed by pulmonary function tests (PFTS) and sleep studies. Unfortunately, pulmonary function tests cannot always be performed due to cognitive or physical disability or age. Sleep studies have the disadvantages that they are obtained in artificial environments, patients may not tolerate the measuring equipment, and patients may not sleep. Thus, there is a need for an objective surrogate for sleep studies to inform the decision as to when NIV is recommended. Electrical impedance tomography (EIT) is a noninvasive, non-ionizing real-time functional imaging technique with no harmful side effects, suitable for patients of any age. We will study the effectiveness of EIT as a measure of lung function longitudinally and as a surrogate for metrics of ventilation obtained during sleep lab studies in patients with muscular dystrophy. We will compare the EIT measures between dystrophies to determine whether their outcomes have the same meaning across the dystrophies. To accomplish this, we will carry out the following specific aims: (1) develop a 3-D algorithm and a new method of segmentation to improve accuracy of EIT images and derived measures of ventilatory heterogeneity. (2) assess lung function and heterogeneity longitudinally in patients with MD using EIT. (3) determine the correlation of EIT measures with ventilation metrics obtained during a sleep study in patients with muscular dystrophy.