Respiratory rate as a X-ray-based biomarker for the longitudinal assessment of lung function and pathology

Kaveh Ahookhosh^1,2Willy Gsell¹Birger Tielemans^1,3Greetje Vande Velde^1*

• ¹Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
• ²Institute of Mechanics, Materials, and Civil Engineering, UCLouvain, Louvain-la-Neuve, Belgium
• ³Institute of Pathology, University clinics Aachen, RWTH University of Aachen, Aachen, Germany

Respiratory rate (RR) is a valuable, yet underexploited lung functional parameter in preclinical lung research, aiding drug toxicity studies, lung disease assessments, stress, pain, and sleep research. It may also enhance translatability between animal and human studies. Longitudinal micro-computed tomography (microCT) lung data acquisitions not only contain spatial information on lung disease, volumes and patterns, but also temporal information covering many breathing cycles. This enables reliable and non-invasive extraction of lung morphological and functional biomarkers, including RR, with a single measurement from free-breathing animals, crucial for accurate measurements. Here, we aimed to develop a non-invasive pipeline, for longitudinal RR monitoring as a biomarker for lung function and pathology based on the X-ray projections of lung microCT acquisitions. First, we mechanically ventilated a mouse and scanned it using microCT at different breathing rates, 60 to 185 breaths per minute (bpm), serving as ground-truth data for our RR measurements. Next, we obtained raw intensity curves from these ground-truth X-ray projections, which contained noise and signals from multiple sources such as respiratory and cardiac cycles. To find the optimal algorithm and isolate the respiratory signals, we post-processed these raw intensity curves with different signal processing techniques. Adept at handling non-uniformly sampled signals in time domain, the Lomb-Scargle (LS) algorithm outperformed the other signal processing techniques, exhibiting robust prediction of RR with an error margin of 3%. Next, we applied this pipeline to benchmark the longitudinal RR data as a biomarker of lung damage and repair in a mouse model of lung epithelial injury. Our RR monitoring pipeline detected a transient loss of lung function in diseased mice, marked by a temporary RR decrease and a simultaneous increase in total lung and aerated lung volumes. Adopting this X-ray-based pipeline would allow lung researchers to non-invasively collect both morphological and functional data in a single measurement, improving insights into lung disease progression and host response thereto by providing relevant biomarkers. This approach contributes to facilitating translation of preclinical study results towards clinical trials.