Mechanical ventilation is a double-edged sword: while life-saving for patients with acute respiratory failure, it may also worsen lung injury and induce respiratory muscle trauma. Adverse events are related to poor interaction between the ventilator and critically ill patient. However, there are no simple, reliable and readily accessible techniques available to clinicians at the bedside to provide lung-protective ventilation tailored to the individual patient’s respiratory physiology. A very promising technology to change clinical practice is Electrical Impedance Tomography (EIT). EIT is gaining popularity worldwide as a bedside non-invasive lung imaging tool: it continuously and real-time visualizes changes in lung volume. Personalizing mechanical ventilation using EIT may ameliorate the risk of death and long-term morbidity, and substantially reduces the burden on our healthcare system. Validated methods to implement EIT information in routine care are yet lacking. Moreover, current available post-processing software depends on the type of EIT device, and do not allow for calculation of advanced respiratory parameters. In this proposal, we focus on developing a novel, robust and clinically meaningful EIT data processing workflow, integrated with important respiratory monitoring modalities in the ICU. Our team has extensive experience in EIT research and advanced analyses; the e-Science center is considered complementary to our expertise and crucial for knowledge translation into a robust and reusable software workflow. This will allow us to fully exploit the clinical benefits of EIT. Furthermore, it will promote sustainability and will accelerate national and international research projects aimed at optimizing personalized mechanical ventilation in the ICU.