A digital lung twin calibrated and updated with ventilator data and bedside imaging for safe mechanical ventilation

Abstract

A mechanical ventilation device having at least one electronic controller configured to receive imaging data and transpulmonary pressure data related to a patient's lungs, perform non-rigid image registration of inspiratory and expiratory images to generate a relative compliance or elasticity map of the lungs, convert the relative compliance or elasticity map into a quantitative compliance or elasticity map of the lungs based on the inspiratory transpulmonary pressure and the expiratory transpulmonary pressure, and display information related to or derived from the quantitative compliance or elasticity map on a display device.

Description

[Technical field]

[0001] This patent application claims priority under 35 U.S.C. § 119 to U.S. Provisional Patent Application No. 63 / 250,253, filed September 30, 2021, the contents of which are incorporated herein by reference.

[0002] The following relates generally to respiratory therapy techniques, respiratory stress and strain techniques, ventilator associated lung injury (VILI) techniques, and related techniques. [Background technology]

[0003] During mechanical ventilation of a patient, clinicians determine the volume of air the ventilator delivers to the patient based on the patient's body size. This volume must provide sufficient ventilation without causing lung injury due to stress (barotrauma), strain (volutrauma), or shear from the cyclic opening and collapse of the alveoli (atelectatic trauma). This type of lung injury caused by the stress and strain of the ventilator is known as ventilator-associated lung injury (VILI).

[0004] The problem in determining ventilator settings to prevent lung injury is that the lung is heterogeneous, either intrinsically due to locally varying structure, geometry and mechanical properties, or secondary to localized damage or fluid accumulation caused by disease or infection, such as chronic obstructive pulmonary disease (COPD), pneumonia, edema, Covid-19, fibrosis, etc. This can result in localized stress and strain concentrations that are much higher than the apparent global stress and strain, as estimated based on the patient's body size, or based on lumped volume and lumped compliance measured by sophisticated ventilators. Summary of the Invention [Problem to be solved by the invention]

[0005] In some current approaches, the solution for the assessment and prevention of VILI involves building a three-dimensional (3D) biophysical model of the patient's lungs based on computed tomography (CT) breath imaging information (see, e.g., Roth, J. et al., 2017, “A comprehensive computational human lung model incorporating inter-acinar dependencies: Application to spontaneous breathing and mechanical ventilation”. Int. J. Numer. Meth. Biomed. Engng. (2017); e02787). With this model, clinicians can try mechanical ventilator (MV) settings and see, via simulation, what happens to the lung (e.g., strain distribution in the parenchymal tissue). The mechanical properties of the lung tissue (i.e., stiffness of the alveolar ducts and inter-alveolar linkers) are chosen so that the lung model simulates the experimental behavior. In this approach, the mechanical properties are not patient-specific and do not vary locally.

[0006] In non-rigid image registration (DIR), two or more images are geometrically mapped to each other using a deformation model. It is applied to find corresponding voxels or regions in two or more medical images (e.g., inhalation and exhalation CT of the lungs) or to construct a deformation map by indicating the relative volumetric changes (i.e., volumetric strain) of corresponding tissue elements. Various deformation models (e.g., rigid, elastic, viscous, sliding surface, etc.) are available. As such, DIR can provide a mapping of local deformations of lung structures and tissues, which provides useful diagnostic information for preventing VILI in emergency patients. For example, (volumetric) strain estimates have been correlated with lung inflammation and injury in mechanically ventilated lungs (see, e.g., Andrade, CI, and Hurtado, DE, 2021, “Inelastic Deformable Image Registration (i-DIR): Capturing Sliding Motion through Automatic Detection of Discontinuities”, Mathematics 2021, 9, 97) and are also used to assess COPD (see, e.g., Galban, C., et al., 2012, “Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression”, Nature Medicine 18(11), 1711; Budduluri, S., 2016, “CT image registration-based lung mechanics In COPD”, PhD (Doctor of Philosophy) thesis, University of Iowa, 2016).

[0007] In the case of a linear elastic mechanical deformation model, the strain map represents the relative compliance map when the forces are uniformly distributed, since in this linear elastic mechanical deformation the compliance C is proportional to the strain divided by the force, i.e. C~e / F. Correspondingly, the modulus of elasticity is E=1 / C, so the inverse of the compliance map is the stiffness map. There is no need to know the corresponding force F. The force F (ventilator pressure) needs to be distributed evenly in the lungs and there needs to be no resistance (zero flow). For this reason, in lung compliance mapping with DIR a breath-hold or breath-pause procedure is used. During this procedure the plateau pressure is determined.

[0008] The construction of pulmonary compliance maps ("pulmonary compliance images") is known in anesthetized, intubated rodents (see, for example, Guerrero, T. et al., 2006, "Novel method to calculate pulmonary compliance images in rodents from computed tomography acquired at constant pressures", Phys. Med. Biol. 51 (2006) 1101-1112). Such maps are generated to calculate the overall pulmonary compliance in a quantitative way by combining the pulmonary compliance map with the overall pressure during a breath-hold delivered by the ventilator at different pressure levels to measure the nonlinear tissue response. Furthermore, such processing builds a map of "pulmonary compliance per unit mass" in terms of milliliters (mL) of air per cmH2O per gram of lung tissue.

[0009] The following discloses specific improvements to overcome these and other problems. [Means for solving the problem]

[0010] In one aspect, a mechanical ventilation apparatus includes at least one electronic controller configured to receive imaging data and transpulmonary pressure data related to the lungs of a patient while the patient is undergoing mechanical ventilation using an artificial ventilator, where the imaging data includes an inspiratory image acquired during an inspiratory phase of mechanical ventilation and an expiratory image acquired during an expiratory phase of mechanical ventilation, and the transpulmonary pressure data includes the inspiratory transpulmonary pressure at the time of acquisition of the inspiratory image and the expiratory transpulmonary pressure at the time of acquisition of the expiratory image, perform non-rigid image registration of the inspiratory and expiratory images to generate a relative compliance or elasticity map of the lungs, convert the relative compliance or elasticity map of the lungs into a quantitative compliance or elasticity map of the lungs based on the inspiratory transpulmonary pressure and the expiratory transpulmonary pressure, and display information related to or derived from the quantitative compliance or elasticity map on a display device.

[0011] In another aspect, a method of mechanical ventilation includes receiving, using at least one electronic controller, imaging data and transpulmonary pressure data related to the lungs of a patient while the patient is undergoing mechanical ventilation therapy using a mechanical ventilation device, the imaging data including an inspiratory image acquired during an inspiratory phase of mechanical ventilation therapy and an expiratory image acquired during an expiratory phase of mechanical ventilation therapy, the transpulmonary pressure data including the inspiratory transpulmonary pressure at the time of acquisition of the inspiratory image and the expiratory transpulmonary pressure at the time of acquisition of the expiratory image; performing non-rigid image registration of the inspiratory image and the expiratory image to generate a relative compliance or elasticity map of the lungs; converting the relative compliance or elasticity map of the lungs into a quantitative compliance or elasticity map of the lungs based on the inspiratory transpulmonary pressure and the expiratory transpulmonary pressure; and displaying, on a display device, information related to or derived from the quantitative compliance or elasticity map.

[0012] One advantage is that it provides a model of the lungs of a patient undergoing mechanical ventilation with non-uniform tissue stiffness, mechanical properties that are patient-specific and calibrated.

[0013] Another advantage resides in providing a model of the lungs of a patient undergoing mechanical ventilation with color-coded tissue map sections corresponding to mechanical properties of the lungs.

[0014] Another advantage is in providing a model of the lungs of a patient undergoing mechanical ventilation on the display of the ventilator, thereby reducing the need for additional computers in the intensive care unit (ICU).

[0015] Another advantage resides in providing a model of the lungs of a patient undergoing mechanical ventilation that is dynamically updated with additional imaging data of the patient.

[0016] Another advantage is that it provides a digital twin of a patient's thoracic cavity, including the lungs, of a patient undergoing mechanical ventilation, which can be used to simulate changes in ventilator settings.

[0017] A given embodiment may provide none, one, two, more, or all of the advantages discussed above, and / or other advantages that will become apparent to those of ordinary skill in the art upon reading and comprehending this disclosure. [Brief description of the drawings]

[0018] The disclosure may take form in various components and arrangements of components, and in various steps and arrangements of steps. [Figure 1] FIG. 1 illustrates a schematic of an exemplary mechanical ventilation system according to the present disclosure. [Diagram 2] FIG. 2 illustrates an exemplary flow chart of operations suitably performed by the system of FIG. [Diagram 3] FIG. 3 shows an example of a map generated by the system of FIG. [Figure 4]FIG. 4 shows an example of a map generated by the system of FIG. [Diagram 5] FIG. 5 illustrates generally an example of a digital twin generated by the system of FIG. [Figure 6] FIG. 6 illustrates an example flowchart of operations for generating the digital twin shown in FIG. [Figure 7] FIG. 7 shows an example of a graph generated by the system of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] In the specification, a plurality is included even if it is not stated otherwise, unless the context clearly dictates otherwise. In the specification, the expression "coupled", "connected" or "engaged" of two or more parts or components shall mean that these parts are coupled, operated or cooperate directly or indirectly, i.e., through one or more intermediate parts or components, so long as they are coupled. Words of direction used in this specification, such as but not limited to top, bottom, left, right, upper, lower, front, rear and their derivatives, relate to the orientation of the elements shown in the drawings and do not limit the scope of the invention as claimed, unless expressly stated herein. The words "having" or "including" do not exclude the presence of elements or steps other than those described in this specification and / or recited in the claims. In a device consisting of several means, several of these means may be embodied by one and the same item of hardware.

[0020] Disclosed herein are systems and methods for assisting bedside clinicians and care providers in providing safe mechanical ventilation, including guiding clinicians to select ventilator settings that will not result in VILI for a particular patient.

[0021] Linear or non-linear local lung compliance numbers (relative numbers) are mapped using non-rigid image registration (DIR) with CT or X-ray. To determine absolute pressure without breath-hold or breath-pause procedures, imaging is triggered or timed at zero flow in the respiratory cycle. Simultaneous with this imaging, a transpulmonary pressure reading is taken. The image is calibrated using ventilator data to convert the relative lung compliance to an absolute number, which uses the transpulmonary pressure value. The output of this process is a quantitative elasticity map.

[0022] A digital twin of the patient's thoracic cavity including the lungs is then generated, i.e. a four-dimensional (4D, spatial and temporal dimensions) biophysical lung model including structural, ventilation and deformation aspects, which is continuously calibrated and updated using ventilator data and bedside imaging information (e.g., using imaging modalities such as X-ray or ultrasound). The digital twin simulates the effects of different ventilator settings on the lungs, in particular tissue stresses. The input to the lung model is the previously obtained quantitative elasticity distribution.

[0023] In some embodiments, the user interface (UI) further comprises an easily readable display. The output of the model is translated into actionable clinical decision support (CDS) information. The ventilator user interface (UI) presents options for the clinician to decide.

[0024] Referring to FIG. 1, a ventilator 2 is shown for providing ventilation therapy to an associated patient P. As shown in FIG. 1, the ventilator 2 includes an outlet 4 connectable to a patient breathing circuit 5 for delivering mechanical ventilation to the patient P. The patient breathing circuit 5 includes typical components of a ventilator, such as, for example, an inspiratory line 6, an optional expiratory line 7 (omitted if the ventilator uses a single-limb patient circuit), a connector or port 8 for connecting to an endotracheal tube (ETT), and one or more respiratory sensors (not shown), such as, for example, a gas flow meter, a pressure sensor, and / or an end-tidal carbon dioxide (etCO2) sensor. The ventilator 2 is designed to deliver air, an air-oxygen mixture, or other breathable gas (not shown) to the outlet 4 at a programmed pressure and / or flow rate to ventilate the patient via the ETT. The ventilator 2 also includes an electronic controller (e.g., a microprocessor) 13 for controlling the operation of the ventilator 2, and a display device 14 for displaying information about the patient P and / or settings of the ventilator 2 during the patient P's mechanical ventilation.

[0025] 1 shows a schematic of a patient P intubated with an ETT 16 (the lower portion of the ETT is shown in perspective since it is inside the patient P). A connector or port 8 operably connects with the ETT 16 for delivering breathable air to the patient P via the ETT 16. Mechanical ventilation provided by the ventilator 2 via the ETT 16 is therapeutic for various diseases, such as various types of lung diseases, e.g., emphysema or pneumonia, viral or bacterial infections that affect breathing, e.g., COVID-19 infection or severe influenza, or cardiovascular diseases, in which the patient P receives oxygen-enriched breathable gas.

[0026] FIG. 1 also shows a medical imaging device 15 (also referred to as image acquisition device, imaging device, etc.). The image acquisition device 15 can be a computed tomography (CT) image acquisition device, a C-arm image acquisition device, other X-ray imaging device, a magnetic resonance (MR) image acquisition device, an ultrasound (US) image acquisition device, or a medical imaging device of another modality. As described primarily herein, the medical imaging device 15 comprises a CT medical imaging device 15. As described herein, the medical imaging device 15 is used to acquire images of the patient P on the basis of which the ETT sizing is performed. It should be noted that the imaging device 15 does not have to be located in the same room or in the same department as the ventilator 2. For example, the medical imaging device 15 may be located in a radiology room, while the ventilator 2 may be located in an intensive care unit (ICU), a coronary care unit (CCU), or a room assigned to the patient P, etc. This is indicated diagrammatically in FIG. 1 by the dividing line L. Additionally or alternatively, a bedside imaging device 15B may be used, such as an exemplary ultrasound imaging device.

[0027] With continued reference to FIG. 1, an electronic processing device 18 is shown configured to generate data related to the patient P and / or settings of the ventilator 2 during mechanical ventilation of the patient P. The electronic processing device 18 may comprise an electronic processing device such as, for example, a workstation computer (more generally, a computer), a smart device (e.g., a smartphone and a tablet, etc.), or a server computer or multiple server computers (e.g., interconnected to form a server cluster, a cloud computing resource, etc.). The electronic processing device 18 includes typical components such as, for example, an electronic controller 20 (e.g., an electronic processor or microprocessor), at least one user input device 22 (e.g., a mouse, a keyboard, a trackball, and / or a finger swipe on a touch screen of a smart device, etc.), and at least one display device 24 (only shown in FIG. 1, e.g., an LCD display, a plasma display, and / or a cathode ray tube display, etc.). In some embodiments, the display device 24 may be a separate component from the electronic processing device 18. The display device 24 may comprise two or more displays.

[0028] The electronic controller 20 is operatively connected to one or more non-transitory storage media 26, which may include, by way of non-limiting illustrative example, one or more of a magnetic disk, RAID or other magnetic storage medium; a solid state drive, a flash drive, an EEROM or other electronic memory; an optical disk or other optical storage device; or various combinations thereof, such as a network storage device, an internal hard drive of the ventilatory support device 18, or various combinations thereof. It should be understood that any reference herein to a non-transitory medium 26 should be broadly interpreted to encompass a single medium or multiple media of the same or different types. Similarly, the electronic controller 20 may be embodied as a single electronic processor or as two or more electronic processors. The non-transitory storage medium 26 stores instructions executable by at least one electronic controller 20, including instructions for generating a graphical user interface (GUI) 28 for display on a remote operator's display device 24.

[0029] Further, as disclosed herein, the non-transitory storage medium 26 stores instructions executable by at least one electronic controller 20 for performing a ventilatory assistance method or process 100 for providing ventilation therapy to a patient P.

[0030] As mentioned above, it is understood that the ventilator 2 may be located in a first room of a medical facility, while the image capture device 15 and electronic processing device 18 may be located in another second room of the medical facility. This is indicated by the dashed line L in the generally "middle" portion of FIG. 1. In another example, the ventilator 2 and electronic processing device 18 may be located in the first room, while the image capture device 15 may be located in the second room of the medical facility. In a further example, each of the ventilator 2, image capture device 15, and electronic processing device 18 may be located in a separate room of the medical facility. Additionally or alternatively, a bedside imaging device 15B may be provided in the patient's room. These are merely illustrative examples.

[0031] As described herein, the method 100 may be performed by the electronic processing device 18 or by the electronic controller 13 of the ventilator 2.

[0032] With continued reference to Figure 1 and with reference to Figure 2, an exemplary embodiment of a method 100 for ventilatory assistance is shown generally as a flow chart. In operation 102, one or more images 34 of a patient are acquired by a medical imaging device 15. In a particular example, the acquired images 34 are CT images 34. To acquire the CT images 34, the electronic controller 20 is configured to control the medical imaging device 15 (i.e., a CT scanner) to acquire CT images 34 of the upper airway or airways of a patient P (e.g., from the nose or mouth to the carina).

[0033] The imaging operation 102 includes analyzing the images 34 to determine an inspiratory image 35 acquired during an inspiratory phase of mechanical ventilation, and an expiratory image 36 acquired during an expiratory phase of mechanical ventilation. For example, the inspiratory image 35 can be acquired during a maximum inhalation by the patient P, and the expiratory image 36 can be acquired during a maximum exhalation by the patient P. In an operation 104, which is performed simultaneously with the imaging operation 102, transpulmonary pressure data associated with the lungs of the patient P is also measured. This transpulmonary pressure data can include, for example, an inspiratory transpulmonary pressure, such as a transpulmonary pressure reading measured during acquisition of the inspiratory image 35, and an expiratory transpulmonary pressure, such as another transpulmonary pressure reading measured during acquisition of the expiratory image 36.

[0034] In some embodiments, imaging operation 102 can include receiving airway airflow as a function of time during mechanical ventilation, with inhalation image 35 and exhalation image 36 selected as images of imaging data acquired when the airway airflow is zero. Transpulmonary pressure can then be measured simultaneously in operation 104 (e.g., using an esophageal catheter with a pressure sensor, or using methods described in, e.g., Umbrello, M. and Chiumello, D., 2018, “Interpretation of the transpulmonary pressure in the critically ill patient”, Ann Transl. Med 2018;6(19):383).

[0035] In operation 106, a non-rigid image registration (DIR) is performed on the inspiration image 35 and the expiration image 36 to generate a lung relative compliance or elasticity map 38. The volumetric strains in the individual lung tissues are determined from the DIR operation 106, and in conjunction with the overall pressure exerted on the individual lung tissues from the ventilator 2 (from operation 104), stiffness values ​​of the individual lung tissues are determined in the relative compliance or elasticity map 38. In one example, for elastic deformation, the elastic modulus is: E local = Δp V local / ΔV local Alternatively, the compliance or elasticity map 38 may be calibrated by scaling the local deformations with the global lung deformation and referencing the global lung compliance C or elasticity E from the ventilator, i.e. E local / E=ΔV global / ΔV local Figure 3 shows an example of a relative compliance or elasticity map 38 using the global pressure Δp.

[0036] In another embodiment, operation 106 is performed using DIR with x-ray or CT, and a similar deformation map can be obtained directly from electrical impedance tomography (EIT) at the bedside of the patient P. Advantageously, since EIT is a wearable technology, the compliance or elasticity map 38 can be continuously updated. Similarly, in operation 102, the bedside imaging device 15B can be used to obtain inhalation and exhalation images. This allows for more frequent updates to the digital twin since bedside imaging is typically performed more frequently.

[0037] In operation 108, the lung relative compliance or elasticity map 38 is converted into a lung quantitative compliance or elasticity map 39 based on the inspiratory transpulmonary pressure and the expiratory transpulmonary pressure (obtained from operation 104). The transpulmonary pressure is the difference between the intra-alveolar pressure and the intra-thoracic pressure in the thoracic cavity. Thus, since the transpulmonary pressure is the actual pressure that induces deformation of the lung tissue, using the transpulmonary pressure as disclosed herein allows operation 108 to convert the relative compliance or elasticity map 38 into a quantitative compliance or elasticity map 39. In some examples, the conversion operation 108 is performed using an airway tree 40 of the lungs of the patient P. This airway tree 40 can advantageously be a patient-specific airway tree extracted from one or more images 34 acquired in operation 102.

[0038] The airway tree 40 can be a one-dimensional (1D) airflow model showing the local resistance of individual lung tissues at different airway generations. The patient-specific parameters of the airway tree 40 can be obtained, for example, from a segmented 3D-CT scan acquired in operation 102 (e.g., the distribution, length and diameter of the airway generations). Alternatively, when a CT scan is not available (e.g., when imaging 102 is performed with an imaging modality that does not effectively image the airway tree 40), a general airway tree network with several airway generations can be used. Optionally, the local resistance of the individual lung tissues can be calibrated with the measured global lung resistance. When the local pressure distribution in the airway tree 40 is known, this local pressure Δp can be used instead of the global pressure Δp. local The local elasticity can be estimated using the local pressure Δp local 4 shows an example of a relative compliance or elasticity map 38 generated using:

[0039] In operation 110, a digital twin 42 of the lungs of patient P is generated by modeling the stress and strain distribution in the patient's lungs, represented by a quantitative compliance or elasticity map 39 of the lungs, in response to mechanical ventilation therapy. As used herein, a "digital twin" of the lungs refers to a virtual representation of the physical lungs. Using computational modeling, the digital twin 42 of the lungs of patient P simulates physiological processes using sensor data and other information continuously acquired from the lungs. The digital twin 42 is continuously updated to assess the function of the lungs of patient P and to reflect the condition of patient P (i.e., lung function and disease progression, etc.) and future predictions of patient P (i.e., optimization of ventilator 2 settings) to provide the best outcome (i.e., faster patient recovery with minimal damage). To do so, updated imaging data (i.e., additional images 34) can be received and used to update the digital twin 42.

[0040] 5 shows a schematic of an example of a digital twin 42 and ancillary components. The digital twin 42 is generated from the images 34 acquired in operation 102 (e.g., via CT, X-ray or ultrasound (US)) and the corresponding transpulmonary pressure readings acquired in operation 104, and the digital twin 42 is stored in the cloud or in a non-transitory computer readable medium 26 of the electronic processing device 18. The digital twin 42 is retrieved and used by a clinician via the ventilator 2 to simulate different ventilator settings in the digital twin 42. Based on these simulations, the clinician can adjust the settings of the ventilator 2 to treat the patient P.

[0041] With reference to FIG. 6 and continuing reference to FIGS. 1 and 5, an exemplary embodiment of a digital twin generation and usage method 200 is illustrated generally as a flow chart. In operation 202, one or more CT images 34 are generated using an image acquisition device 15. In operation 204, an electronic processing device 18 is configured to generate a model of the lung anatomy and geometry of the patient P from said images 34. In operation 206, the mechanical properties of this model are calibrated with the local mechanical properties of the lung tissue using a quantitative compliance or elasticity map 39 of the lung to generate a digital twin 42. In operation 208, a computational fluid dynamics and computational mechanics process is applied to create a lung flow and deformation model. In operation 210, the initial settings of the ventilator are determined (using known protocols accepted by the medical community). In operation 212, a local tissue stress simulation is performed on the digital twin 42 to simulate the effects of different ventilation settings (e.g., flow, volume, pressure, rise time, etc.) on the lung (e.g., tissue stress and strain). From this, updated ventilator settings are determined by the algorithm (or optionally by the clinician) in operation 214. For example, the tidal volume or pressure may be reduced by a predetermined amount to reduce local lung strain or stress so that these values ​​do not exceed a predetermined threshold. In operation 216, one or more setting options, including the resulting lung stress, are displayed on the display 14 of the ventilator 2. In operation 218, the clinician selects one or more of the displayed setting options that are then used by the ventilator 2. In operation 220, the results of mechanical ventilation therapy using the selected setting option are displayed on the display 14 of the ventilator 2 (at which point one or more of operations 204, 206 and / or 212 may be repeated, as indicated by the arrows in FIG. 6). In operation 222, bedside imaging (e.g., EIT imaging or x-ray imaging) of the patient P is performed, and the imaging data may be used to update the digital twin 42, at which point operation 204 may be repeated.In act 224, the clinician may decide whether to continue mechanical ventilation, and if Yes, in act 226, the patient P is extubated (eg, by removing the ETT 16).

[0042] 1 and 2, in operation 112, the ventilator 2 is controlled to adjust one or more parameters of the mechanical ventilation therapy delivered to the patient P using the digital twin 42.

[0043] In operation 112, the quantitative compliance or elasticity map 39 and / or information related to or derived from the digital twin 42 is displayed on the display 14 of the ventilator 2. In one embodiment, a graphical representation of the digital twin 42 is displayed on the display 14. The representation of the digital twin 42 may include clinical decision support (CDS) information and may be presented on the display 14 of the ventilator 2 to enable a clinician to quickly and easily select or determine the best mechanical ventilation therapy scenario. For example, the digital twin 42 may be displayed and include selectable options for progressing mechanical ventilation therapy based on situational needs, the patient's care pathway, and the clinician's expertise.

[0044] Depending on the patient's treatment pathway, clinicians require different types of information and assistance regarding the prevention of VILI in mechanical ventilation to be displayed on the display device 14, including option (1) diagnostic data and information regarding lung heterogeneity (e.g., compressing information from FRI (functional respiratory image) into a single metric), option (2) outcome prediction (i.e., early selection of the ventilator mode providing the shortest possible hospital stay or other treatment options in case of mechanical ventilation failure), option (3) treatment planning assistance (e.g., simulating MV parameters and lung tissue stress), or option (4) monitoring information for performing and maintaining safe mechanical ventilation as well as recommendations for adapting mechanical ventilation settings when necessary.

[0045] Depending on the situation, such as whether the clinician has enough time or is in a hurry, whether the clinician is scheduled for a routine visit or an emergency, the nature of the information or the required action of the system will be different. Different options are then displayed on the display device 14, including option (A) "show what is happening now or what will happen after treatment application" if there is enough time or it is a scheduled routine visit, option (B) "provide recommendations or several scenarios to choose from" if time is limited, the patient P needs help now or it is an emergency, or option (C) "automate the task" if there is no time or the clinician is not available.

[0046] Depending on the clinician's expertise, different options may be displayed on the display 14, including option (i) Pulmonologist, or option (ii) Nurse.

[0047] A lookup table 44 implemented in the electronic processor 13 of the ventilator 2 can be used to determine which options to display on the display 14. For example, the lookup table 44 can receive as inputs a department schedule, ventilator sensor data, and environmental sensor data. From these inputs, the lookup table 44 can select which option (e.g., options 1-4, A-C, or i-ii) to display on the display 14. For example, in a situational aspect, if the department schedule indicates a scheduled routine visit, the lookup table 44 can select option A to display for selection. If the department schedule indicates that it is not a routine visit, and an alarm is sounding, the lookup table 44 can select option B to display for selection. If a caregiver is absent (e.g., as determined from presence detector, camera, microphone, badge reader activity, etc.), the lookup table 44 can select option C to display for selection.

[0048] On the expertise side, if a caregiver is detected to be present (e.g., by activated facial or voice recognition or another automatic identification technology such as information from a badge reader), the level of expertise (e.g., pulmonologist or nurse) is read from the non-transitory computer readable medium 26 and the lookup table 44 can select and display either option (i) or (ii).

[0049] In terms of the patient's care pathway, the steps in the patient's care pathway can be recorded in an emergency room information system, an electronic medical record (EMR) database, or another medical department information system. For example, if patient P is diagnosed with severe COVID-19 and admitted to the ICU, the lookup table 44 can select and display option 3. In another example, if patient P has been on mechanical ventilation in the ICU for a week and his condition is deteriorating, the lookup table 44 can select and display option 4.

[0050] In some embodiments, multiple types of options can be displayed (e.g., displaying options 1, A, and i and displaying options 3, B, and ii). In other embodiments, a push button on display device 14 can be pressed to display a standard or blank user interface (UI), or a button can be pressed to display options selected by lookup table 44.

[0051] In some embodiments, the display operation 112 can include displaying a graph 46 on the display device 14 showing the ventilator 2 settings versus lung strain. In one example, the graph 46 can be a bar graph showing lung tidal volume and lung strain. The bar graph 46 can include two columns representing the right and left lungs along with a tidal volume slider. The percentage of the lung that is above critical strain (e.g., e>2) can also be displayed. The bars can also be color coded. For example, a "green" bar can indicate 0% lung overload (i.e., strain). A "red" bar can indicate that one of the lung lobes is overstrained beyond a threshold (e.g., 30% strain).

[0052] In another example, the graph 46 can be a line graph. The line graph 46 can show the percentage of the lungs above a critical strain (e.g., e>2) as a function of mechanical ventilation settings. In some examples, the critical strain e is patient-specific based on the lung damage (e.g., due to emphysema or fibrosis) shown in the image 34. In another example, the graph 46 can show the effect of changing the mechanical ventilation settings on the predicted outcome based on a patient similarity analysis performed by the electronic controller 13 of the ventilator 2 (or the electronic controller 20 of the electronic processing unit 18). The similarity analysis uses as inputs the long-term mechanical ventilation settings and mechanical ventilation sensor data, diagnostic scans, bedside imaging, patient characteristics and reported outcomes. For example, an icon with green, orange and red areas shows the predicted patient hospitalization (days) as a function of mechanical ventilation settings. For example, from similar Covid-19 patients, it appears that fibrosis is too advanced and it is too late for invasive mechanical ventilation.

[0053] In another example, the line graph 46 may also include real-time (e.g., based on what happens when breathing becomes heavier) stress values ​​or volume distribution of lung tissue during the respiratory cycle of patient P. The line graph 46 may also show other data including the range of compliance or elasticity present in the lungs, and the ratio of biomechanical metrics (volume, elasticity, etc.) in each lung.

[0054] Figure 7 shows an example of a line graph 46 displayed on display device 14. As shown in Figure 7, three lines representing different tidal volumes of the lung are plotted as the percentage of the lung undergoing a particular strain versus the percentage of strain of the lung. The lines representing tidal volumes 1 and 2 indicate that a critical strain value (e.g., e=2) has been met or exceeded, in which case an alert 48 (see Figure 1) is output to the clinician (i.e., as a message on display device 14 or an audible sound).

[0055] In some examples, not shown in Figure 7, different lines can be color coded based on criticality levels for patient P. For example, tidal volume line 1 can be color coded red (indicating high lung strain and thus requiring clinician attention), tidal volume line 2 can be color coded yellow (indicating moderately high lung strain and thus possibly requiring clinician attention), and tidal volume line 3 can be color coded green (indicating low lung strain that does not exceed a critical value and thus does not require clinician attention).

[0056] In other embodiments, the clinician can select a portion of the graph 46 on the display (e.g., by tapping or swiping with a finger indicating user input). Based on the portion of the graph 46 that received the user input, additional information about the portion of the graph 46 can be displayed on the display 14. For example, if the clinician selects the tidal volume line 2, the exact strain value (distribution) for that tidal volume can be displayed and the clinician can decide whether to adjust the settings of the ventilator 2.

[0057] The present disclosure has been described with reference to the preferred embodiment. Modifications and alterations may occur to others upon reading and understanding the above detailed description. It is intended that the exemplary embodiments be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. Receiving imaging data and transpulmonary pressure data related to a patient's lungs while the patient is undergoing mechanical ventilation therapy using a ventilator, wherein the imaging data includes an inhalation image acquired during an inhalation phase of the mechanical ventilation therapy and an exhalation image acquired during an exhalation phase of the mechanical ventilation therapy, and the transpulmonary pressure data includes an inhalation transpulmonary pressure at the time of acquisition of the inhalation image and an exhalation transpulmonary pressure at the time of acquisition of the exhalation image including, performing non-rigid image registration on the inhalation image and the exhalation image to generate a relative compliance or elasticity map of the lungs, converting the relative compliance or elasticity map of the lungs into a quantitative compliance or elasticity map of the lungs based on the inhalation transpulmonary pressure and the exhalation transpulmonary pressure, and displaying information regarding the quantitative compliance or elasticity map or derived from the quantitative compliance or elasticity map on a display device A mechanical ventilation device having at least one electronic controller configured as described above.

2. The at least one electronic controller is receiving airway airflow as a function of time during the mechanical ventilation therapy, and selecting the inhalation image and the exhalation image as images of the imaging data acquired when the airway airflow is zero The device according to claim 1, further configured as described above.

3. The at least one electronic controller is configured to convert the relative compliance or elasticity map of the lungs into a quantitative compliance or elasticity map of the lungs based on the airway tree of the patient's lungs and based on the local pressure distribution in the airway tree, according to claim 1 The device described.

4. The at least one electronic controller is configured to generate an airway tree of the patient's lungs by extracting the airway tree of the patient's lungs from at least one image of the imaging data, according to claim 3 The device described.

5. The at least one electronic controller is configured to generate a digital twin of the patient's lungs by modeling the stress and strain distribution in the patient's lungs represented by the quantitative compliance or elasticity map of the lungs in response to the mechanical ventilation therapy, wherein the digital twin has a model of the anatomical structure and geometric shape of the lungs generated based on the imaging data, The mechanical properties of the model are calibrated by the compliance or elasticity map of the lung, numerical fluid dynamics and computational mechanics processes are applied to model the flow rate and changes of the lung, The device according to claim 1.

6. The at least one electronic controller, receives updated imaging data, and updates the digital twin using the updated imaging data The device according to claim 5, which is programmed to do so.

7. The device further includes a ventilator configured to deliver the mechanical ventilation therapy to the patient, The at least one electronic controller, using the digital twin, is programmed to control the ventilator to adjust one or more parameters of the mechanical ventilation therapy delivered to the patient based on simulating the influence of the parameters of different ventilators on the local tissue stress in the lung. The device according to claim 5.

8. The displayed information regarding the quantitative compliance or elasticity map, or derived from the quantitative compliance or elasticity map, has a graphical representation of the digital twin. The device according to claim 5.

9. The device according to claim 1, further comprising an imaging device configured to acquire the imaging data.

10. The imaging device is a computed tomography (CT) imaging device. The device according to claim 9.

11. The at least one electronic controller, is programmed to display a graph showing the settings of the ventilator with respect to the strain of the lung on a display device of the ventilator. The device according to claim 1.

12. The at least one electronic controller, is programmed to color-code a line on the graph regarding the settings of the ventilator based on the critical level of the patient. The device according to claim 11.

13. The at least one electronic controller, is programmed to output an alert when a critical strain value of the lung occurs. The device according to claim 12.

14. The at least one electronic controller, receives user input from a user regarding a part of the displayed graph on the display device, and displays additional information regarding the part of the graph where the user input is received The apparatus according to claim 11, programmed to operate as such.

15. wherein at least one electronic controller receiving imaging data and transpulmonary pressure data related to the lungs of the patient while the patient is undergoing mechanical ventilation therapy using a ventilator, the imaging data including an inhalation image acquired during the inhalation phase of the mechanical ventilation therapy and an exhalation image acquired during the exhalation phase of the mechanical ventilation therapy, the transpulmonary pressure data including an inhalation transpulmonary pressure at the time of acquisition of the inhalation image and an exhalation transpulmonary pressure at the time of acquisition of the exhalation image, the receiving step; performing non-rigid image registration of the inhalation image and the exhalation image to generate a relative compliance or elasticity map of the lungs; converting the relative compliance or elasticity map of the lungs into a quantitative compliance or elasticity map of the lungs based on the inhalation transpulmonary pressure and the exhalation transpulmonary pressure; and displaying, on a display device, information regarding the quantitative compliance or elasticity map or derived from the quantitative compliance or elasticity map. A mechanical ventilation method having the above steps.

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