Accuracy of noncontact surface imaging for tidal volume and respiratory rate measurements in the ICU.

Tidal volume monitoring may help minimize lung injury during respiratory assistance. Surface imaging using time-of-flight camera is a new, non-invasive, non-contact, radiation-free, and easy-to-use technique that enables tidal volume and respiratory rate measurements. The objectives of the study were to determine the accuracy of Time-of-Flight volume (VTTOF) and respiratory rate (RRTOF) measurements at the bedside, and to validate its application for spontaneously breathing patients under high flow nasal canula. Data analysis was performed within the ReaSTOC data-warehousing project (ClinicalTrials.gov identifier NCT02893462). All data were recorded using standard monitoring devices, and the computerized medical file. Time-of-flight technique used a Kinect V2 (Microsoft, Redmond, WA, USA) to acquire the distance information, based on measuring the phase delay between the emitted light-wave and received backscattered signals. 44 patients (32 under mechanical ventilation; 12 under high-flow nasal canula) were recorded. High correlation (r = 0.84; p < 0.001), with low bias (-1.7 mL) and acceptable deviation (75 mL) was observed between VTTOF and VTREF under ventilation. Similar performance was observed for respiratory rate (r = 0.91; p < 0.001; bias < 1b/min; deviation ≤ 5b/min). Measurements were possible for all patients under high-flow nasal canula, detecting overdistension in 4 patients (tidal volume > 8 mL/kg) and low ventilation in 6 patients (tidal volume < 6 mL/kg). Tidal volume monitoring using time-of-flight camera (VTTOF) is correlated to reference values. Time-of-flight camera enables continuous and non-contact respiratory monitoring under high-flow nasal canula, and enables to detect tidal volume and respiratory rate changes, while modifying flow. It enables respiratory monitoring for spontaneously patients, especially while using high-flow nasal oxygenation.

Noninvasive Tidal Volume Measurements, Using a Time-of-Flight Camera, Under High-Flow Nasal Cannula-A Physiological Evaluation, in Healthy Volunteers.

OBJECTIVES: The mechanisms of high-flow nasal cannula are still debated but may be mediated by the generation of low positive end-expiratory pressure and a washout of the airway dead space. The aims of this study were to assess the effects of high-flow nasal cannula on tidal volume using a noninvasive method using a time-of-flight camera, under various conditions. DESIGN: A physiologic evaluation in healthy volunteers. SETTING: An university hospital ICU. SUBJECTS: Ten healthy volunteers were included in a physiologic study (CamOpt study, ClinicalTrials.gov identifier: NCT04096183). INTERVENTIONS: All volunteers were submitted to 12 different conditions (i.e., gas flow [baseline = 0; 30-60 L/min]; mouth [open/closed]; respiratory rate [baseline; baseline + 10 breaths/min]). Tidal volume measurements were performed every minute, during a 6-minute recording period. In all combinations, reference respiratory rate was measured by using chronometric evaluation, over a 30-second period (RRREF), and by using the time-of-flight camera (RRTOF). MEASUREMENTS AND MAIN RESULTS: Tidal volume increased while increasing gas flow whatever the respiratory rate and mouth condition (p < 0.001). Similar results were observed whatever the experimental conditions (p < 0.01), except one (baseline respiratory rate + 10 breaths/min and mouth closed). Tidal volume increased while decreasing respiratory rate (p < 0.001) and mouth closing (p < 0.05). Proportion of tidal volume greater than 10, 15, and 20 mL/kg changed while increasing the flow. RRTOF was in agreement with RRREF (intraclass correlation coefficient, 0.96), with a low mean bias (0.55 breaths/min) and acceptable deviation. CONCLUSIONS: Time-of-flight enables to detect tidal volume changes under various conditions of high-flow nasal cannula application. Tidal volume increased significantly while increasing gas flow and mouth closing. Such technique might be useful to monitor the risk of patient self-inflicted lung injury or under assistance.

Technical note: Surface imaging for real-time patient positioning in external radiation therapy.

PURPOSE: In the last few years, there has been a growing interest in surface imaging for patient positioning in external radiation therapy. The aim of this study is to evaluate the accuracy of daily patient positioning using the Azure Kinect surface imaging. METHODS: A total of 50 fractions in 10 patients including lung, pelvic, and head and neck tumors were analyzed in real time. A rigid registration algorithm, based on the iterative closest point (ICP) approach, is employed to estimate the patient position in 6 degrees of freedom (DOF). This position is compared to the reference values obtained by the radiograph imaging. The mean setup error and its standard deviation were calculated for all measured fractions. RESULTS: The positioning error showed 1.1 ± 1.1 mm in lateral, 1.8 ± 2.1 mm in longitudinal, and 0.8 ± 1.1 mm in vertical, and 0.3°± 0.4° in yaw, 0.2°± 0.2° in pitch, and 0.2°± 0.2° in roll directions. The larger setup error occurred in pelvic regions. CONCLUSION: We have evaluated in a radiotherapy set-up considering different patient anatomical locations, a depth measurement based surface imaging solution for patient positioning considering the 6 DOF couch motion. We showed that the proposed solution allows an accurate patient positioning without the need for patient markings or the use of additional radiation dose.

Surface imaging for real-time patient respiratory function assessment in intensive care.

PURPOSE: Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients. METHODS: The proposed system uses a Time-of-Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital. RESULTS: The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients. CONCLUSION: This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients.

Technical Note: Kinect V2 surface filtering during gantry motion for radiotherapy applications.

PURPOSE: In radiotherapy, the Kinect V2 camera, has recently received a lot of attention concerning many clinical applications including patient positioning, respiratory motion tracking, and collision detection during the radiotherapy delivery phase. However, issues associated with such applications are related to some materials and surfaces reflections generating an offset in depth measurements especially during gantry motion. This phenomenon appears in particular when the collimator surface is observed by the camera; resulting in erroneous depth measurements, not only in Kinect surfaces itself, but also as a large peak when extracting a 1D respiratory signal from these data. METHODS: In this paper, we proposed filtering techniques to reduce the noise effect in the Kinect-based 1D respiratory signal, using a trend removal filter, and in associated 2D surfaces, using a temporal median filter. Filtering process was validated using a phantom, in order to simulate a patient undergoing radiotherapy treatment while having the ground truth. RESULTS: Our results indicate a better correlation between the reference respiratory signal and its corresponding filtered signal (Correlation coefficient of 0.76) than that of the nonfiltered signal (Correlation coefficient of 0.13). Furthermore, surface filtering results show a decrease in the mean square distance error (85%) between the reference and the measured point clouds. CONCLUSION: This work shows a significant noise compensation and surface restitution after surface filtering and therefore a potential use of the Kinect V2 camera for different radiotherapy-based applications, such as respiratory tracking and collision detection.