Improved resolution of D‐bar images of ventilation using a Schur complement property and an anatomical atlas

Santos, Talles Batista Rattis; Nakanishi, Rafael Mikio; Camargo, Erick Dario León Bueno de; Amato, Marcelo B. P.; Kaipio, Jari P.; Lima, Raúl González; Mueller, Jennifer L.

doi:10.1002/mp.15669

Cited by 5 publications

(4 citation statements)

References 75 publications

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“…In this study, corresponding EIT image sequences were obtained during apnoea using beat-tobeat perfusion and hypertonic saline perfusion, respectively, and we found that changes in beat-tobeat perfusion before and after embolisation were reflected in both end-expiratory and endinspiratory pauses. In addition, the selection of appropriate animal models is important for obtaining reliable and meaningful experimental results [29,30]. As we know, porcine models are the basis for preclinical and translational studies of many diseases treatments [31,32].…”

Section: Discussionmentioning

confidence: 99%

Real-time non-invasive monitoring of pulmonary embolism based on dynamic EIT technology: a new method

Li,

Zhu,

et al. 2024

Preprint

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Background The assessment and monitoring of pulmonary embolism is a key factor in guiding the treatment of critically ill patients. To date, bedside methods used clinically to estimate the physiological correlates of pulmonary embolism (pulmonary blood flow perfusion) are often unreliable or require invasive testing. The aim of this study was to explore a new method for assessing pulmonary embolism detection and non-invasive real-time monitoring based on a high-performance electrical impedance tomography system that acquires imaging of pulmonary blood flow pulsation signals. Methods An anaesthetised porcine model (N = 12) was selected for a before-and-after self-control experiment, and the pulmonary perfusion changes induced before and after pulmonary embolism (artificially induced) were continuously monitored by the vascular pulsatility method (VPM), from which the amplitude, maximal slope (both positive and negative), and waveform area were extracted as indicators for assessing the status of the local pulmonary perfusion. In addition, the degree of ventilation-perfusion matching of the lungs was assessed in conjunction with the analysis of lung ventilation areas. A conventional invasive hypertonic saline (5 ml of 10% NaCl) imaging technique was used as a control to assess the actual pulmonary embolism. Results Areas of perfusion defects before and after embolisation by the vascular pulsatile method showed a high degree of concordance with hypertonic saline in terms of images and indices. All pulmonary blood flow pulsatility indices were significantly reduced in the embolised region relative to the pre-embolisation period, with the most significant changes in waveform area and amplitude during the end-expiratory pause (P < 0.001). Ventilation blood flow matching indices extracted in combination with regional ventilation also showed significant differences, with the most significant changes in ventilation blood flow matching% and deadspace ventilation fraction% (P < 0.001). Conclusions Vascular beat-based EIT imaging methods can be used to assess characteristic changes in pulmonary perfusion before and after embolisation, and this approach is expected to provide new ideas for non-invasive bedside monitoring of pulmonary embolism.

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Section: Discussionmentioning

confidence: 99%

Real-time non-invasive monitoring of pulmonary embolism based on dynamic EIT technology: a new method

Li,

Zhu,

et al. 2024

Preprint

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“…A novel method to quantitatively measure the image quality of 2-D EIT images when a CT scan is available was proposed in [44]. The method uses the structural similarity index (SSIM) [52] or the multiscale structural similarity index (MS-SSIM) [53] to compare to ground truth images that are derived from the CT scans, since direct comparison between EIT images of conductivity and CT images of tissue density would be incorrect due to the differences between the two modalities.…”

Section: Image Quality Evaluationmentioning

confidence: 99%

“…The method uses the structural similarity index (SSIM) [52] or the multiscale structural similarity index (MS-SSIM) [53] to compare to ground truth images that are derived from the CT scans, since direct comparison between EIT images of conductivity and CT images of tissue density would be incorrect due to the differences between the two modalities. The reader is referred to [44] for details of the construction of the ground truth images. Briefly, inspiratory and expiratory CT scans are registered and rescaled, and then segmented to identify the lung region.…”

Section: Image Quality Evaluationmentioning

confidence: 99%

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Resolution improvement and algorithmic dependence of machine learning for post-processing respiratory EIT images

Santos,

Nakanishi,

Olegário

et al. 2023

AMMC

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Electrical impedance tomography (EIT) is an imaging modality in which electric fields arising from currents applied on electrodes are used to form dynamic images of physiological processes, such as respiration, by plotting the reconstructed conductivity distribution. This work investigates the effectiveness of using machine learning to post-process EIT images of respiration, validated against a CT scan taken immediately after the EIT data collection, and the dependence of the results on the reconstruction algorithm used to compute the pre-processed image. Here, a training set for post-processing is computed from a set of CT scans, and a deep learning neural network is used to post-process EIT images from patients with cystic fibrosis reconstructed using (a) the D-bar method and (b) one step of a Gauss-Newton method. The images are compared using the structural similarity index measure (SSIM) to "ground truth" EIT images derived from the CT scans. Results show that while the deep learning post-processing method effectively sharpens edges and reduces blurring, the spatial accuracy depends on the original algorithm used to compute the pre-processed image. The D-bar images result in a higher SSIM than the Gauss-Newton images, and while both sets of images are able to detect a large region of air trapping, they differ when estimating the extent of pathology.

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Improved resolution of D‐bar images of ventilation using a Schur complement property and an anatomical atlas

et al. 2022

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Background: Electrical impedance tomography (EIT) is a nonionizing imaging technique for real-time imaging of ventilation of patients with respiratory distress. Cross-sectional dynamic images are formed by reconstructing the conductivity distribution from measured voltage data arising from applied alternating currents on electrodes placed circumferentially around the chest. Since the conductivity of lung tissue depends on air content, blood flow, and the presence of pathology, the dynamic images provide regional information about ventilation, pulsatile perfusion, and abnormalities. However, due to the ill-posedness of the inverse conductivity problem, EIT images have a coarse spatial resolution. One method of improving the resolution is to include prior information in the reconstruction. Purpose: In this work, we propose a technique in which a statistical prior built from an anatomical atlas is used to postprocess EIT reconstructions of human chest data. The effectiveness of the method is demonstrated on data from two patients with cystic fibrosis. Methods: A direct reconstruction algorithm known as the D-bar method was used to compute a two-dimensional reconstruction of the conductivity distribution in the plane of the electrodes. Reconstructions using one step in an iterative (regularized) Newton's method were also computed for comparison. An anatomical atlas consisting of 1589 synthetic EIT images computed from X-ray computed tomography (CT) scans of 74 adult male subjects was computed for use as a statistical prior. The resolution of the D-bar images was then improved by maximizing the conditional probability density function of an image that is consistent with the a priori information and the statistical model. A new method to evaluate the accuracy of the EIT images using CT scans of the imaged patient as ground truth is presented. The novel approach is tested on data from two patients with cystic fibrosis. Results and Conclusions:The D-bar images resulted in better structural similarity index measures (SSIM) and multiscale (MS) SSIM measures for both subjects using the mask or amplitude evaluation approach than the one-step (regularized) Newton's method. Further improvement was achieved using the Schur complement (SC) approach, with MS-SSIM values of 0.718 and 0.682 using SC evaluated with the mask and amplitude approach, respectively, for PatientThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Improved resolution of D‐bar images of ventilation using a Schur complement property and an anatomical atlas

Cited by 5 publications

References 75 publications

Real-time non-invasive monitoring of pulmonary embolism based on dynamic EIT technology: a new method

Real-time non-invasive monitoring of pulmonary embolism based on dynamic EIT technology: a new method

Resolution improvement and algorithmic dependence of machine learning for post-processing respiratory EIT images

Improved resolution of D‐bar images of ventilation using a Schur complement property and an anatomical atlas

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