Project description:BackgroundAlthough electrical impedance tomography (EIT) is widely used for monitoring regional ventilation distribution, reference values have yet to be established for clinical use. The present study aimed to evaluate the feasibility of creating reference values for standard EIT parameters for potential clinical application.MethodsA total of 75 participants with healthy lungs were included in this prospective study (male:female, 48:27; age, 34±14 years; height, 172±7 cm; weight, 73±12 kg). The subjects were examined during spontaneous breathing in the supine position. EIT measurements were performed at the level of the 4th intercostal space. Commonly used EIT-based parameters, including the center of ventilation (CoV), dorsal and most dorsal fractions of ventilation distribution (TVD and TVROI4 respectively), global inhomogeneity (GI) index, and standard deviation of regional ventilation delay index (RVDSD) were calculated.ResultsFollowing outlier detection, EIT data from 71 subjects were finally evaluated. The values of the evaluated parameters were: CoV, 48.7%±1.7%; TVD, 48.1%±5.4%; TVROI4, 7.1%±1.8%; GI, 0.49±0.04; and RVDSD, 7.0±2.0. The coefficients of variation for CoV and GI were low (0.03 and 0.07, respectively), but those for TVROI4 and RVDSD were comparatively high (0.26 and 0.28, respectively). None of the evaluated parameters showed a significant correlation with age. The GI index showed a weak but significant correlation with body mass index (R=0.29, P=0.01). The RVDSD was slightly higher in males than in females.ConclusionsOur study indicated that CoV and GI were stable parameters with small coefficients of variation in participants with healthy lungs. The creation of EIT parameter reference values for setting treatment targets may be feasible.
Project description:Peripheral vision is fundamentally limited by the spacing between objects. When asked to report a target's identity, observers make erroneous reports that sometimes match the identity of a nearby distractor and sometimes match a combination of target and distractor features. The classification of these errors has previously been used to support competing 'substitution' [1] or 'averaging' [2] models of the phenomenon known as 'visual crowding'. We recently proposed a single model in which both classes of error occur because observers make their reports by sampling from a biologically-plausible population of weighted responses within a region of space around the target [3]. It is critical to note that there is no probabilistic substitution or averaging process in our model; instead, we argue that neither substitution nor averaging occur, but that these are misclassifications of the distribution of reports that emerge when a population response distribution is sampled. This is a fundamentally different way of thinking about crowding, and on this basis we claim to have provided a mechanism unifying categorically distinct perceptual errors. Our goal was not to model all crowding phenomena, such as the release from crowding when target and flanks differ in color or depth [4]. Pachai et al.[5] have suggested that our model is not unifying because it inaccurately predicts perceptual performance for a particular stimulus. Although we agree that our model does not predict their data, this specific demonstration overlooks the critical aspect of the model: perceptual reports are drawn from a weighted population code. We show that Pachai et al.'s [5] own data actually provide evidence for the population code we have described [3], and we suggest a biologically-plausible analysis of their stimuli that provides a computational basis for their 'grouping' account of crowding.