Parametric Level Set Methods for Inverse Problems

In this paper, a parametric level set method for reconstruction of obstacles in general inverse problems is considered. General evolution equations for the reconstruction of unknown obstacles are derived in terms of the underlying level set parameters. We show that using the appropriate form of parameterizing the level set function results a significantly lower dimensional problem, which bypasses many difficulties with traditional level set methods, such as regularization, re-initialization and use of signed distance function. Moreover, we show that from a computational point of view, low order representation of the problem paves the path for easier use of Newton and quasi-Newton methods. Specifically for the purposes of this paper, we parameterize the level set function in terms of adaptive compactly supported radial basis functions, which used in the proposed manner provides flexibility in presenting a larger class of shapes with fewer terms. Also they provide a "narrow-banding" advantage which can further reduce the number of active unknowns at each step of the evolution. The performance of the proposed approach is examined in three examples of inverse problems, i.e., electrical resistance tomography, X-ray computed tomography and diffuse optical tomography

SLC26A Gene Family Participate in pH Regulation during Enamel Maturation.

The bicarbonate transport activities of Slc26a1, Slc26a6 and Slc26a7 are essential to physiological processes in multiple organs. Although mutations of Slc26a1, Slc26a6 and Slc26a7 have not been linked to any human diseases, disruption of Slc26a1, Slc26a6 or Slc26a7 expression in animals causes severe dysregulation of acid-base balance and disorder of anion homeostasis. Amelogenesis, especially the enamel formation during maturation stage, requires complex pH regulation mechanisms based on ion transport. The disruption of stage-specific ion transporters frequently results in enamel pathosis in animals. Here we present evidence that Slc26a1, Slc26a6 and Slc26a7 are highly expressed in rodent incisor ameloblasts during maturation-stage tooth development. In maturation-stage ameloblasts, Slc26a1, Slc26a6 and Slc26a7 show a similar cellular distribution as the cystic fibrosis transmembrane conductance regulator (Cftr) to the apical region of cytoplasmic membrane, and the distribution of Slc26a7 is also seen in the cytoplasmic/subapical region, presumably on the lysosomal membrane. We have also examined Slc26a1 and Slc26a7 null mice, and although no overt abnormal enamel phenotypes were observed in Slc26a1-/- or Slc26a7-/- animals, absence of Slc26a1 or Slc26a7 results in up-regulation of Cftr, Ca2, Slc4a4, Slc4a9 and Slc26a9, all of which are involved in pH homeostasis, indicating that this might be a compensatory mechanism used by ameloblasts cells in the absence of Slc26 genes. Together, our data show that Slc26a1, Slc26a6 and Slc26a7 are novel participants in the extracellular transport of bicarbonate during enamel maturation, and that their functional roles may be achieved by forming interaction units with Cftr

Grain Moisture Sensing Using Electrical Capacitance Tomography

Electrical Capacitance Tomography (ECT) is a non-destructive electrical imaging method used to visualise dielectric permittivity changes across a cross-section of a sample. This paper explores the effectiveness of ECT for moisture sensing in cereal grains, which plays a crucial role in determining grain quality and the likelihood of spoilage during storage. To achieve accurate and comprehensive insights into moisture distribution, tomography emerges as a promising technique due to its capacity to map larger areas and differentiate moisture variations with precision. Considering related research on the dielectric properties of grain, this paper systemically investigates factors known to affect permittivity. It evaluates ECT's ability to image moisture content relative to these influences. Experiments investigated how a sample's moisture, position, size, temperature, and density affect a sensor's ability to detect moisture changes. To date, limited research has focussed on these influences in the context of cereal grain moisture sensing with ECT. All the tests showed the reconstructions to produce results consistent with existing research concerning the dielectric properties of grain. This study concluded that ECT, utilised with a circular array, effectively distinguished between moisture content in reconstructed images, factors significantly affecting the results. ECT images were disturbed by the position and size of samples within the sampling area. The reconstructed images were also heavily dependent on the sample's bulk density and influenced by different moisture contents within the same sampling area

Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring

In recent years, there has been an increasing interest in applying electrical impedance tomography (EIT) in lung monitoring due to its advantages of being noninvasive, nonionizing, real time, and functional imaging with no harmful side effects. However, the EIT images reconstructed by traditional algorithms suffer from low spatial resolution. This article proposes a novel EIT-based lung monitoring scheme by using a 3-D digital twin lung model and a deep learning-based image reconstruction algorithm. Unlike the static numerical or experimental simulations used in other data-driven EIT imaging methods, our digital twin lung model incorporates the biomechanical and electrical properties of the lung to generate a more realistic and dynamic dataset. Additionally, the image reconstruction network (IR-Net) is used to learn the prior information in the dataset and accurately reconstruct the conductivity variation within the lungs during respiration. The results indicate that EIT using a guided digital twin and deep learning-based image reconstruction has better accuracy and anti-noise performance compared to traditional EIT. The proposed EIT imaging framework provides a new idea for efficiently creating labeled EIT data and has potential to be used in various data-driven methods for electrical biomedical imaging.</p

Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring

In recent years, there has been an increasing interest in applying electrical impedance tomography (EIT) in lung monitoring due to its advantages of being noninvasive, nonionizing, real time, and functional imaging with no harmful side effects. However, the EIT images reconstructed by traditional algorithms suffer from low spatial resolution. This article proposes a novel EIT-based lung monitoring scheme by using a 3-D digital twin lung model and a deep learning-based image reconstruction algorithm. Unlike the static numerical or experimental simulations used in other data-driven EIT imaging methods, our digital twin lung model incorporates the biomechanical and electrical properties of the lung to generate a more realistic and dynamic dataset. Additionally, the image reconstruction network (IR-Net) is used to learn the prior information in the dataset and accurately reconstruct the conductivity variation within the lungs during respiration. The results indicate that EIT using a guided digital twin and deep learning-based image reconstruction has better accuracy and anti-noise performance compared to traditional EIT. The proposed EIT imaging framework provides a new idea for efficiently creating labeled EIT data and has potential to be used in various data-driven methods for electrical biomedical imaging.</p

A hybrid image reconstruction for medical magnetic induction tomography:an experimental evaluation

Magnetic induction tomography is atomographic technique with potential medicalapplications. However, the realization of this techniqueremains a challenging topic both in hardware and in imagereconstruction. This paper presents a hybrid imagereconstruction algorithm for the image reconstructionusing experimental data.<br/