Microstrip line (MLIN)-based glucose sensors have been used for noninvasive glucose monitoring. However, the current MLIN-based solutions do not provide enough sensitivity. The reason for this is that they rely on the fringing field of an MLIN that leads to a shallow penetration depth and thus low sensitivity. In this paper, we propose a sensitive MLIN-based glucose sensor using the main field. The idea is illustrated using the following three steps. Step 1 is sensing the glucose level by using the main field, which uses the material under test (MUT) as the substrate of an MLIN terminated with a load; Step 2 is identifying the contribution of different parameters and different components of parameters to sensitivity, which leads to Step 3, in which different parameters and/or different components of parameters are crosschecked for sensing. Two frequency bands, 100 to 500 MHz and 1.4 to 1.9 GHz, were investigated. In Step 1, the main field configuration was compared with its fringing field counterpart. It is shown that the proposed MLIN configuration presents an average sensitivity of 1.8×10 ^-3 dB/(mg/dL) in terms of the reflection coefficient that is more than 10 times higher than that of its counterpart. In Step 2, different components of the reflection coefficient and the input impedance of the proposed configuration were examined, and their diverse contributions to the sensitivity were shown, which allows crosschecking to improve sensing accuracy. This was successfully shown in Step 3, in which algorithms for crosschecking were proposed, and the sensing accuracy was examined. In terms of the optimal sensing frequency band, 1.4 to 1.9 GHz shows higher sensitivity than 100 to 500 MHz, where the molecules may interact the most with the waves. This implies that the interaction between a wave and the sensing structure with the MUT contributes more to sensitivity than the direct interaction between the wave and the MUT. The radiofrequency safety of the proposed configuration was examined by checking the specific absorption rate of the MUT with the proposed structure. This paper offers a new avenue for the development of an MLIN-based noninvasive glucose sensor for continuous monitoring with high sensitivity in the near future. It can be used both as a single sensor and in a multiple-sensor glucose monitoring system.

Electrical properties (EPs) of tissues facilitate early detection of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively probe the EPs of tissues from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) to solve partial differential Equations (PDEs) to reconstruct the EPs. However, they are not practical for clinical data because ND is noise sensitive and the MRI measurements for MREPT are noisy in nature. Recently, Physics informed neural networks (PINNs) have been introduced to solve PDEs by substituting ND with automatic differentiation (AD). To the best of our knowledge, it has not been applied to MREPT due to the challenges in using PINN on MREPT as (i) a PINN requires part of ground-truth EPs as collocation points to optimize the network’s AD, (ii) the noisy input data disrupts the optimization of PINNs despite the noise-filtering nature of NNs and additional denoising processes. In this work, we propose a PINN-MREPT model based on a canonical analytic MREPT model. A reference padding layer with known EPs was added to surround the region of interest for providing additive collocation points. Moreover, an optimizable diffusion coefficient was embedded in the analytic MREPT model used in the PINN-MREPT. The noise robustness of the proposed PINN-MREPT for single-sample reconstruction was tested by using numerical phantoms of human brain with extra tumor-like tissues at different noise levels. The results of numerical experiments show that PINN-MREPT outperforms two typical numerical MREPT methods in terms of reconstruction accuracy, sensitivity to the extra tissues, and the correlations of line profiles in the regions of interest. The advantage of the PINN-MREPT is shown by the results of an experiment on phantom measurement, too. Moreover, it is found that the diffusion term plays an important role to achieve a noise-robust PINN-MREPT. This is an important step moving forward to a clinical application of MREPT.

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based on Maxwell’s equations. Most MREPT methods suffer from artifacts caused by inaccuracy of the hypotheses behind the models, and/or numerical errors. These artifacts can be mitigated by adding coefficients to stabilize the models, however, the selection of such coefficient has been empirical, which limit its medical application. Alternatively, end-to-end Neural networks-based MREPT (NN-MREPT) learns to reconstruct the EPs from training samples, circumventing Maxwell’s equations. However, due to its pattern-matching nature, it is difficult for NN-MREPT to produce accurate reconstructions for new samples. In this work, we proposed a physics-coupled NN for MREPT (PCNN-MREPT), in which an analytic model, cr-MREPT, works with diffusion and convection coefficients, learned by NNs from the difference between the reconstructed and ground-truth EPs to reduce artifacts. With two simulated datasets, three generalization experiments in which test samples deviate gradually from the training samples, and one noise-robustness experiment were conducted. The results show that the proposed PCNN-MREPT achieves higher accuracy than two representative analytic methods. Moreover, compared with an end-to-end NN-MREPT, the proposed method attained higher accuracy in two critical generalization tests. This is an important step to practical MREPT medical diagnoses.