Compressed Sensing MRI Reconstruction using Low Dimensional Manifold Model

In super-resolution magnetic resonance imaging (SR-MRI), the low-resolution scans are acquired keeping the central low-frequency components intact. The scan-time for a given matrix size is shortened by acquiring only the central low-frequency part of the k-space (Fourier space), and filling the unacquired portion with zeroes. While transforming the zero-padded low-resolution acquired k-space to the image domain through inverse Fourier transformation, an inherent blur and high-frequency oscillatory artifacts are manifested in the reconstructed image. State-of-the-art methods including reconstruction-based and deep-learning-based approaches have addressed this problem to a large extent; however, it remains a challenging problem to completely eliminate the remnant effects of blur or ringing in some of the subtle clinical features. In the proposed SR method, we have implemented an interleaved hybrid domain convolutional neural network (CNN) model consisting of a k-space and spatial domain network together with local residual connections. This helps to improve the accuracy of hyper-parameter estimation and effectively reduce the loss function, with the resulting advantage of attaining improved peak-signal-to-noise-ratio (PSNR), structural similarity index measure (SSIM) and normalized root mean square error (NRMSE) as compared to the state-of-the-art methods.

Conventional whole-body (WB) PET imaging using short axial field-of-view (SAFOV) scanners is constrained by low sensitivity and limited temporal resolution, particularly for dynamic and kinetic parametric imaging. This study introduces a supervised deep learning framework to enhance WB Patlak parametric images (Ki) from SAFOV scanners by leveraging high-sensitivity data from long axial field-of-view (LAFOV) uEXPLORER system. Dynamic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${ }^{18}$</tex> F-FDG PET scans were acquired from two cohorts: 15 subjects using the uEXPLORER and 19 subjects using the Siemens Biograph mCT (SAFOV) with a 6 -minute single-bed acquisition in the cardiac region followed by 13 WB passes via continuous bed motion. Low-quality (LQ) WB parametric images were simulated from LAFOV data using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15 \sec \times 13$</tex> frames with 4 -minute interval (short-frame protocol), while how-quality (HQ) parametric images were reconstructed from LAFOV standard protocol. The image data starting from 20 minutes were used to reconstruct Patlak images. A 2D conditional consistency model (denoted as cDDPM-CM) was developed, incorporating LQ Ki images and prior PET images (average of dynamic frames converted to SUV) as conditioning inputs. The model was trained via a 5 -fold cross-validation on LAFOV data (4680 slices). Results demonstrated that the cDDPM-CM significantly outperformed traditional denoising methods (Gaussian filtering, non-local means) and a baseline DDPM-CM model (without prior PET image) in enhancing LQ LAFOV images, achieving a peak signal-to-noise ratio (PSNR) of 39.08, normalized root mean square error (NRMSE) of 0.3445, and structural similarity index measure (SSIM) of 0.9433. Similar improvements were observed in SAFOV images, with enhanced clarity and reduced noise.

In the last few years there has been rapid progress in the application of nuclear magnetic resonance clinical imaging as a diagnostic aid (Partain et al, 1983; Steiner, 1983; Worthington, 1983). Records kept by the operators of NMR imaging equipment indicate that about 1800 individuals have now been examined without incident within the United Kingdom. In view of this progress, an advisory group (Appendix) appointed by the National Radiological Protection Board recommends some modifications to the original advice given in 1981 (NRPB, 1981). The principal modifications are that: (1) It is no longer considered necessary to exclude volunteers with a history of epilepsy or cardiac disease. (2) It is not obligatory that a registered medical practitioner trained in resuscitation techniques be present during an exposure. (3) Higher limits are recommended for rates of change of magnetic flux density when the period of change is less than 10 ms. (4) The radiofrequency limit of 15 MHz has been removed. Theoretical considerations indicate that the interaction of an applied magnetic field with the intrinsic magnetic moments of protons and electrons within a molecule could alter its rate of chemical reaction (McLauchlan, 1981). Chemical reactions involving radical pair intermediates are expected to be sensitive to magnetic fields for this reason and a number of enzyme reactions that might be expected to be affected have been identified (A. Cass, personal communication). However, there is no experimental confirmation of these theoretical possibilities at present.

Attenuation correction (AC) is important for an accurate interpretation and quantitative analysis of SPECT myocardial perfusion imaging. Dedicated cardiac SPECT systems have invaluable efficacy in the evaluation and risk stratification of patients with known or suspected cardiovascular disease. However, most dedicated cardiac SPECT systems are standalone, not combined with a transmission imaging capability such as computed tomography (CT) for generating attenuation maps for AC. To address this problem, we propose to apply a conditional generative adversarial network (cGAN) for generating attenuation-corrected SPECT images (SPECTGAN ) directly from non-corrected SPECT images (SPECTNC ) in image domain as a one-step process without requiring additional intermediate step. The proposed network was trained and tested for 100 cardiac SPECT/CT data from a GE Discovery NM 570c SPECT/CT, collected retrospectively at Yale New Haven Hospital.The generated images were evaluated quantitatively through the normalized root mean square error (NRMSE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) and statistically through joint histogram and error maps. In comparison to the reference CT-based correction (SPECTCTAC ), NRMSEs were 0.2258±0.0777 and 0.1410±0.0768 (37.5% reduction of errors); PSNRs 31.7712±2.9965 and 36.3823±3.7424 (14.5% improvement in signal to noise ratio); SSIMs 0.9877±0.0075 and 0.9949±0.0043 (0.7% improvement in structural similarity) for SPECTNC and SPECTGAN , respectively. This work demonstrates that the conditional adversarial training can achieve accurate CT-less attenuation correction for SPECT MPI, that is quantitatively comparable to CTAC. Standalone dedicated cardiac SPECT scanners can benefit from the proposed GAN to reduce attenuation artifacts efficiently.

We estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multisalinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty‐five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time ( of 0.69 and 0.33 and normalized root mean square errors (NRMSE) of 0.16 and 0.21, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE=0.23). We demonstrate that that permeability estimates from the joint‐NMR‐CC model (NRMSE=0.13) compare favorably to estimates from the Katz and Thompson model (NRMSE=0.074). This model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.

Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

Speech compression plays a significant role in speech communication systems. It yields a compact data representation allowing an efficient storage and transmission of information. The potentiality of speech compression has been essential to the technologies of robust long-distance communication, high-quality speech storage, and message encryption. Storage and archival of large volumes of spoken information makes speech compression essential and which improves the capacity of communications relatively of unlimited bandwidth. The main objective of this research work is to present a good quality speech data at a low bit rate. In order to accomplish this, the most powerful speech analysis and compression techniques such as Linear Predictive Coding (LPC), Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) are adopted for Tamil speech database. The adopted techniques are evaluated based on Compression ratio, Peak Signal to Noise Ratio (PSNR), Normalized Root Mean Square Error (NRMSE) and Mean Opinion Score (MOS). Among these methods the DWT achieves greater performance than other two techniques employed in this research work.

In recent years, data security has been a challenging endeavor, especially when the data is being transmitted every second. Internet of Things (IoT) involves continuously sending data over public networks, making the data vulnerable to various security threats. Therefore, ensuring the secure end-to-end communication of IoT data is critical. Cryptography and steganography have proven effective in providing secure connectivity for IoT devices. However, challenges in existing approaches include scalability, computational complexity, implementation, key management, trade-offs, evolving threats, and hyperparameter tuning. Therefore, in this paper, we propose EGCrypto, an efficient and secure model for IoT networks. EGCrypto utilizes a low-complexity elliptic Galois cryptography approach along with matrix XOR steganography to enhance security. To optimize its performance, we employ the zoning evolution of control attributes and adaptive mutation based self-adaptive differential evolution with fitness and diversity ranking. These techniques are utilized to fine-tune the hyperparameters of EGCrypto, enhancing its effectiveness and efficiency. The confidential IoT data is encrypted using low-complexity elliptic Galois cryptography. Following encryption, the encrypted data is embedded or hidden into cover blocks of an image, which are selected using the optimization algorithm. This ensures secure data communication in IoT architectures, as the encrypted data is transferred safely and can be easily recovered and decrypted at the receiving end. The experimental results demonstrate that EGCrypto outperforms competitive models with improvements of 1.8473% in peak signal to noise ratio (PSNR), 1.5490% in strutural similarity index metric (SSIM), 1.7682% in normalized root mean square error (NRMSE), 1.3829% in carrier capacity, and 1.9372% in embedding efficiency.

&lt;span lang="EN-US"&gt;Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.&lt;/span&gt;

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

Application of Landsat-8 and Sentinel-2 for retrieval of chlorophyll-a in a shallow freshwater lake

Electroencephalographic (EEG) recordings are often contaminated with artefacts, such as eye blinks, which complicate their analysis. While various methods exist to address, identify, and mitigate artefacts, many require human intervention. This study introduces a novel, self-supervised, fully automated approach for identifying and reducing artefacts in EEG signals using a Variational Autoencoder (VAE) architecture. In detail, subject-specific VAEs, with convolutional layers, are trained from spatially preserved EEG topographic maps. A sample-wise strategy based on the negative log-likelihood of activated latent vectors from training data is proposed to identify anomalous topomaps. This assigns an anomaly score to each model’s input. The vectors of input topomaps above a chosen threshold are automatically clipped with a percentile-based approach of activated latent space components. Eventually, the reconstructed EEG signals are compared with a baseline built upon an offline ICA method with automatic detection of artefactual components inspired by the FASTER methodology. Results show that the signal-to-noise ratio (SNR) and the peak signal-to-noise ratio (PSNR) of the FP1, FP2, and other channels were higher, while the remaining channels were similar to ICA Fast. Similarly, mean absolute error (MAE), normalised root mean square error (NRMSE), and correlation coefficients indicated comparable signals from both methods. In addition, findings demonstrate the method’s strength in avoiding signal updates in non-artefactual segments, preserving their neural dynamics. The contribution to the body of knowledge is a fully automated, subject-specific method for identifying and denoising EEG signals.

An effective method for the rapid detection of microplastics in soil

Soil water content indicators, particularly field capacity (FC) and permanent wilting point (PWP), are fundamental parameters influencing irrigation management, water retention capacity, infiltration, drainage, hydraulic conductivity, plant water stress, and solute dynamics. Their direct measurement is often costly. To address this, pedotransfer functions (PTFs) have emerged as efficient, rapid, and economical alternatives for estimating these characteristics. This study evaluates the capability of multiple linear regression, classification and regression trees (C&RT), artificial neural networks (ANN), k-nearest neighbor (k-NN), and adaptive neuro-fuzzy inference system (ANFIS), for estimating soil FC and PWP. A total of 240 soil samples were collected and split into training and testing datasets. Key input variables such as clay, silt, sand, and bulk density were considered, while FC and PWP served as the outputs. Sensitivity analysis and statistical validation were used to assess model performance, primarily based on the determination coefficient (R2) and normalized root mean square error (NRMSE). Among the models tested, ANFIS (for FC; R2 = 0.77, NRMSE = 0.138 and PWP; R2 = 0.79, NRMSE = 0.149) demonstrated superior predictive accuracy, making it the most suitable approach for estimating soil water content in diverse soil textures. Consequently, the integration of ANFIS into the development of soil PTFs is recommended for soils in northwestern Iran.