MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.

Background: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

The role of cardiac remodeling associated with renal function in mediating cardiovascular event outcomes.

The potential impact of renal function-related cardiovascular remodeling on associated cardiovascular risk has not been previously investigated. Hence, we conducted multiple mediation analyses in the UK Biobank study to evaluate this association. Using multiple Cox models, we found lower renal function (estimated glomerular filtration rate based on cystatin C, eGFR-cysC) was independently related to increased risks of various cardiovascular events and mortalities. Multivariable linear regression revealed a progressive relationship between declining eGFR-cysC and adverse left ventricular (LV) remodeling and impaired systolic function. In Cox models, larger LV volume, mass, as well as decreased systolic function, were significantly correlated with adverse events, particularly in heart failure. Mediation analyses showed that undesirable LV remodeling and cardiometabolic diseases were independent mediators. Our study explores the connections between reduced renal function and poor cardiovascular phenotypes, as well as their significant independent role in mediating renal function-cardiovascular outcome relationships.

Identification of a novel compound heterozygous mutation and a homozygous mutation of SLURP1 in Chinese families with Mal de Meleda.

BACKGROUND: Mal de Meleda is an autosomal recessive palmoplantar keratoderma, with SLURP1 identified as the pathogenic gene responsible. Although over 20 mutations in SLURP1 have been reported, only the mutation c.256G > A (p.G87R) has been detected in Chinese patients. Here, we report a novel heterozygous SLURP1 mutation in a Chinese family. METHODS: We assessed the clinical manifestations of two Chinese patients with Mal de Meleda and collected specimens from the patients and other family members for whole-exome and Sanger sequencing. We used algorithms (MutationTaster, SIFT, PolyPhen-2, PROVEAN, PANTHER, FATHMM, mCSM, SDM and DUET) to predict the pathogenetic potential of the mutation detected. We also employed AlphaFold2 and PyMOL for protein structure analysis. RESULTS: Both patients displayed the typical manifestation of palmoplantar keratoderma. In Proband 1, we detected a novel compound heterozygous mutation (c.243C > A and c.256G > A) in exon 3 of SLURP1. Proband 2 was an adult female born to a consanguineous family and carried a homozygous mutation (c.211C > T). Algorithms indicated both mutations to be probably disease causing. We used AlphaFold2 to predict the protein structure of these mutations and found that they cause instability, as shown by PyMOL. CONCLUSIONS: Our study identified a novel compound heterozygous mutation (c.243C > A and c.256G > A) in a Chinese patient with Mal de Meleda that has the potential to cause instability in protein structure. Moreover, this study expands on the existing knowledge of SLURP1 mutations and contributes to knowledge of Mal de Meleda.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

PURPOSE: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. METHODS: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. RESULTS: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. CONCLUSION: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

Two novel mutations of SERPINB7 in eight cases of Nagashima-type palmoplantar keratosis in the Chinese population.

Nagashima-type palmoplantar keratosis (NPPK) is a diffuse, autosomal recessive, and non-epidermolytic palmoplantar keratosis caused by mutations in the SERPINB7 gene, a member of the serine protease inhibitor superfamily. Genetic studies and case reports suggest that NPPK is the most common palmoplantar keratosis in East Asia but rare in Western countries. This study reports eight NPPK patients in seven pedigrees of the Chinese Han ethnicity with two novel (c.530T>C and c.643A>G) and two recurrent mutations (c.796C>T and c.455G>T) in SERPINB7. The diagnosis of NPPK is now well-defined because of the typical manifestations and pathogenic gene tests. However, its pathomechanism is still obscure, and treatment remains a challenge. This study reviewed all 15 pathogenic mutations and related data in the 1000 Genomes Project to elucidate the founder effect of SERPINB7. Also, several latest cases of NPPK in areas outside East Asia are presented, including France, Finland, and Thailand. Further clinical investigation and genetic studies are crucial for identifying the pathomechanism of NPPK. Also, large-scale control studies are required to determine the safety and curative effects of available therapies.

Low Frequency Transcranial Alternating Current Stimulation Accelerates Sleep Onset Process.

GOAL: The aim of this study is to find a kind of low frequency oscillation transcranial alternating current stimulation, which is directly applied to the scalp epidermal, to stimulate the cerebral cortex with a large spatial range of electric field oscillation across the brain hemisphere, and then trigger the start of the Top-Down processing of sleep homeostasis, in the daytime nap. METHODS: Thirty healthy subjects, to take naps, underwent an intervention of electrical stimulation at 5 Hz, applied to the dorsal lateral prefrontal cortex. The subjects in the experiments were strictly controlled, and opened their eyes when stimulation was transmitted. Subsequently, after 15 min transcranial alternating current stimulation, subjects entered the experimental procedure of sleep. Electroencephalograph was taken at baseline and during sleep. Behavioral indicators were also added to the experiment. RESULTS: We found that the total power of Electroencephalograph activity in the theta band, as well as low-frequency power at 1-7 Hz, was significantly entrained and increased, and that alpha activity was attenuated faster and spindle activity active earlier. Even more, the transition from awake to Non-rapid eye movement stages occurs earlier. Alertness also decreased when the subjects woke up after brief sleep. CONCLUSION: The intervention of low frequency brain rhythmic transcranial alternating current stimulation may induce accelerated effect on sleep onset process, thereby possibly alleviating the problems related to sleep disorders such as difficulty to reach the real sleep state quickly after lying down.