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1.
IEEE Trans Biomed Eng ; PP2022 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-35853075

RESUMO

OBJECTIVE: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. METHODS: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. RESULTS: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24dB in the brain and 21.2dB in tumor regions. CONCLUSION AND SIGNIFICANCE: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning. Code is available at https://github.com/chenchao666/Contrast-enhanced-MRI-Synthesis.

2.
Int J Comput Assist Radiol Surg ; 17(10): 1845-1853, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35867303

RESUMO

PURPOSE: The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that smaller than 0.5 cm, the early detection of PM is still a difficult problem. This study is aiming at constructing a deep convolution neural network classifier based on meta-learning to predict PM. METHOD: Peritoneal metastases are delineated on enhanced CT. The model is trained based on meta-learning, and features are extracted using multi-modal deep Convolutional Neural Network(CNN) with enhanced CT to classify PM. Besides, we evaluate the performance on the test dataset, and compare it with other PM prediction algorithm. RESULTS: The training datasets are consisted of 9574 images from 43 patients with PM and 67 patients without PM. The testing datasets are consisted of 1834 images from 21 testing patients. To increase the accuracy of the prediction, we combine the multi-modal inputs of plain scan phase, portal venous phase and arterial phase to build a meta-learning-based multi-modal PM predictor. The classifier shows an accuracy of 87.5% with Area Under Curve(AUC) of 0.877, sensitivity of 73.4%, specificity of 95.2% on the testing datasets. The performance is superior to routine PM classify based on logistic regression (AUC: 0.795), a deep learning method named ResNet3D (AUC: 0.827), and a domain generalization (DG) method named MADDG (AUC: 0.834). CONCLUSIONS: we proposed a novel training strategy based on meta-learning to improve the model's robustness to "unseen" samples. The experiments shows that our meta-learning-based multi-modal PM predicting classifier obtain more competitive results in synchronous PM prediction compared to existing algorithms and the model's improvements of generalization ability even with limited data.


Assuntos
Aprendizado Profundo , Neoplasias Peritoneais , Algoritmos , Humanos , Redes Neurais de Computação , Neoplasias Peritoneais/diagnóstico por imagem
3.
Materials (Basel) ; 15(13)2022 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-35806729

RESUMO

The crystallization and viscosity of modified blast furnace slag are key factors in fiber forming conditions. In this paper, the crystallization behavior of modified blast furnace slag under continuous cooling conditions was studied by differential scanning calorimetry, and its crystallization kinetics with different acidity coefficients were established. On this basis, the evolution law of the crystallization phase and the influence of crystallization on the viscosity of modified blast furnace slag with different acidity coefficients were analyzed. The results indicated that the crystallization phases of slag with acidity coefficients of 1.05 and 1.20 were, respectively, Melilite and Anorthite. During the cooling process at the acidity coefficient of 1.05, the critical rates of precipitation of Melilite and Anorthite were 50 °C/s and 20 °C/s, respectively, while they were 20 °C/s and 15 °C/s, respectively, at the acidity coefficient of 1.20. With the increase of the acidity coefficient, the crystal growth mode of slag changed from two-dimensional and three-dimensional mixed crystallization to surface nucleation and one-dimensional crystallization. The crystallization activation energy of slag with acidity coefficients of 1.05 and 1.20 were 698.14 kJ/mol and 1292.50 kJ/mol, respectively. In addition, the change trend of viscosity was related to crystal size and content.

4.
Chem Asian J ; 17(16): e202200296, 2022 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-35713338

RESUMO

Chemodynamic therapy (CDT) based on Fenton and Fenton-like reactions induces cancer cell killing via in situ catalyzing H2 O2 and generating highly oxidative hydroxyl radicals (⋅OH) in tumor sites. Their application is not limited by tumor grown depth or hypoxic microenvironment. However, the reaction efficiency is still hampered due to the structure of catalytic agents and the requirement for low pH environment. Here, we design a porous CuO nanocluster (CuO NC) through self-assembly of oleylamine stabilized CuO NPs (OAm-CuO NPs), and functionalize it with folic acid (CuO NC-FA) for specific tumor cell targeting. It can catalyze H2 O2 with high efficiency in nearly neutral environment. Besides, the porous structure of CuO NC also helps the diffusion of H2 O2 to the interior of nanocluster to further improve Fenton-like reaction efficiency. The convenient synthesis of CuO NC-FA with good Fenton-like reaction efficiency at neutral environment demonstrates good chemodynamic therapy effect.


Assuntos
Nanopartículas , Neoplasias , Linhagem Celular Tumoral , Cobre/química , Cobre/farmacologia , Humanos , Peróxido de Hidrogênio , Radical Hidroxila , Nanopartículas/química , Neoplasias/patologia , Oxirredução , Microambiente Tumoral
5.
J Agric Food Chem ; 70(18): 5499-5515, 2022 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-35473317

RESUMO

Detoxification plays an important role in herbicide action. Herbicide safeners selectively protect crops from herbicide injury without reducing the herbicidal efficiency against the target weeds. With the large-scale use of herbicides, herbicide safeners have been widely used in sorghum, wheat, rice, corn, and other crops. In recent years, an increasing number of unexpected new herbicide safeners have been designed. The varieties, structural characteristics, uses, and synthetic routes of commercial herbicide safeners are reviewed in this paper. The design ideas and structural characteristics of novel herbicide safeners are summarized, which provide a basis for the design of bioactive molecules as new herbicide safeners in the future.


Assuntos
Herbicidas , Herbicidas/química , Herbicidas/farmacologia , Plantas Daninhas , Triticum , Zea mays/química
6.
Science ; 376(6591): 371-377, 2022 04 22.
Artigo em Inglês | MEDLINE | ID: mdl-35446634

RESUMO

Relaxor-lead titanate (PbTiO3) crystals, which exhibit extremely high piezoelectricity, are believed to possess high electro-optic (EO) coefficients. However, the optical transparency of relaxor-PbTiO3 crystals is severely reduced as a result of light scattering and reflection by domain walls, limiting electro-optic applications. Through synergistic design of a ferroelectric phase, crystal orientation, and poling technique, we successfully removed all light-scattering domain walls and achieved an extremely high transmittance of 99.6% in antireflection film-coated crystals, with an ultrahigh EO coefficient r33 of 900 picometers per volt (pm V-1), >30 times as high as that of conventionally used EO crystals. Using these crystals, we fabricated ultracompact EO Q-switches that require very low driving voltages, with superior performance to that of commercial Q-switches. Development of these materials is important for the portability and low driving voltage of EO devices.

7.
J Hazard Mater ; 429: 128369, 2022 05 05.
Artigo em Inglês | MEDLINE | ID: mdl-35236039

RESUMO

To properly manage nuclear wastes is critical to sustainable utilization of nuclear power and environment health. Here, we show an innovative carbiding strategy for sustainable management of radioactive graphite through digestion of carbon in H2O2. The combined action of intermolecular oxidation of graphite by MoO3 and molybdenum carbiding demonstrates success in gasifying graphite and sequestrating uranium for a simulated uranium-contaminated graphite waste. The carbiding process plays a triple role: (1) converting graphite into atomic carbon digestible in H2O2, (2) generating oxalic ligands in the presence of H2O2 to favor U-precipitation, and (3) delivering oxalic ligands to coordinate to MoVI-oxo anionic species to improve sample batching capacity. We demonstrate > 99% of uranium to be sequestrated for the simulated waste with graphite matrix completely gasifying while no detectable U-migration occurred during operation. This method has further been extended to removal of surface carbon layers for graphite monolith and thus can be used to decontaminate monolithic graphite waste with emission of a minimal amount of secondary waste. We believe this work not only provides a sustainable approach to tackle the managing issue of heavily metal contaminated graphite waste, but also indicates a promising methodology toward surface decontamination for irradiated graphite in general.


Assuntos
Grafite , Resíduos Radioativos , Radioatividade , Urânio , Carbono , Digestão , Resíduos Perigosos , Peróxido de Hidrogênio , Molibdênio , Resíduos Radioativos/análise , Resíduos Radioativos/prevenção & controle
8.
Med Image Anal ; 77: 102346, 2022 04.
Artigo em Inglês | MEDLINE | ID: mdl-35030342

RESUMO

With 3D magnetic resonance imaging (MRI), a tradeoff exists between higher image quality and shorter scan time. One way to solve this problem is to reconstruct high-quality MRI images from undersampled k-space. There have been many recent studies exploring effective k-space undersampling patterns and designing MRI reconstruction methods from undersampled k-space, which are two necessary steps. Most studies separately considered these two steps, although in theory, their performance is dependent on each other. In this study, we propose a joint optimization model, trained end-to-end, to simultaneously optimize the undersampling pattern in the Fourier domain and the reconstruction model in the image domain. A 2D probabilistic undersampling layer was designed to optimize the undersampling pattern and probability distribution in a differentiable manner. A 2D inverse Fourier transform layer was implemented to connect the Fourier domain and the image domain during the forward and back propagation. Finally, we discovered an optimized relationship between the probability distribution of the undersampling pattern and its corresponding sampling rate. Further testing was performed using 3D T1-weighted MR images of the brain from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset and locally acquired brain 3D T1-weighted MR images of healthy volunteers and contrast-enhanced 3D T1-weighted MR images of high-grade glioma patients. The results showed that the recovered MR images using our 2D probabilistic undersampling pattern (with or without the reconstruction network) significantly outperformed those using the existing start-of-the-art undersampling strategies for both qualitative and quantitative comparison, suggesting the advantages and some extent of the generalization of our proposed method.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador , Encéfalo/diagnóstico por imagem , Humanos , Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos
9.
IEEE J Biomed Health Inform ; 26(3): 1208-1218, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-34232898

RESUMO

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective. In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra annotations. Specifically, we first train a classification model to learn the attention maps of the discriminative regions, finding the hand region, the most discriminative region (the carpal bones), and the next most discriminative region (the metacarpal bones). Guided by those attention maps, we then crop the informative local regions from the original image and aggregate different regions for BAA. Instead of taking BAA as a general regression task, which is suboptimal due to the label ambiguity problem in the age label space, we propose using joint age distribution learning and expectation regression, which makes use of the ordinal relationship among hand images with different individual ages and leads to more robust age estimation. Extensive experiments are conducted on the RSNA pediatric bone age data set. Without using extra manual annotations, our method achieves competitive results compared with existing state-of-the-art deep learning-based methods that require manual annotation. Code is available at https://github.com/chenchao666/Bone-Age-Assessment.


Assuntos
Determinação da Idade pelo Esqueleto , Atenção , Criança , Humanos
10.
Quant Imaging Med Surg ; 11(9): 3978-3989, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34476183

RESUMO

BACKGROUND: Finding methods to accurately predict the final infarct volumes for acute ischemic stroke patients with full or no recanalization would significantly help to evaluate the potential benefits of thrombolytic therapy. We proposed such a method by constructing a model of ensemble deep learning and machine learning using diffusion-weighted imaging (DWI) only. METHODS: The proposed prediction model (named AUNet) combines an adaptive linear ensemble model (ALEM) of machine learning and a deep U-Net network with an accelerated non-local module (U-NL-Net) to learn voxel-wise and spatial features, respectively. Of 40 patients with acute ischemic stroke who received thrombolytic therapy, 17 were fully recanalized, 14 were not recanalized, and nine were partially recanalized. The AUNet was separately trained for full recanalization conditions (AUNetR) and no recanalization (AUNetN) as the best and worst outcomes of thrombolysis, respectively. RESULTS: AUNet performed significantly better in predicting the final infarct volumes in both the recanalization and non-recanalization conditions [area under the receiver operating characteristic curve (AUC) =0.898±0.022, recanalization; AUC =0.875±0.036, non-recanalization: Matthew's correlation coefficient (MCC) =0.863±0.033, recanalization; MCC =0.851±0.025, non-recanalization] than the fixed-thresholding method (AUC =0.776±0.021, P<0.0001, recanalization; AUC =0.692±0.023, P<0.0001, non-recanalization: MCC =0.742±0.035, recanalization; MCC =0.671±0.024, non-recanalization), the logistic regression method (AUC =0.797±0.023, P<0.003, recanalization; AUC =0.751±0.030, P<0.003, non-recanalization: MCC =0.762±0.035, recanalization; MCC =0.730±0.031, non-recanalization), and a recently developed convolutional neural network (AUC =0.814±0.013, P<0.003, recanalization; AUC =0.781±0.027, P<0.003, non-recanalization: MCC =792±0.022, recanalization; MCC =0.758±0.016, non-recanalization). The potential benefit of thrombolysis calculated from AUNetR and AUNetN showed large individual differences (from 12.81% to 239.73%). CONCLUSIONS: AUNet improved predictive accuracy over current state-of-the-art methods. More importantly, the accurate prediction of infarct volumes under different recanalization conditions may provide benefitial information for physicians in selecting suitable patients for thrombolytic therapy.

11.
ACS Appl Mater Interfaces ; 13(27): 31452-31461, 2021 Jul 14.
Artigo em Inglês | MEDLINE | ID: mdl-34197086

RESUMO

Chemodynamic therapy (CDT) is a promising therapeutic modality with transition metal ions and endogenous H2O2 as reagents, but its efficiency is impaired by low endogenous H2O2 levels and nonregeneration of metal ions. Most intracellular H2O2 supplement strategies use oxidases and are intensively dependent on oxygen participation. The hypoxia microenvironments of solid tumors weaken their performance. Here, we develop a near-infrared II light powered nanoamplifier to improve the local oxygen level and to enhance CDT. The nanoamplifier CPNP-Fc/Pt consists of ferrocene (Fc)- and cisplatin prodrug (Pt(IV))-modified conjugated polymer nanoparticles (CPNPs). CPNP has a donor-acceptor structure and demonstrates a good photothermal effect under 1064 nm light irradiation, which accelerates blood flow and efficiently elevates the local oxygen content. In response to intracellular glutathione, Pt(II) is released from CPNP-Fc/Pt and triggers enzymatic cascade reactions with nicotinamide adenine dinucleotide phosphate oxidase (NADPH oxidase) and superoxide dismutase to convert oxygen into H2O2. The enhanced oxygen level results in efficient intracellular H2O2 supply. Fc is reacted with H2O2 and converted to Fc+ via the Fenton reaction, with the generation of hydroxyl radicals for CDT. Unlike free metal ions, the Fe(III) in Fc+ is reduced to Fe(II) by intracellular NAD(P)H, which achieves the regeneration of Fc. The sufficient intracellular H2O2 supply and efficient Fc regeneration effectively enhance the Fenton reaction and demonstrate good in vivo CDT results with tumor growth suppression. This design offers a promising strategy to enhance CDT efficiency in the hypoxia microenvironment of solid tumors.


Assuntos
Compostos Ferrosos/química , Raios Infravermelhos , Metalocenos/química , Nanomedicina/métodos , Nanopartículas/química , Linhagem Celular Tumoral , Humanos , Espaço Intracelular/efeitos dos fármacos , Espaço Intracelular/metabolismo , NADPH Oxidases/metabolismo , Oxigênio/metabolismo , Superóxido Dismutase/metabolismo
12.
J Agric Food Chem ; 69(23): 6701-6709, 2021 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-34100285

RESUMO

Walnut oil is vulnerable to oxidation due to its high content of polyunsaturated fatty acids and adulteration due to its high price. This study investigated 12 typical walnut oils from six main walnut-producing areas in China, using differential scanning calorimetry (DSC), Rancimat test, gas chromatography (GC), elemental analyzer-isotope ratio mass spectrometry (EA-IRMS) and GC-IRMS combined with oxidation kinetic analysis, Pearson correlation analysis, and principal component analysis (PCA). The melting and crystallization profiles using DSC indicated that walnut oils with a relatively high crystal onset temperature tended to be more stable against oxidation. Oleic acid was found to be the most characteristic fatty acid in walnut oil, with a content ranging from 13.84 to 35.08%. Two walnut oils with the highest oleic acid contents of 35.08 and 32.78% had the highest activation energies in nonisothermal DSC. Their predicted shelf lives based on the Rancimat test were 3.5-4.0 times longer than that of the oil with the highest α-linolenic acid at 4 °C and 3.1-3.5 times longer at 25 °C. The δ13C values of walnut oils were determined by EA-IRMS, and the δ13C values of fatty acids were determined by GC-IRMS. Fatty acid stable carbon isotope ratios combined with PCA were successfully applied to intuitively discriminate different walnut oils. The results suggested that fatty acid δ13C values determined by IRMS combined with chemometrics and lipid compositions are promising as a powerful means of vegetable oil authentication and discrimination.


Assuntos
Ácidos Graxos , Juglans , Carbono , Isótopos de Carbono/análise , China , Cromatografia Gasosa-Espectrometria de Massas , Cinética , Óleos Vegetais/análise
13.
J Magn Reson Imaging ; 53(6): 1898-1910, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33382513

RESUMO

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA-Dixon-TWIST DCE-MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans ), blood volume fraction (vp ), and volume fraction of extracellular extravascular space (ve ), from DCE-MRI data of tumor and normal-appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans , greater than 0.965 for vp , and greater than 0.948 for ve . The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal-to-noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.


Assuntos
Meios de Contraste , Imageamento por Ressonância Magnética , Humanos , Análise dos Mínimos Quadrados , Redes Neurais de Computação , Estudos Retrospectivos
14.
Magn Reson Imaging ; 70: 134-144, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32353530

RESUMO

With the aim of developing a fast algorithm for high-quality MRI reconstruction from undersampled k-space data, we propose a novel deep neural Network, which is inspired by Iterative Shrinkage Thresholding Algorithm with Data consistency (NISTAD). NISTAD consists of three consecutive blocks: an encoding block, which models the flow graph of ISTA, a classical iteration-based compressed sensing magnetic resonance imaging (CS-MRI) method; a decoding block, which recovers the image from sparse representation; a data consistency block, which adaptively enforces consistency with the acquired raw data according to learned noise level. The ISTA method is thereby mapped to an end-to-end deep neural network, which greatly reduces the reconstruction time and simplifies the tuning of hyper-parameters, compared to conventional model-based CS-MRI methods. On the other hand, compared to general deep learning-based MRI reconstruction methods, the proposed method uses a simpler network architecture with fewer parameters. NISTAD has been validated on retrospectively undersampled diencephalon standard challenge data using different acceleration factors, and compared with DAGAN and Cascade CNN, two state-of-the-art deep neural network-based methods which outperformed many other state-of-the-art model-based and deep learning-based methods. Experimental results demonstrated that NISTAD reconstruction achieved comparable image quality with DAGAN and Cascade CNN reconstruction in terms of both PSNR and SSIM metrics, and subjective assessment, though with a simpler network structure.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética , Humanos , Estudos Retrospectivos
15.
Sci Rep ; 10(1): 285, 2020 01 14.
Artigo em Inglês | MEDLINE | ID: mdl-31937887

RESUMO

Four californite samples from Pakistan with yellowish-green, green and reddish-brown colors were investigated by combining the methods of µ-XRF mapping, XRD, Raman spectra, optical spectra, EPMA and XPS. The results show that the californite is composed mainly of microcrystalline vesuvianite and smaller amounts of clinochlore. Based on the distribution of the clinochlore, the californite can be divided into three types. The gem-quality californite is composed of microcrystalline vesuvianite and has a translucent appearance. The ordinary-quality californite contains microcrystalline vesuvianite as well as clinochlore, and it has an opaque appearance. The transitional-type has properties that are intermediate between those of gem- and ordinary-quality californite. Octahedrally coordinated iron and chromium in the clinochlore reduce the transparency and contribute to the opaque green and yellowish-green colors of the californite. At sites where there is no clinochlore, Cr3+ in the octahedrally coordinated site Y3 of the vesuvianite is mainly responsible for the green tone of the californite, Fe3+ and Mn3+ at the Y3 site contribute mainly to the yellowish-green and reddish-brown colors, respectively. The Fe2+ → Fe3+ charge transfer also occurs in vesuvianite and partly influences the appearance of the californite. The actual color of californite that lacks clinochlore is due to the synergy of Cr3+, Fe3+ and Mn3+ crystal field transfers at the octahedral site Y3 as well as the Fe2+ → Fe3+ charge transfer in the vesuvianite. Vesuvianite in the californite can be assigned to the P4/n space group, and the occurrence of clinochlore reflects the fact that the californite from Pakistan formed under medium-grade metamorphic conditions at temperatures of ~300-500 °C. The content of clinochlore provides a basis for grading the quality of the californite.

16.
Magn Reson Imaging ; 66: 104-115, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31278998

RESUMO

Various sparse transform models have been explored for compressed sensing-based dynamic cardiac MRI reconstruction from vastly under-sampled k-space data. Recently emerged low rank tensor model using Tucker decomposition could be viewed as a special form of sparse model, where the core tensor, which is obtained using high-order singular value decomposition, is sparse in the sense that only a few elements have dominantly large magnitude. However, local details tend to be over-smoothed when the entire image is conventionally modeled as a global tensor. Moreover, low rankness is sensitive to motion as spatiotemporal correlation is corrupted by spatial misalignment between temporal frames. To overcome these limitations, this paper presents a novel motion aligned locally low rank tensor (MALLRT) model for dynamic MRI reconstruction. In MALLRT, low rank constraint is enforced on image patch-based local tensors, which correspond to overlapping blocks extracted from the reconstructed high-dimensional image after group-wise inter-frame motion registration. For solving the proposed model, this paper presents an efficient optimization algorithm by using variable splitting and alternating direction method of multipliers (ADMM). MALLRT demonstrated promising performance as validated on one cardiac perfusion MRI dataset and two cardiac cine MRI datasets using retrospective under-sampling with various acceleration factors, as well as one prospectively under-sampled cardiac perfusion MRI dataset. Compared to four state-of-the-art methods, MALLRT achieved substantially better image reconstruction quality in terms of both signal to error ratio (SER) and structural similarity index (SSIM) metrics, and visual perception in preserving spatial details and capturing temporal variations.


Assuntos
Coração/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Algoritmos , Conjuntos de Dados como Assunto , Humanos , Imagem Cinética por Ressonância Magnética , Movimento (Física) , Estudos Prospectivos , Estudos Retrospectivos
17.
Magn Reson Imaging ; 58: 56-66, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30658071

RESUMO

Liver dynamic contrast-enhanced MRI (DCE-MRI) requires high spatiotemporal resolution and large field of view to clearly visualize all relevant enhancement phases and detect early-stage liver lesions. The low-rank plus sparse (L + S) reconstruction outperforms standard sparsity-only-based reconstruction through separation of low-rank background component (L) and sparse dynamic components (S). However, the L + S decomposition is sensitive to respiratory motion so that image quality is compromised when breathing occurs during long time data acquisition. To enable high quality reconstruction for free-breathing liver 4D DCE-MRI, this paper presents a novel method called SMC-LS, which incorporates Sliding Motion Compensation into the standard L + S reconstruction. The global superior-inferior displacement of the internal abdominal organs is inferred directly from the undersampled raw data and then used to correct the breathing induced sliding motion which is the dominant component of respiratory motion. With sliding motion compensation, the reconstructed temporal frames are roughly registered before applying the standard L + S decomposition. The proposed method has been validated using free-breathing liver 4D MRI phantom data, free-breathing liver 4D DCE-MRI phantom data, and in vivo free breathing liver 4D MRI dataset. Results demonstrated that SMC-LS reconstruction can effectively reduce motion blurring artefacts and preserve both spatial structures and temporal variations at a sub-second temporal frame rate for free-breathing whole-liver 4D DCE-MRI.


Assuntos
Abdome/diagnóstico por imagem , Meios de Contraste/química , Fígado/diagnóstico por imagem , Imageamento por Ressonância Magnética , Movimento (Física) , Adulto , Artefatos , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Imageamento Tridimensional , Masculino , Imagens de Fantasmas , Respiração , Estudos Retrospectivos , Fatores de Tempo , Adulto Jovem
18.
J AOAC Int ; 102(4): 1174-1180, 2019 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-30609947

RESUMO

Background: Cabernet Sauvignon wine enjoys large market in China, and its adulteration has become a well-known problem and challenge. Objective: This study aims to evaluate the capabilities of multiple techniques, including headspace-solid-phase microextraction-GC-MS (HS-SPME-GC-MS), electronic tongue (E-tongue) spectroscopy, mid-infrared (MIR) spectroscopy, and near-infrared (NIR) spectroscopy, to differentiate this popular imported wine in China. Methods: MIR spectroscopy, NIR spectroscopy, E-tongue spectroscopy, and HS-SPME-GC-MS were used. Multivariate analysis techniques were applied to further explore the instrumental determination data for the wine discrimination. Results: Joint use of MIR and NIR with Grey relational analysis (GRA), E-tongue with principal component analysis (PCA) and hierarchical cluster analysis, and HS-SPME-GC-MS with PCA allowed unanimous differentiation of the wines. Conclusions: The approach described herein offers both ecologically friendly and multiperspective mutual corroboration techniques for Cabernet Sauvignon wine discrimination. The integrative methodology could be used as a reference for wine authentication. Highlights: GRA was first applied to discriminate the wine samples. Mutual corroboration was verified by multivariate statistics combined with MIR, NIR, E-tongue, and SPME-GC/MS. Integrated techniques pointed to a unanimous authentication of the wine samples.


Assuntos
Contaminação de Alimentos/análise , Vinho/análise , Análise por Conglomerados , Cromatografia Gasosa-Espectrometria de Massas/métodos , Análise Multivariada , Análise de Componente Principal , Microextração em Fase Sólida/métodos , Espectrofotometria/métodos
19.
Appl Biochem Biotechnol ; 184(3): 1009-1023, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-28933034

RESUMO

A novel ZnO nanowire/macroporous SiO2 composite was used as a support to immobilize chloroperoxidase (CPO) by in situ cross-linking method. An anionic bi-epoxy compound was synthesized and used as a long-chained anionic cross-linker, and it was adsorbed on the surface of ZnO nanowires through static interaction before reaction with CPO, creating a new approach to change the structure, property, and catalytic performance of the produced cross-linking enzyme aggregates (CLEAs) of CPO. The immobilized CPO showed high activity in the decolorization of three azo dyes. The effect of various conditions such as the loading amount of CPO, solution pH, temperature, and dye concentration was optimized on the decolorization. Under optimized conditions, the decolorization percentage of Acid Blue 113, Direct Black 38, and Acid Black 10 BX reached as high as 95.4, 92.3, and 89.1%, respectively. The immobilized CPO exhibited much better thermostability and resistance to pH inactivation than free CPO. The storage stability and reusability were greatly improved through the immobilization. It was found from the decolorization of Acid Blue 113 that 83.6% of initial activity retained after incubation at 4 °C for 60 days and that 80.9% of decolorization efficiency retained after 12 cycles of reuses.


Assuntos
Compostos Azo/química , Cloreto Peroxidase/química , Enzimas Imobilizadas/química , Nanocompostos/química , Dióxido de Silício/química , Óxido de Zinco/química
20.
Biotechnol Appl Biochem ; 65(2): 220-229, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-28220537

RESUMO

A zinc oxide (ZnO) nanowires/macroporous silicon dioxide composite was used as support to immobilize horseradish peroxidase (HRP) simply by in situ cross-linking method. As cross-linker was adsorbed on the surface of ZnO nanowires, the cross-linked HRP was quite different from the traditional cross-linking enzyme aggregates on both structure and catalytic performance. Among three epoxy compounds, diethylene glycol diglycidyl ether (DDE) was the best cross-linker, with which the loading amount of HRP with pI of 5.3 reached as high as 118.1 mg/g and specific activity was up to 14.9 U/mg-support. The mass loss of HRP cross-linked with DDE was negligible during 50-H leaching at 4 °C, and the thermal stability of the immobilized HRP was also quite good. The catalytic performance of immobilized HRP to decolorize anthraquinone dye was explored by using Reactive Blue 19 (RB 19) and Acid Violet 109 (AV 109) as model substrates. The results indicated that the immobilized HRP exhibited high decolorization efficiency and good reusability. The decolorization efficiency reached 94.3% and 95.9% for AV 109 and RB 19 within the first 30 Min, respectively. A complete decolorization of these two dyes has been realized within 2-3 H by using this new biocatalysis system.


Assuntos
Antraquinonas/isolamento & purificação , Corantes/isolamento & purificação , Poluentes Ambientais/isolamento & purificação , Peroxidase do Rábano Silvestre/química , Nanofios/química , Dióxido de Silício/química , Óxido de Zinco/química , Biocatálise , Reagentes de Ligações Cruzadas/química , Estabilidade Enzimática , Enzimas Imobilizadas/química , Compostos de Epóxi/química
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