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BACKGROUND: Automatic segmentation of the pancreas and its tumor region is a prerequisite for computer-aided diagnosis. OBJECTIVE: In this study, we focus on the segmentation of pancreatic cysts in abdominal computed tomography (CT) scan, which is challenging and has the clinical auxiliary diagnostic significance due to the variability of location and shape of pancreatic cysts. METHODS: We propose a convolutional neural network architecture for segmentation of pancreatic cysts, which is called pyramid attention and pooling on convolutional neural network (PAPNet). In PAPNet, we propose a new atrous pyramid attention module to extract high-level features at different scales, and a spatial pyramid pooling module to fuse contextual spatial information, which effectively improves the segmentation performance. RESULTS: The model was trained and tested using 1,346 CT slice images obtained from 107 patients with the pathologically confirmed pancreatic cancer. The mean dice similarity coefficient (DSC) and mean Jaccard index (JI) achieved using the 5-fold cross-validation method are 84.53% and 75.81%, respectively. CONCLUSIONS: The experimental results demonstrate that the proposed new method in this study enables to achieve effective results of pancreatic cyst segmentation.

BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome.

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn’s disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.

Although the traditional financial poverty alleviation model has provided great convenience and played a great role in China's targeted poverty alleviation to a large extent, the problem of information asymmetry between farmers, the government and financial institutions cannot be effectively solved. solve. Therefore, if new financial technologies such as blockchain, big data, and cloud computing can be flexibly applied to financial poverty alleviation, it will have a great boosting effect on consolidating the achievements of poverty alleviation. This paper mainly analyzes the defects of the traditional financial poverty alleviation model and the development prospects of the financial technology innovative financial poverty alleviation model, and finally summarizes and puts forward relevant suggestions.

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.约束球面反卷积可以从大脑扩散磁共振成像数据中量化白质纤维取向分布信息，该方法仅适用于单壳的扩散磁共振成像数据；在包含各向同性扩散信号的白质组织中，该方法会提供错误的纤维方向信息。针对这一不足，本文在约束球面反卷积的基础上，结合多壳数据和多种扩散模型下估计的响应函数，提出一种基于多模型响应函数的约束球面反卷积方法。多壳数据可以提高纤维方向估计的稳定性，多模型响应函数可以衰减脑白质中各向同性扩散信号，提供更加准确的纤维方向信息。为了验证算法的有效性，利用模拟数据和来自公开数据库的真实人脑数据进行对比实验。结果表明，本文算法可以衰减白质组织中的各向同性扩散信号，克服部分容积效应的影响，能更准确地进行纤维方向估计；重建的纤维方向分布稳定，伪峰少，而且对交叉纤维的识别能力也更强，为纤维束追踪技术的进一步研究奠定了基础。.

In the various stages from life chemistry to biological evolution, major geological events are usually accompanied by the extinction of species and the gestation of new life, which are important factors that determine the direction of ecological evolution. The study of the terrane accretive orogeny during the Jinning–Chengjiang period has had a great impact on the crustal rock composition, the atmospheric environment and the water circulation system, etc. The living environment of the primitive biological community has undergone tremendous changes; for example, the crust produced a superimposed contraction and extensional rift effect, resulting in collisional magmatic activity and strong earthquakes, and a large number of species became extinct. In turn, biological activity and organic matter also contributed significantly to migration. Given the periodic cycle of geological processes, and being fully utilized by nature, the origin and evolution of life grow in a spiral. Based on the analysis of the petrochemical composition characteristics of metamorphic volcanic–sedimentary rock series and intermediate-acid intrusive rocks in Pengguan terrane, this paper proposes that the subduction mechanism of oceanic crust in the study area is formed by the NW subduction of the Longmenshan–Anning oceanic crust on the SE side. The relationship between the average change of chemical composition of the diabase in the Masongling and Xinkaidong areas and around the world, ion radius and compressibility, shows that when the element ion compressibility is less than 4.46 compared with the average world value, the oxide content of Si, Ti, Al and P is markedly lower, while when it is equal to or greater than 4.46, the oxide content of Fe, Mn, Mg and K increases, as opposed to that of Ca and Na.

BACKGROUND: Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE: The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS: We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network’s attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS: Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS: This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice.

BACKGROUND: Effective detection of Alzheimer’s disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE: To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS: Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS: The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS: This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data.

The synchrosqueezing generalized S-transform (SSGST) is commonly used to generate an isofrequency component of a signal by squeezing the decomposed frequency components of the signal. However, for seismic signals, the single-trace process can have a lack of lateral information in the squeezed results and lead to some discontinuous geologic information that will mislead the interpreter. Thus, to improve the stability of SSGST, we have developed a multichannel seismic trace squeezing method. Multichannel SSGST (MSSGST) considers the decomposed frequency components of neighboring traces of the analysis seismic trace and then reconstructs the center trace. Therefore, compared with SSGST, the multichannel processing improves the stability of the squeezing and produces more laterally continuous results that properly follow the geologic phenomenon. The effectiveness of MSSGST is validated using various field data. We use the application to demonstrate the potential of multichannel squeezing to perform well at (1) improving the energy focusing and continuity of the decomposed frequency components, (2) depicting the boundaries of geologic structures, and (3) identifying the thin layers.

Sparse representation (SR)-based denoising method attenuates random seismic noise trace-by-trace, which may not be able to employ the coherency between neighboring traces. To address this, we propose a multiple measurement vector (MMV)-based algorithm for robust denoising of multichannel seismic gathers. Seismic reflectors in common offset gather (COG) is characterized by horizontal events, which satisfy the common sparsity assumption, making it ideal for the MMV approach. $\ell _{2,1}$ -norm regularization, which enforces two constraints on source matrix, i.e., temporal sparsity and the horizontal continuity, is then adopted in the MMV model to stabilize the lateral variation between channels. Besides, $\ell _{2,1}$ -norm regularization provides a reasonable intrinsic structure to reduce the multisolution of the algorithm. Celebrating the strengths of iteratively reweighted (IR), we present a novel MMV algorithm, IR $\ell _{2,1}$ norm minimization (IR- $\ell _{2,1}$ ), that further improves the performance. The formulated IR- $\ell _{2,1}$ can be minimized by the alternating direction method of multipliers. Both synthetic and field data applications confirm the effectiveness of the proposed denoising method.

BACKGROUND: Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning. OBJECTIVE: Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method. METHOD: The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem. RESULTS AND CONCLUSION: Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment.

The efficient integration of satellite and terrestrial networks has become an important component for 6G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area. As the satellite and cellular networks are developed separately these years, the integrated network should synergize the communication, storage, computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence. This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks (DILIGENT). Leveraging the boost development of multi-access edge computing (MEC) technology and artificial intelligence (AI), the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks. In this article, we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization. Then we present the overall design of the proposed DILIGENT architecture, where the advantages are discussed and summarized. Strategies of task offloading, content caching and distribution are presented. Numerical results show that the proposed network architecture outperforms the existing integrated networks.

Population registration has been proposed for normalizing a large group of images into a common space, which is important in many clinical and research studies, such as brain development, aging, and atlas construction. Different from pairwise registration problem that aligns the target image to the reference directly, determining the reference or the hidden common space with the least bias is important in population registration. In order to decrease this bias, a lot of work takes the arithmetic mean image as the reference. However, the arithmetic mean image is usually too smooth to guide the population registration. This work presents an efficient symmetric population registration strategy for brain template construction, which defines the symmetric population center guiding population registration. This is important because the population registration problem can be translated into a series of pairwise registration problem which is easier to optimize and implement. Another prominent merit of proposed population registration algorithm is reference-free, which eliminates the reference dependency-related bias in population registration. Based on this symmetric population registration, the brain template is constructed by approximating both the population's intensity and gradient information. In addition, we also present a new measurement named with average bias for evaluating the unbiasedness of brain template. Experiments were first carried out on four synthetic images created with controllable transforms, which aim at comparing the difference between conventional method and proposed method. Further experiment is designed for reference-free validation. Finally, in real inter-subject brain data, twenty MRI T1 volumes with size 256 × 256 × 176 are used to construct a symmetric brain template with proposed population registration method. The constructed brain template has a small bias and clear brain details comparing with DARTEL.

Some studies have found that heart-type fatty acid-binding protein (H-FABP) is related to the prognosis of patients with sepsis. This study aimed to explore whether H-FABP could predict the 28-day mortality in patients with sepsis.Seven databases were searched, and the studies were screened based on the inclusion and exclusion criteria to assess the quality. The pooled sensitivity (SEN), specificity (SPE) positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve were calculated along with the 95% confidence interval (CI) values. Deeks' funnel plot was used to ascertain any publication bias. Meta-regression analysis was performed to explore the possible sources of heterogeneity.Seven studies were assessed that included 822 patients with sepsis. The pooled SEN was 0.76 (95% CI, 0.71-0.81), SPE was 0.66 (95% CI, 0.61-0.70), PLR was 2.21 (95% CI, 1.73-2.83), NLR was 0.36 (95% CI, 0.29-0.54), DOR was 6.23 (95% CI, 4.27-9.11) and the pooled AUC was 0.8137. There was no publication bias. Race, literature language, sampling time, threshold division and threshold effect were not the causes for the large heterogeneity.This meta-analysis suggests that H-FABP has high accuracy in predicting the 28-day mortality rate of patients with sepsis.

In order to research the effect pattern of MnS inclusions on free-cutting steel, we study the microstructure evolution, the damage mechanism and the mechanical properties in free-cutting steel in the presence of MnS inclusions. Spindle shaped MnS is added as inclusions within the free-cutting steel. The mechanical properties were found to change when inclusions were present. The gained results show that the formation of voids causing fracture starts from the interface inside the matrix close to the MnS. From the point of view of nanocomposite strength, the main effect of MnS inclusions is related to stress concentration, leading to the effect of increased stresses near the interface between the interior of the matrix and the inclusions. The inclusions have lower Young's modulus and lower dislocation activity, resulting in smaller deformation of the alloy system, larger interfacial stress concentrations and earlier hole formation. The maximum strain and stress regions of the alloy also appear near the MnS inclusions, which leads to the formation of defects near the MnS inclusions and then fracture of the alloy. MnS inclusions adversely affect the tensile properties of the alloy, such as Young's modulus, yield stress and yield strain. By comparing the stress-strain curves of single crystal iron and alloy containing MnS inclusions, it is indicated that the yield strength of the latter decreases. Slip bands and dislocation lines are first generated around the MnS inclusion, and the phase transition is induced from the original single BCC structure to FCC, HCP and amorphous structures, and the atoms of FCC, HCP and amorphous structures increase with increasing strain, while those of BCC structure decrease, especially after yield strain. This study is significant for understanding the effect of inclusions on the mechanical laws and fracture mechanisms of the alloy.

To explore the CT and MR imaging findings of non-Hodgkin's lymphoma (NHL) involving the jaw bones.Gnathic NHLs were retrospectively analyzed in 52 patients. The imaging findings were observed from the lesion number, location, size, shape, margin, density and signal intensity, CT value, bone structure change, and soft tissue mass.Of the 52 subjects, there were 43 with solitary gnathic NHL, and 9 with multicentric gnathic NHLs. The maximum diameter of all the 72 lesions ranged from 1.5 to 9.5 cm (mean 4.6 ± 1.67 cm). Of the 72 lesions, 90.3% were identified as irregular shapes, 87.5% as ill-defined margins, and 76.4% as homogeneous densities and signal intensities. The osteolytic destruction was found in 84.7% lesions, cortical disruption in 84.7%, and no periosteal reaction in 93.1%. The soft tissue masses surrounding the gnathic lesions were shown in 93.1% lesions. On MR imaging, the gnathic NHLs (18 lesions) showed as iso-signal intensity on T1WI, slightly higher signal intensity on T2WI, and high signal intensity on DWI. After contrast media administration, 83.3% lesions showed slightly homogeneous enhancement on T1WI.On CT and MR imaging, gnathic NHLs usually reveal as osteolytic lesions with irregular shapes, ill-defined margins, homogeneous densities and signal intensities, and surrounded by soft tissue mass. Some gnathic NHLs are characterized by multicentricity, which is important for indicating the condition.

PURPOSE The shape and position of the inferior alveolar canal (IAC) are analyzed to effectively reduce the risk of iatrogenic injury based on cone-beam computer tomography (CBCT). To assist dental clinicians to make better use of the IAC information, we propose an IAC segmentation method based on CBCT images. METHODS In this paper, CBCT images are first preprocessed by the Hounsfield unit values clipping and gray normalization. Secondly, based on the multi-plane reconstruction (MPR) and curved surface reconstruction, the curved MPR image sets are generated by the smooth dental arch curve with a sampling distance of 1.00 pixels. Then, the K-means clustering algorithm is used to cluster the texture parameters of the gray level-gradient co-occurrence matrix enhanced by the gradient directions to improve the image contrast of the IAC. Finally, the IAC edges are roughly segmented by the 2D line-tracking method, and smoothed by the fourth-order polynomial to obtain the final segmentation result. RESULTS Twenty-one real clinical dental CBCT datasets were used to test the proposed method. The manual segmentation results of two specialized dental clinicians were used as quantitative evaluation criteria. The dice similarity index (DSI), average symmetric surface distance (ASSD), and mean curve distance (MCD) of the left IAC are 0.93 (SD = 0.01), 0.16 mm (SD = 0.05 mm), and 1.59 mm (SD = 0.25 mm), respectively; the DSI, ASSD, and MCD of the right IAC are 0.93 (SD = 0.02), 0.16 mm (SD = 0.05 mm), and 1.60 mm (SD = 0.30 mm), respectively. CONCLUSIONS The proposed method provides an effective image enhancement and segmentation solution to analyze the shape and position of the IAC. Experimental results show that the relationships between the IAC and other structures can be accurately reflected in the panoramic images without superimposition and geometric distortion, and the smooth edges of the IAC can be segmented.

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor voxel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

Purpose RNA interference (RNAi) was used to silence the expression of Livin gene in human pancreatic adenocarcinoma cell line (PANC1), and to investigate the chemosensitivity of PANC1on gemcitabine after the Livin gene knockdown.Methods Using LipofectamineTM2000 as a carrier, a small interfering RNA (siRNA) targeting Livin gene was transfected into PANC1 cells. The mRNA and protein expression of Livin, cleaved PARP (cPARP) and caspase-3 (cCaspase-3) in PANC1 cells were measured by RT-PCR and Western Blot. The protein bands density was analyzed by Image J software, and expressed by the density ratio with the corresponding internal control protein (β-actin). The density ratios of the cPARP and cCaspase-3 protein bands were analyzed by one-way analysis of variance and Student's t-test for comparison of multiple or two groups. A p < 0.05 was considered statistically significant.Results Livin mRNA expression of PANC1 cells significantly decreased after transfection with Livin-siRNA # 1. The cPARP and cCaspase-3 induced by 50 nM gemcitabine were stronger than those induced by 25 nM after 48 hours and 72 hours in PANC1 cells.But the cPARP and cCaspase-3 induced by 100 nM gemcitabine did not significantly change compared with 50 nM. After transfection of Livin-siRNA # 1, cPARP and cCaspase-3 induced by both 25 nM and 50 nM gemcitabine were enhanced. This indicated that the apoptosis of PANC1 cells induced by gemcitabine was enhanced after Livin knockdown.Conclusions RNAi-targeted Livin promotes apoptosis of PANCl cells in vitro through PARP and caspase-3 pathways, and enhances its chemosensitivity to gemcitabine.

High-resolution reflectivity inversion is termed as a fundamental yet essential step for the prediction of thin-bedded hydrocarbon reservoirs. However, algorithms suffer from two key issues: (1) seismic inversion is an ill-posed problem that has multiple solutions, and the results of trace-by-trace seismic inversion are quite poor in lateral continuity, and (2) algorithm stability is likely to be decreased owing to the noise and distortion associated with the acquisition and processing flows. In the current article, we formulate a new joint sparse representation through the combination with L2,1-norm misfit function, which possesses superior noise robustness, in particular in the presence of outliers. On the basis of the L2,1-norm regularization, this specific approach enforces a common sparsity profile, together with consistently lowering the multiplicity of solution. Subsequent to that, the resultant algorithm is applied to the multi-trace seismic inversion. Besides, the wedge model trial and practical applications suggest that the proposed inversion algorithm is stable, in addition to having good noise robustness and lateral continuity; moreover, the vertical resolution of λ/8 is realized under the noise and outliers interference. The logging data calibration illustrates that the proposed methodology is accurate and credible.

Gradient based image fusion can more effectively incorporate edge details using structure tensor, which is successfully used in 2D image fusion. In this study, we generalized and applied this gradient based image fusion method into 3D for non-small cell lung cancer PET/CT image fusion. According the characteristic of lung PET/CT images, we proposed a novel 3D structure tensor based feature, which can be used to construct a weighted structure tensor containing important local detail of both PET and CT images. The fusion gradient domain is deduced from a rank one tensor, which is the closest approximation of the weighted structure tensor in geometry. Based on the fusion gradient domain, final PET/CT fusion image is obtained by solving a Poisson equation. Comparing with the wavelet transform based fusion result, the average information entropy and average gradient measure of proposed fusion method increase 13.5% and 42.3%, respectively. The experimental results show that the proposed fusion method enables to effectively preserve lung vessel structure and sphere-like lesion detail while produces clear, stable and well consistent fusion images.

Diffusion tensor imaging (DTI) data interpolation is important for DTI processing, which could affect the precision and computational complexity in the process of denoising, filtering, regularization, and DTI registration and fiber tracking. In this paper, we propose a novel DTI interpolation framework named with spectrum-sine (SS) focusing on tensor orientation variation in DTI processing. Compared with the state-of-the-art DTI interpolation method using Euler angles or quaternion to represent the orientation of DTI tensors, this method does not need to convert eigenvectors into Euler angles or quaternions, but interpolates each tensor’s unit eigenvector directly. The prominent merit of this tensor interpolation method lies in tensor orientation information preservation, which is different from the existing DTI tensor interpolation methods that interpolating tensor’s orientation information in a scalar way. The experimental results from both synthetic and real human brain DTI data demonstrated the proposed SS interpolation scheme not only maintains the advantages of Log-Euclidean and Riemannian interpolation frameworks, such as preserving the tensor’s symmetric positive definiteness and the monotonic determinant variation, but also preserve the tensor’s anisotropy property which was proposed in the spectral quaternion (SQ) method.