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Background: The existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points., Methods: This work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face., Results: The proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods., Conclusions: Our experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interest related to this article., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective. In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability. Approach. To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments. Main results. We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration. Significance. The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications., (© 2023 Institute of Physics and Engineering in Medicine.)

The classic N-wire phantom has been widely used in the calibration of freehand ultrasound probes. One of the main challenges of the phantom is accurately identifying N-fiducials in ultrasound images, especially with multiple N-wire structures. In this study, a method using a multilayer N-wire phantom for the automatic spatial calibration of ultrasound images is proposed. All dots in the ultrasound image are segmented, scored, and classified according to the unique geometric features of the multilayer N-wire phantom. A recognition method for identifying N-fiducials from the dots is proposed for calibrating the spatial transformation of the ultrasound probe. At depths of 9, 11, 13, and 15 cm, the reconstruction error of 50 points is 1.24 ± 0.16, 1.09 ± 0.06, 0.95 ± 0.08, 1.02 ± 0.05 mm, respectively. The reconstruction mockup test shows that the distance accuracy is 1.11 ± 0.82 mm at a depth of 15 cm., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Vessel centerline extraction from x-ray angiography images is essential for vessel structure analysis in the diagnosis of coronary artery disease. However, complete and continuous centerline extraction remains a challenging task due to image noise, poor contrast, and complexity of vessel structure. Thus, an iterative multi-path search framework for automatic vessel centerline extraction is proposed. First, the seed points of the vessel structure are detected and sorted by confidence. With the ordered seed points, multi-bifurcation centerline is searched through multi-path propagation of wavefront and accumulated voting. Finally, the centerline is further extended piecewise by wavefront propagation on the basis of keypoint detection. The latter two steps are performed alternately to obtain the final centerline result. The proposed method is qualitatively and quantitatively evaluated on 1260 synthetic images and 50 clinical angiography images. The results demonstrate that our method has a highF1score of 87.8% ± 2.7% for the angiography images and achieves accurate and continuous results of vessel centerline extraction., (© 2021 Institute of Physics and Engineering in Medicine.)

Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound (US) images. We propose a 3D tensor-based active contour model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D US images. Experimental results in clinical 3D US and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768 ± 0.0597, 0.9978 ± 0.0013 and 0.9971 ± 0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast., (© 2021 Institute of Physics and Engineering in Medicine.)

Liver is an incompressible organ that maintains its volume during the respiration-induced deformation. Quantifying this deformation with the incompressible constraint is significant for liver tracking. The constraint can be accomplished with retaining the divergence-free field obtained by the deformation decomposition. However, the decomposition process is time-consuming, and the removal of non-divergence-free field weakens the deformation. In this study, a divergence-free fitting-based registration method is proposed to quantify the incompressible deformation rapidly and accurately. First, the deformation to be estimated is mapped to the velocity in a diffeomorphic space. Then, this velocity is decomposed by a fast Fourier-based Hodge-Helmholtz decomposition to obtain the divergence-free, curl-free, and harmonic fields. The curl-free field is replaced and fitted by the obtained harmonic field with a translation field to generate a new divergence-free velocity. By optimizing this velocity, the final incompressible deformation is obtained. Moreover, a deep learning framework (DLF) is constructed to accelerate the incompressible deformation quantification. An incompressible respiratory motion model is built for the DLF by using the proposed registration method and is then used to augment the training data. An encoder-decoder network is introduced to learn appearance-velocity correlation at patch scale. In the experiment, we compare the proposed registration with three state-of-the-art methods. The results show that the proposed method can accurately achieve the incompressible registration of liver with a mean liver overlap ratio of 95.33%. Moreover, the time consumed by DLF is nearly 15 times shorter than that by other methods.

Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from x-ray angiographic image sequences. In x-ray-based vascular interventional therapy, motion modeling can simulate the process of organ deformation driven by motion signals to display a dynamic organ on angiograms without contrast agent injection. Automatic respiratory signal estimation from x-ray angiographic image sequences is essential for motion compensation and modeling. The effects of respiratory motion, cardiac impulses, and tremors on structures in the chest and abdomen bring difficulty in extracting accurate respiratory signals individually. In this study, an end-to-end deep learning framework based on a motion-flow-guided recurrent network is proposed to address the aforementioned problem. The proposed method utilizes a convolutional neural network to learn the spatial features of every single frame, and a recurrent neural network to learn the temporal features of the entire sequence. The combination of the two networks can effectively analyze the image sequence to realize respiratory signal estimation. In addition, the motion-flow between consecutive frames is introduced to provide a dynamic constraint of spatial features, which enables the recurrent network to learn better temporal features from dynamic spatial features than from static spatial features. We demonstrate the advantages of our approach on designed datasets which contain coronary and hepatic angiographic sequences with diaphragm structures, and coronary angiographic sequences without diaphragm structures. Our method improves over state-of-the-art manifold-learning-based methods by 85.7%, 81.5% and 75.3% in respiratory signal accuracy metric on these datasets. The results demonstrate that the proposed method can effectively estimate respiratory signals from multiple motion patterns.

Graph-based groupwise registration methods are widely used in atlas construction. Given a group of images, a graph is built whose nodes represent the images, and whose edges represent a geodesic path between two nodes. The distribution of images on an image manifold is explored through edge traversal in a graph. The final atlas is a mean image at the population center of the distribution on the manifold. The procedure of warping all images to the mean image turns to dynamic graph shrinkage in which nodes become closer to each other. Most conventional groupwise registration frameworks construct and shrink a graph without considering the local distribution of images on the dataset manifold and the local structure variations between image pairs. Neglecting the local information fundamentally decrease the accuracy and efficiency when population atlases are built for organs with large inter-subject anatomical variabilities. To overcome the problem, this paper proposes a global-local graph shrinkage approach that can generate accurate atlas. A connected graph is constructed automatically based on global similarities across the images to explore the global distribution. A local image distribution obtained by image clustering is used to simplify the edges of the constructed graph. Subsequently, local image similarities refine the deformation estimated through global image similarity for each image warping along the graph edges. Through the image warping, the overall simplified graph shrinks gradually to yield the atlas with respecting both global and local features. The proposed method is evaluated on 61 synthetic and 20 clinical liver datasets, and the results are compared with those of six state-of-the-art groupwise registration methods. The experimental results show that the proposed method outperforms non-global-local method approaches in terms of accuracy., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The 3D/2D registration of pre-operative computed tomography angiography (CTA) and intra-operative x-ray angiography (XRA) images in vascular intervention is imperative for guiding surgical instruments and reducing the dosage of toxic contrast agents. In this study, 3D/2D vascular registration is formulated as a search tree problem on the basis of the topological continuity of vessels and the fact that matching can be decomposed into continuous states. In each node of the tree, a closed-solution of 3D/2D transformation is used to obtain the registration results based on the dense correspondences of vessel points, and the results of matching and registration are calculated and recorded. Then, a hand-crafted score that quantifies the qualities of matching and registration of vessels is used, and the remaining problem focuses on finding the highest score in the search tree. An improved heuristic tree search strategy is also proposed to find the best registration. The proposed method is evaluated and compared with four state-of-the-art methods. Experiments on simulated data demonstrate that our method is insensitive to initial pose and robust to noise and deformation. It outperforms other methods in terms of registering real model data and clinical coronary data. In the 3D/2D registration of uninitialized and initialized coronary arteries, the average registration errors are 1.85 and 1.79 mm, respectively. Given that the proposed method is independent of the initial pose, it can be used to navigate vascular intervention for clinical practice.

Background: Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures., Methods: A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method., Results: Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence., Conclusions: The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

A graph-based groupwise shape registration algorithm for building statistical shape model (SSM) is proposed, which has been successfully applied to shape prediction of foot scans. Establishing unbiased and effective shape correspondences of large-scale data sets is extremely challenging, for the inappropriate selection of initial mean shape and non-rigid registration of shape with large-scale deformation. To address these issues, first, we use a simplified graph to model the shape distribution in metric space and an edge-guided graph shrinkage to deform the shapes. Then, the groupwise registration is performed by iteratively performing the graph shrinkage until the shape converges. And, the correspondences of training shapes are obtained by propagating the converged shape to the original data along each shrinkage path. Compared with traditional forward and backward models of groupwise registration, the proposed method is data-driven without initial mean shape as input. Moreover, under the constraint of the established graph, the non-rigid registration can perform more accurately by restricting shape register to its neighbors. Based on the shape correspondence, the SSM of foot shapes is constructed and applied to shape prediction by taking the collected anthropometric information as predictor. Experiments demonstrate that the proposed method can obtain robust shape correspondences and SSM capability with respect to model generalization, specificity, and compactness. The application of shape prediction model shows an average prediction error lower than 1% for general foot size. Graphical abstract The graphical abstract of unbiased groupwise registration for foot prediction.

The structural information of coronary arteries has important clinical value for quantitative diagnosis and treatment of coronary artery disease. In this study, a deep feature regression (DFR) method based on a convolutional regression network (CRN) and a stable point clustering mechanism for 3D vessel segmentation is proposed. First, the vessel model is constructed by a vessel section generator and a series of deviation parameter estimators. The generator provides 2D images for the training and prediction processes, while the estimators calculate pose parameters of an input vessel section. Second, estimators are trained by a series of CRNs, in which deep vessel features are automatically learned from 600 000 sample images. Third, we propose a stable point clustering mechanism that evaluates the reliability of the CRN estimation through iterative regression of vessel parameters. This mechanism eliminates the outliers, thereby increasing tracking robustness. Finally, we present a vessel segmentation algorithm using trained deviation parameter estimators. And, the termination criteria are designed based on both the number of stable points and an intensity constraint. The proposed method is evaluated on a public coronary artery data set. The average overlapping ratio and error are 97.5% and 0.27 mm, respectively. A quantitative test on a public cerebral artery data set demonstrates that the proposed DFR method tracks the vessel centerline with high accuracy, for which the average error is less than 0.33 mm.

Background: Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities., Methods: We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third "mediator" variable. These "mediator" variables appear to modulate the associations between pairs of bacteria., Results: Using this analysis, we were able to assess the OTUs' ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors., Conclusions: By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download ( http://bitbucket.org/charade/elsa ).

The imbalance of human gut microbiota has been associated with colorectal cancer. In recent years, metagenomics research has provided a large amount of scientific data enabling us to study the dedicated roles of gut microbes in the onset and progression of cancer. We removed unrelated and redundant features during feature selection by mutual information. We then trained a random forest classifier on a large metagenomics dataset of colorectal cancer patients and healthy people assembled from published reports and extracted and analysed the information from the learned decision trees. We identified key microbial species associated with colorectal cancers. These microbes included Porphyromonas asaccharolytica , Peptostreptococcus stomatis , Fusobacterium, Parvimonas sp., Streptococcus vestibularis and Flavonifractor plautii . We obtained the optimal splitting abundance thresholds for these species to distinguish between healthy and colorectal cancer samples. This extracted consensus decision tree may be applied to the diagnosis of colorectal cancers., Competing Interests: The authors declare no conflicts interests.

In the diagnosis and interventional treatment of coronary artery disease, the 3D[Formula: see text]time reconstruction of the coronary artery on the basis of x-ray angiographic image sequences can provide dynamic structural information. The synchronization of cardiac phases in the sequences is essential for minimizing the influence of cardiorespiratory motion and realizing precise 3D[Formula: see text]time reconstruction. Key points are initially extracted from the first image of a sequence. Matching grid points between consecutive images in the sequence are extracted by a multi-layer matching strategy. Then deep motion tracking (DMT) of key points is achieved by local deformation based on the neighboring grid points of key points. The local deformation is optimized by the Random sample consensus (RANSAC) algorithm. Then, a simple harmonic motion (SHM) model is utilized to distinguish cardiac motion from other motion sources (e.g. respiratory, patient movement, etc). Next, the signal which is composed of cardiac motions is filtered by a band-pass filter to reconstruct the cardiac phases. Finally, the synchronization of cardiac phases from different imaging angles is realized by a piece-wise linear transformation. The proposed method was evaluated using clinical x-ray angiographic image sequences from 13 patients. [Formula: see text] matching points can be accurately computed by the DMT method. The mean peak temporal distance (MPTD) between the reconstructed cardiac phases and the electrocardiograph signal is [Formula: see text] s. The correlation between the cardiac phases of the same patient is over [Formula: see text]. Compared with three other state-of-the-art methods, the proposed method accurately reconstructs and synchronizes the cardiac phases from different sequences of the same patient. The proposed DMT method is robust and highly effective in synchronizing cardiac phases of angiographic image sequences captured from different imaging angles.

Statistical shape models are capable of extracting shape prior information, and are usually utilized to assist the task of segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, and it also is difficult to achieve satisfactory results for complex shapes. This study proposed a novel statistical model for cervical vertebra segmentation, called sparse intervertebral fence composition (SiFC), which can reconstruct the boundary between adjacent vertebrae by modeling intervertebral fences. The complex shape of the cervical spine is replaced by a simple intervertebral fence, which considerably reduces the difficulty of cervical segmentation. The final segmentation results are obtained by using a 3D active contour deformation model without shape constraint, which substantially enhances the recognition capability of the proposed method for objects with complex shapes. The proposed segmentation framework is tested on a dataset with CT images from 20 patients. A quantitative comparison against corresponding reference vertebral segmentation yields an overall mean absolute surface distance of 0.70 mm and a dice similarity index of 95.47% for cervical vertebral segmentation. The experimental results show that the SiFC method achieves competitive cervical vertebral segmentation performances, and completely eliminates inter-process overlap.

Background: 3D ultrasound volume reconstruction from B-model ultrasound slices can provide more clearly and intuitive structure of tissue and lesion for the clinician., Methods: This paper proposes a novel Global Path Matching method for the 3D reconstruction of freehand ultrasound images. The proposed method composes of two main steps: bin-filling scheme and hole-filling strategy. For the bin-filling scheme, this study introduces two operators, including the median absolute deviation and the inter-quartile range absolute deviation, to calculate the invariant features of each voxel in the 3D ultrasound volume. And the best contribution range for each voxel is obtained by calculating the Euclidian distance between current voxel and the voxel with the minimum invariant features. Hence, the intensity of the filling vacant voxel can be obtained by weighted combination of the intensity distribution of pixels in the best contribution range. For the hole-filling strategy, three conditions, including the confidence term, the data term and the gradient term, are designed to calculate the weighting coefficient of the matching patch of the vacant voxel. While the matching patch is obtained by finding patches with the best similarity measure that defined by the three conditions in the whole 3D volume data., Results: Compared with VNN, PNN, DW, FMM, BI and KR methods, the proposed Global Path Matching method can restore the 3D ultrasound volume with minimum difference., Conclusions: Experimental results on phantom and clinical data sets demonstrate the effectiveness and robustness of the proposed method for the reconstruction of ultrasound volume.

Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods., Competing Interests: The authors have declared that no competing interests exist.

This study proposes a novel geometrical force constraint method for 3-D vasculature modeling and angiographic image simulation. For this method, space filling force, gravitational force, and topological preserving force are proposed and combined for the optimization of the topology of the vascular structure. The surface covering force and surface adhesion force are constructed to drive the growth of the vasculature on any surface. According to the combination effects of the topological and surface adhering forces, a realistic vasculature can be effectively simulated on any surface. The image projection of the generated 3-D vascular structures is simulated according to the perspective projection and energy attenuation principles of X-rays. Finally, the simulated projection vasculature is fused with a predefined angiographic mask image to generate a realistic angiogram. The proposed method is evaluated on a CT image and three generally utilized surfaces. The results fully demonstrate the effectiveness and robustness of the proposed method.

Background: Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies., Results: By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns., Availability: The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

Background: Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise., Methods: Considering the characteristics of both Rician noise and the NLM filter, this study proposes a frame for a pre-smoothing NLM (PSNLM) filter combined with image transformation. In the PSNLM frame, noisy MRI is first transformed into an image in which noise can be treated as additive noise. Second, the transformed MRI is pre-smoothed via a traditional denoising method. Third, the NLM filter is applied to the transformed MRI, with weights that are computed from the pre-smoothed image. Finally, inverse transformation is performed on the denoised MRI to obtain the denoising results., Results: To test the performance of the proposed method, both simulated and real patient data are used, and various pre-smoothing (Gaussian, median, and anisotropic filters) and image transformation [squared magnitude of the MRI, and forward and inverse variance-stabilizing trans-formations (VST)] methods are used to reduce noise. The performance of the proposed method is evaluated through visual inspection and quantitative comparison of the peak signal-to-noise ratio of the simulated data. The real data include Alzheimer's disease patients and normal controls. For the real patient data, the performance of the proposed method is evaluated by detecting atrophy regions in the hippocampus and the parahippocampal gyrus., Conclusions: The comparison of the experimental results demonstrates that using a Gaussian pre-smoothing filter and VST produce the best results for the peak signal-to-noise ratio (PSNR) and atrophy detection.