Computed tomography (CT) is a widely used imaging technique in both medical and industrial applications. However, accurate CT reconstruction requires complete projection data, while incompletedata can result in signi...
详细信息
Computed tomography (CT) is a widely used imaging technique in both medical and industrial applications. However, accurate CT reconstruction requires complete projection data, while incompletedata can result in significant artifacts in the reconstructed images, compromising their reliability for subsequent detection and diagnosis. As a result, accurate CT reconstructionfromincomplete projection data remains a challenging research area in radiology. With the rapid development of deep learning (DL) techniques, many DL-based methods have been proposed for CT reconstructionfromincomplete projection data. However, there are limited comprehensive surveys that summarize recent advances in this field. This article provides a comprehensive overview of the current state-of-the-art DL-based CT reconstructionfromincomplete projection data, including acrlong SV reconstruction, acrlong LA reconstruction, acrlong MAR, acrlong IT, and ring artifact reduction. This survey covers various DL-based solutions to the five problems, potential limitations of existing methods, and future research directions.
BACKGROUND: For sparse and limited angle projection Computed Tomography (CT), the reconstructed image usually suffers from considerable artifacts due to undersampled data. OBJECTIVE: To improve imagereconstruction qu...
详细信息
BACKGROUND: For sparse and limited angle projection Computed Tomography (CT), the reconstructed image usually suffers from considerable artifacts due to undersampled data. OBJECTIVE: To improve imagereconstruction quality of sparse and limited angle projection CT, this study tested a novel reconstruction algorithm based on Dictionary Learning (DL) from sparse and limited projections. METHODS: The study used signal sparse representation and feature extraction to render the DL technology, which is constrained by L2 and Lp norms, respectively. A Lp Norm Dictionary Learning term is suitable for regular term of objective function for CT imagereconstruction. This is helpful for solving the objective function by combining algorithm of ART. Based on these features, the new algorithm of ART-DL-Lp is proposed for CT imagereconstruction. The alternate solving strategy of the algorithm of "ART first, then adaptive DL" is provided in sequence. The impact on reconstruction results of ART-DL-Lp at different p values (0 < p < 1) is also considered. RESULTS: For non-ideal projections with noise, the digital experiments show that ART-DL-Lp data were superior to those of ART, SART, and ART-DL-L2. Accordingly, the objective evaluation metrics for non-ideal situation of RMSE, MAE, PSNR, Residuals and SSIM are all better than those of contrasted three algorithms. The metrics curves of ART-DL-Lp algorithm are recorded as the best. In both incomplete projection situations, smaller p-value of ART-DL-Lp algorithm induces more close reconstructed images to the original form and better five objective evaluation metrics. CONCLUSIONS: Overall, the reconstruction efficiency of the proposed ART-DL-Lp for CT imaging using the noisy incomplete projections outperforms ART, SART and ART-DL-L2 algorithms. For ART-DL-Lp algorithm, lower p-values result in better reconstruction performance.
Use of incompleteimagedata has become a prominent research issue in recent years, driven by the development of space variant image sensors. Whilst imagereconstruction techniques have been developed that enable the ...
详细信息
ISBN:
(纸本)9781424417650
Use of incompleteimagedata has become a prominent research issue in recent years, driven by the development of space variant image sensors. Whilst imagereconstruction techniques have been developed that enable the subsequent use of standard image processing algorithms, the development of image processing algorithms that can be applied directly to incompleteimagedata has received less attention. The problem of interest point detection for incompleteimages is addressed by presenting an algorithm that can be applied directly to incompleteimagedata without the requirement of imagereconstruction, and the accurate performance of the algorithm is illustrated through visual results and ROC curves.
Electrocardiograms (ECG) enable the straightforward identification of cardiovascular diseases (CVD). However, the complexity of ECG graphs often challenges physicians in making precise and confident diagnoses. This st...
详细信息
ISBN:
(数字)9798331530259
ISBN:
(纸本)9798331530266
Electrocardiograms (ECG) enable the straightforward identification of cardiovascular diseases (CVD). However, the complexity of ECG graphs often challenges physicians in making precise and confident diagnoses. This study aims to digitize electrocardiograms using a publicly available ECG imagedataset from the Mendeley database and classify the signals into four categories: normal, arrhythmia, myocardial infarction (MI), and past myocardial infarction (PMI). The proposed framework includes a comprehensive preprocessing pipeline involving greyscale conversion, noise elimination, segmentation, and normalization. The classification process employs advanced deep learning models like EfficientNet-1D and ResNet-1D alongside machine learning (ML) models such as XGBoost, Support Vector Machines, Logistic Regression, and k-Nearest Neighbours (kNN). The models were evaluated using performance metrics, including F1-score, accuracy, recall, and precision. The Voting Ensemble Classifier outperformed achieving the highest overall accuracy of 93.2%. Among the deep learning models, ResNet-1D achieved an accuracy of 86.6%, while EfficientNet-1D attained 92.2%, demonstrating strong performance in ECG classification. ResNet-1D excelled in detecting normal and arrhythmic signals, while EfficientNet-1D demonstrated superior effectiveness in identifying myocardial infarctions. Both models exhibited high precision and recall, with ResNet-1D achieving exceptional success. This study underscores the importance of digitizing printed ECG records for advanced medical diagnostics and highlights the role of performance metrics in evaluating model efficacy. The proposed method enhances ECG classification accuracy and is well-suited for real-time clinical applications and automated cardiac disease detection systems.
系统地回顾大数据在旅游研究中的应用,对于理解旅游研究范式的转型,响应新涌现的科学问题和实践应用问题具有重要意义。对Web of Science、Archive和中国知网3个数据库中的2477篇旅游大数据文献进行了综述。研究发现:(1)从2010年开始,...
详细信息
系统地回顾大数据在旅游研究中的应用,对于理解旅游研究范式的转型,响应新涌现的科学问题和实践应用问题具有重要意义。对Web of Science、Archive和中国知网3个数据库中的2477篇旅游大数据文献进行了综述。研究发现:(1)从2010年开始,旅游大数据研究文献数量逐年增长。中国研究者发表了838篇旅游大数据研究论文,占文献总量的33.83%。(2)接近50.00%的论文以会议和学位论文的形式发表,超过70.00%的文献发表在非旅游类期刊。(3)62.65%的论文利用了TripAdvisor、携程旅行网、马蜂窝等的UGC数据。(4)预测旅游需求、旅游推荐、旅游消费行为、游客流动模式、旅游目的地形象、游客满意度、景观评价和方法创新是当前研究聚焦的八大场景。(5)目前旅游研究领域对大数据应用方法的创新贡献不足,主要通过迁移数据科学与信息科学等已经发展较为成熟的方法,结合旅游情景和数据开展研究。
Imaging interferometry suffers from sparse Fourier measurements, and, at the visible wavelengths, a lack of phase information, creating a need for an imagereconstruction algorithm. A support constraint is useful for ...
详细信息
ISBN:
(纸本)9780819492173
Imaging interferometry suffers from sparse Fourier measurements, and, at the visible wavelengths, a lack of phase information, creating a need for an imagereconstruction algorithm. A support constraint is useful for optimization but is often not known a priori. The two-point rule for finding an object support from the autocorrelation is limited in usefulness by the sparsity and non-uniformity of the Fourier data and is insufficient for imagereconstruction. Compactness, a common prior, does not require knowledge of the support. Compactness penalizes solutions that have bright pixels away from the center, favoring soft-edged objects with a bright center and darker extremities. With regards to imaging hard-edged objects such as satellites, a support constraint is desired but unknown and compactness may be unfavorable. Combining various techniques, a method of simultaneously estimating the object's support and the object's intensity distribution is presented. Though all the optimization parameters are in the image domain, we are effectively performing phase retrieval at the measurement locations and interpolation between the sparse data points.
A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described. The method is based on a Gaussian mi...
详细信息
ISBN:
(纸本)9780819482969
A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described. The method is based on a Gaussian mixture description of the heterogeneity and is motivated by cryo electron microscopy of biological objects.
In recent years, dictionary learning has shown to be an efficient tool in recovering images from their degraded, damaged or incomplete version. Especially, for medical images that contain significant details and chara...
详细信息
In recent years, dictionary learning has shown to be an efficient tool in recovering images from their degraded, damaged or incomplete version. Especially, for medical images that contain significant details and characteristics. In this work, the authors are interested in this unsupervised learning technique for discovering and visualising the underlying structure of a medical image. Therefore, an adaptive bi-dictionary learning model for recovering magnetic resonance (MR) imagefrom undersampled measurements is introduced. The proposed model learns two dictionaries, one over the underlying image and the other over its sparse gradient. Hence, the algorithm minimises a linear combination of three terms corresponding to the least-squares data fitting, dictionary learning over the pixel domain, and gradient-based dictionary. Numerically, experimental results on several MR images demonstrate that the proposed bi-dictionary framework can improve reconstruction accuracy over other methods.
Optical diffusion imaging is a new imaging modality that promises great potential in applications such as medical imaging, environmental sensing and nondestructive testing. It presents a difficult nonlinear image reco...
详细信息
ISBN:
(纸本)0819437689
Optical diffusion imaging is a new imaging modality that promises great potential in applications such as medical imaging, environmental sensing and nondestructive testing. It presents a difficult nonlinear imagereconstruction problem however. An inversion algorithm is formulated in a Bayesian framework, and an efficient optimization technique that uses iterative coordinate descent is presented. A general multigrid optimization technique for nonlinear imagereconstruction problems is developed and applied to the optical diffusion imaging problem. Numerical results show that this approach improves the quality of reconstructions and dramatically decreases computation times.
Measuring a series of far-field intensity patterns from an object, taken after a, transverse translation of the object with respect to a known illumination pattern, has been shown to make the problem of image reconstr...
详细信息
ISBN:
(纸本)9780819472960
Measuring a series of far-field intensity patterns from an object, taken after a, transverse translation of the object with respect to a known illumination pattern, has been shown to make the problem of imagereconstruction by phase retrieval much more robust. However, previously reported reconstruction algorithms [Phys. Rev. Lett. 93, 023903 (2004)] rely oil an accurate knowledge of the translations and illumination pattern for a successful reconstruction. We developed a nonlinear optimization algorithm that allows optimization over the translations and illumination pattern, dramatically improving the reconstructions if the system parameters are inaccurately known [Opt. Express 16, 7264 (2008)]. In this paper we compare reconstructions obtained with these algorithms under realistic experimental scenarios.
暂无评论