This paper presents a novel method based on leveraging physics-informed neural networks for magnetic resonance electrical property tomography (MREPT). MREPT is a noninvasive technique that can retrieve the spatial distribution of electrical properties (EPs) of scanned tissues from measured transmit radiofrequency (RF) in magnetic resonance imaging (MRI) systems. The reconstruction of EP values in MREPT is achieved by solving a partial differential equation derived from Maxwell’s equations that lacks a direct solution. Most conventional MREPT methods suffer from artifacts caused by the invalidation of the assumption applied for simplification of the problem and numerical errors caused by numerical differentiation. Existing deep learning-based (DL-based) MREPT methods comprise data-driven methods that need to collect massive datasets for training or model-driven methods that are only validated in trivial cases. Hence we proposed a model-driven method that learns mapping from a measured RF, its spatial gradient and Laplacian to EPs using fully connected networks (FCNNs). The spatial gradient of EP can be computed through the automatic differentiation of FCNNs and the chain rule. FCNNs are optimized using the residual of the central physical equation of convection-reaction MREPT as the loss function (L). To alleviate the ill condition of the problem, we added multiconstraints, including the similarity constraint between permittivity and conductivity and the ℓ1norm of spatial gradients of permittivity and conductivity, to theL. We demonstrate the proposed method with a three-dimensional realistic head model, a digital phantom simulation, and a practical phantom experiment at a 9.4T animal MRI system.

Photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) are two advanced imaging techniques widely used in pre-clinical research. PAT has high optical contrast and deep imaging range but poor soft tissue contrast, whereas MRI provides excellent soft tissue information but poor temporal resolution. Despite recent advances in medical image fusion with pre-aligned multimodal data, PAT-MRI image fusion remains challenging due to misaligned images and spatial distortion. To address these issues, we propose an unsupervised multi-stage deep learning framework called PAMRFuse for misaligned PAT and MRI image fusion. PAMRFuse comprises a multimodal to unimodal registration network to accurately align the input PAT-MRI image pairs and a self-attentive fusion network that selects information-rich features for fusion. We employ an end-to-end mutually reinforcing mode in our registration network, which enables joint optimization of cross-modality image generation and registration. To the best of our knowledge, this is the first attempt at information fusion for misaligned PAT and MRI. Qualitative and quantitative experimental results show the excellent performance of our method in fusing PAT-MRI images of small animals captured from commercial imaging systems.

Current survival analysis of cancers confronts two key issues. While comprehensive perspectives provided by data from multiple modalities often promote the performance of survival models, data with inadequate modalities at the testing phase are more ubiquitous in clinical scenarios, which makes multi-modality approaches not applicable. Additionally, incomplete observations (i.e., censored instances) bring a unique challenge for survival analysis, to tackle which, some models have been proposed based on certain strict assumptions or attribute distributions that, however, may limit their applicability. In this paper, we present a mutual-assistance learning paradigm for standalone mono-modality survival analysis of cancers. The mutual assistance implies the cooperation of multiple components and embodies three aspects: 1) it leverages the knowledge of multi-modality data to guide the representation learning of an individual modality via mutual-assistance similarity and geometry constraints; 2) it formulates mutual-assistance regression and ranking functions independent of strong hypotheses to estimate the relative risk, in which a bias vector is introduced to efficiently cope with the censoring problem; 3) it integrates representation learning and survival modeling into a unified mutual-assistance framework for alleviating the requirement of attribute distributions. Extensive experiments on several datasets demonstrate our method can significantly improve the performance of mono-modality survival model.

The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information. However, how to efficiently fuse and select the complementary information of high-dimensional multi-modal data remains challenging for Cox model, as it generally does not equip with feature fusion/selection mechanism. Many previous studies typically perform feature fusion/selection in the original feature space before Cox modeling. Alternatively, learning a latent shared feature space that is tailored for Cox model and simultaneously keeps sparsity is desirable. In addition, existing Cox-based models commonly pay little attention to the actual length of the observed time that may help to boost the model's performance. In this article, we propose a novel Cox-driven multi-constraint latent representation learning framework for prognosis analysis with multi-modal data. Specifically, for efficient feature fusion, a multi-modal latent space is learned via a bi-mapping approach under ranking and regression constraints. The ranking constraint utilizes the log-partial likelihood of Cox model to induce learning discriminative representations in a task-oriented manner. Meanwhile, the representations also benefit from regression constraint, which imposes the supervision of specific survival time on representation learning. To improve generalization and alleviate overfitting, we further introduce similarity and sparsity constraints to encourage extra consistency and sparseness. Extensive experiments on three datasets acquired from The Cancer Genome Atlas (TCGA) demonstrate that the proposed method is superior to state-of-the-art Cox-based models.

Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., background) and lesion regions (i.e., foreground) bring challenges for lesion segmentation. Considering that such rich texture information is contained in background, very few methods have tried to explore and exploit background-salient representations for assisting foreground segmentation. Additionally, other characteristics of BUS images, i.e., 1) low-contrast appearance and blurry boundary, and 2) significant shape and position variation of lesions, also increase the difficulty in accurate lesion segmentation. In this paper, we present a saliency-guided morphology-aware U-Net (SMU-Net) for lesion segmentation in BUS images. The SMU-Net is composed of a main network with an additional middle stream and an auxiliary network. Specifically, we first propose generation of saliency maps which incorporate both low-level and high-level image structures, for foreground and background. These saliency maps are then employed to guide the main network and auxiliary network for respectively learning foreground-salient and background-salient representations. Furthermore, we devise an additional middle stream which basically consists of background-assisted fusion, shape-aware, edge-aware and position-aware units. This stream receives the coarse-to-fine representations from the main network and auxiliary network for efficiently fusing the foreground-salient and background-salient features and enhancing the ability of learning morphological information for network. Extensive experiments on five datasets demonstrate higher performance and superior robustness to the scale of dataset than several state-of-the-art deep learning approaches in breast lesion segmentation in ultrasound image.

As an emerging molecular imaging modality, Photoacoustic Tomography (PAT) is capable of mapping tissue physiological metabolism and exogenous contrast agent information with high specificity. Due to its ultrasonic detection mechanism, the precise localization of targeted lesions has long been a challenge for PAT imaging. The poor soft-tissue contrast of the PAT image makes this process difficult and inaccurate. To meet this challenge, in this study, we first make use of the rich and clear structural information brought about by another advanced imaging modality, Magnetic Resonance Imaging (MRI), to assist organ segmentation and correct for the light fluence attenuation of PAT. We demonstrate improved feature visibility and enhanced localization of endogenous and exogenous agents in the fluence corrected PAT images. Compared with PAT-based methods, the contrast-to-noise ratio (CNR) of our MRI-assisted method increases by 29.1% in live animal experiments. Furthermore, we show that the co-registered MRI image can also be incorporated into PAT image restoration, and achieves improved anatomical landscape and soft-tissue contrast (CNR increased by 25.36%) while preserving similar spatial resolution. This PAT-MRI combination provides excellent structural, functional and molecular images of the subject, and may enable more comprehensive analysis of various preclinical research applications.

The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.

The spatial resolution of photoacoustic tomography (PAT) can be characterized by the point spread function (PSF) of the imaging system. Due to the tomographic detection geometry, the PAT image degradation model could be generally described by using spatially variant PSFs. Deconvolution of the PAT image with these PSFs could restore image resolution and recover object details. Previous PAT image restoration algorithms assume that the degraded images can be restored by either a single uniform PSF, or some blind estimation of the spatially variant PSFs. In this work, we propose a PAT image restoration method to improve image quality and resolution based on experimentally measured spatially variant PSFs. Using photoacoustic absorbing microspheres, we design a rigorous PSF measurement procedure, and successfully acquire a dense set of spatially variant PSFs for a commercial cross-sectional PAT system. A pixel-wise PSF map is further obtained by employing a multi-Gaussian-based fitting and interpolation algorithm. To perform image restoration, an optimization-based iterative restoration model with two kinds of regularizations is proposed. We perform phantom and in vivo mice imaging experiments to verify the proposed method, and the results show significant image quality and resolution improvement.

Given the complicated relationship between the magnetic resonance imaging (MRI) signals and the attenuation values, the attenuation correction in hybrid positron emission tomography (PET)/MRI systems remains a challenging task. Currently, existing methods are either time-consuming or require sufficient samples to train the models. In this paper, an efficient approach for predicting pseudo computed tomography (CT) images from T1- and T2-weighted MRI data with limited data is proposed. The proposed approach uses improved neighborhood anchored regression (INAR) as a baseline method to pre-calculate projected matrices to flexibly predict the pseudo CT patches. Techniques, including the augmentation of the MR/CT dataset, learning of the nonlinear descriptors of MR images, hierarchical search for nearest neighbors, data-driven optimization, and multi-regressor ensemble, are adopted to improve the effectiveness of the proposed approach. In total, 22 healthy subjects were enrolled in the study. The pseudo CT images obtained using INAR with multi-regressor ensemble yielded mean absolute error (MAE) of 92.73 ± 14.86 HU, peak signal-to-noise ratio of 29.77 ± 1.63 dB, Pearson linear correlation coefficient of 0.82 ± 0.05, dice similarity coefficient of 0.81 ± 0.03, and the relative mean absolute error (rMAE) in PET attenuation correction of 1.30 ± 0.20% compared with true CT images. Moreover, our proposed INAR method, without any refinement strategies, can achieve considerable results with only seven subjects (MAE 106.89 ± 14.43 HU, rMAE 1.51 ± 0.21%). The experiments prove the superior performance of the proposed method over the six innovative methods. Moreover, the proposed method can rapidly generate the pseudo CT images that are suitable for PET attenuation correction.

Multispectral photoacoustic tomography (PAT) is capable of resolving tissue chromophore distribution based on spectral un-mixing. It works by identifying the absorption spectrum variations from a sequence of photoacoustic images acquired at multiple illumination wavelengths. Due to multispectral acquisition, this inevitably creates a large dataset. To cut down the data volume, sparse sampling methods that reduce the number of detectors have been developed. However, image reconstruction of sparse sampling PAT is challenging because of insufficient angular coverage. During spectral un-mixing, these inaccurate reconstructions will further amplify imaging artefacts and contaminate the results. To solve this problem, we present the interlaced sparse sampling (ISS) PAT, a method that involved: 1) a novel scanning-based image acquisition scheme in which the sparse detector array rotates while switching illumination wavelength, such that a dense angular coverage could be achieved by using only a few detectors; and 2) a corresponding image reconstruction algorithm that makes use of an anatomical prior image created from the ISS strategy to guide PAT image computation. Reconstructed from the signals acquired at different wavelengths (angles), this self-generated prior image fuses multispectral and angular information, and thus has rich anatomical features and minimum artefacts. A specialized iterative imaging model that effectively incorporates this anatomical prior image into the reconstruction process is also developed. Simulation, phantom, and in vivo animal experiments showed that even under 1/6 or 1/8 sparse sampling rate, our method achieved comparable image reconstruction and spectral un-mixing results to those obtained by conventional dense sampling method.

For decades, commercial X-ray computed tomography (CT) scanners have been using the filtered backprojection (FBP) algorithm for image reconstruction. However, the desire for lower radiation doses has pushed the FBP algorithm to its limit. Previous studies have made significant efforts to improve the results of FBP through preprocessing the sinogram, modifying the ramp filter, or postprocessing the reconstructed images. In this paper, we focus on analyzing and processing the stacked view-by-view backprojections (named VVBP-Tensor) in the FBP algorithm. A key challenge for our analysis lies in the radial structures in each backprojection slice. To overcome this difficulty, a sorting operation was introduced to the VVBP-Tensor in its z direction (the direction of the projection views). The results show that, after sorting, the tensor contains structures that are similar to those of the object, and structures in different slices of the tensor are correlated. We then analyzed the properties of the VVBP-Tensor, including structural self-similarity, tensor sparsity, and noise statistics. Considering these properties, we have developed an algorithm using the tensor singular value decomposition (named VVBP-tSVD) to denoise the VVBP-Tensor for low-mAs CT imaging. Experiments were conducted using a physical phantom and clinical patient data with different mAs levels. The results demonstrate that the VVBP-tSVD is superior to all competing methods under different reconstruction schemes, including sinogram preprocessing, image postprocessing, and iterative reconstruction. We conclude that the VVBP-Tensor is a suitable processing target for improving the quality of FBP reconstruction, and the proposed VVBP-tSVD is an effective algorithm for noise reduction in low-mAs CT imaging. This preliminary work might provide a heuristic perspective for reviewing and rethinking the FBP algorithm.

Predicting malignant potential is one of the most critical components of a computer-aided diagnosis system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on the basis of subjective computed tomography findings. Among various methodologies, radiomics, and deep learning algorithms, specifically convolutional neural networks (CNNs), have recently been confirmed to achieve significant success by outperforming the state-of-the-art performance in medical image pattern classification and have rapidly become leading methodologies in this field. However, the existing methods generally use radiomics or deep convolutional features independently for pattern classification, which tend to take into account only global or local features, respectively. In this paper, we introduce and evaluate a hybrid structure that includes different features selected with radiomics model and CNNs and integrates these features to deal with GISTs classification. The Radiomics model and CNNs are constructed for global radiomics and local convolutional feature selection, respectively. Subsequently, we utilize distinct radiomics and deep convolutional features to perform pattern classification for GISTs. Specifically, we propose a new pooling strategy to assemble the deep convolutional features of 54 three-dimensional patches from the same case and integrate these features with the radiomics features for independent case, followed by random forest classifier. Our method can be extensively evaluated using multiple clinical datasets. The classification performance (area under the curve (AUC): 0.882; 95% confidence interval (CI): 0.816-0.947) consistently outperforms those of independent radiomics (AUC: 0.807; 95% CI: 0.724-0.892) and CNNs (AUC: 0.826; 95% CI: 0.795-0.856) approaches.

Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A "correlation-weighted" constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients' map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency.

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.

Lung field segmentation in chest radiographs (CXRs) is an essential preprocessing step in automatically analyzing such images. We present a method for lung field segmentation that is built on a high-quality boundary map detected by an efficient modern boundary detector, namely a structured edge detector (SED). A SED is trained beforehand to detect lung boundaries in CXRs with manually outlined lung fields. Then, an ultrametric contour map (UCM) is transformed from the masked and marked boundary map. Finally, the contours with the highest confidence level in the UCM are extracted as lung contours. Our method is evaluated using the public Japanese Society of Radiological Technology database of scanned films. The average Jaccard index of our method is 95.2%, which is comparable with those of other state-of-the-art methods (95.4%). The computation time of our method is less than 0.1 s for a CXR when executed on an ordinary laptop. Our method is also validated on CXRs acquired with different digital radiography units. The results demonstrate the generalization of the trained SED model and the usefulness of our method.

Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.

Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.

Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)." In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems.

Accurate segmentation of the prostate in computed tomography (CT) images is important in image-guided radiotherapy; however, difficulties remain associated with this task. In this study, an automatic framework is designed for prostate segmentation in CT images. We propose a novel image feature extraction method, namely, variant scale patch, which can provide rich image information in a low dimensional feature space. We assume that the samples from different classes lie on different nonlinear submanifolds and design a new segmentation criterion called local independent projection (LIP). In our method, a dictionary containing training samples is constructed. To utilize the latest image information, we use an online updated strategy to construct this dictionary. In the proposed LIP, locality is emphasized rather than sparsity; local anchor embedding is performed to determine the dictionary coefficients. Several morphological operations are performed to improve the achieved results. The proposed method has been evaluated based on 330 3-D images of 24 patients. Results show that the proposed method is robust and effective in segmenting prostate in CT images.

Lung four-dimensional computer tomography (4D-CT) data resolution enhancement is helpful for lung cancer accurate radiotherapy. Sparse representation based algorithm has been proposed to reconstruct the resolution enhancement image, and achieved state-of-art performance. In this paper, based on the sparse representation algorithm, we present an adaptively patch partition approach to divide the slices into adaptively scaled patches. This approach will catch more anatomical nuances and improve the reconstruction. The quad tree-based algorithm is employed in our method to partition the slices. Moreover, a jointly intensity-feature homogeneity is defined to determinate the patch division criterion. The effectiveness of the proposed method is demonstrated by the experiments.

4D-CT plays an important role in lung cancer treatment. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical. As a result, artifacts such as lung vessel discontinuity and partial volume are typical in 4D-CT images and might mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for super-resolution enhancement of the 4D-CT images along the superior-inferior direction. Our working premise is that the anatomical information that is missing at one particular phase can be recovered from other phases. Based on this assumption, we employ a patch-based mechanism for guided reconstruction of super-resolution axial slices. Specifically, to reconstruct each targeted super-resolution slice for a CT image at a particular phase, we agglomerate a dictionary of patches from images of all other phases in the 4D-CT sequence. Then we perform a sparse combination of the patches in this dictionary to reconstruct details of a super-resolution patch, under constraint of similarity to the corresponding patches in the neighboring slices. By iterating this procedure over all possible patch locations, a superresolution 4D-CT image sequence with enhanced anatomical details can be eventually reconstructed. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in terms of preserving image details and suppressing misleading artifacts.

4D-CT plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.

This letter presents an adaptive denoising method based on the singular value decomposition (SVD). By incorporating a global subspace analysis into the scheme of local basis selection, the problems of previous adaptive methods are effectively tackled. Experimental results show that the proposed method achieves outstanding preservation of image details, and at high noise levels it provides improvements in both objective and subjective quality of the denoised image when compared to the state-of-the-art methods.

In this letter, a two-step texture-preserving image deblurring method is proposed. This method restores the blurred image in the frequency domain to obtain a noisy result with minimal loss of signal components, followed by a modified non-local means (NLM) filter to attenuate the leaked colored noise. This modified NLM filter computes the weights not only using the self-similarity of the neighbors, but also on spatial distance from the pixel in question. Experimental results demonstrate that the proposed algorithm can effectively obtain a visually pleasant texture-preserving result with compared to the state-of-the-art total variation method.

Rough set theory is a relatively new mathematical tool to deal with imprecise, incomplete and inconsistent data. A method of multispectral image classification using rough set theory is proposed. First, to decrease computational time and complexity, band reduction of multispectral image using attribute reduct concept in rough set theory and information entropy is performed. Then, mixture model initial parameters of remote sensing image are mapped from crude classes, which are generated using equivalent relation. Finally image cluster is obtained unsupervised with Gaussian mixture model whose parameters are refined by Expectation Maximization algorithm. The proposed method is performed on a multispectral image, and the experimental results show the feasibility and effectiveness of the algorithm by means of comparison and analysis.

Three-dimensional reconstruction of vessels from digital X-ray angiographic images is a powerful technique that compensates for limitations in angiography. It can provide physicians with the ability to accurately inspect the complex arterial network and to quantitatively assess disease induced vascular alterations in three dimensions. In this paper, both the projection principle of single view angiography and mathematical modeling of two view angiographies are studied in detail. The movement of the table, which commonly occurs during clinical practice, complicates the reconstruction process. On the basis of the pinhole camera model and existing optimization methods, an algorithm is developed for 3-D reconstruction of coronary arteries from two uncalibrated monoplane angiographic images. A simple and effective perspective projection model is proposed for the 3-D reconstruction of coronary arteries. A nonlinear optimization method is employed for refinement of the 3-D structure of the vessel skeletons, which takes the influence of table movement into consideration. An accurate model is suggested for the calculation of contour points of the vascular surface, which fully utilizes the information in the two projections. In our experiments with phantom and patient angiograms, the vessel centerlines are reconstructed in 3-D space with a mean positional accuracy of 0.665 mm and with a mean back projection error of 0.259 mm. This shows that the algorithm put forward in this paper is very effective and robust.

Because the medical CT scanner is rapidly evolving from fan-beam to cone-beam geometry, we motivate to take advantages of Noo's formula for cone-beam reconstruction with higher temporal resolution. Feldkampetal.proposed a practical cone-beam reconstruction algorithm for full scan data collected on a circular locus in 1984. It is known that the Feldkamp-type reconstruction framework is compatible with any fan-beam reconstruction formula. Therefore, the Noo's formula can be generalized into Feldkamp-type algorithm for the satisfactory reconstruction of a volume of interest (VOI). Our method is the first attempt to combines the pseudo-lambda tomography (PLT) and Feldkamp-type algorithm together. Simulation using the 3D differentiable Shepp-Logan phantom is performed demonstrate the utility of this technique.

We propose an improved super-short-scan reconstruction algorithm for fan-beam computed tomography based on pi-lines in this paper. Within the framework of the classic FBP algorithm, this new algorithm can achieve exact reconstruction of the region of interest (ROI), if and only if all lines passing through the ROI intersect the source trajectory. The new reconstruction formula successfully avoids the direct derivative of projection data and is expressed as the combination of Hilbert filter and Ramp filter rather than weighted Hilbert filter. This helps increase the numerical stability and improves the quality of reconstructed images, as the real data reconstruction involves discrete data. A preliminary computer simulation study has been done, and a comparison with other classic super-short-scan reconstruction algorithms proves the validation of this new algorithm.

Since the ionizing radiation may induce cancers and genetic damages in the patient, it is highly desirable to minimize the X-ray dose during a CT scan. As one of the local imaging techniques, the Lambda imaging reduces the X-ray dose and imaging time. But the existence of the singular values results in the low quality of the image. The broad applications of the Pi-lines proof that it can deal with the truncated projections effectively. In this work, we propose a new exact Lambda imaging algorithm based on Wang G's local imaging method and Pi-lines segment to reconstruct an image with utilizing a Gaussian kernel function convoluting the projection data. We also analyze how to choose the parameters of the Gaussian kernel function. Numerical simulations support our new reconstruction algorithm with high quality reconstruction image.

For the rigid registration of multi-modality medical images, mutual information (MI) technique is unsuitable to clinical diagnose because of high computational cost and low robustness. In this paper, a new concept of equivalent meridian plane (EMP) is proposed, and the EMP and other two normal feature planes are determined using principal component analysis (PCA); the rough registrations of those 2D planes are to be realized at six freedom degree; finally, the refine registrations can be completed using MI in a small neighboring region. This method is called as EMP based MI registration technique. The accuracy and robustness of EMP-MI approach can be verified by applying it to the simulated and real brain image data (CT, MR, PET, and SPECT). The experimental results indicate that the proposed algorithm reduces computational time distinctly and is a global optimal strategy.

We have developed a geometric deformable model that employs neighborhood influence to achieve robust segmentation for noisy and broken edges. The fundamental power of this strategy rests with the explicitly combination of regional inter-point constraints, image forces, and a priori boundary information for each geometric contour point within its adaptively determined local influence domain. This formulation thus naturally unifies the essences of the geometric and parametric snakes through automatic local scale selection, and exhibits their respective fundamental strengths of allowing stable boundary detection when the edge information is weak and possibly discontinuous, while maintaining the abilities to handle topological changes during front evolution. In particular, this paper presents an implementation of the method through local integration of the level set function and the image/prior-driven evolution forces, where the resulting partial differential equation is solved numerically using standard finite difference method. Experimental results on synthetic and real images demonstrate its superior performance.

Intensity-modulated radiotherapy (IMRT) is being increasingly used for treatment of malignant cancer. Beam angle optimization (BAO) is an important problem in IMRT. In this paper, an emerging population-based meta-heuristic algorithm named ant colony optimization (ACO) is introduced to solve the BAO problem. In the proposed algorithm, a multi-layered graph is designed to map the BAO problem to ACO, and a heuristic function based on the beam's-eye-view dosimetrics (BEVD) score is introduced. In order to verify the feasibility of the presented algorithm, a clinical prostate tumor case is employed, and the preliminary results demonstrate that ACO appears more effcient than genetic algorithm (GA) and can find the optimal beam angles within a clinically acceptable computation time.

This paper reports a new delay subspace decomposition (DSD) algorithm. Instead of using the canonical zero-delay correlation matrix, the new DSD algorithm introduces a delay into the correlation matrix of the subspace decomposition to suppress noises in the data. The algorithm is applied to functional magnetic resonance imaging (fMRI) to detect the regions of focal activities in the brain. The efficiency is evaluated by comparing with independent component analysis and principal component analysis method of fMRI.

For the segmentation and robust tracking of the cardiac left ventricle (LV) in MRI sequences, an optimized algorithm is presented; it is based on the active contour framework. To use the active contours model (ACM) (Kass, M. et al., Int. J. Comput. Vision, vol.1, p.321-31, 1998) to estimate cardiac motion, a new concept of generalized fuzzy gradient vector flow (GFGVF) is presented and compared with the classical gradient vector flow (GVF) (Chenyang Xu and Prince, J.L., "Gradient Vector Flow Deformable Models", Academic Press, 2000; Chung-Chu Leung and Wufan Chen, Proc. IEEE ICIP Conf., 2003). Then, a modified ACM is proposed for motion tracking, which is based on two new external forces: one is the GFGVF field; the other is the relativity of the optical flow field (OFF) on the predictive contour. For robust tracking of the outline of interest, a set of motion equations is presented to describe two correlative updating steps. Also, given some prior terms and likelihood one, the motion state of each point can be found by the maximum a posteriori probability (MAP).

Boundary detection in MR image with brain tumor is an important image processing technique applied in radiology for 3D reconstruction. The nonhomogeneities density tissue of the brain with tumor can result in achieving the inaccurate location in any boundary detection algorithms. Recently, some studies using the contour deformable model with regional base technique, the performance is insufficient to obtain the fine edge in the tumor, and the considerable error in accuracy is existed. Moreover, even in some of the normal tissue region, edge created by this method has also been encompassed. In this paper, we propose a new approach to detect the boundary of brain tumor based on the generalized fuzzy operator (GFO). One typical example is used for evaluating this method with the contour deformable model.

The model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered. In this paper, two multiclass approaches, based on the model of piecewise fuzzy Gibbs random fields (PFGRF) and that of generalized fuzzy Gibbs random fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical "hard" one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.

Morphometric assessment of tumor cells is important in the prediction of biological behavior of thyroid cancer. In order to automate the process, the computer-based system has to recognize the boundary of the cells. Many methods for the boundary detection have appeared in the literature and some of them applied to microscopic slice analysis. However, there is no reliable method since the gray-levels in the nuclei are uneven and are similar to the background. In the paper, a fuzzy edge detection method is used and is based on an improved generalized fuzzy operator. The method enhances the nuclei and effectively separates the cells from the background.

In this paper, a novel algorithm for image restoration is proposed based on constrained total least-squares (CTLS) estimation, that is, adaptively regularized CTLS (ARCTLS). It is well known that in the regularized CTLS (RCTLS) method, selecting a proper regularization parameter is very difficult. For solving this problem, we take the first-order partial derivative of the classic equation of RCTLS image restoration and do some simplification with it. Then, we deduce an approximate formula, which can be used to adaptively calculate the best regularization parameter along with the degraded image to be restored. We proved that the convergence and the stability of the solution could be well satisfied. The results of our experiments indicate that using this method can make an arbitrary initial parameter be an optimal one, which results in a good restored image of high quality.

With the novel idea of describing an image in characteristic subspace (DCS), a new approach of a more efficient image compression for some types of images, such as medical sciagraph, is proposed. Firstly, we obtain the coefficient image of the original image by its description in the DCS; then we decompose the coefficient image with a forward wavelet transform (WT) with multi-resolution to remove the redundant information; and finally encode the decomposed image by vector quantization (VQ). In comparison with WT-VQ or DCT-VQ, the new method (DCS-WT-VQ) has been proved by experiments to have a much higher compression ratio and SNR, requiring a lower computational time in the coding and decoding process.