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  • Artikel  (957)
  • 2015-2019  (957)
  • Technik allgemein  (957)
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  • Artikel  (957)
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  • 1
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    Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publikationsdatum: 2018-04-04
    Beschreibung: Hyperpolarized MRI with 13 C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of 13 C data, both for clinical and research purposes, typically relies on the use of region-of-interest analysis to detect and compare regions of altered metabolism. However, it is not clear how this should be determined from the five-dimensional data produced and most standard methodologies are unable to exploit the multidimensional nature of the data. Here we propose a solution to the novel problem of 13 C image segmentation using a hybrid Markov random field model with continuous fuzzy logic. The algorithm fully utilizes the multi-dimensional data format in order to classify each voxel into one of six distinct classes based on its metabolic characteristics. Bayesian priors fully incorporate spatial, temporal and ratiometric contextual information whilst image contrast from multiple spectral dimensions are considered concurrently by using an analogy from color image segmentation. Performance of the algorithm is demonstrated on in silico data, where the superiority of the approach over a reference thresholding method is consistently observed. Application to in vivo animal data from a pre-clinical subcutaneous tumor model illustrates the ability of the MRF algorithm to successfully detect tumor location whilst avoiding image artifacts. This work has the potential to assist the analysis of human hyperpolarized 13 C data in the future.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
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    Multi-Scale Modeling and Oxygen Impact on Tumor Temporal Evolution: Application on Rectal Cancer During Radiotherapy (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publikationsdatum: 2018-04-04
    Beschreibung: We present a multi-scale approach of tumor modeling in order to predict its evolution during radiotherapy. Within this context we focus on three different scales of tumor modeling: microscopic (individual cells in a voxel), mesoscopic (population of cells in a voxel) and macroscopic (whole tumor), with transition interfaces between these three scales. At the cellular level, the description is based on phase transfer probabilities in the cellular cycle. At the mesoscopic scale we represent populations of cells according to different stages in a cell cycle. Finally, at the macroscopic scale, the tumor description is based on the use of FDG PET image voxels. These three scales exist naturally: biological data are collected at the macroscopic scale, but the pathological behavior of the tumor is based on an abnormal cell-cycle at the microscopic scale. On the other hand, the introduction of a mesoscopic scale is essential in order to reduce the gap between the two extreme, in terms of resolution, description levels. It also reduces the computational burden of simulating a large number of individual cells. As an application of the proposed multi-scale model, we simulate the effect of oxygen on tumor evolution during radiotherapy. Two consecutive FDG PET images of 17 rectal cancer patients undergoing radiotherapy are used to simulate the tumor evolution during treatment. The simulated results are compared with those obtained on a third FDG PET image acquired two weeks after the beginning of the treatment.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
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    2.5-D Extended Field-of-View Ultrasound (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publikationsdatum: 2018-04-04
    Beschreibung: Recently, the growing emphasis on medical ultrasound (US) has led to a rapid development of US extended field-of-view (EFOV) techniques. US EFOV techniques can be classified into three categories: 2-D US EFOV, 3-D US, and 3-D US EFOV. In this paper, we propose a novel EFOV method called 2.5-D US EFOV that combines both the advantages of the 2-D US EFOV and the 3-D US by generating a panorama on a curved image plane guided by a curved scanning trajectory of the US probe. In 2.5-D US EFOV, the real-time position and orientation of the US image plane can be recorded via an electromagnetic spatial sensor attached to the probe. The scanning direction is not necessarily straight and can be curved according to the regions of interest (ROI). To form the curved panorama, an image cutting method is proposed. Finally, the curved panorama is rendered in a 3-D space using a surface rendering based on a texture mapping technique. This allows 3-D measurements of lines and angles. Phantom experiments demonstrated that 2.5-D US EFOV images could show anatomical structures of ROI accurately and rapidly. The overall average errors for the distance and angle measurements are −0.097 ± 0.128 cm (−1% ± 1.2%) and 1.50° ± 1.60° (1.9% ± 2%), respectively. A typical extended US image can be reconstructed from 321 B-scans images within 3 s. The satisfying quantitative result on the spinal tissues of a scoliosis subject demonstrates that our system has potential applications in the assessment of musculoskeletal issues.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
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  • 4
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    Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publikationsdatum: 2018-04-04
    Beschreibung: Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors. First, we developed a dictionary learning- and sparse representation-based feature extraction method that exploits the statistical characteristics of the lesion area, leading to fine and more effective feature extraction compared with the traditional explicitly calculation-based methods. Then, we set up an iterative sparse representation method to solve the redundancy problem of the extracted features. Finally, we proposed a novel multi-feature collaborative sparse representation classification framework that introduces a new coefficient of regularization term to combine features from multi-modal images at the sparse representation coefficient level. Two clinical problems were used to validate the performance and usefulness of the proposed SRR system. One was the differential diagnosis between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and the other was isocitrate dehydrogenase 1 estimation for gliomas. The SRR system had superior PCNSL and GBM differentiation performance compared with some advanced imaging techniques and yielded 11% better performance for estimating IDH1 compared with the traditional radiomics methods.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
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    Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publikationsdatum: 2018-04-04
    Beschreibung: 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 ${D}_{98%}$ , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
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    Online Robust Projective Dictionary Learning: Shape Modeling for MR-TRUS Registration (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publikationsdatum: 2018-04-04
    Beschreibung: Robust and effective shape prior modeling from a set of training data remains a challenging task, since the shape variation is complicated, and shape models should preserve local details as well as handle shape noises. To address these challenges, a novel robust projective dictionary learning (RPDL) scheme is proposed in this paper. Specifically, the RPDL method integrates the dimension reduction and dictionary learning into a unified framework for shape prior modeling, which can not only learn a robust and representative dictionary with the energy preservation of the training data, but also reduce the dimensionality and computational cost via the subspace learning. In addition, the proposed RPDL algorithm is regularized by using the $ell _{1}$ norm to handle the outliers and noises, and is embedded in an online framework so that of memory and time efficiency. The proposed method is employed to model prostate shape prior for the application of magnetic resonance transrectal ultrasound registration. The experimental results demonstrate that our method provides more accurate and robust shape modeling than the state-of-the-art methods do. The proposed RPDL method is applicable for modeling other organs, and hence, a general solution for the problem of shape prior modeling.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
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    Table of contents (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publikationsdatum: 2018-04-04
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
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    IEEE Transactions on Medical Imaging publication information (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publikationsdatum: 2018-04-04
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
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    Neuron Segmentation With High-Level Biological Priors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publikationsdatum: 2018-04-04
    Beschreibung: We present a novel approach to the problem of neuron segmentation in image volumes acquired by an electron microscopy. Existing methods, such as agglomerative or correlation clustering, rely solely on boundary evidence and have problems where such an evidence is lacking (e.g., incomplete staining) or ambiguous (e.g., co-located cell and mitochondria membranes). We investigate if these difficulties can be overcome by means of sparse region appearance cues that differentiate between pre- and postsynaptic neuron segments in mammalian neural tissue. We combine these cues with the traditional boundary evidence in the asymmetric multiway cut (AMWC) model, which simultaneously solves the partitioning and the semantic region labeling problems. We show that AMWC problems over superpixel graphs can be solved to global optimality with a cutting plane approach, and that the introduction of semantic class priors leads to significantly better segmentations.
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
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    Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publikationsdatum: 2018-04-04
    Beschreibung: Recent advances in imaging genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data. In this paper, we present a novel method to detect conditional associations between imaging genetics data. We use projected distance correlation to build a conditional dependency graph among high-dimensional mixed data, and then use multiple testing to detect significant group level associations (e.g., regions of interest-gene). In addition, we introduce a scalable algorithm based on orthogonal greedy algorithm, yielding the greedy projected distance correlation (G-PDC). This can reduce the computational cost, which is critical for analyzing large volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with G-PDC than distance correlation, Pearson’s correlation, and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies. The MATLAB code is available at https://sites.google.com/site/jianfang86/gPDC .
    Print ISSN: 0278-0062
    Digitale ISSN: 1558-254X
    Thema: Medizin , Technik allgemein
    Publiziert von Institute of Electrical and Electronics Engineers (IEEE)
    Standort Signatur Einschränkungen Verfügbarkeit
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