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Purpose: To identify optimal machine learning methods for radiomics-based differentiation of local recurrence versus inflammation from post-treatment nasopharyngeal positron emission tomography/X-ray computed tomography (PET/CT) images.Procedures: Seventy-six nasopharyngeal carcinoma (NPC) patients were enrolled (41/35 local recurrence/inflammation as confirmed by pathology). Four hundred eighty-seven radiomics features were extracted from PET images for each patient. The diagnostic performance was investigated for 42 cross-combinations derived from 6 feature selection methods and 7 classifiers. Of the original cohort, 70 % was applied for feature selection and classifier development, and the remaining 30 % used as an independent validation set. The diagnostic performance was evaluated using area under the ROC curve (AUC), test error, sensitivity, and specificity. Furthermore, the performance of the radiomics signatures against routine features was statistically compared using DeLong's method.Results: The cross-combination fisher score (FSCR) + k-nearest neighborhood (KNN), FSCR + support vector machines with radial basis function kernel (RBF-SVM), FSCR + random forest (RF), and minimum redundancy maximum relevance (MRMR) + RBF-SVM outperformed others in terms of accuracy (AUC 0.883, 0.867, 0.892, 0.883; sensitivity 0.833, 0.864, 0.831, 0.750; specificity 1, 1, 0.873, 1) and reliability (test error 0.091, 0.136, 0.150, 0.136). Compared with conventional metrics, the radiomics signatures showed higher AUC values (0.867-0.892 vs. 0.817), though the differences were not statistically significant (p = 0.462-0.560).Conclusion: This study identified the most accurate and reliable machine learning methods, which could enhance the application of radiomics methods in the precision of diagnosis of NPC. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the prognostic performance of radiomics features, as extracted from positron emission tomography (PET) and X-ray computed tomography (CT) components of baseline 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT images and integrated with clinical parameters, in patients with nasopharyngeal carcinoma (NPC).Procedures: One hundred twenty-eight NPC patients (85 vs. 43 for training vs. validation), containing a subset of 86 patients with local-regional advanced stage, were enrolled. All patients underwent pretreatment PET/CT scans (mean follow-up time 24 ± 14 months). Three thousand two hundred seventy-six radiomics features extracted from PET or CT components and 13 clinical parameters were used to predict progression-free survival (PFS). Univariate analysis with Benjamini-Hochberg false discovery rate (FDR) correction was first used to screen significant features, and redundant features with Spearman's correlation > 0.8 were further eliminated. Then, seven multivariate models involving PET features and/or CT features and/or clinical parameters (denoted as clinical, PET, CT, clinical + PET, clinical + CT, PET + CT and clinical + PET + CT) were constructed by forward stepwise multivariate Cox regression. Model performance was evaluated by concordance index (C-index).Results: Sixty patients encountered events (28 recurrences, 17 metastases, and 15 deaths). Six clinical parameters, 3 PET features, and 14 CT features in training cohort and 4 clinical parameters, 10 PET features, and 4 CT features in subset of local-regional advanced stage were significantly associated with PFS. Combining PET and/or CT features with clinical parameters showed equal or higher prognostic performance than models with PET or CT or clinical parameters alone (C-index 0.71-0.76 vs. 0.67-0.73 and 0.62-0.75 vs. 0.54-0.75 for training and validation cohorts, respectively), while the prognostic performance was significantly improved in local-regional advanced cohort (C-index 0.67-0.84 vs. 0.64-0.77, p value 0.001-0.059).Conclusion: Radiomics features extracted from the PET and CT components of baseline PET/CT images provide complementary prognostic information and improved outcome prediction for NPC patients compared with use of clinical parameters alone. [ABSTRACT FROM AUTHOR]

Purpose: Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging and to enable enhanced prediction of therapy response and outcome. An important ingredient to success in translation of radiomic features to clinical reality is to quantify and ascertain their robustness. In the present work, we studied the impact of segmentation and discretization on 88 radiomic features in 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) and [11C]methyl-choline ([11C]choline) positron emission tomography/X-ray computed tomography (PET/CT) imaging of nasopharyngeal carcinoma.Procedures: Forty patients underwent [18F]FDG PET/CT scans. Of these, nine patients were imaged on a different day utilizing [11C]choline PET/CT. Tumors were delineated using reference manual segmentation by the consensus of three expert physicians, using 41, 50, and 70 % maximum standardized uptake value (SUVmax) threshold with background correction, Nestle's method, and watershed and region growing methods, and then discretized with fixed bin size (0.05, 0.1, 0.2, 0.5, and 1) in units of SUV. A total of 88 features, including 21 first-order intensity features, 10 shape features, and 57 second- and higher-order textural features, were extracted from the tumors. The robustness of the features was evaluated via the intraclass correlation coefficient (ICC) for seven kinds of segmentation methods (involving all 88 features) and five kinds of discretization bin size (involving the 57 second- and higher-order features).Results: Forty-four (50 %) and 55 (63 %) features depicted ICC ≥0.8 with respect to segmentation as obtained from [18F]FDG and [11C]choline, respectively. Thirteen (23 %) and 12 (21 %) features showed ICC ≥0.8 with respect to discretization as obtained from [18F]FDG and [11C]choline, respectively. Six features were obtained from both [18F]FDG and [11C]choline having ICC ≥0.8 for both segmentation and discretization, five of which were gray-level co-occurrence matrix (GLCM) features (SumEntropy, Entropy, DifEntropy, Homogeneity1, and Homogeneity2) and one of which was an neighborhood gray-tone different matrix (NGTDM) feature (Coarseness).Conclusions: Discretization generated larger effects on features than segmentation in both tracers. Features extracted from [11C]choline were more robust than [18F]FDG for segmentation. Discretization had very similar effects on features extracted from both tracers. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is associated with abnormal resting-state network (RSN) architecture of the default mode network (DMN), the dorsal attention network (DAN), the executive control network (CON), the salience network (SAL), and the sensory-motor network (SMN). However, little is known about the disrupted intra- and inter-network architecture in mild cognitive impairment (MCI). Here, we employed a priori defined regions of interest to investigate the intra- and inter-network functional connectivity profiles of these RSNs in longitudinal participants, including normal controls (n = 23), participants with early MCI (n = 26), and participants with late MCI (n = 19). We found longitudinal alterations of functional connectivity within the DMN, where they were correlated with variation in cognitive ability. The SAL as well as the interaction between the DMN and the SAL were disrupted in MCI. Furthermore, our results demonstrate that longitudinal alterations of functional connectivity are more profound in earlier stages as opposed to later stages of the disease. The increased severity of cognitive impairment is associated with increasingly altered RSN connectivity patterns, suggesting that disruptions in functional connectivity may contribute to cognitive dysfunction and may represent a potential biomarker of impaired cognitive ability in MCI. Earlier prevention and treatment may help to delay disease progression to AD. [ABSTRACT FROM AUTHOR]

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images. [ABSTRACT FROM AUTHOR]

Ultrasound elastography is a novel approach of evaluating regional myocardial systolic function and detecting infarcted area. This study aims to evaluate the radial motion of myocardial infarction (MI) area in left ventricular parasternal short axis (PSAX) view using a hybrid method of fast normalized cross-correlation and global analytic minimization (FNCCGLAM) and polar transformation. Fifteen rats were randomly selected for sham group, MI group and ischemia-reperfusion (IR) group (N = 5 for each group). The ultrasound radiofrequency data of the PSAX view of rat heart were acquired. After polar transformation of the data, the infarcted myocardium with the change of mechanical property was tracked over one myocardial systolic phase by the proposed method in comparison with fast normalized cross-correlation (FNCC) and dynamic programming analytic minimization (DPAM). To obtain a clear visualization of the myocardium, the inverse polar transformation was performed. The results indicated that the use of FNCCGLAM refined the myocardial displacements to obtain high-quality myocardial elastographic map with a higher contrast-to-noise ratio and dynamically tracked the infarcted myocardial segment with a higher success rate in comparison with FNCC and DPAM. It was found that the radial systolic motion of the infarcted anterior segment in the MI group reduced significantly (p < 0.05) in comparison with the sham group, while the systolic function of that myocardial segment in the IR group recovered at some extent. The results in this study suggest that FNCCGLAM is superior to FNCC and DPAM with the improved accuracy and robustness of motion estimation and has potentials as displacement estimator in ultrasound elastography. [ABSTRACT FROM AUTHOR]

The denoising of magnetic resonance (MR) images is important to improve the inspection quality and reliability of quantitative image analysis. Nonlocal filters by exploiting similarity and/or sparseness among patches or cubes achieve excellent performance in denoising MR images. Recently, higher-order singular value decomposition (HOSVD) has been demonstrated to be a simple and effective method for exploiting redundancy in the 3D stack of similar patches during denoising 2D natural image. This work aims to investigate the application and improvement of HOSVD to denoising MR volume data. The wiener-augmented HOSVD method achieves comparable performance to that of BM4D. For further improvement, we propose to augment the standard HOSVD stage by a second recursive stage, which is a repeated HOSVD filtering of the weighted summation of the residual and denoised image in the first stage. The appropriate weights have been investigated by experiments with different image types and noise levels. Experimental results over synthetic and real 3D MR data demonstrate that the proposed method outperforms current state-of-the-art denoising methods. [ABSTRACT FROM AUTHOR]

Abstract: Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. [Copyright &y& Elsevier]

Abstract: The advantages of open, vertical-field, magnetic resonance-guided, focused ultrasound surgery (MRgFUS) are attractive. The inverse technique using the bi-boundary conditions is proposed to design a uterine-oriented intraoperative RF coil with an ultrasound aperture for the MRgFUS system. In the current proposed scheme, the desired magnetic field of the RF coil was set to completely overlap the target organ. The current density distribution on the RF coil surface, accounting for the expected magnetic field, was solved using the inverse technique. The stream function was available through the ‘discretization’ of the current density distribution on the RF coil surface. The coil windings were obtained from the contour plot of the stream function. As a modification of previous designs, the bi-boundary conditions are proposed in the inverse technique for the existence of the ultrasound aperture. Based on the obtained coil windings, a prototype coil was constructed. MR imaging of the phantom and the human body was performed to show the efficacy of the prototype coil. The results of temperature measurement using the prototype coil in a 0.4-T MR system were satisfactory. The performance of the prototype coil improved compared with the previously reported design. [Copyright &y& Elsevier]