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Purpose Magnetoacoustic tomography with magnetic induction (MAT-MI) is a technique that utilizes the acoustic signals induced by magnetic stimulation to reconstruct the electrical impedance distribution in biological tissues. Most algorithms ignored the fact that acoustic properties in human tissues are heterogeneous, which lead to distortion and blurring of small reconstructed objects. In this study, a novel algorithm is proposed for exact reconstruction of the sound source distribution in acoustic heterogeneous tissues. Methods Based on the ring transducer array, we develop an algorithm which combines algebraic reconstruction technique (ART) and time reversal method. Different to existing reconstruction methods, the ultrasonic transmission tomography (UTT) and the MAT-MI can be completed in same system, which decreases the system complexity. The sound velocity distribution is reconstructed with the ART so that the propagation time of the magnetoacoustic signals in the heterogeneous tissue is corrected. And then, the sound source image is reconstructed based on the time reversal method from new sound pressure data. Both numerical simulations and phantom experiments are established to validate the proposed method. Results Compared with the results without consideration of the variation on acoustic speed, sound sources reconstructed by our method are more consistent with the model in terms of size and shape. Conclusions The novel algorithm can be used to reconstruct the high-accuracy image of MAT-MI sound source in the sound velocity inhomogeneous media. In addition, this method is applicable to scenarios that the prior knowledge of the imaging targets is unknown. The signal acquisition time of MAT-MI in acoustically heterogeneity media is greatly reduced due to the introduction of ring transducer array into the imaging system. Therefore, our method will promote the application of MAT-MI in noninvasive early diagnosis of tumor for preclinical study.

Objective The interaction between tumor microenvironment and tumor cells plays a key role in tumor progression. However, the mechanisms by which this interaction promotes the transdifferentiation of normal fibroblasts (NFs) to cancer-associated fibroblasts (CAFs) are still unclear. The aim of this study was to investigate whether ovarian cancer (OvCa) cells-derived microRNAs were involved in the transition of resident fibroblasts to CAFs, and in promoting tumorigenesis. Methods CAFs and NFs were isolated from the same ovarian site in OvCa and noncancerous prophylactic oophorectomy specimens. The effect of exosomes on the motility of CAFs or NFs was analyzed by wound healing and Transwell assays. The expression of CAFs marker α-smooth muscle actin (α-SMA) and fibroblast activated protein (FAP) were determined by quantitative real-time PCR and Western blotting. A luciferase reporter assay was used to test the interaction between miR-124 and sphingosine kinase 1 (SPHK1). Results NFs with downregulated miR-124 displayed the characteristics of CAFs, including overexpression of α-SMA and FAP and increased migratory and invasive ability. Overexpression of miR-124 in CAFs reversed some traits of NFs. Human ovarian surface epithelial cells-secreted miR-124 could be transferred via exosomes to CAFs and resulted in decreased α-SMA and FAP expression and attenuated cell motility. Moreover, our finding showed that the expression of SPHK1, a potential target of miR-124, was significantly elevated in CAFs. Conclusions The present study provides important and novel perspective into OvCa CAF differentiation and extracellular matrix remodeling, which trigger the tumor progression.

Past neuroimaging studies have focused on identifying specialized functional brain systems for processing different components of reading, such as orthography, phonology, and semantics. More recently, a few experiments have been performed to look into the integration and interaction of distributed neural systems for visual word recognition, suggesting that lexical processing in alphabetic languages involves both ventral and dorsal neural pathways originating from the visual cortex. In the present functional magnetic resonance imaging study, we tested the multiple pathways model with Chinese character stimuli and examined how the neural systems interacted in reading Chinese. Using dynamic causal modeling, we demonstrated that visual word recognition in Chinese engages the ventral pathway from the visual cortex to the left ventral occipitotemporal cortex, but not the dorsal pathway from the visual cortex to the left parietal region. The ventral pathway, however, is linked to the superior parietal lobule and the left middle frontal gyrus (MFG) so that a dynamic neural network is formed, with information flowing from the visual cortex to the left ventral occipitotemporal cortex to the parietal lobule and then to the left MFG. The findings suggest that cortical dynamics is constrained by the differences in how visual orthographic symbols in writing systems are linked to spoken language. Hum Brain Mapp 36:2580–2591, 2015. © 2015 Wiley Periodicals, Inc.

Research on how lexical tone is neuroanatomically represented in the human brain is central to our understanding of cortical regions subserving language. Past studies have exclusively focused on tone perception of the spoken language, and little is known as to the lexical tone processing in reading visual words and its associated brain mechanisms. In this study, we performed two experiments to identify neural substrates in Chinese tone reading. First, we used a tone judgment paradigm to investigate tone processing of visually presented Chinese characters. We found that, relative to baseline, tone perception of printed Chinese characters were mediated by strong brain activation in bilateral frontal regions, left inferior parietal lobule, left posterior middle/medial temporal gyrus, left inferior temporal region, bilateral visual systems, and cerebellum. Surprisingly, no activation was found in superior temporal regions, brain sites well known for speech tone processing. In activation likelihood estimation (ALE) meta-analysis to combine results of relevant published studies, we attempted to elucidate whether the left temporal cortex activities identified in Experiment one is consistent with those found in previous studies of auditory lexical tone perception. ALE results showed that only the left superior temporal gyrus and putamen were critical in auditory lexical tone processing. These findings suggest that activation in the superior temporal cortex associated with lexical tone perception is modality-dependent.

Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals.

Purpose: To assist radiologists and decrease interobserver variability when using 2D ultrasonography(US) to locate the standardized plane of early gestational sac (SPGS) and to perform gestational sac (GS) biometric measurements. Methods: In this paper, the authors report the design of the first automatic solution, called “intelligent scanning” (IS), for selecting SPGS and performing biometric measurements using real-time 2D US. First, the GS is efficiently and precisely located in each ultrasound frame by exploiting a coarse to fine detection scheme based on the training of two cascade AdaBoost classifiers. Next, the SPGS are automatically selected by eliminating false positives. This is accomplished using local context information based on the relative position of anatomies in the image sequence. Finally, a database-guided multiscale normalized cuts algorithm is proposed to generate the initial contour of the GS, based on which the GS is automatically segmented for measurement by a modified snake model. Results: This system was validated on 31 ultrasound videos involving 31 pregnant volunteers. The differences between system performance and radiologist performance with respect to SPGS selection and length and depth (diameter) measurements are 7.5% ± 5.0%, 5.5% ± 5.2%, and 6.5% ± 4.6%, respectively. Additional validations prove that the IS precision is in the range of interobserver variability. Our system can display the SPGS along with biometric measurements in approximately three seconds after the video ends, when using a 1.9 GHz dual-core computer. Conclusions: IS of the GS from 2D real-time US is a practical, reproducible, and reliable approach.

Integration of magnetic resonance imaging (MRI) and other imaging modalities is promising to furnish complementary information for accurate cancer diagnosis and imaging-guided therapy. However, most gadolinium (Gd)-chelator MR contrast agents are limited by their relatively low relaxivity and high risk of released-Gd-ions-associated toxicity. Herein, a radionuclide-64 Cu-labeled doxorubicin-loaded polydopamine (PDA)-gadolinium-metallofullerene core-satellite nanotheranostic agent (denoted as CDPGM) is developed for MR/photoacoustic (PA)/positron emission tomography (PET) multimodal imaging-guided combination cancer therapy. In this system, the near-infrared (NIR)-absorbing PDA acts as a platform for the assembly of different moieties; Gd3 N@C80 , a kind of gadolinium metallofullerene with three Gd ions in one carbon cage, acts as a satellite anchoring on the surface of PDA. The as-prepared CDPGM NPs show good biocompatibility, strong NIR absorption, high relaxivity (r 1 = 14.06 mM-1 s-1 ), low risk of release of Gd ions, and NIR-triggered drug release. In vivo MR/PA/PET multimodal imaging confirms effective tumor accumulation of the CDPGM NPs. Moreover, upon NIR laser irradiation, the tumor is completely eliminated with combined chemo-photothermal therapy. These results suggest that the CDPGM NPs hold great promise for cancer theranostics.

Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to markmore » voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. Conclusions: The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.« less