A smart adhesive capable of binding to a wetted surface was prepared by copolymerizing dopamine methacrylamide (DMA) and 3-acrylamido phenylboronic acid (AAPBA). pH was used to control the oxidation state and the adhesive property of the catechol side chain of DMA and to trigger the catechol–boronate complexation. FTIR spectroscopy confirmed the formation of the complex at pH 9, which was not present at pH 3. The formation of the catechol–boronate complex increased the cross-linking density of the adhesive network. Most notably, the loss modulus values of the adhesive were more than an order of magnitude higher for adhesive incubated at pH 9 when compared to those measured at pH 3. This drastic increase in the viscous dissipation property is attributed to the introduction of reversible complexation into the adhesive network. Based on the Johnson Kendall Roberts (JKR) contact mechanics test, adhesive containing both DMA and AAPBA demonstrated strong interfacial binding properties (work of adhesion (Wadh) = 2000 mJ/m2) to borosilicate glass wetted with an acidic solution (pH 3). When the pH was increased to 9, Wadh values (180 mJ/m2) decreased by more than an order of magnitude. During successive contact cycles, the adhesive demonstrated the capability to transition reversibly between its adhesive and nonadhesive states with changing pH. Adhesive containing only DMA responded slowly to repeated changes in pH and became progressively oxidized without the protection of boronic acid. Although adhesive containing only AAPBA also demonstrated strong wet adhesion (Wadh ∼ 500 mJ/m2), its adhesive properties were not pH responsive. Both DMA and AAPBA are required to fabricate a smart adhesive with tunable and reversible adhesive properties.

We establish the feasibility of imaging the linear and nonlinear elastic properties of soft tissue using ultrasound. We report results for breast tissue where it is conjectured that these properties may be used to discern malignant tumors from benign tumors. We consider and compare three different quantities that describe nonlinear behavior, including the variation of strain distribution with overall strain, the variation of the secant modulus with overall applied strain and finally the distribution of the nonlinear parameter in a fully nonlinear hyperelastic model of the breast tissue.

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.

We reconstruct the in vivo spatial distribution of linear and nonlinear elastic parameters in ten patients with benign (five) and malignant (five) tumors. The mechanical behavior of breast tissue is represented by a modified Veronda–Westmann model with one linear and one nonlinear elastic parameter. The spatial distribution of these elastic parameters is determined by solving an inverse problem within the region of interest (ROI). This inverse problem solution requires the knowledge of the displacement fields at small and large strains. The displacement fields are measured using a free-hand ultrasound strain imaging technique wherein, a linear array ultrasound transducer is positioned on the breast and radio frequency echo signals are recorded within the ROI while the tissue is slowly deformed with the transducer. Incremental displacement fields are determined from successive radio-frequency frames by employing cross-correlation techniques. The rectangular regions of interest were subjectively selected to obtain low noise displacement estimates and therefore were variables that ranged from 346 to 849.6 mm2. It is observed that malignant tumors stiffen at a faster rate than benign tumors and based on this criterion nine out of ten tumors were correctly classified as being either benign or malignant.

This study developed an improved motion estimation algorithm for ultrasonic strain imaging that employs a dynamic programming technique. In this paper, we model the motion estimation task as an optimization problem. Since tissue motion under external mechanical stimuli often should be reasonably continuous, a set of cost functions combining correlation and various levels of motion continuity constraint were used to regularize the motion estimation. To solve the optimization problem with a reasonable computational load, a dynamic programming technique that does not require iterations was used to obtain displacement vectors in integer precision. Then, a sub-sample estimation algorithm was used to calculate local displacements in fractional precision. Two implementation schemes were investigated with in vivo ultrasound echo data sets.We found that the proposed algorithm provides more accurate displacement estimates than our previous algorithm for in vivo clinical data. In particular, the new algorithm is capable of tracking motion in more complex anatomy and increases strain image consistency in a sequence of images. Preliminary results also suggest that a significantly longer sequence of high contrast strain images could be obtained with the new algorithm compared to the previous algorithm. The new algorithm can also tolerate larger motion discontinuities (e.g. cavity in an anthropomorphic uterine phantom).