Indocyanine green based fluorescent polymeric nanoprobes for in vitro imaging.

Indocyanine green (ICG) provides an advantage in the imaging of deep tumors as it can reach deeper location without being absorbed in the upper layers of biological tissues in the wavelengths, which named "therapeutic window" in the tissue engineering. Unfortunately, rapid elimination and short-term stability in aqueous media limited its use as a fluorescence probe for the early detection of cancerous tissue. In this study, stabilization of ICG was performed by encapsulating ICG molecules into the biodegradable polymer composited with poly(l-lactic acid) and poly(ε-caprolactone) via a simple one-step multiaxial electrospinning method. Different types of coaxial and triaxial structure groups were performed and compared with single polymer only groups. Confocal microscopy was used to image the encapsulated ICG (1 mg/mL) within electrospun nanofibers and in vitro ICG uptake by MIA PaCa-2 pancreatic cancer cells. Stability of encapsulated ICG is demonstrated by the in vitro sustainable release profile in PBS (pH = 4 and 7) up to 21 days. These results suggest the potential of the ability of internalization and accommodation of ICG into the pancreatic cell cytoplasm from in vitro implanted ICG-encapsulated multiaxial nanofiber mats. ICG-encapsulated multilayer nanofibers may be promising for the local sustained delivery system to eliminate loss of dosage caused by direct injection of ICG-loaded nanoparticles in systemic administration.

Breast Cancer Detection with Reduced Feature Set.

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering.

BACKGROUND: After the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems. METHODS: In this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV. RESULTS: A tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV. CONCLUSIONS: RMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization.

BACKGROUND: Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS) techniques. This can be done by minimizing the l1 norm of the gradient of the image which can also be defined as total variation (TV) minimization. In tomosynthesis imaging problem, this idea was utilized by minimizing total variation of image reconstructed by algebraic reconstruction technique (ART). Previous studies have largely addressed 2-dimensional (2D) TV minimization and only few of them have mentioned 3-dimensional (3D) TV minimization. However, quantitative analysis of 2D and 3D TV minimization with ART in DBT imaging has not been studied. METHODS: In this paper two different DBT image reconstruction algorithms with total variation minimization have been developed and a comprehensive quantitative analysis of these two methods and ART has been carried out: The first method is ART + TV2D where TV is applied to each slice independently. The other method is ART + TV3D in which TV is applied by formulating the minimization problem 3D considering all slices. RESULTS: A 3D phantom which roughly simulates a breast tomosynthesis image was designed to evaluate the performance of the methods both quantitatively and qualitatively in the sense of visual assessment, structural similarity (SSIM), root means square error (RMSE) of a specific layer of interest (LOI) and total error values. Both methods show superior results in reducing out-of-focus slice blur compared to ART. CONCLUSIONS: Computer simulations show that ART + TV3D method substantially enhances the reconstructed image with fewer artifacts and smaller error rates than the other two algorithms under the same configuration and parameters and it provides faster convergence rate.

Classification of pulmonary nodules by using hybrid features.

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).

Anesthetic gas concentration changes related to the temperature and humidity in high and low flow anesthesia.

The objective of this study, is to investigate the effects of the temperature and the humidity to the amount of anesthetic gas output from the vaporizer in both high and low flow anesthesia, and to attract attention to this relationship during the delivery of anesthesia and the calibration measurements. The performance of the anesthesia vaporizer (isoflurane and sevoflurane) was evaluated in both high and low flow anesthesia, and it was investigated whether the vaporizer supplied the preset amount of anesthetic to the patient. Then, through changing the temperature of the anesthetic device between 20 and 36 degrees C, and through changing the humidity between 20% and 42%, the changes in the gas concentration were measured. After measurements, the results were studied statically. For high and low flow anesthesia, the regression equations were obtained and it was seen that these equations were acceptable.