Mammography is the only technique currently used for detecting microcalcification (MC) clusters, an early indicator of breast cancer. However, mammographic images superimpose a three-dimensional compressed breast image onto two-dimensional projection views, resulting in overlapped anatomical breast structures that may obscure the detection and visualization of MCs. One possible solution to this problem is the use of cone beam computed tomography (CBCT) with a flat-panel (FP) digital detector. Although feasibility studies of CBCT techniques for breast imaging have yielded promising results, they have not shown how radiation dose and x-ray tube voltage affect the accuracy with which MCs are detected by CBCT experimentally. We therefore conducted a phantom study using a FP-based CBCT system with various mean glandular doses and kVp values. An experimental CBCT scanner was constructed with a data acquisition rate of 7.5 frames/s. 10.5 and 14.5 cm diameter breast phantoms made of gelatin were used to simulate uncompressed breasts consisting of 100% glandular tissue. Eight different MC sizes of calcium carbonate grains, ranging from 180-200 microm to 355-425 microm, were used to simulate MCs. MCs of the same size were arranged to form a 5 x 5 MC cluster and embedded in the breast phantoms. These MC clusters were positioned at 2.8 cm away from the center of the breast phantoms. The phantoms were imaged at 60, 80, and 100 kVp. With a single scan (360 degrees), 300 projection images were acquired with 0.5 x, 1x, and 2x mean glandular dose limit for 10.5 cm phantom and with 1x, 2x, and 4x for 14.5 cm phantom. A Feldkamp algorithm with a pure ramp filter was used for image reconstruction. The normalized noise level was calculated for each x-ray tube voltage and dose level. The image quality of the CBCT images was evaluated by counting the number of visible MCs for each MC cluster for various conditions. The average percentage of the visible MCs was computed and plotted as a function of the MGD, the kVp, and the average MC size. The results showed that the MC visibility increased with the MGD significantly but decreased with the breast size. The results also showed that the x-ray tube voltage affects the detection of MCs under different circumstances. With a 50% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 348(+/-2), 288(+/-7), 257(+/-2) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 355 (+/-1), 307 (+/-7), 275 (+/-5) microm at 6, 12, and 24 mGy, respectively. With a 75% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 367 (+/-1), 316 (+/-7), 265 (+/-3) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 377 (+/-3), 334 (+/-5), 300 (+/-2) microm at 6, 12, and 24 mGy, respectively.

Background and methods: Silica-coated magnetic nanoparticle (SiO 2 -MNP) prepared by the sol-gel method was studied as a nanocarrier for targeted delivery of tissue plasminogen activator (tPA). The nanocarrier consists of a superparamagnetic iron oxide core and an SiO 2 shell and is characterized by transmission electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, superconducting quantum interference device, and thermogravimetric analysis. An amine-terminated surface silanizing agent (3-aminopropyltrimethoxysilane) was used to functionalize the SiO 2 surface, which provides abundant -NH 2 functional groups for conjugating with tPA. Results: The optimum drug loading is reached when 0.5 mg/mL tPA is conjugated with 5 mg SiO 2 -MNP where 94% tPA is attached to the carrier with 86% retention of amidolytic activity and full retention of fibrinolytic activity. In vitro biocompatibility determined by lactate dehydrogenase release and cell proliferation indicated that SiO 2 -MNP does not elicit cytotoxicity. Hematological analysis of blood samples withdrawn from mice after venous administration indicates that tPA-conjugated SiO 2 -MNP (SiO 2 -MNP-tPA) did not alter blood component concentrations. After conjugating to SiO 2 -MNP, tPA showed enhanced storage stability in buffer and operation stability in whole blood up to 9.5 and 2.8-fold, respectively. Effective thrombolysis with SiO 2 -MNP-tPA under magnetic guidance is demonstrated in an ex vivo thrombolysis model where 34% and 40% reductions in blood clot lysis time were observed compared with runs without magnetic targeting and with free tPA, respectively, using the same drug dosage. Enhanced penetration of SiO 2 -MNP-tPA into blood clots under magnetic guidance was confirmed from microcomputed tomography analysis. Conclusion: Biocompatible SiO 2 -MNP developed in this study will be useful as a magnetic targeting drug carrier to improve clinical thrombolytic therapy.

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

A formalism is presented for analytically obtaining the probability density function, Pn(s), for the random distance s between two random points in an n-dimensional spherical object of radius R. Our formalism allows Pn(s) to be calculated for a spherical n-ball having an arbitrary volume density, and reproduces the well-known results for the case of uniform density. The results find applications in stochastic geometry, computational science, molecular biological systems, statistical physics, astrophysics, condensed matter physics, nuclear physics, and elementary particle physics. As one application of these results, we propose a new statistical method obtained from our formalism to study random number generators in n-dimensions used in Monte Carlo simulations.