Regression learning for 2D/3D image registration

Image registration is a common technique in medical image analysis. The goal of image registration is to discover the underlying geometric transformation of target objects or regions appearing in two images. This dissertation investigates image registration methods for lung Image-Guided Radiation Therapy (IGRT). The goal of lung IGRT is to lay the radiation beam on the ever-changing tumor centroid but avoid organs at risk under the patient's continuous respiratory motion during the therapeutic procedure. To achieve this goal, I developed regression learning methods that compute the patient's 3D deformation between a treatment-time acquired x-ray image and a treatment-planning CT image (2D/3D image registration) in real-time. The real-time computation involves learning x-ray to 3D deformation regressions from a simulated patient-specific training set that captures credible deformation variations obtained from the patient's Respiratory-Correlated CT (RCCT) images. At treatment time, the learned regressions can be applied efficiently to the acquired x-ray image to yield an estimation of the patient's 3D deformation. In this dissertation, three regression learning methods - linear, non-linear, and locally-linear regression learning methods are presented to approach this 2D/3D image registration problem.Doctor of Philosoph

Mechanical ball shear, electromigration, and thermal cycling reliability testing on novel solder interconnects of highly integrated chips for advanced applications

In the near future, Ultra Large Scale Integrated Circuits (ULSI) with high integration has drawn the huge attention because of its potential applications in VR, AI, IoTs and automotive regions. Thermal budget and reliability concerns are two major issues that are urgently needed to be solved for these technologies. Since the increasing integration of ICs might lead to low yield concern, low fabrication temperature is expected to reduce the thermal impact on ICs properties. Besides, better reliability is also required to the electric devices for those to work under harsh outdoor environments. This study is tended to be focused on the novel solder bonds for the advanced ICs, including low temperature solder, Cu-core solder ball, and their response under various reliability tests. Three main reliability tests: (1) ball shear test, (2) electromigration test (EM) and (3) thermal cycling test (TCT), are conducted to evaluate the reliability of solder bonds. In this work, the novel Bi-40In solder alloy with improved mechanical property and the EM-resisted Cu-core solder ball are demonstrated. The re-designed low temperature solder joint reveals the superior ball shear strength than that of conventional eutectic Bi-33In joint. Additionally, the interconnects using Cu-core solder ball show the high resistance against EM under current stressing. Regarding TCT, the assemble joints with various grain structures are tested to realize the effects of Sn grain size on joint degradation and the possible ways for relieving the thermomechanical stress caused by TCT. The microstructure, elemental characteristics and grain structure are analyzed by FE-SEM, FE-EPMA and EBSD, respectively. The failure mechanisms for all reliability tests are addressed and discussed in details as well

Local Metric Learning in 2D/3D Deformable Registration With Application in the Abdomen

In image-guided radiotherapy (IGRT) of disease sites subject to respiratory motion, soft tissue deformations can affect localization accuracy. We describe the application of a method of 2D/3D deformable registration to soft tissue localization in abdomen. The method, called registration efficiency and accuracy through learning a metric on shape (REALMS), is designed to support real-time IGRT. In a previously developed version of REALMS, the method interpolated 3D deformation parameters for any credible deformation in a deformation space using a single globally-trained Riemannian metric for each parameter. We propose a refinement of the method in which the metric is trained over a particular region of the deformation space, such that interpolation accuracy within that region is improved. We report on the application of the proposed algorithm to IGRT in abdominal disease sites, which is more challenging than in lung because of low intensity contrast and nonrespiratory deformation. We introduce a rigid translation vector to compensate for nonrespiratory deformation, and design a special region-of-interest around fiducial markers implanted near the tumor to produce a more reliable registration. Both synthetic data and actual data tests on abdominal datasets show that the localized approach achieves more accurate 2D/3D deformable registration than the global approach

2D/3D image registration using regression learning

In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object’s 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region’s motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method’s application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof

In-depth analysis of music structure as a self-organized network

Words in a natural language not only transmit information but also evolve with the development of civilization and human migration. The same is true for music. To understand the complex structure behind the music, we introduced an algorithm called the Essential Element Network (EEN) to encode the audio into text. The network is obtained by calculating the correlations between scales, time, and volume. Optimizing EEN to generate Zipfs law for the frequency and rank of the clustering coefficient enables us to generate and regard the semantic relationships as words. We map these encoded words into the scale-temporal space, which helps us organize systematically the syntax in the deep structure of music. Our algorithm provides precise descriptions of the complex network behind the music, as opposed to the black-box nature of other deep learning approaches. As a result, the experience and properties accumulated through these processes can offer not only a new approach to the applications of Natural Language Processing (NLP) but also an easier and more objective way to analyze the evolution and development of music.Comment: 5 page

High levels of serum macrophage migration inhibitory factor and interleukin 10 are associated with a rapidly fatal outcome in patients with severe sepsis

SummaryObjectivesThe aim of this study was to delineate the association between high macrophage migration inhibitory factor (MIF) and interleukin 10 (IL-10) levels in the early phase of sepsis and rapidly fatal outcome.MethodsOne hundred and fifty-three adult subjects with the main diagnosis of severe sepsis (including septic shock) admitted directly from the emergency department of two tertiary medical centers and one regional teaching hospital between January 2009 and December 2011, were included prospectively. MIF and IL-10 levels were measured and outcomes were analyzed by Cox regression analysis according to the following outcomes: rapidly fatal outcome (RFO, death within 48h), late fatal outcome (LFO, death between 48h and 28 days), and survival at 28 days.ResultsAmong the three outcome groups, IL-10 levels were significantly higher in the RFO group (p < 0.001) and no significant differences were seen between the LFO and survivor groups. After Cox regression analysis, each incremental elevation of 1000 pg/ml in both IL-10 and MIF was independently associated with RFO in patients with severe sepsis. Each incremental elevation of 1000 pg/ml in IL-10 increased the RFO risk by a factor of 1.312 (95% confidence interval 1.094–1.575; p=0.003); this was the most significant factor leading to RFO in patients with severe sepsis.ConclusionsPatients with RFO exhibited simultaneously high MIF and IL-10 levels in the early phase of severe sepsis. Incremental increases in both IL-10 and MIF levels were associated with RFO in this patient group, and of the two, IL-10 was the most significant factor linked to RFO

Hypermethylation of the TGF-β target, ABCA1 is associated with poor prognosis in ovarian cancer patients

Background The dysregulation of transforming growth factor-β (TGF-β) signaling plays a crucial role in ovarian carcinogenesis and in maintaining cancer stem cell properties. Classified as a member of the ATP-binding cassette (ABC) family, ABCA1 was previously identified by methylated DNA immunoprecipitation microarray (mDIP-Chip) to be methylated in ovarian cancer cell lines, A2780 and CP70. By microarray, it was also found to be upregulated in immortalized ovarian surface epithelial (IOSE) cells following TGF-β treatment. Thus, we hypothesized that ABCA1 may be involved in ovarian cancer and its initiation. Results We first compared the expression level of ABCA1 in IOSE cells and a panel of ovarian cancer cell lines and found that ABCA1 was expressed in HeyC2, SKOV3, MCP3, and MCP2 ovarian cancer cell lines but downregulated in A2780 and CP70 ovarian cancer cell lines. The reduced expression of ABCA1 in A2780 and CP70 cells was associated with promoter hypermethylation, as demonstrated by bisulfite pyro-sequencing. We also found that knockdown of ABCA1 increased the cholesterol level and promoted cell growth in vitro and in vivo. Further analysis of ABCA1 methylation in 76 ovarian cancer patient samples demonstrated that patients with higher ABCA1 methylation are associated with high stage (P = 0.0131) and grade (P = 0.0137). Kaplan-Meier analysis also found that patients with higher levels of methylation of ABCA1 have shorter overall survival (P = 0.019). Furthermore, tissue microarray using 55 ovarian cancer patient samples revealed that patients with a lower level of ABCA1 expression are associated with shorter progress-free survival (P = 0.038). Conclusions ABCA1 may be a tumor suppressor and is hypermethylated in a subset of ovarian cancer patients. Hypermethylation of ABCA1 is associated with poor prognosis in these patients

Dihydroartemisinin Enhances Apo2L/TRAIL-Mediated Apoptosis in Pancreatic Cancer Cells via ROS-Mediated Up-Regulation of Death Receptor 5

BACKGROUND: Dihydroartemisinin (DHA), a semi-synthetic derivative of artemisinin, has recently shown antitumor activity in various cancer cells. Apo2 ligand or tumor necrosis factor-related apoptosis-inducing ligand (Apo2L/TRAIL) is regarded as a promising anticancer agent, but chemoresistance affects its efficacy as a treatment strategy. Apoptosis induced by the combination of DHA and Apo2L/TRAIL has not been well documented, and the mechanisms involved remain unclear. METHODOLOGY/PRINCIPAL FINDINGS: Here, we report that DHA enhances the efficacy of Apo2L/TRAIL for the treatment of pancreatic cancer. We found that combined therapy using DHA and Apo2L/TRAIL significantly enhanced apoptosis in BxPC-3 and PANC-1 cells compared with single-agent treatment in vitro. The effect of DHA was mediated through the generation of reactive oxygen species, the induction of death receptor 5 (DR5) and the modulation of apoptosis-related proteins. However, N-acetyl cysteine significantly reduced the enhanced apoptosis observed with the combination of DHA and Apo2L/TRAIL. In addition, knockdown of DR5 by small interfering RNA also significantly reduced the amount of apoptosis induced by DHA and Apo2L/TRAIL. CONCLUSIONS/SIGNIFICANCE: These results suggest that DHA enhances Apo2L/TRAIL-mediated apoptosis in human pancreatic cancer cells through reactive oxygen species-mediated up-regulation of DR5