ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures

Propensity score regression for causal inference with treatment heterogeneity

Understanding how treatment effects vary on individual characteristics is critical in the contexts of personalized medicine, personalized advertising and policy design. When the characteristics are of practical interest are only a subset of full covariate, non-parametric estimation is often desirable; but few methods are available due to the computational difficult. Existing non-parametric methods such as the inverse probability weighting methods have limitations that hinder their use in many practical settings where the values of propensity scores are close to 0 or 1. We propose the propensity score regression (PSR) that allows the non-parametric estimation of the heterogeneous treatment effects in a wide context. PSR includes two non-parametric regressions in turn, where it first regresses on the propensity scores together with the characteristics of interest, to obtain an intermediate estimate; and then, regress the intermediate estimates on the characteristics of interest only. By including propensity scores as regressors in the non-parametric manner, PSR is capable of substantially easing the computational difficulty while remain (locally) insensitive to any value of propensity scores. We present several appealing properties of PSR, including the consistency and asymptotical normality, and in particular the existence of an explicit variance estimator, from which the analytical behaviour of PSR and its precision can be assessed. Simulation studies indicate that PSR outperform existing methods in varying settings with extreme values of propensity scores. We apply our method to the national 2009 flu survey (NHFS) data to investigate the effects of seasonal influenza vaccination and having paid sick leave across different age groups

InstanT: Semi-supervised Learning with Instance-dependent Thresholds

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning pseudo-labels to confident unlabeled instances and incorporating them into the training set. Therefore, the selection criteria of confident instances are crucial to the success of SSL. Recently, there has been growing interest in the development of SSL methods that use dynamic or adaptive thresholds. Yet, these methods typically apply the same threshold to all samples, or use class-dependent thresholds for instances belonging to a certain class, while neglecting instance-level information. In this paper, we propose the study of instance-dependent thresholds, which has the highest degree of freedom compared with existing methods. Specifically, we devise a novel instance-dependent threshold function for all unlabeled instances by utilizing their instance-level ambiguity and the instance-dependent error rates of pseudo-labels, so instances that are more likely to have incorrect pseudo-labels will have higher thresholds. Furthermore, we demonstrate that our instance-dependent threshold function provides a bounded probabilistic guarantee for the correctness of the pseudo-labels it assigns.Comment: Accepted as poster for NeurIPS 202

Dirac quantum spin liquid emerging in a kagome-lattice antiferromagnet

Emerging quasi-particles with Dirac dispersion in condensed matter physics are analogous to their cousins in high-energy physics in that both of them can be described by the Dirac equation for relativistic electrons. Recently, these Dirac fermions have been widely found in electronic systems, such as graphene and topological insulators. At the conceptual level, since the charge is not a prerequisite for Dirac fermions, the emergence of Dirac fermions without charge degree of freedom has been theoretically predicted to be realized in Dirac quantum spin liquids. In such case, the Dirac quasiparticles are charge-neutral and carry a spin of 1/2, known as spinons. Despite of theoretical aspirations, spectra evidence of Dirac spinons remains elusive. Here we show that the spin excitations of a kagome antiferromagnet, YCu$_3$(OD)$_6$Br$_2$[Br$_{x}$(OD)$_{1-x}$], are conical with a spin continuum inside, which are consistent with the convolution of two Dirac spinons. The spinon velocity obtained from the spin excitations also quantitatively reproduces the low-temperature specific heat of the sample. Interestingly, the locations of the conical spin excitations differ from those calculated by the nearest neighbor Heisenberg model, suggesting an unexpected origin of the Dirac spinons. Our results thus provide strong spectra evidence for the Dirac quantum-spin-liquid state emerging in this kagome-lattice antiferromagnet.Comment: 7 pages, 4 figure

Safety, tolerance, and pharmacokinetics of salvianolic acid B in healthy Chinese volunteers: A randomized, double-blind, placebo-controlled phase 1 clinical trial

Background: Salvianolic acid B (Sal B) is one of the main active ingredients of Salvia miltiorrhiza Bunge. In China, many traditional Chinese medicines have been modified into injections for higher bioavailability and better efficacy. Salvianolic acid injection has been widely used in the clinic.Objective: This phase 1, randomized, double-blind, placebo-controlled, single-center study aimed to evaluate the safety, tolerance, and pharmacokinetics of Sal B injection in healthy Chinese volunteers.Methods: For the single-ascending-dose study, forty-seven healthy volunteers were randomly divided into 25, 75, 150, 200, 250, and 300 mg groups. For the multiple-ascending-dose study, sixteen healthy volunteers were randomly divided into 150 and 300 mg groups. In each group, volunteers were treated with Sal B or placebo randomly. Their safety was evaluated by a skin test, physical examination, vital sign, laboratory examination, 12-lead electrocardiogram, Holter, and clinical symptoms and signs. Blood samples were collected in 75, 150, and 300 mg single-ascending-dose study groups and 150 mg multiple-ascending-dose study groups to determine the concentration of salvianolic acid B.Results: In single-ascending-dose study groups, there were 41 adverse events in 24 cases (51.1%, 24/47). In multiple-ascending-dose study groups, there were 13 adverse events in eight cases (50.0%, 8/16). Sixty-six volunteers received the skin test, and three of them were excluded because of the positive result. Adverse events related to the treatment included increased alanine aminotransferase (4.0%), increased bilirubin (2.0%), increased creatinine kinase-MB (2.0%), increased brain natriuretic peptide (8.0%), increased urine N-acetyl-β-D-glucosidase (4.0%), dizziness (2.0%), and chest discomfort (2.0%). No serious adverse events occurred. No volunteers withdrew from the trial. Peak plasma concentration and the area under the plasma concentration–time curve of salvianolic acid B progressively increased in a dose-dependent manner in 75, 150, and 300 mg single-ascending-dose study groups. There was no accumulation after 5 consecutive days of administration of 150 mg salvianolic acid B.Conclusion: Salvianolic acid B injections administered up to 300 mg in a single dose and 250 mg for 5 consecutive days showed excellent safety and tolerability in healthy Chinese volunteers.Clinical Trial Registration:www.chinadrugtrials.org.cn, identifier CTR2019223

Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing

Background: Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. Results: We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). Conclusions: Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe

Phase-Contrast X-Ray Imaging for Small Animal Lungs

From the Washington University Undergraduate Research Digest: WUURD, Volume 11, 2015-2016. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Kristin Sobotka, Editor; Jennifer Kohl. Mentor: Mark Anastasi