Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Improved test-retest reliability of R2∗\textit{R}_2^* and susceptibility quantification using multi-shot multi echo 3D EPI

This study aimed to evaluate the potential of 3D echo-planar imaging (EPI) for improving the reliability of $T_2^*$-weighted ($T_2^*w$) data and quantification of $\textit{R}_2^*$ decay rate and susceptibility ($\chi$) compared to conventional gradient echo (GRE)-based acquisition. Eight healthy subjects in a wide age range were recruited. Each subject received repeated scans for both GRE and EPI acquisitions with an isotropic 1 mm resolution at 3 T. Maps of $\textit{R}_2^*$ and $\chi$ were quantified and compared using their inter-scan difference to evaluate the test-retest reliability. Inter-protocol differences of $\textit{R}_2^*$ and $\chi$ between GRE and EPI were also measured voxel by voxel and in selected ROIs to test the consistency between the two acquisition methods. The quantifications of $\textit{R}_2^*$ and $\chi$ using EPI protocols showed increased test-retest reliability with higher EPI factors up to 5 as performed in the experiment and were consistent with those based on GRE. This result suggested multi-shot multi-echo 3D EPI can be a useful alternative acquisition method for $T_2^*w$ MRI and quantification of $\textit{R}_2^*$ and $\chi$ with reduced scan time, improved test-retest reliability and similar accuracy compared to commonly used 3D GRE.Comment: 18 pages, 8 figures and 1 tabl

Early detection of patient deterioration in patients with infection or sepsis

Sepsis is the leading cause of death and critical illness worldwide. Despite treatment, one in five patients deteriorate within 48 hours from admission. Deterioration includes the development of (multiple) organ dysfunction, the need for ICU admission or death. How patients can be effectively monitored for signs of deterioration remains largely unknown. In this thesis, we explore infection and sepsis-related deterioration from different perspectives, using a variety of instruments ranging from clinical impression, clinical scoring systems and laboratory parameters (biomarkers), to continuous analysis of vital signs (heart rate, blood pressure, respiratory rate, oxygen saturation). We explored whether these instruments can detect (early) signs of patient deterioration in patients presenting with infection or sepsis to the emergency department. The clinical impression of the nurse or treating physician is most helpful to decide whether patients can be admitted to the general ward or need ICU treatment. Clinical scoring systems are most helpful to predict long-term mortality outcomes. Biomarkers lack sensitivity and specificity for their clinical application and (novel) biomarkers are not readily available in the ED. Patterns in the continuous analysis of vital signs, contain valuable information about patient deterioration. However, the main challenge remains to improve their modeling and condense the contained information about the risk of deterioration for individual patients into a usable and understandable format for the clinician. Once these issues are solved, continuous analysis of vital signs could be an easily applicable method for the early warning of deterioration in patients in throughout the hospital

Developing a three-dimensional (3D) assessment method for clubfoot-A study protocol

© 2018 Ganesan, Luximon, Al-Jumaily, Yip, Gibbons and Chivers. Background: Congenital talipes equinovarus (CTEV) or clubfoot is a common pediatric congenital foot deformity that occurs 1 in 1,000 live births. Clubfoot is characterized by four types of foot deformities: hindfoot equinus; midfoot cavus; forefoot adductus; and hindfoot varus. A structured assessment method for clubfoot is essential for quantifying the initial severity of clubfoot deformity and recording the progress of clubfoot intervention. Aim: This study aims to develop a three-dimensional (3D) assessment method to evaluate the initial severity of the clubfoot and monitor the structural changes of the clubfoot after each casting intervention. In addition, this study explores the relationship between the thermophysiological changes in the clubfoot at each stage of the casting intervention and in the normal foot. Methods: In this study, a total of 10 clubfoot children who are < 2 years old will be recruited. Also, the data of the unaffected feet of a total of 10 children with unilateral clubfoot will be obtained as a reference for normal feet. A Kinect 3D scanner will be used to collect the 3D images of the clubfoot and normal foot, and an Infrared thermography camera (IRT camera) will be used to collect the thermal images of the clubfoot. Three-dimensional scanning and IR imaging will be performed on the foot once a week before casting. In total, 6-8 scanning sessions will be performed for each child participant. The following parameters will be calculated as outcome measures to predict, monitor, and quantify the severity of the clubfoot: Angles cross section parameters, such as length, width, and the radial distance; distance between selected anatomical landmarks, and skin temperature of the clubfoot and normal foot. The skin temperature will be collected on selected areas (forefoot, mid foot, and hindfoot) to find out the relationship between the thermophysiological changes in the clubfoot at each stage of the casting treatment and in the normal foot. Ethics: The study has been reviewed and approved on 17 August 2016 by the Sydney Children's Hospitals Network Human Research Ethics Committee (SCHN HREC), Sydney, Australia. The Human Research Ethics Committee (HREC) registration number for this study is: HREC/16/SCHN/163

Physical and statistical shape modelling in craniomaxillofacial surgery: a personalised approach for outcome prediction

Orthognathic surgery involves repositioning of the jaw bones to restore face function and shape for patients who require an operation as a result of a syndrome, due to growth disturbances in childhood or after trauma. As part of the preoperative assessment, three-dimensional medical imaging and computer-assisted surgical planning help to improve outcomes, and save time and cost. Computer-assisted surgical planning involves visualisation and manipulation of the patient anatomy and can be used to aid objective diagnosis, patient communication, outcome evaluation, and surgical simulation. Despite the benefits, the adoption of three-dimensional tools has remained limited beyond specialised hospitals and traditional two-dimensional cephalometric analysis is still the gold standard. This thesis presents a multidisciplinary approach to innovative surgical simulation involving clinical patient data, medical image analysis, engineering principles, and state-of-the-art machine learning and computer vision algorithms. Two novel three-dimensional computational models were developed to overcome the limitations of current computer-assisted surgical planning tools. First, a physical modelling approach – based on a probabilistic finite element model – provided patient-specific simulations and, through training and validation, population-specific parameters. The probabilistic model was equally accurate compared to two commercial programs whilst giving additional information regarding uncertainties relating to the material properties and the mismatch in bone position between planning and surgery. Second, a statistical modelling approach was developed that presents a paradigm shift in its modelling formulation and use. Specifically, a 3D morphable model was constructed from 5,000 non-patient and orthognathic patient faces for fully-automated diagnosis and surgical planning. Contrary to traditional physical models that are limited to a finite number of tests, the statistical model employs machine learning algorithms to provide the surgeon with a goal-driven patient-specific surgical plan. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

Instrument design and optimization of interferometric reflectance imaging sensors for in vitro diagnostics

Thesis (Ph.D.)--Boston UniversityIn the field of drug discovery and disease diagnostics, protein microarrays have generated much enthusiasm for their high-throughput monitoring of biomarkers; however, this technology has yet to translate from research laboratories to commercialization. The hindrance is the considerable uncertainty and skepticism regarding data obtained. The disparity in results from different laboratories performing identical tests is attributed to a lack of assay quality control. Unlike DNA microarrays, protein microarrays have a higher level of bioreceptor immobilization variability and non-specific binding because of the more complex molecular structure and broader physiochemical properties. Traditional assay detection modalities, such as fluorescence microscopy and surface plasmon resonance, are unable to overcome both of these sources of variation. This dissertation describes the hardware and software design and biological validation of three complementary platforms that overcome bioreceptor variability and non-specific binding for diagnostics. In order to quantify the bioreceptor quality, a label-free, nondestructive, low cost, and high-throughput interferometric sensor has been developed as a quality control tool. The quality control tool was combined with a wide-field fluorescence imaging system to improve fluorescence experimental repeatability. Lastly, a novel high-throughput and label-free platform for quality control and specific protein microarray detection is described. This platform overcomes the additional complexities and time required with labeled assays by discriminating between specific and nonspecific detection by including sizing of individual binding events. Protein microarrays may one day emerge as routine clinical laboratory tests; however, it is important that the proper quality control procedures are in place to minimize erroneous results. These platforms provide reliable and repeatable protein microarray measurements for new advancements in disease diagnostics with the potential for drug discovery