Automatic segmentation, detection and quantification of coronary artery stenoses on CTA

Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step. The resulting centerlines are used as an initialization for lumen segmentation, performed using graph cuts. Then, the expected diameter of the healthy lumen is estimated by applying robust kernel regression to the coronary artery lumen diameter profile. Finally, stenoses are detected and quantified by computing the difference between estimated and expected diameter profiles. We evaluated our method using the data provided in the Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Using 30 testing datasets, the method achieved a detection sensitivity of 29 % and a positive predi

Designing for Risk Assessment Systems for Patient Triage in Primary Health Care:A Literature Review

Background: This literature review covers original journal papers published between 2011 and 2015. These papers review the current status of research on the application of human factors and ergonomics in risk assessment systems’ design to cope with the complexity, singularity, and danger in patient triage in primary health care. Objective: This paper presents a systematic literature review that aims to identify, analyze, and interpret the application of available evidence from human factors and ergonomics to the design of tools, devices, and work processes to support risk assessment in the context of health care. Methods: Electronic search was performed on 7 bibliographic databases of health sciences, engineering, and computer sciences disciplines. The quality and suitability of primary studies were evaluated, and selected papers were classified according to 4 classes of outcomes. Results: A total of 1845 papers were retrieved by the initial search, culminating in 16 selected for data extraction after the application of inclusion and exclusion criteria and quality and suitability evaluation. Conclusions: Results point out that the study of the implications of the lack of understanding about real work performance in designing for risk assessment in health care is very specific, little explored, and mostly focused on the development of tool

Age-Specific 18F-FDG Image Processing Pipelines and Analysis Are Essential for Individual Mapping of Seizure Foci in Paediatric Patients with Intractable Epilepsy

Fluoro-18-deoxyglucose positron emission tomography (FDG-PET) is an important tool for the pre-surgical assessment of children with drug-resistant epilepsy. Standard assessment is carried out visually and this is often subjective and highly user-dependent. Voxel-wise statistics can be used to remove user-dependent biases by automatically identifying areas of significant hypo/hyper-metabolism, associated to the epileptogenic area. In the clinical settings, this analysis is carried out using commercially available software. These software packages suffer from two main limitations when applied to paediatric PET data: 1) paediatric scans are spatially normalised to an adult standard template and 2) statistical comparisons use an adult control dataset. The aim of this work is to provide a reliable observer-independent pipeline for the analysis of paediatric FDG-PET scans, as part of pre-surgical planning in epilepsy. METHODS: A pseudo-control dataset (n = 19 for 6-9y, n = 93 for 10-20y) was used to create two age-specific FDG-PET paediatric templates in standard paediatric space. The FDG-PET scans of 46 epilepsy patients (n = 16 for 6-9y, n = 30 for 10-17y) were retrospectively collated and analysed using voxel-wise statistics. This was implemented with the standard pipeline available in the commercial software Scenium and an in-house Statistical Parametric Mapping v.8 (SPM8) pipeline (including age-specific paediatric templates and normal database). A kappa test was used to assess the level of agreement between findings of voxel-wise analyses and the clinical diagnosis of each patient. The SPM8 pipeline was further validated using post-surgical seizure-free patients. RESULTS: Improved agreement with the clinical diagnosis was reported using SPM8, in terms of focus localisation, especially for the younger patient group: kScenium=0.489 versus kSPM=0.805. The proposed pipeline also showed a sensitivity of ~70% in both age ranges, for the localisation of hypo-metabolic areas on paediatric FDG-PET scans in post-surgical seizure-free patients. CONCLUSION: We show that by creating age-specific templates and using paediatric control databases, our pipeline provides an accurate and sensitive semi-quantitative method for assessing FDG-PET scans of patients under 18y

Multi-parametric Imaging Using Hybrid PET/MR to Investigate the Epileptogenic Brain

Neuroimaging analysis has led to fundamental discoveries about the healthy and pathological human brain. Different imaging modalities allow garnering complementary information about brain metabolism, structure and function. To ensure that the integration of imaging data from these modalities is robust and reliable, it is fundamental to attain deep knowledge of each modality individually. Epilepsy, a neurological condition characterised by recurrent spontaneous seizures, represents a field in which applications of neuroimaging and multi-parametric imaging are particularly promising to guide diagnosis and treatment. In this PhD thesis, I focused on different imaging modalities and investigated advanced denoising and analysis strategies to improve their application to epilepsy. The first project focused on fluorodeoxyglucose (FDG) positron emission tomography (PET), a well-established imaging modality assessing brain metabolism, and aimed to develop a novel, semi-quantitative pipeline to analyse data in children with epilepsy, thus aiding presurgical planning. As pipelines for FDG-PET analysis in children are currently lacking, I developed age-appropriate templates to provide statistical parametric maps identifying epileptogenic areas on patient scans. The second and third projects focused on two magnetic resonance imaging (MRI) modalities: resting-state functional MRI (rs-fMRI) and arterial spin labelling (ASL), respectively. The aim was to i) probe the efficacy of different fMRI denoising pipelines, and ii) formally compare different ASL data acquisition strategies. In the former case, I compared different pre-processing methods and assessed their impact on fMRI signal quality and related functional connectivity analyses. In the latter case, I compared two ASL sequences to investigate their ability to quantify cerebral blood flow and interregional brain connectivity. The final project addressed the combination of rs-fMRI and ASL, and leveraged graph-theoretical analysis tools to i) compare metrics estimated via these two imaging modalities in healthy subjects and ii) assess topological changes captured by these modalities in a sample of temporal lobe epilepsy patients