Role of computed tomography and magnetic resonance imaging in patients with cardiovascular disease

Background: Although there have been recent advances, cardiovascular disease remains the commonest cause of premature death in the United Kingdom. There is a need to develop safe non-invasive techniques to aid the diagnosis and treatment of patients with cardiovascular disease.Objectives: The aims of this thesis are: (i) to establish whether coronary artery calcification can be measured reproducibly by helical computed tomography; (ii) to establish the effect of lipid lowering therapy on the progression of coronary calcification; (iii) to determine whether multidetector computed tomography can predict graft patency in patients who have undergone coronary artery bypass grafting; and (iv), to investigate the role of magnetic resonance imaging to assess plaque characteristics following acute carotid plaque rupture.Methods: In 16 patients, coronary artery calcification was assessed twice within 4 weeks by helical computed tomography. As part of a randomised controlled trial, patients received atorvastatin 80 mg daily or matching placebo, and had coronary calcification assessed annually. Fifty patients with previous coronary artery bypass surgery who were listed for diagnostic coronary angiography underwent contrast enhanced computed tomography angiography using a 16-slice multidetector computed tomography scanner. Finally, 15 patients with recent symptoms and signs of an acute transient ischaemic attack, amaurosis fugax or stroke underwent magnetic resonance angiography of the carotid arteries using dedicated surface coils. Plaque volume, regional plaque densities and neovascularisation were determined before and after gadolinium enhancement.Results: Quantification of coronary artery calcification demonstrated good reproducibility in patients with scores > 100 AU. Despite reducing systemic inflammation and halving serum low-density lipoprotein cholesterol concentrations, atorvastatin therapy did not affect the rate of progression of coronary artery calcification. Computed tomography angiography was found to be highly specific for the detection of graft patency. Assessment of plaque characteristics by magnetic resonance scanning in patients with recent acute carotid plaque was feasible and reproducible.Conclusions: Coronary artery calcium scores can be determined in a reproducible manner. Although they correlate well with the presence of atherosclerosis and predict future coronary risk. there is little role for monitoring progression of coronary artery calcification in order to assess the response to lipid lowering therapy. Computed tomography can be used reliably to predict graft patency in patients who have undergone coronary artery bypass grafting, and is an acceptable non-invasive alternative to invasive coronary angiography. Magnetic resonance imaging techniques ' can be employed in a feasible, timely and reproducible manner to detect plaque characteristics associated with acute atherothrombotic disease

Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes

The purpose of this study was to investigate whether radiomics features measured from weekly 4-dimensional computed tomography (4DCT) images of non-small cell lung cancers (NSCLC) change during treatment and if those changes are prognostic for patient outcomes or dependent on treatment modality. Radiomics features are quantitative metrics designed to evaluate tumor heterogeneity from routine medical imaging. Features that are prognostic for patient outcome could be used to monitor tumor response and identify high-risk patients for adaptive treatment. This would be especially valuable for NSCLC due to the high prevalence and mortality of this disease. A novel process was designed to select feature-specific image preprocessing and remove features that were not robust to differences in CT model or tumor volumes. These features were then measured from weekly 4DCT images. These features were evaluated to determine at which point in treatment they first begin changing if those changes were different for patients treated with protons versus photons. A subset of features demonstrated significant changes by the second or third week of treatment, however changes were never significantly different between patient groups. Delta-radiomics features were defined as relative net changes, linear regression slopes, and end of treatment feature values. Features were then evaluated in univariate and multivariate models for overall survival, distant metastases, and local-regional recurrence. In general, the delta-radiomics features were not more prognostic than models built using clinical factors or features at pre-treatment. However one shape descriptor measured at pre-treatment significantly improved model fit and performance for overall survival and distant metastases. Additionally for local-regional recurrence, the only significant covariate was texture strength measured at the end of treatment. A separate study characterized radiomics feature variability in cone-beam CT images to increased scatter, increased motion, and different scanners. Features were affected by all three parameters and specifically by motion amplitudes greater than 1 cm. This study resulted in strong evidence that a set of robust radiomics features change significantly during treatment. While these changes were not prognostic or dependent on treatment modality, future studies may benefit from the methodologies described here to explore delta-radiomics in alternative tumor sites or imaging modalities

Improving the Access of Cardiac Magnetic Resonance in Low-Middle Income Countries to Improve Cardiac Care: Rapid CMR

Non-communicable diseases– cancer and cardiovascular disease - are the leading causes of death in high-income countries (HICs). Cardiovascular disease, however, is increasing in Low-Middle Income Countries (LMICs) and is the emergent primary cause of mortality. Part of the reason is suboptimal therapies– from primary prevention to more advanced tertiary care. Not only are advanced therapies scarce but advanced diagnostic tests which apply to them are not fully available, and so diagnoses could be inaccurate and treatments poorly targeted. Within the portfolio and hierarchy of cardiovascular diagnostic testing, Cardiac Magnetic Resonance (CMR) is a crucial diagnostic imaging test that redefines diagnosis and enables targeted therapies, but is expensive with inadequate training and poor availability in LMICs countries. I demonstrated that CMR could be made fast, easy, and cheap – sufficient for delivery in five LMICs countries in three continents. To achieve this, I developed an abbreviated CMR protocol, focused on the core of CMR - volumes, function, and scar imaging (with selected additions like iron quantification), and by embedding the technical quality protocol within clinical care, training, and mentoring, so it proved to have diagnostic utility and change management, as well being a self-sustaining and essential service. I also used CMR as a research method in LMICs specifically to complement research in areas of a specific need to those countries, exploiting opportunities that were previously unavailable, with one chapter dedicated to evaluating early cardiovascular involvement in treated and non-treated people living with HIV in Peru, and a second chapter of the potential utility of CMR for screening cardiotoxicity and its comparison in precision with other cardiac imaging modalities in the UK, potentially extending the role of rapid CMR in HICs. Unlike traditional PhDs in medicine, my research involved technology adaptation, transfer, and collaboration. The project was multi-layered with political, social, educational, training, and partnership aspects, along with more traditional aspects such as clinical effectiveness and cost-effectiveness analysis. I showed the use of advanced cardiac imaging in LMICs by breaking down barriers, demonstrating that Rapid CMR can be possible in new clinical environments where much need exists

Investigation of intra-tumour heterogeneity to identify texture features to characterise and quantify neoplastic lesions on imaging

The aim of this work was to further our knowledge of using imaging data to discover image derived biomarkers and other information about the imaged tumour. Using scans obtained from multiple centres to discover and validate the models has advanced earlier research and provided a platform for further larger centre prospective studies. This work consists of two major studies which are describe separately: STUDY 1: NSCLC Purpose The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). Patients and methods Pre-therapy PET scans from 358 Stage I–III NSCLC patients scheduled for radical radiotherapy/chemoradiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. Using a semiautomatic threshold method to segment the primary tumors, radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. Results Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. Conclusion PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy. STUDY 2: Ovarian Cancer Purpose The 5-year survival of epithelial ovarian cancer is approximately 35-40%, prompting the need to develop additional methods such as biomarkers for personalised treatment. Patient and Methods 657 texture features were extracted from the CT scans of 364 untreated EOC patients. A 4-texture feature ‘Radiomic Prognostic Vector (RPV)’ was developed using machine learning methods on the training set. Results The RPV was able to identify the 5% of patients with the worst prognosis, significantly improving established prognostic methods and was further validated in two independent, multi-centre cohorts. In addition, the genetic, transcriptomic and proteomic analysis from two independent datasets demonstrated that stromal and DNA damage response pathways are activated in RPV-stratified tumours. Conclusion RPV could be used to guide personalised therapy of EOC. Overall, the two large datasets of different imaging modalities have increased our knowledge of texture analysis, improving the models currently available and provided us with more areas with which to implement these tools in the clinical setting.Open Acces

Assessment of Image Quality of a PET/CT scanner for a Standarized Image situation Using a NEMA Body Phantom. “The impact of Different Image Reconstruction Parameters on Image quality”

Radiologists and medical practitioners are working daily with images from integrated Positron Emission Tomography/ Computed Tomography (PET/CT) scanners in order to detect potentially lethal diseases. It is thus very important to ensure that these images have adequate image quality. For the staff responsible of quality assurance of the applied scanner, it is important to ensure that the reconstruction procedures and image protocols in use enable acquisition of image with a high quality with respect to resolution and contrast, while the data sets are containing as little noise as possible. The goal of the quality assurance work will be to continuously make sure that, data acquisition settings and especially the reconstruction procedure that is utilized for routine and daily clinical purposes, enables lesions or cancer cells and diseases to be detected. This master thesis project aims at evaluating a reconstruction algorithm (iterative reconstruction) and some key parameters applied in image reconstruction. These parameters include selected filters (Gaussian, median, Hann and Butterworth filter), selected full width at half maximum values (FWHM: 3, 5, and 7 mm) and image matrix sizes (128 x 128 and 168 x 168 pixels respectively), in order to provide information on how these key parameters will affect image quality. The National Electrical Manufacturers Association (NEMA) International Electrotechnical Commission (IEC) Body Phantom Set was used in this work. It consists of a lid with six fillable spheres (with internal diameters 37, 28, 22, 17, 13 and 10 mm respectively), lung insert, body phantom (which represent the background volume) and a test phantom. The work in this thesis project has been carried out using the radiopharmaceutical tracer an F-18 FDG, fluotodeoxyglucose, produced with a cyclotron, a General Electric’s PETtrace 6 cyclotron, at the Center for Nuclear Medicine/PET at Haukeland University Hospital in Bergen, Norway. The applied radiopharmaceutical F-18 FDG was produced in a 2.5 ml target volume at the cyclotron. After the production, this volume was delivered from the cyclotron into a 20 ml sealed cylindrical glass already containing 17.5 ml of non-radioactive water. The activity level in this new solution with 20 ml F-18 FDG and water was measured in a dose calibrator (ISOMED 2010TM). The solution was diluted further, in an iterative process, a number of times in order to acquire the necessary activity concentrations for both the selected hot spheres and the background volume. The aim was to obtain activity concentrations for sphere-to-background ratios of either 4:1 or 8:1. The sphere-to-background ratio in this work is the ratio between the radioactivity level in four small spheres (with diameters 22, 17, 13 and 10 mm respectively, and having a total volume of 9.8 ml for all the 4 spheres) and the radioactivity level in the main body of the applied phantom; the so-called background volume (9708 ml). The two bigger spheres (28 and 37 mm) were filled with non-radioactive water in order to represent areas without radioactivity, i.e. “cold spheres”. When the spheres and volumes under study were filled with the desired level of activity and the activity level was measured, the spheres were positioned into the applied body phantom and the phantom was sealed to avoid spillage. The prepared NEMA IEC body phantom was placed on the table of a Siemens Biograph 40 PET/CT scanner in a predetermined reproducible position and scanned using a standard clinical whole body PET/CT protocol. The acquired images were reconstructed. Three repetitive studies were done for each concentration ratio. For each experiment performed, the sphere-to-background ratios were either 4:1 or 8:1. A selection of different standardized reconstruction parameters and different image corrections were applied. This was done in order to study what impact changes of the reconstruction parameters will have on the image quality. The image quality being defined by a quantification of the measured relative contrast in the images studied. The procedures followed while performing the PET/CT were in compliance with the recommended procedure presented in the NEMA NU2 – 2007 manual (from the manufacturer of the NEMA IEC body phantom described above). The reconstructed images were analyzed manually on a PET/CT workstation and also analyzed automatically with python programming software specially developed for the purpose of this work. The image quality results obtained from analyzes of the reconstructed images when different reconstruction parameters were used, were thereafter compared to the standardized protocol for reconstruction of PET/CT images. Lastly, the results have been compared with other similar work on the same subject by Helmar Bergmann et al (2005).Master i Medisinsk biologiMAMD-MEDBIBMED39

MRI-based radiomics: Quantifying the stability and reproducibility of tumour heterogeneity in vivo and in a 3D printed phantom

Magnetic resonance imaging (MRI) is a key component in the oncology workflow. Radiomics analysis is a new approach that uses standard of care (SOC) magnetic resonance (MR) images to non-invasively characterise tumour heterogeneity. For radiomics to be reliable, the imaging features measured must be stable and reproducible. This thesis aims to quantify the stability and reproducibility of MRI-based radiomics in vivo and in a 3D printed phantom. Chapter 4 explores the feasibility of constructing a 3D printed phantom using an MRI visible material (‘red resin’). The study shows that the material used to construct an anthropomorphic skull phantom mimicked human cortical bone with a T2* of 411 ± 19 µs. The phantom material provided sufficient signal for tissue segmentation however was only visible with an ultrashort echo time sequence, not commonly used in SOC imaging. Chapter 5 investigates a high temperature resin (‘white resin’) where a texture object was developed for analysis. The ‘white resin’ was visible using SOC sequences. The interscanner repeatability measurements of the texture phantom demonstrated high reproducibility with 76% of texture features having an ICC > 0.9. In chapter 6, further texture and shape objects were developed and employed in a multi-centre study assessing inter and intrascanner variation of MRI-based radiomics. The phantom was stable over a period of 12 months, with a T1 and T2 of 150.7 ± 6.7 ms and 56.1 ± 3.9 ms, respectively. The study also found that histogram features were more stable (ICC > 0.8 for 67%) compared to texture (ICC > 0.8 for 58%) and shape texture (ICC > 0.8 for 0%) across the 8 scanners. In chapter 7, phantom measurements found that radiomics features were more sensitive to changes of image resolution and noise. The in vivo test-retest component of chapter 7 detected many unstable features not suitable for use in a radiomics prognostic model. In chapter 8, of the 83 features computed only 19 features had significant changes between the baseline, mid and post radiation treatment and may be informative to assess rectal cancer treatment response. When considering using radiomics analysis for SOC MRI scans, caution must be taken to ensure imaging protocols, imaging equipment including scanners and coils are consistent to improve intra and inter-institutional feature robustness. This can be achieved with regular quality assurance (QA) of imaging protocols using a suitable phantom and appropriate feature selection using phantom and in vivo datasets

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

Artificial Intelligence and Chest Computational Tomography to predict prognosis in Pulmonary Hypertension and lung disease.

Pulmonary hypertension (PH) is an incurable severe condition with poor survival and multiple clinically distinct sub-groups and phenotypes. Accurate diagnosis and identification of the underlying phenotype is an integral step in patient management as it informs treatment choice. Outcomes vary significantly between phenotypes. Patients presenting with signs of both PH and lung disease pose a clinical dilemma between two phenotypes - idiopathic pulmonary arterial hypertension (IPAH) and pulmonary hypertension secondary to lung disease (PH-CLD) as they can present with overlapping features. The impact of lung disease on outcomes is not well understood and this is a challenging area in the literature with limited progress. All patients suspected with PH undergo routine chest Computed Tomography Pulmonary Angiography (CTPA) imaging. Despite this, the prognostic significance of commonly visualised lung parenchymal patterns is currently unknown. Current radiological assessment is also limited by its visual and subjective nature. Recent breakthroughs in deep-learning Artificial Intelligence (AI) approaches have enabled automated quantitative analysis of medical imaging features. This thesis demonstrates the prognostic impact of common lung parenchymal patterns on CT in IPAH and PH-CLD. It describes how this data could aid in phenotyping, and in identification of new sub-groups of patients with distinct clinical characteristics, imaging features and prognostic profiles. It further develops and clinically evaluates an automated CT AI model which quantifies the percentage of lung involvement of prognostic lung parenchymal patterns. Combining this AI model with radiological assessment improves the prognostic predictive strength of lung disease severity in these patients