Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Improved quantification in small animal PET/MR

In translational medicine, complementary functional and morphological imaging techniques are used extensively to observe physiological processes in vivo and to assess structural changes as a result of disease progression. The combination of magnetic resonance imaging (MRI) and positron emission tomography (PET) provides excellent soft tissue contrast from MRI with exceptional sensitivity and specificity from PET. This thesis explores the use of sequentially acquired PET and MR images to improve the quantification of small animal PET data. The primary focus was to improve image-based estimates of the arterial input function (AIF), which defines the amount of PET tracer within blood plasma over time. The AIF is required to produce physiological parameters quantifying key processes such as metabolism or perfusion from dynamic PET images. The gold standard for AIF measurement, however, requires serial blood sampling over the course of a PET scan, which is invasive in rat studies but prohibitive in mice due to small total blood volumes. To address this issue, the geometric transfer matrix (GTM) and recovery coefficient (RC) techniques were applied using anatomical MR images to enable the extraction of partial volume corrected image based AIFs from mouse PET images. A non-invasive AIF extraction method was also developed for rats, beginning with the optimization of an automated voxel selection algorithm to assist in extracting MR contrast agent signal time courses from dynamic susceptibility contrast (DSC) MRI data. This procedure was then combined with dynamic contrast enhanced (DCE) MRI to track a combined injection of Gadolinium-based contrast agent and PET tracer through the rat brain. By comparison with gold standard tracer blood sample data, it was found that normalized MRI-based AIFs could be successfully converted into PET tracer AIFs in the first pass phase when fitted with gamma variate functions. Finally, a MR image segmentation method used to provide PET attenuation correction in mice was validated using the Cambridge split magnet PET/MR scanner’s transmission scanning capabilities. This work recommends that contributions from MR hardware in the PET field of view must be accounted forto gain accurate estimates of tracer uptake and standard uptake values (SUVs). This thesis concludes that small animal MR data taken in the same imaging session can provide non-invasive methods to improve PET image quantification, giving added value to combined PET/MR studies over those conducted using PET alone.This PhD project was supported by a Medical Research Council (MRC) studentship, with grant code SRAG/038

Automated morphometry of transgenic mouse brains in MR images

Quantitative and local morphometry of mouse brain MRI is a relatively new field of research, where automated methods can be exploited to rapidly provide accurate and repeatable results. In this thesis we reviewed several existing methods and applications of quantitative morphometry to brain MR images. The Moore-Rayleigh test is proposed as new method to quantitatively analyze mouse brain MRI using deformation-based morphometry. This method has been validated on multiple datasets and proved to be reliable, accurate and repeatable, without the loss of computational time.This work was supported by funds from CYTTRON within the BSIK program (Besluit subsidies investeringen kennisinfrastructuur)UBL - phd migration 201

Added value of acute multimodal CT-based imaging (MCTI) : a comprehensive analysis

Introduction: MCTI is used to assess acute ischemic stroke (AIS) patients.We postulated that use of MCTI improves patient outcome regardingindependence and mortality.Methods: From the ASTRAL registry, all patients with an AIS and a non-contrast-CT (NCCT), angio-CT (CTA) or perfusion-CT (CTP) within24 h from onset were included. Demographic, clinical, biological, radio-logical, and follow-up caracteristics were collected. Significant predictorsof MCTI use were fitted in a multivariate analysis. Patients undergoingCTA or CTA&CTP were compared with NCCT patients with regards tofavourable outcome (mRS ≤ 2) at 3 months, 12 months mortality, strokemechanism, short-term renal function, use of ancillary diagnostic tests,duration of hospitalization and 12 months stroke recurrence

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Use of thrombolytic therapy beyond current recommendations for acute ischaemic stroke

In Chapter 1, I introduce ischaemic stroke, thrombolytic therapy, thrombolysis trials and then discuss the rationale for exclusion criteria in stroke thrombolysis guidelines.In Chapter 2, I describe methods for examining outcomes in patients that are currently recommended for exclusions from receiving alteplase for acute ischaemic stroke. In Chapter 3, I examine Virtual International Stroke Trials Archive (VISTA) data to test whether current European recommendation suggesting exclusion of elderly patients (older than 80 years) from thrombolysis for acute ischaemic stroke is justified. Employing non-randomised controlled comparison of outcomes, I show better outcomes amongst all patients (P 30 years. Outcomes assessed by National Institutes of Health Scale (NIHSS) score and dichotomised modified Rankin Scale score are consistently similar. In Chapter 4, I compare thrombolysed patients in Safe Implementation of Thrombolysis in Stroke International Stroke Thrombolysis Register (SITS-ISTR) with VISTA non-thrombolysed patients ("comparators" or "controls") and test exactly similar question as in Chapter 3. Distribution of scores on modified Rankin scale are better amongst all thrombolysis patients than controls (odds ratio 1.6, 95% confidence interval 1.5 to 1.7; Cochran-Mantel-Haenszel P80 (OR 1.4, 95% CI 1.3 to 1.6; P<0.001; n=3439). Odds ratios are consistent across all 10 year age ranges above 30, and benefit is significant from age 41 to 90; dichotomised outcomes (score on modified Rankin scale 0-1 v 2-6; 0-2 v 3-6; and 6 (death) versus rest) are consistent with the results of ordinal analysis. These findings are consistent with results from VISTA reported in Chapter 3. Age alone should not be a criterion for excluding patients from receiving thrombolytic therapy.In Chapter 5, I employ VISTA data to examine whether patients having diabetes and previous stroke have improved outcomes from use of alteplase in acute ischaemic stroke. Employing a non-randomised controlled comparison, I show that the functional outcomes are better for thrombolysed patients versus nonthrombolysed comparators amongst non-diabetic (P < 0.0001; OR 1.4 [95% CI 1.3-1.6]) and diabetic (P = 0.1; OR 1.3 [95% CI1.05-1.6]) patients. Similarly, outcomes are better for thrombolysed versus nonthrombolysed patients who have not had a prior stroke (P < 0.0001; OR 1.4 [95% CI1.2-1.6]) and those who have (P = 0.02; OR 1.3 [95% CI1.04-1.6]). There is no interaction of diabetes and prior stroke with treatment (P = 0.8). Neurological outcomes (NIHSS) are consistent with functional outcomes (mRS). In Chapter 6, I undertake a non-randomised controlled comparison of SITS-ISTR data with VISTA controls and examine whether patients having diabetes and previous stroke have improved outcomes from use of alteplase in acute ischaemic stroke. I show that adjusted mRS outcomes are better for thrombolysed versus non-thrombolysed comparators amongst patients with diabetes mellitus (OR 1.45[95% CI1.30-1.62], N=5354), previous stroke (OR 1.55[95% CI1.40-1.72], N=4986), or concomitant diabetes mellitus and previous stroke (OR 1.23 [95% CI 0.996-1.52], P=0.05, N=1136), all CMH p<0.0001. These are comparable to outcomes between thrombolysed and non-thrombolysed comparators amongst patients suffering neither diabetes mellitus nor previous stroke: OR=1.53(95%CI 1.42-1.63), p<0.0001, N=19339. There are no interaction between diabetes mellitus and previous stroke with alteplase treatment (t-PA*DM*PS, p=0.5). Present data supports results obtained from the analyses of VISTA data in chapter 5. There is no statistical evidence to recommend exclusion of patients with diabetes and previous stroke from receiving alteplase.In Chapter 7, I examine VISTA data to test whether exclusion of patients having a mild or severe stroke at baseline would be justified. Stratifying baseline stroke severity for quintiles of NIHSS scores, I observe that there are significant associations of use of alteplase with improved outcomes for baseline NIHSS levels from 5 to 24 (p<0.05). This association lose significance for baseline NIHSS categories 1 to 4 (P = 0.8; OR, 1.1; 95% CI, 0.3-4.4; N = 8/161) or ≥ 25 (P = 0.08; OR, 1.1; 95% CI, 0.7-1.9; N = 64/179) when sample sizes are small and confidence interval wide. These findings fail to provide robust evidence to support the use of alteplase in the mild or severe stroke patients, though potential for benefit appears likely.In Chapter 8, I present a meta-analysis of trials that investigated mismatch criteria for patients’ selection to examine whether present evidence supports delayed thrombolysis amongst patients selected according to mismatch criteria. I collate outcome data for patients who were enrolled after 3 hours of stroke onset in thrombolysis trials and had mismatch on pre-treatment imaging. I compare favourable outcome, reperfusion and/or recanalisation, mortality, and symptomatic intracerebral haemorrhage between the thrombolysed and non-thrombolysed groups of patients and the probability of a favourable outcome among patients with successful reperfusion and clinical findings for 3 to 6 versus 6 to 9 hours from post stroke onset. I identify articles describing the DIAS, DIAS II, DEDAS, DEFUSE, and EPITHET trials, giving a total of 502 mismatch patients thrombolysed beyond 3 hours. The combined adjusted odds ratios (a-ORs) for favourable outcomes are greater for patients who had successful reperfusion (a-OR=5.2; 95% CI, 3 to 9; I2=0%). Favourable clinical outcomes are not significantly improved by thrombolysis (a-OR=1.3; 95% CI, 0.8 to 2.0; I2=20.9%). Odds for reperfusion/recanalisation are increased amongst patients who received thrombolytic therapy (a-OR=3.0; 95% CI, 1.6 to 5.8; I2=25.7%). The combined data show a significant increase in mortality after thrombolysis (a-OR=2.4; 95% CI, 1.2 to 4.9; I2=0%), but this is not confirmed when I exclude data from desmoteplase doses that are abandoned in clinical development (a-OR=1.6; 95% CI, 0.7 to 3.7; I2=0%). Symptomatic intracerebral haemorrhage is significantly increased after thrombolysis (a-OR=6.5; 95% CI, 1.2 to 35.4; I2=0%) but not significant after exclusion of abandoned doses of desmoteplase (a-OR=5.4; 95% CI, 0.9 to 31.8; I2=0%). Delayed thrombolysis amongst patients selected according to mismatch imaging is associated with increased reperfusion/recanalisation. Recanalisation/reperfusion is associated with improved outcomes. However, delayed thrombolysis in mismatch patients was not confirmed to improve clinical outcome, although a useful clinical benefit remains possible. Thrombolysis carries a significant risk of symptomatic intracerebral haemorrhage and possibly increased mortality. Criteria to diagnose mismatch are still evolving. Validation of the mismatch selection paradigm is required with a phase III trial. Pending these results, delayed treatment, even according to mismatch selection, cannot be recommended as part of routine care.In Chapter 9, I summarise the findings of my research, discuss its impact on the research community, and discuss weaknesses inherent in registry data and limitation of statistical methods. Then, I elaborate the future directions I may take to further research on the theme of this thesis.

Diseases of the Brain, Head and Neck, Spine 2020–2023

This open access book offers an essential overview of brain, head and neck, and spine imaging. Over the last few years, there have been considerable advances in this area, driven by both clinical and technological developments. Written by leading international experts and teachers, the chapters are disease-oriented and cover all relevant imaging modalities, with a focus on magnetic resonance imaging and computed tomography. The book also includes a synopsis of pediatric imaging. IDKD books are rewritten (not merely updated) every four years, which means they offer a comprehensive review of the state-of-the-art in imaging. The book is clearly structured and features learning objectives, abstracts, subheadings, tables and take-home points, supported by design elements to help readers navigate the text. It will particularly appeal to general radiologists, radiology residents, and interventional radiologists who want to update their diagnostic expertise, as well as clinicians from other specialties who are interested in imaging for their patient care

A patch-based convolutional neural network for localized MRI brain segmentation.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Accurate segmentation of the brain is an important prerequisite for effective diagnosis, treatment planning, and patient monitoring. The use of manual Magnetic Resonance Imaging (MRI) segmentation in treating brain medical conditions is slowly being phased out in favour of fully-automated and semi-automated segmentation algorithms, which are more efficient and objective. Manual segmentation has, however, remained the gold standard for supervised training in image segmentation. The advent of deep learning ushered in a new era in image segmentation, object detection, and image classification. The convolutional neural network has contributed the most to the success of deep learning models. Also, the availability of increased training data when using Patch Based Segmentation (PBS) has facilitated improved neural network performance. On the other hand, even though deep learning models have achieved successful results, they still suffer from over-segmentation and under-segmentation due to several reasons, including visually unclear object boundaries. Even though there have been significant improvements, there is still room for better results as all proposed algorithms still fall short of 100% accuracy rate. In the present study, experiments were carried out to improve the performance of neural network models used in previous studies. The revised algorithm was then used for segmenting the brain into three regions of interest: White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). Particular emphasis was placed on localized component-based segmentation because both disease diagnosis and treatment planning require localized information, and there is a need to improve the local segmentation results, especially for small components. In the evaluation of the segmentation results, several metrics indicated the effectiveness of the localized approach. The localized segmentation resulted in the accuracy, recall, precision, null-error, false-positive rate, true-positive and F1- score increasing by 1.08%, 2.52%, 5.43%, 16.79%, -8.94%, 8.94%, 3.39% respectively. Also, when the algorithm was compared against state of the art algorithms, the proposed algorithm had an average predictive accuracy of 94.56% while the next best algorithm had an accuracy of 90.83%