Optimization of CT scanning protocol of Type B aortic dissection follow-up through 3D printed model

This research aims to develop and evaluate a human tissue-like material 3D printed model used as a phantom in determining optimized scanning parameters to reduce the radiation dose for Type B aortic dissection patients after thoracic endovascular aortic repair. The results show that radiation risk for follow-up Type B aortic dissection patients can be potentially reduced. Further, the value of using 3D printed model in studying CT scanning protocols was further validated

International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches

Cardiovascular disease (CVD) is the leading cause of mortality and disability in developed countries. According to WHO, an estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to major adverse cardiac and cerebral events. Early detection and care for individuals at high risk could save lives, alleviate suffering, and diminish economic burden associated with these diseases. Carotid artery disease is not only a well-established risk factor for ischemic stroke, contributing to 10%–20% of strokes or transient ischemic attacks (TIAs), but it is also a surrogate marker of generalized atherosclerosis and a predictor of cardiovascular events. In addition to diligent history, physical examination, and laboratory detection of metabolic abnormalities leading to vascular changes, imaging of carotid arteries adds very important information in assessing stroke and overall cardiovascular risk. Spanning from carotid intima-media thickness (IMT) measurements in arteriopathy to plaque burden, morphology and biology in more advanced disease, imaging of carotid arteries could help not only in stroke prevention but also in ameliorating cardiovascular events in other territories (e.g. in the coronary arteries). While ultrasound is the most widely available and affordable imaging methods, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), their combination and other more sophisticated methods have introduced novel concepts in detection of carotid plaque characteristics and risk assessment of stroke and other cardiovascular events. However, in addition to robust progress in usage of these methods, all of them have limitations which should be taken into account. The main purpose of this consensus document is to discuss pros but also cons in clinical, epidemiological and research use of all these techniques

Detection of anatomical structures in medical datasets

Detection and localisation of anatomical structures is extremely helpful for many image analysis algorithms. This thesis is concerned with the automatic identification of landmark points, anatomical regions and vessel centre lines in three-dimensional medical datasets. We examine how machine learning and atlas-based ideas may be combined to produce efficient, context-aware algorithms. For the problem of anatomical landmark detection, we develop an analog to the idea of autocontext, termed atlas location autocontext, whereby spatial context is iteratively learnt by the machine learning algorithm as part of a feedback loop. We then extend our anatomical landmark detection algorithm from Computed Tomography to Magnetic Resonance images, using image features based on histograms of oriented gradients. A cross-modality landmark detector is demonstrated using unsigned gradient orientations. The problem of brain parcellation is approached by independently training a random forest and a multi-atlas segmentation algorithm, then combining them by a simple Bayesian product operation. It is shown that, given classifiers providing complementary information, the hybrid classifier provides a superior result. The Bayesian product method of combination outperforms simple averaging where the classifiers are sufficiently independent. Finally, we present a system for identifying and tracking major arteries in Magnetic Resonance Angiography datasets, using automatically detected vascular landmarks to seed the tracking. Knowledge of individual vessel characteristics is employed to guide the tracking algorithm by two means. Firstly, the data is pre-processed using a top-hat transform of size corresponding to the vessel diameter. Secondly, a vascular atlas is generated to inform the cost function employed in the minimum path algorithm. Fully automatic tracking of the major arteries of the body is satisfactorily demonstrated

Deep Representation Learning with Limited Data for Biomedical Image Synthesis, Segmentation, and Detection

Biomedical imaging requires accurate expert annotation and interpretation that can aid medical staff and clinicians in automating differential diagnosis and solving underlying health conditions. With the advent of Deep learning, it has become a standard for reaching expert-level performance in non-invasive biomedical imaging tasks by training with large image datasets. However, with the need for large publicly available datasets, training a deep learning model to learn intrinsic representations becomes harder. Representation learning with limited data has introduced new learning techniques, such as Generative Adversarial Networks, Semi-supervised Learning, and Self-supervised Learning, that can be applied to various biomedical applications. For example, ophthalmologists use color funduscopy (CF) and fluorescein angiography (FA) to diagnose retinal degenerative diseases. However, fluorescein angiography requires injecting a dye, which can create adverse reactions in the patients. So, to alleviate this, a non-invasive technique needs to be developed that can translate fluorescein angiography from fundus images. Similarly, color funduscopy and optical coherence tomography (OCT) are also utilized to semantically segment the vasculature and fluid build-up in spatial and volumetric retinal imaging, which can help with the future prognosis of diseases. Although many automated techniques have been proposed for medical image segmentation, the main drawback is the model's precision in pixel-wise predictions. Another critical challenge in the biomedical imaging field is accurately segmenting and quantifying dynamic behaviors of calcium signals in cells. Calcium imaging is a widely utilized approach to studying subcellular calcium activity and cell function; however, large datasets have yielded a profound need for fast, accurate, and standardized analyses of calcium signals. For example, image sequences from calcium signals in colonic pacemaker cells ICC (Interstitial cells of Cajal) suffer from motion artifacts and high periodic and sensor noise, making it difficult to accurately segment and quantify calcium signal events. Moreover, it is time-consuming and tedious to annotate such a large volume of calcium image stacks or videos and extract their associated spatiotemporal maps. To address these problems, we propose various deep representation learning architectures that utilize limited labels and annotations to address the critical challenges in these biomedical applications. To this end, we detail our proposed semi-supervised, generative adversarial networks and transformer-based architectures for individual learning tasks such as retinal image-to-image translation, vessel and fluid segmentation from fundus and OCT images, breast micro-mass segmentation, and sub-cellular calcium events tracking from videos and spatiotemporal map quantification. We also illustrate two multi-modal multi-task learning frameworks with applications that can be extended to other domains of biomedical applications. The main idea is to incorporate each of these as individual modules to our proposed multi-modal frameworks to solve the existing challenges with 1) Fluorescein angiography synthesis, 2) Retinal vessel and fluid segmentation, 3) Breast micro-mass segmentation, and 4) Dynamic quantification of calcium imaging datasets

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool

Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan

INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar ligament on MRI between male and female. The specific objectives are to assess the prevalence of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and signal homogeneity and to find differences in alar ligament signal intensity between male and female. This study also aims to determine the association between the heights of respondents with alar ligament signal intensity and dimensions. MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar ligament is depicted in 3 planes and the visualization and variability of the ligament courses, shapes and signal intensity characteristics were determined. The alar ligament dimensions were also measured. RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial planes. The orientations were laterally ascending in most of the subjects (60%), predominantly oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar ligament signal intensity between male and female respondents. No significant association was found between the heights of the respondents with alar ligament signal intensity and dimensions. CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as depicted in our data shows that caution needs to be exercised when evaluating alar ligament, especially during circumstances of injury