Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery

The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Integrated Structural And Functional Biomarkers For Neurodegeneration

Alzheimer\u27s Disease consists of a complex cascade of pathological processes, leading to the death of cortical neurons and development of dementia. Because it is impossible to regenerate neurons that have already died, a thorough understanding of the earlier stages of the disease, before significant neuronal death has occurred, is critical for developing disease-modifying therapies. The various components of Alzheimer\u27s Disease pathophysiology necessitate a variety of measurement techniques. Image-based measurements known as biomarkers can be used to assess cortical thinning and cerebral blood flow, but non-imaging characteristics such as performance on cognitive tests and age are also important determinants of risk of Alzheimer\u27s Disease. Incorporating the various imaging and non-imaging sources of information into a scientifically interpretable and statistically sound model is challenging. In this thesis, I present a method to include imaging data in standard regression analyses in a data-driven and anatomically interpretable manner. I also introduce a technique for disentangling the effect of cortical structure from blood flow, enabling a clearer picture of the signal carried by cerebral blood flow beyond the confounding effects of anatomical structure. In addition to these technical developments in multi-modal image analysis, I show the results of two clinically-oriented studies focusing on the relative importance of various biomarkers for predicting presence of Alzheimer\u27s Disease pathology in the earliest stages of disease. In the first, I present evidence that white matter hyperintensities, a marker of small vessel disease, are more highly associated with Alzheimer\u27s Disease pathology than current mainstream imaging biomarkers in elderly control patients. In the second, I show that once Alzheimer\u27s Disease has progressed to the point of noticeable cognitive decline, cognitive tests are as predictive of presence of Alzheimer\u27s pathology as standard imaging biomarkers. Taken together, these studies demonstrate that the relative importance of biomarkers and imaging modalities changes over the course of disease progression, and sophisticated data-driven methods for combining a variety of modalities is likely to lead to greater biological insight into the disease process than a single modality

Novel Methods for Multi-Shape Analysis

Multi-shape analysis has the objective to recognise, classify, or quantify morphological patterns or regularities within a set of shapes of a particular object class in order to better understand the object class of interest. One important aspect of multi-shape analysis are Statistical Shape Models (SSMs), where a collection of shapes is analysed and modelled within a statistical framework. SSMs can be used as (statistical) prior that describes which shapes are more likely and which shapes are less likely to be plausible instances of the object class of interest. Assuming that the object class of interest is known, such a prior can for example be used in order to reconstruct a three-dimensional surface from only a few known surface points. One relevant application of this surface reconstruction is 3D image segmentation in medical imaging, where the anatomical structure of interest is known a-priori and the surface points are obtained (either automatically or manually) from images. Frequently, Point Distribution Models (PDMs) are used to represent the distribution of shapes, where each shape is discretised and represented as labelled point set. With that, a shape can be interpreted as an element of a vector space, the so-called shape space, and the shape distribution in shape space can be estimated from a collection of given shape samples. One crucial aspect for the creation of PDMs that is tackled in this thesis is how to establish (bijective) correspondences across the collection of training shapes. Evaluated on brain shapes, the proposed method results in an improved model quality compared to existing approaches whilst at the same time being superior with respect to runtime. The second aspect considered in this work is how to learn a low-dimensional subspace of the shape space that is close to the training shapes, where all factors spanning this subspace have local support. Compared to previous work, the proposed method models the local support regions implicitly, such that no initialisation of the size and location of these regions is necessary, which is advantageous in scenarios where this information is not available. The third topic covered in this thesis is how to use an SSM in order to reconstruct a surface from only few surface points. By using a Gaussian Mixture Model (GMM) with anisotropic covariance matrices, which are oriented according to the surface normals, a more surface-oriented fitting is achieved compared to a purely point-based fitting when using the common Iterative Closest Point (ICP) algorithm. In comparison to ICP we find that the GMM-based approach gives superior accuracy and robustness on sparse data. Furthermore, this work covers the transformation synchronisation method, which is a procedure for removing noise that accounts for transitive inconsistency in the set of pairwise linear transformations. One interesting application of this methodology that is relevant in the context of multi-shape analysis is to solve the multi-alignment problem in an unbiased/reference-free manner. Moreover, by introducing an improvement of the numerical stability, the methodology can be used to solve the (affine) multi-image registration problem from pairwise registrations. Compared to reference-based multi-image registration, the proposed approach leads to an improved registration accuracy and is unbiased/reference-free, which makes it ideal for statistical analyses

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation

Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom über zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekürzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein Verstärken oder Dämpfen von Hirnaktivität zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur Unterstützung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe Variabilität im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollständiges Verständnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als Schlüsselkomponente für das Verständnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer größeren elektrischen Feldstärke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle für die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen Leitfähigkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. Außerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale Abschätzung der Ausbreitung des elektrischen Feldes anhand der bloßen Position der Stimulationselektroden ist daher nicht ausreichend genau für eine zielgerichtete Stimulation. Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und für ein breites Feld an Forschern zugänglich machen, wurden in den vergangenen Jahren für den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden Läsionen eine nicht-triviale Aufgabe. Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verändertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunächst für gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle überführt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden. Im Verlauf des normalen Alterns ist das Gehirn strukturellen Veränderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer Veränderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese Phänomene des normalen Alterns zu berücksichtigen. Die vordergründige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der veränderten Hirnmikrostruktur, da die resultierenden Veränderungen im Leitfähigkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen Leitfähigkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen Leitfähigkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der veränderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese Gewebsveränderungen hochgradig lokal und im Allgemeinen gering auswirken. Schließlich wurden in einem dritten Schritt Simulationen für Schlaganfallpatienten durchgeführt. Ihre großen, strukturzerstörenden Läsionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren Leitfähigkeiten gearbeitet, was zu unsicheren elektrischen Feldabschätzungen führte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter Berücksichtigung der inhärenten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergründen, ob die Hirnstimulation einen positiven Einfluss auf die Hirnaktivität der Patienten im Kontext von Rehabilitationstherapie ausüben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstützen kann. Während ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschließend zu klären.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms Bibliograph

Deep learning for fast and robust medical image reconstruction and analysis

Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging. This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

Deep learning for accelerated magnetic resonance imaging

Medical imaging has aided the biggest advance in the medical domain in the last century. Whilst X-ray, CT, PET and ultrasound are a form of imaging that can be useful in particular scenarios, they each have disadvantages in cost, image quality, ease-of-use and ionising radiation. MRI is a slow imaging protocol which contributes to its high cost to run. However, MRI is a very versatile imaging protocol allowing images of varying contrast to be easily generated whilst not requiring the use of ionising radiation. If MRI can be made to be more efficient and smart, the effective cost of running MRI may be more affordable and accessible. The focus of this thesis is decreasing the acquisition time involved in MRI whilst maintaining the quality of the generated images and thus diagnosis. In particular, we focus on data-driven deep learning approaches that aid in the image reconstruction process and streamline the diagnostic process. We focus on three particular aspects of MR acquisition. Firstly, we investigate the use of motion estimation in the cine reconstruction process. Motion allows us to combine an abundance of imaging data in a learnt reconstruction model allowing acquisitions to be sped up by up to 50 times in extreme scenarios. Secondly, we investigate the possibility of using under-acquired MR data to generate smart diagnoses in the form of automated text reports. In particular, we investigate the possibility of skipping the imaging reconstruction phase altogether at inference time and instead, directly seek to generate radiological text reports for diffusion-weighted brain images in an effort to streamline the diagnostic process. Finally, we investigate the use of probabilistic modelling for MRI reconstruction without the use of fully-acquired data. In particular, we note that acquiring fully-acquired reference images in MRI can be difficult and nonetheless may still contain undesired artefacts that lead to degradation of the dataset and thus the training process. In this chapter, we investigate the possibility of performing reconstruction without fully-acquired references and furthermore discuss the possibility of generating higher quality outputs than that of the fully-acquired references.Open Acces