In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.

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We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords “Brain Aging” and “Magnetic Resonance”; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid‐life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1, T2, T2*, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.

Cerebrovascular imaging is of great interest in the understanding of neurological disease. MRI is a non-invasive technology that can visualize and provide information on: (i) the structure of major blood vessels; (ii) the blood flow velocity in these vessels; and (iii) the microcirculation, including the assessment of brain perfusion. Although other medical imaging modalities can also interrogate the cerebrovascular system, MR provides a comprehensive assessment, as it can acquire many different structural and functional image contrasts whilst maintaining a high level of patient comfort and acceptance. The extent of examination is limited only by the practicalities of patient tolerance or clinical scheduling limitations. Currently, MRI methods can provide a range of metrics related to the cerebral vasculature, including: (i) major vessel anatomy via time-of-flight and contrast-enhanced imaging; (ii) blood flow velocity via phase contrast imaging; (iii) major vessel anatomy and tissue perfusion via arterial spin labeling and dynamic bolus passage approaches; and (iv) venography via susceptibility-based imaging. When designing an MRI protocol for patients with suspected cerebral vascular abnormalities, it is appropriate to have a complete understanding of when to use each of the available techniques in the 'MR angiography toolkit'. In this review article, we: (i) overview the relevant anatomy, common pathologies and alternative imaging modalities; (ii) describe the physical principles and implementations of the above listed methods; (iii) provide guidance on the selection of acquisition parameters; and (iv) describe the existing and potential applications of MRI to the cerebral vasculature and diseases. The focus of this review is on obtaining an understanding through the application of advanced MRI methodology of both normal and abnormal blood flow in the cerebrovascular arteries, capillaries and veins.

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