Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.

In this article, we provide an overview of current neuroimaging methods for studying perivascular spaces (PVS) in humans using brain MRI. In recent years, an increasing number of studies highlighted the role of PVS in cerebrospinal/interstial fluid circulation and clearance of cerebral waste products and their association with neurological diseases. Novel strategies and techniques have been introduced to improve the quantification of PVS and to investigate their function and morphological features in physiological and pathological conditions. After a brief introduction on the anatomy and physiology of PVS, we examine the latest technological developments to quantitatively analyze the structure and function of PVS in humans with MRI. We describe the applications, advantages, and limitations of these methods, providing guidance and suggestions on the acquisition protocols and analysis techniques that can be applied to study PVS in vivo. Finally, we review the human neuroimaging studies on PVS across the normative lifespan and in the context of neurological disorders.

In recent years, ultra-high field MRI (7 T and above) has received more interest for clinical imaging. Indeed, a number of studies have shown the benefits from the application of this powerful tool not only for research purposes, but also in realms of improved diagnostics and patient management. The increased signal-to-noise ratio and higher spatial resolution compared with conventional and high-field clinical scanners allow imaging of small anatomical detail and subtle pathological findings. Furthermore, greater spectral resolution achieved at ultra-high field allows the resolution of metabolites for MR spectroscopic imaging. All these advantages have a significant impact on many neurological diseases, including multiple sclerosis, cerebrovascular disease, brain tumors, epilepsy and neurodegenerative diseases, in part because the pathology can be subtle and lesions small in these diseases, therefore having higher signal and resolution will help lesion detection. In this review, we discuss the main clinical neurological applications and some technical challenges which remain with ultra-high field MRI.