Demyelination causes slowed or failed neuronal conduction and is a driver of disability in multiple sclerosis and other neurological diseases. Currently, the gold standard for imaging demyelination is MRI, but despite its high spatial resolution and sensitivity to demyelinated lesions, it remains challenging to obtain specific and quantitative measures of molecular changes involved in demyelination. To understand the contribution of demyelination in different diseases and to assess the efficacy of myelin-repair therapies, it is critical to develop new in vivo imaging tools sensitive to changes induced by demyelination. Upon demyelination, axonal K+ channels, normally located underneath the myelin sheath, become exposed and increase in expression, causing impaired conduction. Here, we investigate the properties of the K+ channel PET tracer [ 18 F]3F4AP in primates and its sensitivity to a focal brain injury that occurred three years prior to imaging. [ 18 F]3F4AP exhibited favorable properties for brain imaging including high brain penetration, high metabolic stability, high plasma availability, high reproducibility, high specificity, and fast kinetics. [ 18 F]3F4AP showed preferential binding in areas of low myelin content as well as in the previously injured area. Sensitivity of [ 18 F]3F4AP for the focal brain injury was higher than [ 18 F]FDG, [ 11 C]PiB, and [ 11 C]PBR28, and compared favorably to currently used MRI methods.

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging. KEYWORDSapplications, human study, methods and engineering, methods and engineering, neurological, perfusion and permeability methods, perfusion spin labeling methods, post-acquisition processing, reconstruction INTRODUCTIONPerfusion is an important physiological biomarker which is commonly used to assess tissue viability in clinical settings. Arterial spin labeling (ASL) is a powerful magnetic resonance imaging (MRI) technique that measures blood perfusion without exposure to ionizing radiation or involvement of contrast agent injection. 1,2 Due to the non-invasive, repeatable, and quantitative nature of the technique, ASL has great potential for studying perfusion in research settings as well as in clinical scenarios with contraindications of gadolinium, especially for pediatric patients or patients with renal failure. However, since only a small portion of blood is labeled compared to the whole tissue volume, conventional ASL suffers from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time.Because ASL has great potential in clinical and basic research applications, developing advanced data acquisition schemes and processing methods for fast, high-resolution, high-SNR ASL has been a very active research area. For data acquisition, three-dimensional (3D) ASL sequences, e.g., 3D rapid acquisition with relaxation enhancement (RARE) stack-of-spirals, 3,4 3D gradient and spin echo (GRASE), 5-9 and 3D balanced steady-state free precession (bSSFP), 10,11 have been developed to achieve whole-brain perfusion mapping within a clinically feasible † Kuang Gong...