Feasibility of filter-exchange imaging (FEXI) in measuring different exchange processes in human brain.

Transmembrane water exchange, including intra-to-extravascular and intra-to-extracellular ones, are potential biomarkers in the diagnosis and understanding of cancers, brain disorders, and other diseases. Filter-exchange imaging (FEXI), a special case of diffusion exchange spectroscopy (DEXSY) adapted for clinical applications, has the potential to reveal different physiological water exchange processes using the same MRI sequence. In this study, we aim to explore the feasibility of FEXI in measuring different water exchange processes by modulating the diffusion filter (bf) and detection blocks in FEXI. Two FEXI protocols were implemented on a 3T MRI clinical scanner and reveal distinct apparent exchange rate (AXR) contrast in brain tissues in seven healthy volunteers. AXR estimated from a FEXI protocol with bf â€‹= â€‹250 â€‹s/mm2, which is expected to filter out the vascular water specifically, are significantly larger than those of a FEXI protocol with bf â€‹= â€‹900 â€‹s/mm2. Besides, the filter efficiency of FEXI with bf â€‹= â€‹250 â€‹s/mm2 shows a strong correlation with vascular density, a metric estimated as the fraction of water exhibiting intravoxel incoherent motion (IVIM). AXR of FEXI with bf â€‹= â€‹250 â€‹s/mm2 agrees with the vascular water efflux rate constants reported by other independent measurements, although the physiological basis of the AXR of FEXI with bf â€‹= â€‹900 â€‹s/mm2 is not clear yet. Collectively, our current results demonstrate the feasibility of FEXI in measuring different water exchange processes in vivo, and that FEXI targeting the vascular water could help characterize the intra-to-extravascular water exchange process.

A reliable subtyping of de novo Parkinson disease: biomarkers, medication effects and longitudinal progression.

Divergent clinical symptoms and pathological progression suggest multiple subtypes of Parkinson disease (PD). Here, we proposed a reliable PD subtyping approach that quantifies the disturbance of an individual patient to the reference structural covariance networks derived from healthy controls. We revealed two subtypes of de novo PD patients by using longitudinal data from the PPMI dataset. Compared to the conventional clinical TD/PIGD phenotypes, our subtyping was highly stable in 5 years' visits. The two subtypes of PD showed significant differences in motor symptoms, medication effects, CSF biomarkers, and longitudinal progression. Moreover, patients of subtype 2 showed widespread lower cortical-to-dorsal raphe nucleus (DRN) connections and higher medication effects on motor symptoms which was regulated by 5-HT neurons in DRN. Our results suggest distinct neuropathological pathways underlying the two subtypes, such that, in contrast to the typical PD subtype, patients of subtype 2 may be affected by serotonergic modulation on dopaminergic neurons in striatum. Our study opens new avenue to precision medicine and personalized treatments in PD and may be applicable to other neurodegenerative diseases.

2D probabilistic undersampling pattern optimization for MR image reconstruction.

With 3D magnetic resonance imaging (MRI), a tradeoff exists between higher image quality and shorter scan time. One way to solve this problem is to reconstruct high-quality MRI images from undersampled k-space. There have been many recent studies exploring effective k-space undersampling patterns and designing MRI reconstruction methods from undersampled k-space, which are two necessary steps. Most studies separately considered these two steps, although in theory, their performance is dependent on each other. In this study, we propose a joint optimization model, trained end-to-end, to simultaneously optimize the undersampling pattern in the Fourier domain and the reconstruction model in the image domain. A 2D probabilistic undersampling layer was designed to optimize the undersampling pattern and probability distribution in a differentiable manner. A 2D inverse Fourier transform layer was implemented to connect the Fourier domain and the image domain during the forward and back propagation. Finally, we discovered an optimized relationship between the probability distribution of the undersampling pattern and its corresponding sampling rate. Further testing was performed using 3D T1-weighted MR images of the brain from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset and locally acquired brain 3D T1-weighted MR images of healthy volunteers and contrast-enhanced 3D T1-weighted MR images of high-grade glioma patients. The results showed that the recovered MR images using our 2D probabilistic undersampling pattern (with or without the reconstruction network) significantly outperformed those using the existing start-of-the-art undersampling strategies for both qualitative and quantitative comparison, suggesting the advantages and some extent of the generalization of our proposed method.