Arterial pulsation dependence of perivascular cerebrospinal fluid flow measured by dynamic diffusion tensor imaging in the human brain.

Perivascular cerebrospinal fluid (pCSF) flow is a key component of the glymphatic system. Arterial pulsation has been proposed as the main driving force of pCSF influx along the superficial and penetrating arteries; however, evidence of this mechanism in humans is limited. We proposed an experimental framework of dynamic diffusion tensor imaging with low b-values and ultra-long echo time (dynDTIlow-b) to capture pCSF flow properties during the cardiac cycle in human brains. Healthy adult volunteers (aged 17-28 years; seven men, one woman) underwent dynDTIlow-b using a clinical 3T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany) with simultaneously recorded cardiac output. The results showed that diffusion tensors reconstructed from pCSF were mainly oriented in the direction of the neighboring arterial flow. When switching from vasoconstriction to vasodilation, the axial and radial diffusivities of the pCSF increased by 5.7% and 4.94%, respectively, suggesting that arterial pulsation alters the pCSF flow both parallel and perpendicular to the arterial wall. DynDTIlow-b signal intensity at b=0 s/mm2 (i.e., T2-weighted, [S(b=0 s/mm2)]) decreased in systole, but this change was ∼7.5% of a cardiac cycle slower than the changes in apparent diffusivity, suggesting that changes in S(b=0 s/mm2) and apparent diffusivity arise from distinct physiological processes and potential biomarkers associated with perivascular space volume and pCSF flow, respectively. Additionally, the mean diffusivities of white matter showed cardiac-cycle dependencies similar to pCSF, although a delay relative to the peak time of S(b=0 s/mm2) was present, suggesting that dynDTIlow-b could potentially reveal the dynamics of magnetic resonance imaging-invisible pCSF surrounding small arteries and arterioles in white matter; this delay may result from pulse wave propagation along penetrating arteries. In conclusion, the vasodilation-induced increases in axial and radial diffusivities of pCSF and mean diffusivities of white matter are consistent with the notion that arterial pulsation can accelerate pCSF flow in human brain. Furthermore, the proposed dynDTIlow-b technique can capture various pCSF dynamics in artery pulsation.

Using deep learning to accelerate magnetic resonance measurements of molecular exchange.

Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements.

Age- and time-of-day dependence of glymphatic function in the human brain measured via two diffusion MRI methods.

Advanced age, accompanied by impaired glymphatic function, is a key risk factor for many neurodegenerative diseases. To study age-related differences in the human glymphatic system, we measured the influx and efflux activities of the glymphatic system via two non-invasive diffusion magnetic resonance imaging (MRI) methods, ultra-long echo time and low-b diffusion tensor imaging (DTIlow-b) measuring the subarachnoid space (SAS) flow along the middle cerebral artery and DTI analysis along the perivascular space (DTI-ALPS) along medullary veins in 22 healthy volunteers (aged 21-75 years). We first evaluated the circadian rhythm dependence of the glymphatic activity by repeating the MRI measurements at five time points from 8:00 to 23:00 and found no time-of-day dependence in the awake state under the current sensitivity of MRI measurements. Further test-retest analysis demonstrated high repeatability of both diffusion MRI measurements, suggesting their reliability. Additionally, the influx rate of the glymphatic system was significantly higher in participants aged >45 years than in participants aged 21-38, while the efflux rate was significantly lower in those aged >45 years. The mismatched influx and efflux activities in the glymphatic system might be due to age-related changes in arterial pulsation and aquaporin-4 polarization.

The Consistence of Dynamic Contrast-Enhanced MRI and Filter-Exchange Imaging in Measuring Water Exchange Across the Blood-Brain Barrier in High-Grade Glioma.

BACKGROUND: Water exchange across blood-brain barrier (BBB) (WEXBBB ) is an emerging biomarker of BBB dysfunction with potential applications in many brain diseases. Several MRI methods have been proposed to measure WEXBBB , but evidence remains scarce whether different methods can produce comparable WEXBBB . PURPOSE: To explore whether dynamic contrast-enhanced (DCE)-MRI and vascular water exchange imaging (VEXI) could produce comparable WEXBBB in high-grade glioma (HGG) patients. STUDY TYPE: Prospective cross-sectional. SUBJECTS: 13 HGG patients (58.4 ± 9.4 years, 9 females, 4 WHO III and 9 WHO IV). FIELD STRENGTH/SEQUENCE: A 3 T, spoiled gradient-recalled-echo DCE-MRI and VEXI containing two pulsed-gradient spin-echo blocks separated by a mixing block. ASSESSMENTS: The enhanced tumor and contralateral normal-appearing white matter (cNAWM) volume-of-interests (VOIs) were drew by two neuroradiologists. And whole-brain NAWM and normal-appearing gray matter (NAGM) without tumor-affected regions were segmented by automated segmentation algorithm in FSL. STATISTICAL TESTS: Student's t-test was used to evaluate parameters difference between cNAWM and tumor, NAGM and NAWM, respectively. The correlation between vascular water efflux rate constant (kbo ) from DCE-MRI and apparent exchange rate across BBB (AXRBBB ) from VEXI was evaluated by Pearson correlation. P < 0.05 was considered statistically significant. RESULTS: Compared with cNAWM, both kbo and AXRBBB were significantly reduced in tumor (kbo = 3.50 ± 1.18 sec-1 vs. 1.03 ± 0.75 sec-1 ; AXRBBB = 3.54 ± 1.11 sec-1 vs. 1.94 ± 1.04 sec-1 ). Both kbo and AXRBBB showed significantly higher values in NAWM than NAGM (kbo = 3.50 ± 0.59 sec-1 vs. 2.10 ± 0.56 sec-1 ; AXRBBB = 3.35 ± 0.77 sec-1 vs. 2.07 ± 0.52 sec-1 ). The VOI-averaged kbo and AXRBBB were also linearly correlated in tumor, NAWM, and NAGM (r = 0.59). DATA CONCLUSION: DCE-MRI and VEXI showed comparable and correlated WEXBBB in HGG patients, suggesting that the consistence and reliability of these two MRI methods in measuring WEXBBB . EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 1.

Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas.

The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.

Water exchange detected by shutter speed dynamic contrast enhanced-MRI help distinguish solitary brain metastasis from glioblastoma.

PURPOSE: This study aimed to explore the feasibility of transmembrane water exchange parameters detected by brain shutter speed (BSS) dynamic contrast enhanced (DCE)MRI, which is validated to be associated with aquaporin-4 expression, in distinguishing glioblastoma (GBM) from solitary brain metastasis (SBM). METHODS: 40 patients (mean age: 58.6 ± 11.7 years old, male/female: 23/17) with GBM and 48 patients (mean age: 61.7 ± 10.5 years old, male/female: 28/20) with SBM were enrolled in this observational study. BSS DCE-MRI was performed before operation. Intravascular water efflux rate constant (kbo) and intracellular water efflux rate constant (kio) within the peritumoral region and enhancing tumor were calculated from SS-DCE, respectively. The difference of these two parameters between GBM and SBM was explored. Immunohistochemical staining aquaporin-4 of was performed to validate its underlying biological mechanism. RESULTS: The kbo was found to be statistically different within both peritumoral region {SBM vs. GBM (s-1): 1.0[0.4,1.7] vs. 1.5[0.9,2.1], p = 0.009} and enhanced tumor {SBM vs. GBM (s-1): 0.2[0.1,0.5] vs. 0.4[0.1,1.3], p = 0.034}. Immunohistochemical analysis reveals the high perivascular aquaporin-4 expression in GBM may contribute the higher kbo value than that of SBM. CONCLUSIONS: kbo derived from BSS DCE-MRI was an independent pathophysiological parameter for separating GBM from SBM, in which kbo might be associated with the perivascular aquaporin-4 expression.

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.

An Ultrahigh-Field-Tailored T1 -T2 Dual-Mode MRI Contrast Agent for High-Performance Vascular Imaging.

The assessment of vascular anatomy and functions using magnetic resonance imaging (MRI) is critical for medical diagnosis, whereas the commonly used low-field MRI system (≤3 T) suffers from low spatial resolution. Ultrahigh field (UHF) MRI (≥7 T), with significantly improved resolution and signal-to-noise ratio, shows great potential to provide high-resolution vasculature images. However, practical applications of UHF MRI technology for vascular imaging are currently limited by the low sensitivity and accuracy of single-mode (T1 or T2 ) contrast agents. Herein, a UHF-tailored T1 -T2 dual-mode iron oxide nanoparticle-based contrast agent (UDIOC) with extremely small core size and ultracompact hydrophilic surface modification, exhibiting dually enhanced T1 -T2 contrast effect under the 7 T magnetic field, is reported. The UDIOC enables clear visualization of microvasculature as small as ≈140 µm in diameter under UHF MRI, extending the detection limit of the 7 T MR angiography. Moreover, by virtue of high-resolution UHF MRI and a simple double-checking process, UDIOC-based dual-mode dynamic contrast-enhanced MRI is successfully applied to detect tumor vascular permeability with extremely high sensitivity and accuracy, providing a novel paradigm for the precise medical diagnosis of vascular-related diseases.

Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging.

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA-Dixon-TWIST DCE-MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans ), blood volume fraction (vp ), and volume fraction of extracellular extravascular space (ve ), from DCE-MRI data of tumor and normal-appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans , greater than 0.965 for vp , and greater than 0.948 for ve . The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal-to-noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.