Morphological Study on Lenticulostriate Arteries in Patients With Middle Cerebral Artery Stenosis at 7 T MRI.

Background: Middle cerebral artery (MCA) stenosis affects lenticulostriate arteries (LSAs) that supply the basal ganglia. Increased spatial resolution and signal-to-noise ratio of 7 T could facilitate morphological imaging of very-small-diameter LSAs.

Purpose: To evaluate differences in morphological characteristics of LSA among different MCA stenoses.

Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.

Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures predictive of tremor improvements. Fifty-four ET patients (mean age = 63.

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI.

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices.

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep convolutional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality.

Alteration of White Matter Connectivity for MR-Guided Focused Ultrasound in the Treatment of Essential Tremor.

Background: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy has been implemented as a therapeutic alternative for the treatment of drug-refractory essential tremor (ET). However, its impact on the brain structural network is still unclear.

Purpose: To investigate both global and local alterations of the white matter (WM) connectivity network in ET after MRgFUS thalamotomy.

Apparent Diffusion Coefficient as Imaging Biomarker for Identifying IDH Mutation, 1p19q Codeletion, and MGMT Promoter Methylation Status in Patients With Glioma.

Background: Glioma genotypes are of importance for clinical decision-making. This data can only be acquired through histopathological analysis based on resection or biopsy. Consequently, there is a need for alternative biomarkers that noninvasively provide reliable information for preoperatively identifying molecular characteristics.

Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China.

Background And Objectives: The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions' involvement in the long-term functional outcomes of acute ischemic stroke (AIS).

Materials And Methods: Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included.

Arterial Spin Labeling-Based MRI Estimation of Penumbral Tissue in Acute Ischemic Stroke.

Background: Arterial spin labeling (ASL) has shown potential for the assessment of penumbral tissue in patients with acute ischemic stroke (AIS). The postlabeling delay (PLD) parameter is sensitive to arterial transit delays and influences cerebral blood flow measurements.

Purpose: To assess the impact of ASL acquisition at different PLDs for penumbral tissue quantification and to compare their performance regarding assisting patient selection for endovascular treatment with dynamic susceptibility contrast MRI (DSC-MRI) as the reference method.

FLAIR vessel hyperintensities predict functional outcomes in patients with acute ischemic stroke treated with medical therapy.

Objectives: The prognostic value of fluid-attenuated inversion recovery vessel hyperintensity (FVH) remains controversial in acute ischemic stroke (AIS). The objective was to investigate whether the presence of FVH could predict long-term functional outcomes in patients with AIS receiving medical therapy.

Methods: Consecutive AIS patients with anterior circulation large vessel stenosis (LVS) in multiple centers between January 2019 and December 2020 were studied.

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

Objectives: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and evaluate the clinical feasibility of this approach.

Methods: A complex-valued convolutional neural network (ComplexNet) was developed to reconstruct high-quality SWI from highly accelerated k-space data.

Pretherapeutic functional connectivity of tractography-based targeting of the ventral intermediate nucleus for predicting tremor response in patients with Parkinson's disease after thalamotomy with MRI-guided focused ultrasound.

Objective: Tractography-based direct targeting of the ventral intermediate nucleus (T-VIM) is a novel method that provides patient-specific VIM coordinates. This study aimed to explore the accuracy and predictive value of using T-VIM in combination with tractography and resting-state functional connectivity techniques to perform magnetic resonance imaging-guided focused ultrasound (MRgFUS) thalamotomy as a treatment of Parkinson's disease (PD).

Methods: PD patients underwent MRgFUS thalamotomy and were recruited for functional MRI scanning.

Severity of Intracranial Large Artery Disease Correlates With Cerebral Small Vessel Disease.

Background: Small vessel disease (SVD) shares common vascular risk factors with large artery disease (LAD). However, little is known about the relationship between intracranial artery stenosis and SVD burden.

Purpose: To investigate whether SVD burden correlates with severity of intracranial LAD.

Correlation of visual area with tremor improvement after MRgFUS thalamotomy in Parkinson's disease.

Objective: MRI-guided focused ultrasound (MRgFUS) thalamotomy is a novel and minimally invasive alternative for medication-refractory tremor in Parkinson's disease (PD). However, the impact of MRgFUS thalamotomy on spontaneous neuronal activity in PD remains unclear. The purpose of the current study was to evaluate the effects of MRgFUS thalamotomy on local fluctuations in neuronal activity as measured by the fractional amplitude of low-frequency fluctuations (fALFF) in patients with PD.

Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning.

Objectives: Multiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and quantitative assessment of lung morphometry, but its long acquisition time is not well-tolerated by patients. We aimed to accelerate multiple b-value gas DW-MRI for lung morphometry using deep learning.

Methods: A deep cascade of residual dense network (DC-RDN) was developed to reconstruct high-quality DW images from highly undersampled k-space data.

Fast and accurate reconstruction of human lung gas MRI with deep learning.

Purpose: To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning.

Methods: The scheme was comprised of coarse-to-fine nets (C-net and F-net). Zero-filling images from retrospectively undersampled k-space at an acceleration factor of 4 were used as input for C-net, and then output intermediate results which were fed into F-net.

Highly and Adaptively Undersampling Pattern for Pulmonary Hyperpolarized Xe Dynamic MRI.

Hyperpolarized (HP) gas (e.g., He or Xe) dynamic MRI could visualize the lung ventilation process, which provides characteristics regarding lung physiology and pathophysiology.

Considering low-rank, sparse and gas-inflow effects constraints for accelerated pulmonary dynamic hyperpolarized Xe MRI.

Dynamic hyperpolarized (HP) Xe MRI is able to visualize the process of lung ventilation, which potentially provides unique information about lung physiology and pathophysiology. However, the longitudinal magnetization of HP Xe is nonrenewable, making it difficult to achieve high image quality while maintaining high temporal-spatial resolution in the pulmonary dynamic MRI. In this paper, we propose a new accelerated dynamic HP Xe MRI scheme incorporating the low-rank, sparse and gas-inflow effects (L + S + G) constraints.

Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm2.

Objectives: To evaluate the diagnostic performance of different mathematical models and different b-value ranges of diffusion-weighted imaging (DWI) in peripheral zone prostate cancer (PZ PCa) detection.

Methods: Fifty-six patients with histologically proven PZ PCa who underwent DWI-magnetic resonance imaging (MRI) using 21 b-values (0-4500 s/mm2) were included. The mean signal intensities of the regions of interest (ROIs) placed in benign PZs and cancerous tissues on DWI images were fitted using mono-exponential, bi-exponential, stretched-exponential, and kurtosis models.