Jianqi Li

Quantitative assessment of extraocular muscles (EOMs) is crucial for the clinical management of thyroid-associated ophthalmopathy, and other orbital disord

Quantitative susceptibility mapping (QSM) is a post‐processing magnetic resonance imaging technique that quantifies the magnetic susceptibility of biological tissue and provides insights into factors such as iron deposition, hemorrhage, calcification, myelin content, and oxygen extraction fraction. A variety of analytical approaches have been developed to interpret QSM data from different perspectives, including region‐of‐interest‐based, depth‐wise, surface‐based, network‐based, and voxel‐wise methods. Among these, voxel‐wise analysis has gained increasing prominence due to its ability to perform a detailed examination of the entire brain without anatomically predefined regions. This approach is especially valuable for investigating neurological pathologies and aging‐related changes, including neurodegenerative and neuropsychiatric disorders. This article aims to comprehensively summarize voxel‐wise analysis in QSM by outlining key methodological considerations and clinical applications. Moreover, it offers practical data processing recommendations to advance the reproducibility and transparency of voxel‐wise QSM research.

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies tissue magnetic susceptibility by deconvolving the measured signal phase data. Accurate background field removal is essential for QSM, especially in surface regions of the brain, such as the cerebral cortex, where the background field interference is substantial. Existing methods have errors in estimating background field near the boundary of an organ, such as those of the brain, due to assumptions or loss of low-frequency information. A novel Green’s function total field inversion (gTFI) method is proposed here to model the background field using integral equations composed of Green’s function and boundary conditions, thereby eliminating the need for traditional filtering, assumption or regularization. The gTFI method simultaneously determines the background field at the boundary and the tissue susceptibility from the measured phase data. Numerical simulations and in vivo experiments demonstrate that the gTFI effectively separates the background field and reconstructs whole-brain QSM images without boundary erosion, offering superior performance over existing methods, particularly in cortical regions.

This study aims to comprehensively assess microstructural abnormalities in both gray matter (GM) and white matter (WM) in patients with moyamoya disease (MMD) using neurite orientation dispersion and density imaging (NODDI). The analysis integrates GM–based and tract-based spatial statistics (GBSS and TBSS, respectively). Diffusion-weighted imaging was performed on 26 healthy controls and 15 patients with MMD. NODDI metrics—including the neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF)—as well as diffusion tensor imaging (DTI) parameters—fractional anisotropy and mean diffusivity (MD)—were estimated and compared using GBSS and TBSS approaches. The analysis revealed significant microstructural alterations in both GM and WM among patients with MMD. In GM, reduced ODI was observed in multiple regions, including areas associated with the default mode network, executive control network, visual cortex, auditory cortex, sensorimotor cortex, and insula. In WM, decreased NDI and increased ISOVF were identified, predominantly in the corpus callosum, corona radiata, and bilateral frontal and parietal lobes. Although both DTI and NODDI metrics showed similar spatial distribution patterns of WM changes, the alterations detected by NODDI were more widespread. This suggests that NODDI may provide superior sensitivity for identifying microstructural changes associated with MMD. The integration of NODDI with GBSS and TBSS enhances the detection of cerebral microstructural alterations in MMD. These findings highlight the potential of NODDI-based metrics as valuable imaging biomarkers for improving diagnostic accuracy in MMD.

Background Neonatal hypoxic-ischemic encephalopathy (HIE) diagnosis is confounded by heterogeneous neural injury and metabolic dysfunction. Multi-pool chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) uniquely quantifies amide proton transfer, nuclear Overhauser enhancement, and magnetization transfer signals, providing multi-parametric assessment of HIE pathophysiology. Objective To investigate whether multi-pool CEST MRI can serve as a molecular-specific biomarker for histopathological alterations in HIE and assess its efficacy in grading disease severity. Materials and methods This prospective study included 20 neonates with HIE and 42 age-matched controls undergoing 3.0-T CEST MRI. Imaging data were spatially normalized to a neonatal atlas for region-specific analysis (caudate, putamen, thalamus, pallidum, amygdala, hippocampus). Group differences in CEST signals (amide proton transfer, nuclear Overhauser enhancement, magnetization transfer) were analyzed via Wilcoxon tests, with diagnostic performance evaluated through receiver operating characteristic analysis. Results Compared to controls, HIE neonates showed significant reductions in amide proton transfer (bilateral putamen, right hippocampus/pallidum/amygdala, left thalamus/caudate), nuclear Overhauser enhancement (left thalamus/caudate/putamen), and magnetization transfer signals (bilateral thalamus/pallidum/putamen, left caudate; all PP<0.05). Notably, conventional amide proton transfer-weighted imaging showed no significant changes, as the reduction in amide proton transfer signal was offset by a concurrent decrease in the nuclear Overhauser enhancement, highlighting the superiority of multi-pool analysis. Left hippocampal nuclear Overhauser enhancement demonstrated exceptional severity discrimination (area under curve (AUC)=0.96), while a multi-region integrated model achieved perfect staging accuracy (AUC=1.00). Conclusion Multi-pool CEST MRI effectively captures histopathological changes in neonatal HIE, with left hippocampal nuclear Overhauser enhancement emerging as a precise biomarker for severity stratification. The combined dynamics of amide proton transfer, nuclear Overhauser enhancement, and magnetization transfer signals provide noninvasive insights into metabolic-pathological correlations, highlighting its transformative potential for early diagnosis and targeted therapeutic monitoring.

To simultaneously fit multiple‐pool effects, spectrally selective 3D chemical exchange saturation transfer (CEST) imaging typically requires single‐shot readouts to save time. However, to date, fast low angle shot (FLASH) and echo planar imaging (EPI) have been the primary pulse sequences used for this purpose. They suffer from low signal‐to‐noise ratio (SNR) or image distortion related to B0 field inhomogeneity. In this work, we developed a 3D single‐shot CEST sequence using true fast imaging with steady‐state precession (True FISP) readout, also known as balanced steady state free precession (bSSFP), and optimized the scanning parameters through simulations. The performance of the CEST sequence was validated using an egg white phantom, 10 healthy volunteers, and two patients with brain tumors on a 3T human scanner. Subsequently, the proposed CEST sequence using True FISP was compared with the commonly used FLASH‐based CEST sequence, focusing on SNR and image contrast, while maintaining identical pre‐saturation modes, repetition time, echo time, and scan time. In the simulation experiments, the maximum CEST signal obtained from the True FISP was significantly greater than that obtained from the FLASH sequence. In the egg white phantom, the SNRs of amide proton transfer‐weighted (APTw) and nuclear Overhauser enhancement (NOE) effect images obtained from the True FISP were 68.3% and 57.0% higher than those obtained from the FLASH sequence, respectively. In healthy volunteers, saturated images collected with the True FISP sequence at 3.5 ppm showed an approximately 84% increase in mean temporal SNR compared to those collected with the FLASH sequence. Compared to the FLASH sequence, the CEST images obtained from the True FISP sequence could display more detailed brain tissue structures of both normal individuals and patients with brain tumors. Therefore, due to the high SNR inherent in the sequence, True FISP has the potential to be used for fast and high‐quality 3D image readout of CEST contrasts in clinical applications.