UNASSIGNED: With better visual contrast and the ability for magnetic susceptibility quantification analysis, quantitative susceptibility mapping (QSM) has emerged as an important magnetic resonance imaging (MRI) method for basal ganglia studies. Precise segmentation of basal ganglia is a prerequisite for quantification analysis of tissue magnetic susceptibility, which is crucial for subsequent disease diagnosis and surgical planning. The conventional method of localizing and segmenting basal ganglia heavily relies on layer-by-layer manual annotation by experts, resulting in a tedious amount of workload. Although several morphology registration and deep learning based methods have been developed to automate segmentation, the voxels around the nuclei boundary remain a challenge to distinguish due to insufficient tissue contrast. This paper proposes AGSeg, an active gradient guidance-based susceptibility and magnitude information complete (MIC) network for real-time and accurate basal ganglia segmentation.
UNASSIGNED: Various datasets, including clinical scans and data from healthy volunteers, were collected across multiple centers with different magnetic field strengths (3T/5T/7T), with a total of 210 three-dimensional (3D) susceptibility measurements. Manual segmentations following fixed rules for anatomical borders annotated by experts were used as ground truth labels. The proposed network took QSM maps and Magnitude images as two individual inputs, of which the features are selectively enhanced in the proposed magnitude information complete (MIC) module. AGSeg utilized a dual-branch architecture, with Seg-branch aiming to generate a proper segmentation map and Grad-branch to reconstruct the gradient map of regions of interest (ROIs). With the support of the newly designed active gradient module (AGM) and gradient guiding module (GGM), the Grad-branch provided attention guidance for the Seg-branch, facilitating it to focus on the boundary of target nuclei.
UNASSIGNED: Ablation studies were conducted to assess the functionality of the proposed modules. Significant performance decrement was observed after ablating relative modules. AGSeg was evaluated against several existing methods on both healthy and clinical data, achieving an average Dice similarity coefficient (DSC) =0.874 and average 95% Hausdorff distance (HD95) =2.009. Comparison experiments indicated that our model had superior performance on basal ganglia segmentation and better generalization ability over existing methods. The AGSeg outperformed all implemented comparison deep learning algorithms with average DSC enhancement ranging from 0.036 to 0.074.
UNASSIGNED: The current work integrates a deep learning-based method into automated basal ganglia segmentation. The high processing speed and segmentation robustness of AGSeg contribute to the feasibility of future surgery planning and intraoperative navigation. Experiments show that leveraging active gradient guidance mechanisms and magnitude information completion can facilitate the segmentation process. Moreover, this approach also offers a portable solution for other multi-modality medical image segmentation tasks.

UNASSIGNED: Parkinson disease (PD) and multiple system atrophy (MSA) are neurodegenerative disorders characterized by the accumulation of alpha-synuclein. Distinguishing between these conditions remains a significant challenge. This study thus employed quantitative susceptibility mapping (QSM) to evaluate subcortical iron deposition and its clinical implications in patients with PD or MSA and a group of healthy controls (HCs).
UNASSIGNED: The study included 26 patients with MSA, 40 patients with PD, and 35 HCs. We used magnetic resonance imaging (MRI)-based QSM to measure iron accumulation in the substantia nigra pars compacta (SNc), substantia nigra pars reticulata (SNr), and globus pallidus internus (GPi). We assessed differences between groups, examined correlations with clinical scores, and conducted receiver operating characteristic (ROC) curve analysis.
UNASSIGNED: Compared to those with PD, patients with MSA showed more severe motor and nonmotor impairment. QSM analysis indicated a significant increase in iron levels in the SNc, SNr, and GPi regions in patient groups compared to HCs. In patients with MSA, a notable positive correlation was found between SNc QSM values and Non-Motor Symptoms Scale scores (r=0.4; P=0.043). In patients with PD, a positive association was observed between iron levels in the SNc and Unified Parkinson\'s Disease Rating Scale Part III (UPDRS-III) (r=0.395; P=0.012) and Hamilton Depression Rating Scale scores (r=0.313; P=0.049). Furthermore, iron content in the GPi inversely correlated with rapid-eye movement sleep behavior disorder questionnaire-Hong Kong scores (r=-0.342; P=0.031). The SNr region demonstrated the best ability to discriminate between MSA and PD with an area under the curve (AUC) of 0.67, followed by the GPi (AUC =0.64) and SNc (AUC =0.57).
UNASSIGNED: QSM effectively quantified subcortical iron deposition in the PD, MSA, and HC groups. The correlations found between iron levels and clinical manifestations provide insights into the pathophysiological processes of these disorders, highlighting the potential of QSM as a diagnostic tool for differentiation.

OBJECTIVE: This review aims to explore the role of Quantitative Susceptibility Mapping (QSM) in the early detection of neurodegenerative diseases, particularly Alzheimer\'s disease (AD) and Lewy body dementia (LBD). By examining QSM\'s ability to map brain iron deposition, we seek to highlight its potential as a diagnostic tool for preclinical dementia.
METHODS: QSM techniques involve the advanced processing of MRI phase images to reconstruct tissue susceptibility, employing methods such as spherical mean value filtering and Tikhonov regularization for accurate background field removal. This review discusses how these methodologies enable the precise quantification of iron and other elements within the brain.
RESULTS: QSM has demonstrated effectiveness in identifying early pathological changes in key brain regions, including the hippocampus, basal ganglia, and substantia nigra. These regions are significantly impacted in the early stages of AD and LBD. Studies reviewed indicate that QSM can detect subtle neurodegenerative changes, providing valuable insights into disease progression. However, challenges remain in standardizing QSM processing algorithms to ensure consistent results across different studies.
CONCLUSIONS: QSM emerges as a promising tool for early dementia detection, offering precise measurements of brain iron deposition and other critical biomarkers. The review underscores the importance of refining QSM methodologies and integrating them with other imaging modalities to improve early diagnosis and management of neurodegenerative diseases. Future research should focus on standardizing QSM techniques and exploring their synergistic use with other neuroimaging methods to enhance its clinical utility.

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP\'s excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32 % accuracy improvement than supervised deep learning methods. It is also 33 % more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 min.

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a simulated-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms. The PnP OA-LFE module\'s versatility was further demonstrated by its successful application to xQSM, a distinct DL-QSM network for dipole inversion. In conclusion, this work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.

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Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson\'s disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.

UNASSIGNED: Quantitative susceptibility mapping (QSM) is a novel imaging method for detecting iron content in the brain. The study aimed determine whether the iron deposition in the brains of people with Parkinson\'s disease (PD) is correlated with freezing of gait (FOG).
UNASSIGNED: We retrospectively collected the data of 24 patients with PD from the Movement Disorders Program and 36 healthy controls (HCs) from January 2021 to December 2021. Clinical assessments included mental intelligence scales, Parkinson rating scales, motor-related scales, and clinical gait assessments. All exercise scales and gait assessments were performed in the \"ON\" and \"OFF\" states. Magnetic resonance imaging (MRI) data were collected using 3-dimensional fast low-angle shot sequences. We chose the bilateral red nucleus, substantia nigra, thalamus, putamen, caudate nucleus, and globus pallidus as regions of interest for QSM analysis.
UNASSIGNED: The iron deposition in the substantia nigra of the PD group was significantly higher than that of the HC group (P<0.01). In the PD group, the iron deposition in the substantia nigra of patients with FOG was significantly higher than that in patients without FOG (P=0.04). The iron deposition in the substantia nigra was positively correlated with the New Freezing of Gait Questionnaire (P=0.03). The scores for depression and anxiety of the PD group were significantly higher than those of the HC group, while the Berg balance scale score was significantly lower (P<0.01).
UNASSIGNED: The iron deposition in the substantia nigra of patients with PD is increased compared with that of controls and is associated with FOG. QSM can be used to detect brain iron deposition in patients with PD, which would help to explore the mechanism of abnormal neurobiological activity in FOG.

UNASSIGNED: Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC).
UNASSIGNED: This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance.
UNASSIGNED: This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively.
UNASSIGNED: QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.

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