Explore the vast range of titles available.

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 s using the trained iQFM and iQSM neural networks. [Display omitted]

A new dipole field inversion method for whole head quantitative susceptibility mapping (QSM) is proposed. Instead of performing background field removal and local field inversion sequentially, the proposed method performs dipole field inversion directly on the total field map in a single step. To aid this under-determined and ill-posed inversion process and obtain robust QSM images, Tikhonov regularization is implemented to seek the local susceptibility solution with the least-norm (LN) using the L-curve criterion. The proposed LN-QSM does not require brain edge erosion, thereby preserving the cerebral cortex in the final images. This should improve its applicability for QSM-based cortical grey matter measurement, functional imaging and venography of full brain. Furthermore, LN-QSM also enables susceptibility mapping of the entire head without the need for brain extraction, which makes QSM reconstruction more automated and less dependent on intermediate pre-processing methods and their associated parameters. It is shown that the proposed LN-QSM method reduced errors in a numerical phantom simulation, improved accuracy in a gadolinium phantom experiment, and suppressed artefacts in nine subjects, as compared to two-step and other single-step QSM methods. Measurements of deep grey matter and skull susceptibilities from LN-QSM are consistent with established reconstruction methods. •The proposed LN-QSM method generates susceptibility maps in a single step without the need for background field removal.•LN-QSM enables full brain susceptibility mapping without any brain edge erosion.•LN-QSM demonstrates the feasibility of whole head susceptibility mapping (including skull) without any brain extraction.•Deep grey matter and skull susceptibilities are consistent with established reconstruction methods.

Most studies of brain iron relied on the effect of the iron on magnetic resonance (MR) relaxation properties, such as R2∗, and bulk tissue magnetic susceptibility, as measured by quantitative susceptibility mapping (QSM). The present study exploited the dependence of R2∗ and magnetic susceptibility on physical interactions at different length-scales to retrieve information about the tissue microenvironment, rather than the iron concentration. We introduce a method for the simultaneous analysis of brain tissue magnetic susceptibility and R2∗ that aims to isolate those biophysical mechanisms of R2∗ -contrast that are associated with the micro- and mesoscopic distribution of iron, referred to as the Iron Microstructure Coefficient (IMC). The present study hypothesized that changes in the deep gray matter (DGM) magnetic microenvironment associated with aging and pathological mechanisms of multiple sclerosis (MS), such as changes of the distribution and chemical form of the iron, manifest in quantifiable contributions to the IMC. To validate this hypothesis, we analyzed the voxel-based association between R2∗ and magnetic susceptibility in different DGM regions of 26 patients with multiple sclerosis and 33 age- and sex-matched normal controls. Values of the IMC varied significantly between anatomical regions, were reduced in the dentate and increased in the caudate of patients compared to controls, and decreased with normal aging, most strongly in caudate, globus pallidus and putamen. •Voxel-wise analysis of R2* and susceptibility reveals their differential dependency on brain iron.•The Iron Microstructure Coefficient (IMC) varies with brain regions and decreases with age.•Multiple Sclerosis is associated with increased IMC in dentate and decreased IMC in caudate.

•An end-to-end deep neural network was developed for quantitative susceptibility mapping (QSM) of arbitrary acquisition orientation and spatial resolution.•The proposed method significantly improved the generalizability of deep learning QSM, leading to more robust and accurate susceptibility maps from oblique and anisotropic scans.•The proposed neural network can perform dipole inversion of arbitrary image resolution and acquisition orientation in seconds without re-training the model. Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0.6 mm isotropic at the finest. The AFTER-QSM neural network starts with a forward affine transformation layer, followed by a Unet for dipole inversion, then an inverse affine transformation layer, followed by a Residual Dense Network (RDN) for QSM refinement. Simulation and in-vivo experiments demonstrated that the proposed AFTER-QSM network architecture had excellent generalizability. It can successfully reconstruct susceptibility maps from highly oblique and anisotropic scans, leading to the best image quality assessments in simulation tests and suppressed streaking artifacts and noise levels for in-vivo experiments compared with other methods. Furthermore, ablation studies showed that the RDN refinement network significantly reduced image blurring and susceptibility underestimation due to affine transformations. In addition, the AFTER-QSM network substantially shortened the reconstruction time from minutes using conventional methods to only a few seconds.

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds. A deep-learning based method – DCRNet is developed from a deep residual network backbone using complex convolutional operations to recover both MRI magnitude and quantitative phase images from incoherent undersampled MRI data, thus enabling the acceleration of R2* and QSM from undersampled multi-echo GRE acquisitions. [Display omitted]

•A method named DECOMPOSE-QSM is developed to decompose sub-voxel paramagnetic and diamagnetic susceptibilities only use multi-echo GRE data.•The method is validated with numerical simulations, phantom, ex vivo and in vivo experiments.•The resulting susceptibility composition maps reveal more detailed subregion structures than conventional QSM.•The proposed method may be applied to various susceptibility-based studies. A method named DECOMPOSE-QSM is developed to decompose bulk susceptibility measured with QSM into sub-voxel paramagnetic and diamagnetic components based on a three-pool complex signal model. Multi-echo gradient echo signal is modeled as a summation of three weighted exponentials corresponding to three types of susceptibility sources: reference susceptibility, diamagnetic and paramagnetic susceptibility relative to the reference. Paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) maps are constructed to represent the sub-voxel compartments by solving for linear and nonlinear parameters in the model. Numerical forward simulation and phantom validation confirmed the ability of DECOMPOSE-QSM to separate the mixture of paramagnetic and diamagnetic components. The PCS obtained from temperature-variant brainstem imaging follows the Curie's Law, which further validated the model and the solver. Initial in vivo investigation of human brain images showed the ability to extract sub-voxel PCS and DCS sources that produce visually enhanced contrast between brain structures comparing to threshold QSM.

•A method called APART-QSM is proposed to separate the opposing susceptibilities within a single voxel using a more comprehensive signal model.•APART-QSM is evaluated with phantom, ex-vivo and in-vivo experiments.•APART-QSM demonstrates an improved ability to accurately quantify iron and myelin, and also reveals fine brain subregion details.•APART-QSM can handle an arbitrary number of input GRE measurements. The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2′ in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2′ and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2′ relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.

Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications. [Display omitted] •New QSM reconstruction, QSMnet, is developed using a deep neural network.•QSMnet generates a highly accurate QSM map close to a gold standard (COSMOS) map.•Processing time of QSMnet is only a few seconds, achieving real-time processing.•In patients, QSMnet delivers similar lesion contrasts to conventional QSM.

BackgroundIron accumulation and neuroinflammation are both pathological processes that are associated with disease progression of Huntington’s Disease (HD). However, their intercorrelation remains unclear, in both HD and other neurodegenerative diseases.AimsThis study aims to investigate the correlation between altered brain iron content and altered neuroinflammatory markers in the basal ganglia of HD gene expansion carriers.MethodsA total of 30 participants (11 premanifest, 7 manifest and 12 healthy controls) were scanned on a 7T MRI scanner (Philips, Netherlands), as part of a larger (EHDN seed fund) project (n=70). Quantitative susceptibility mapping (QSM) was used to quantify iron concentration in the brain. 1H Magnetic Resonance Spectroscopy (MRS) was used to measure levels of myoinositol (Ins) and choline (Cho) in a volume-of-interest (VOI) located in the basal ganglia. Ins and Cho are two metabolites that are preferentially located in glial cells and astrocytes and can serve as neuroinflammation markers.OutcomeAs a first step, a subpopulation, consisting of 3 age- and sex-matched subjects per group (9 in total), will be analyzed with QSM and MRS. Magnetic susceptibility and metabolite concentrations within the MRS-VOI will be quantified and correlated.ConclusionsOur unique protocol will lead us one step closer to the missing link between neuroinflammation and iron accumulation, using QSM and MRS. This could help determining the order and distribution of these pathological processes in the different stages of HD. This novel approach could create new opportunities for biomarker development and therapeutic interventions.

Objectives: This study aimed to investigate differences in brain imaging characteristics among patients with Parkinson’s disease with cognitive impairment (PDCI), Parkinson’s disease without cognitive impairment (PDNCI), and healthy controls (HC), and to develop machine learning–based models for the early diagnosis of PDCI. A total of 48 patients with PDCI, 50 patients with PDNCI, and 47 age- and sex-matched healthy controls were enrolled, all of whom underwent magnetic resonance imaging using a 3.0 T MRI scanner. Arterial spin labeling (ASL) was applied to quantify cerebral blood flow (CBF), and quantitative susceptibility mapping (QSM) was used to assess magnetic susceptibility, while cognitive function was evaluated using standardized neuropsychological scales. Group differences were examined using one-way analysis of variance (ANOVA), and seven machine learning classifiers, including random forest (RF), K-nearest neighbors (KNN), and Extreme Gradient Boosting (XGB), were constructed to discriminate among the PDCI, PDNCI, and HC groups. The ANOVA results revealed significant differences in both CBF and magnetic susceptibility between the HC group and the two PD groups, whereas no significant differences were observed between the PDCI and PDNCI groups. Compared with normative data, patients with PDCI exhibited cognitive impairments exceeding 2 standard deviations in the domains of language, attention, and working memory, as well as impairments exceeding 1 standard deviation in visuospatial function, memory, and executive function. Among the machine learning models, RF, KNN, and XGB achieved perfect classification performance, with all evaluation metrics reaching 1.000, indicating excellent discriminative capability. Feature importance analysis identified increased CBF and magnetic susceptibility in regions such as the left precuneus (Precuneus_L) and left postcentral gyrus (Postcentral_L) as key imaging features distinguishing PDCI, and correlation analyses further demonstrated significant associations between cognitive deficits and alterations in CBF and magnetic susceptibility. These findings suggest that ASL- and QSM-derived imaging features have substantial potential as non-invasive biomarkers for the early diagnosis of PDCI, that patients with PDCI exhibit widespread impairments across multiple cognitive domains—particularly in language, attention, and working memory—and that machine learning models integrating multimodal imaging features provide a reliable and effective approach for early diagnosis and may facilitate personalized treatment strategies in Parkinson’s disease, although future studies with larger sample sizes and independent validation cohorts are warranted to enhance the robustness and generalizability of these models.