Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method

An ill-posed inverse problem in quantitative susceptibility mapping （QSM） is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. However, in clinical diagnosis, it is necessary to reconstruct a susceptibility map efficiently with an appropriate method. Here, a modified QSM reconstruction method called weighted total variation using split Bregman （WTVSB） is proposed. It reconstructs the susceptibility map with fast computational speed and effective artifact suppression by incorporating noise-suppressed data weighting with split Bregman iteration. The noise-suppressed data weighting is determined using the Laplacian of the calculated local field, which can prevent the noise and errors in field maps from spreading into the susceptibility inversion. The split Bregman iteration accelerates the solution of the Ll-regularized reconstruction model by utilizing a preconditioned conjugate gradient solver. In an experiment, the proposed reconstruction method is compared with truncated k-space division （TKD）, morphology enabled dipole inversion （MEDI）, total variation using the split Bregman （TVSB） method for numerical simulation, phantom and in vivo human brain data evaluated by root mean square error and mean structure similarity. Experimental results demonstrate that our proposed method can achieve better balance between accuracy and efficiency of QSM reconstruction than conventional methods, and thus facilitating clinical applications of QSM.

Deep Learning‑Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas

This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss.

A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.

The neuroimaging of neurodegenerative and vascular disease in the secondary prevention of cognitive decline

Structural brain changes indicative of dementia occur up to 20 years before the onset of clinical symptoms. Efforts to modify the disease process after the onset of cognitive symptoms have been unsuccessful in recent years. Thus, future trials must begin during the preclinical phases of the disease before symptom onset. Age related cognitive decline is often the result of two coexisting brain pathologies: Alzheimer's disease(amyloid, tau, and neurodegeneration) and vascular disease. This review article highlights some of the common neuroimaging techniques used to visualize the accumulation of neurodegenerative and vascular pathologies during the preclinical stages of dementia such as structural magnetic resonance imaging, positron emission tomography, and white matter hyperintensities. We also describe some emerging neuroimaging techniques such as arterial spin labeling, diffusion tensor imaging, and quantitative susceptibility mapping. Recent literature suggests that structural imaging may be the most sensitive and cost-effective marker to detect cognitive decline, while molecular positron emission tomography is primarily useful for detecting disease specific pathology later in the disease process. Currently, the presence of vascular disease on magnetic resonance imaging provides a potential target for optimizing vascular risk reduction strategies, and the presence of vascular disease may be useful when combined with molecular and metabolic markers of neurodegeneration for identifying the risk of cognitive impairment.

IMPROVED HARMONIC INCOMPATIBILITY REMOVAL FOR SUSCEPTIBILITY MAPPING VIA REDUCTION OF BASIS MISMATCH

In quantitative susceptibility mapping(QSM),the background field removal is an essential data acquisition step because it has a significant effect on the restoration quality by generating a harmonic incompatibility in the measured local field data.Even though the sparsity based first generation harmonic incompatibility removal(1GHIRE)model has achieved the performance gain over the traditional approaches,the 1GHIRE model has to be further improved as there is a basis mismatch underlying in numerically solving Poisson’s equation for the background removal.In this paper,we propose the second generation harmonic incompatibility removal(2GHIRE)model to reduce a basis mismatch,inspired by the balanced approach in the tight frame based image restoration.Experimental results shows the superiority of the proposed 2GHIRE model both in the restoration qualities and the computational efficiency.