Quantitative Susceptibility Mapping Images Research Articles

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson’s disease

ObjectivesThe altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson’s disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM).MethodsIn our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model.ResultsThe hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral “swallow tail” sign region was significant for classification.ConclusionThe integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

Quantitative susceptibility mapping and a nonlinearly transformed atlas for targeting the ventral intermediate nucleus of the thalamus in a patient with tremor and thalamic hypertrophy: illustrative case.

The ventral intermediate nucleus (Vim) of the thalamus is a surgical target for treating various types of tremor. Because it is difficult to visualize the Vim using standard magnetic resonance imaging, the structure is usually targeted based on the anterior and posterior commissures. This standard targeting method is practical in most patients but not in those with thalamic asymmetry. The authors examined the usefulness of quantitative susceptibility mapping (QSM) and transformed Vim atlas images to estimate the Vim localization in a patient with tremor and significant thalamic hypertrophy. A 51-year-old right-handed female had experienced a predominant left-hand action tremor for 6 years. Magnetic resonance imaging showed significant hypertrophy of the right thalamus and caudal shift of the thalamic ventral border. The authors referred to the QSM images to localize the decreased susceptibility area within the lateral ventral thalamic nuclei to target the Vim. In addition, the nonlinearly transformed Vim atlas images complemented the imaging-based targeting. The radiofrequency thalamotomy at the modified Vim target relieved the tremor completely. A combination of QSM and nonlinear transformation of the thalamic atlas can be helpful in the targeting method of the Vim for tremor patients with thalamic asymmetry.

In vivo mapping of hippocampal venous vasculature and oxygenation using susceptibility imaging at 7T

Mapping the small venous vasculature of the hippocampus in vivo is crucial for understanding how functional changes of hippocampus evolve with age. Oxygen utilization in the hippocampus could serve as a sensitive biomarker for early degenerative changes, surpassing hippocampal tissue atrophy as the main source of information regarding tissue degeneration. Using an ultrahigh field (7T) susceptibility-weighted imaging (SWI) sequence, it is possible to capture oxygen-level dependent contrast of submillimeter-sized vessels. Moreover, the quantitative susceptibility mapping (QSM) results derived from SWI data allow for the simultaneous estimation of venous oxygenation levels, thereby enhancing the understanding of hippocampal function. In this study, we proposed two potential imaging markers in a cohort of 19 healthy volunteers aged between 20 and 74 years. These markers were: 1) hippocampal venous density on SWI images and 2) venous susceptibility (Δχvein) in the hippocampus-associated draining veins (the inferior ventricular veins (IVV) and the basal veins of Rosenthal (BVR) using QSM images). They were chosen specifically to help characterize the oxygen utilization of the human hippocampus and medial temporal lobe (MTL). As part of the analysis, we demonstrated the feasibility of measuring hippocampal venous density and Δχvein in the IVV and BVR at 7T with high spatial resolution (0.25 × 0.25 × 1 mm3). Our results demonstrated the in vivo reconstruction of the hippocampal venous system, providing initial evidence regarding the presence of the venous arch structure within the hippocampus. Furthermore, we evaluated the age effect of the two quantitative estimates and observed a significant increase in Δχvein for the IVV with age (p=0.006, r2 = 0.369). This may suggest the potential application of Δχvein in IVV as a marker for assessing changes in atrophy-related hippocampal oxygen utilization in normal aging and neurodegenerative diseases such as AD and dementia.

Quantitative susceptibility mapping for detection of kidney stones, hemorrhage differentiation, and cyst classification in ADPKD.

The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI). Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w, and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated. QSM visualized two renal calcifications measuring 9 and 10mm and three pelvic phleboliths measuring 2mm but missed 24 calcifications measuring 1mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) Cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage. Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation and able to identify properties of complex cysts unattainable with traditional approaches.

Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks

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.

Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer.

The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.

Probing Brain Magnetic Susceptibility Alterations in a Mouse Model of Multiple Sclerosis

Multiple sclerosis (MS) is an increasing cause of disability in the United States, characterized by autoimmune demyelinating events in the central nervous system. MS patients can present with a varying range of symptoms and disability due to the chronic inflammatory and relapsing events associated with the disease, such as impaired mobility, slowed cognitive processing, and high probability of disease progression.&#x0D; MS can be morphologically characterized by the damage to the myelin sheath that surrounds axons, oligodendrocytes that produce myelin, and axons that transmit information to different neurons. Due to the complicated mechanism of MS, the advanced MRI methods which can provide both sensitive and specific assessment of MS tissue injury hold the promise to provide critical mechanistic guidance of MS and serve as robust imaging biomarkers for MS. Several animal models have been established to understand the distinct aspects of the disease. Cuprizone, a copper chelator, creates demyelination events comparable to MS, allowing the mouse model to be a representation QSM’s ability to accurately distinguish between and predict acute and chronic demyelination events. Recently, we have demonstrated that the novel quantitative susceptibility mapping (QSM) can identify between the demyelination andremyelination process in the corpus callosum of mice treated with cuprizone. However, how early QSM can detect brain alterations has not been investigated.&#x0D; We acquired high-resolution in vivo whole-brain QSM, T-2 weighted, and magnetization transfer ratio (MTR) images at 0, 2, and 4 weeks after cuprizone administration. The corpus callosum exhibited significant changes in T2-weighted and MTR images at 4 weeks after cuprizone administration. In contrast, QSM decreased significantly in QSM at 2 weeks after cuprizone administration. Since dramatic demyelination only starts after oligodendrocytes depletion (3 weeks after cuprizone treatment), QSM may be able to detect the alteration of oligodendrocytes and serve as a sensitive biomarker even before demyelination occurs.

Central intra-lesional iron deposits as a possible novel imaging marker at 7 Tesla MRI in Susac Syndrome - an exploratory study.

Susac syndrome (SuS) is a rare autoimmune disease that leads to hearing impairment, visual field deficits, and encephalopathy due to an occlusion of precapillary arterioles in the brain, retina, and inner ear. Given the potentially disastrous outcome and difficulties in distinguishing SuS from its differential diagnoses, such as multiple sclerosis (MS), our exploratory study aimed at identifying potential new SuS-specific neuroimaging markers. Seven patients with a definite diagnosis of SuS underwent magnetic resonance imaging (MRI) at 7 Tesla (7T), including T2* weighted and quantitative susceptibility mapping (QSM) sequences. T2 weighted hyperintense lesions were analyzed with regard to number, volume, localization, central vein sign, T1 hypointensity, and focal iron deposits in the center of SuS lesions ("iron dots"). Seven T MRI datasets from the same institute, comprising 75 patients with, among others, MS, served as controls. The "iron dot" sign was present in 71.4% (5/7) of the SuS patients, compared to 0% in our control cohort. Thus, sensitivity was 71.4% and specificity 100%. A central vein sign was only incidentally detected. We are the first to demonstrate this type of "iron dot" lesions on highly resolving 7T T2*w and QSM images in vivo as a promising neuroimaging marker of SuS, corroborating previous histopathological ex vivo findings.

Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.

Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. To automatically detect CMB on QSM, we proposed a two-stage deep learning pipeline. Retrospective. A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy-eight subjects (70 ± 13) were used as external testing. 3 T/QSM. The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one-layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V-Net was utilized to reduce false positives. The V-Net was trained using CMB and non CMB labels, which allowed for high-level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi-automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. 2 TECHNICAL EFFICACY: Stage 2.

Assessment of quantitative susceptibility mapping (QSM) and oxygen extraction fraction (OEF) in the spectrum of Alzheimer’s disease clinical presentations

AbstractBackgroundDifferent methods have been proposed to assess vascular dysfunction in Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects including quantitative susceptibility mapping (QSM), and recently oxygen extraction fraction (OEF). QSM is sensitive to the presence of iron and is a non‐invasive MR approach to measure local tissue susceptibility with high spatial resolution. OEF is a measure of the percentage of oxygen taken from the brain’s blood supply and relates directly to brain oxygen metabolism. Here, we aimed to examine QSM (Figure1) and OEF (Figure1) potential in differentiating AD, MCI, and CU.MethodA cohort of 310 subjects with AD (n = 48), MCI (n = 80), and CU (n = 182) were recruited. All the patients received 3D gradient‐recalled echo sequence MRI. All QSM images were constructed by MEDI+0 and OEF images were constructed by QSM+qBOLD model with CCTV (Temporal clustering, tissue composition, and total variation). Eighty‐two various brain regions of interest (ROI) and 6 Braak ROI were investigated in the current study. ANOVA and Posthoc least significant difference (LSD) tests were implemented for each ROI over AD, MCI, and CU.ResultOver Braak ROI analysis, the ANOVA test showed that the average susceptibility of QSM with Braak 4 ROI is significantly different (p&lt;0.001). LSD test showed that Braak 4 ROI of QSM is significantly different between AD vs. MCI and AD vs. CU (p&lt;0.001). Braak 2 ROI of OEF was significantly different between AD vs. MCI and AD vs. CU (p = 0.21 and p = 0.32, respectively). Over brain ROI, the ANOVA test showed QSM right and left posterior cingulate and right Caudate were significantly different (p&lt;0.001). Besides, these ROIs were significantly different between AD vs. MCI and AD vs. CU, according to the LSD test. Over brain ROI, OEF right Caudate was significantly different (p&lt;0.001) followed by left Caudate and Right transverse temporal with p = 0.002 based on the ANOVA test. However, right and left Caudate were just significantly different between AD vs. CU (p&lt;0.001) based on the LSD test (Table 1 and 2).ConclusionQSM and OEF are affordable non‐invasive MR approaches to assess vascular dysfunction in the spectrum of clinical presentations of AD.

Pulvinar quantitative susceptibility mapping predicts visual hallucinations post-deep brain stimulation in Parkinson's disease.

We have reported the relationship between low pulvinar nuclei (PN) intensity in susceptibility-weighted imaging and the appearance of visual hallucinations and cognitive function. The aim of the study was to examine the changes in the quantitative susceptibility mapping (QSM) in patients with Parkinson's disease (PD) who underwent deep brain stimulation (DBS) and verify whether the PN susceptibility value (SV) on QSM can predict visual hallucination and cognitive changes after DBS. This study examined 24 patients with PD who underwent DBS along with QSM imaging on magnetic resonance imaging (MRI). All MRIs were performed within 3 months before surgery. The PN SV was further assessed based on the QSM. Then, associations were examined among cognitive changes, hallucination, and PN SV. The cognitive function of the patient was compared immediately before surgery and at 1 year postoperatively. Visual hallucinations were observed in seven patients during the follow-up period. The PN SV was ≥0.045ppm in nine patients with PD, and six of them had visual hallucinations, whereas only one of 15 patients with PD with SV of<0.045ppm had visual hallucinations (Fisher's exact test, p = .0037). The SV of>0.045ppm at the PN in QSM in patients with PD may provide useful information suggesting visual hallucination and cognitive deterioration after DBS treatment.

Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative susceptibility mapping

PurposeTo develop a method for predicting amyloid positron emission tomography (PET) positivity based on multiple regression analysis of quantitative susceptibility mapping (QSM). Materials and methodsThis prospective study included 39 patients with suspected dementia from four centers. QSM images were obtained through a 3-T, three-dimensional radiofrequency-spoiled gradient-echo sequence with multiple echoes. The cortical standard uptake value ratio (SUVR) was obtained using amyloid PET with 18F-flutemetamol, and susceptibility in the brain regions was obtained using QSM. A multiple regression model to predict cortical SUVR was constructed based on susceptibilities in multiple brain regions, with the constraint that cortical SUVR and susceptibility were positively correlated. The discrimination performance of the Aβ-positive and Aβ-negative cohorts was evaluated based on the predicted SUVR using the area under the receiver operating characteristic curve (AUC) and Mann–Whitney U test. ResultsThe correlation coefficients between true and predicted SUVR were increased by incorporating the constraint, and the AUC to discriminate between the Aβ-positive and Aβ-negative cohorts reached to 0.79 (p < 0.01). ConclusionThese preliminary results suggest that a QSM-based multiple regression model can predict amyloid PET positivity with fair accuracy.

Deep gray matters iron deposition is positively associated with white matter hyperintensity in hypertension.

The association and underlying mechanisms between iron deposition and white matter hyperintensity (WMH) remain unclear. In this study, quantitative susceptibility mapping (QSM) was used to quantify deep gray matters iron deposition and to explore the association from both global and regional perspectives. A total of 84 patients with hypertension and 26 healthy controls underwent a strategically acquired gradient echo (STAGE) protocol, and the multi-echo data were used to reconstruct QSM images. The susceptibilities were used to describe iron content. Global region (RI) susceptibilities were measured in regions of interest, and age-related thresholds were used to determine high-iron content region (RII) susceptibilities. Compared with healthy controls, hypertension had higher total WMH scores and regional scores (all p=.001) and higher susceptibilities using the RI or RII analysis (all p<.05). In healthy controls, there was no significant association between susceptibilities and WMH scores. In hypertension, the susceptibilities of deep gray matters were positively correlated with WMH scores (RI analysis: right putamen; RII analysis: bilateral caudate nucleus head, putamen, red nucleus, substantia nigra, and dentate nucleus; age and education corrected p<.05). These findings suggest that iron deposition in deep gray matters was positively associated with WMH in hypertension, especially using the RII analysis.

Increased Brain Iron Deposition in Episodic Migraine: A Pilot Voxel-based Quantitative Susceptibility Mapping

Although iron deposition has been identified as a significant migraine trigger, the key structures in episodic migraine (EM) remain unknown. The aim of this study is to investigate cerebral iron deposition in EM using an advanced voxel-based quantitative susceptibility mapping (QSM). A multi-echo gradient echo sequence MR was performed in 15 episodic migraine patients (EMs) and 27 normal control subjects (NCs). The reconstructed quantitative susceptibility mapping images and voxel-based analysis were performed over the entire brain. The susceptibility value of all brain regions with altered iron deposition was extracted, and the correlations between susceptibility value and clinical variables (including HAMA, HAMD, MoCA, VAS, MIDAS score, diseased duration, and headache frequency) were calculated. EM patients presented increased susceptibility value in the left putamen and bilateral substantia nigra (SN) compared with NC. There was no correlation between susceptibility value and the clinical variables. Increased brain iron deposition in the extrapyramidal system may be a biomarker for migraine, and abnormal iron metabolism may be involved in the extrapyramidal mechanism. The QSM technique would be an optimal and simple tool for clinical practice and research in iron measurement.

Change of iron content in brain regions after intravenous iron therapy in restless legs syndrome: quantitative susceptibility mapping study.

The pathomechanism of restless legs syndrome (RLS) is related to brain iron deficiency and iron therapy is effective for RLS; however, the effect of iron therapy on human brain iron state has never been studied with magnetic resonance imaging. This study aimed to investigate the change of brain iron concentrations in patients with RLS after intravenous iron therapy using quantitative susceptibility mapping (QSM). We enrolled 31 RLS patients and 20 healthy controls. All participants underwent initial baseline (t0) assessment using brain magnetic resonance imaging, serum iron status, and sleep questionnaires including international RLS Study Group rating scale (IRLS). RLS patients underwent follow-up tests at 6 and 24 weeks (t1 and t2) after receiving 1000 mg ferric carboxymaltose. Iron content of region-of-interest on QSM images was measured for 13 neural substrates using the fixed-shaped method. RLS symptoms evaluated using IRLS were significantly improved after iron treatment (t0: 29.7 ± 6.5, t1: 19.5 ± 8.5, t2: 21.3 ± 10.1; p < .001). There was no significant difference in susceptibility values between the controls and RLS patients at t0. In the caudate nucleus, putamen, and pulvinar thalamus of RLS patients, the QSM values differed significantly for three timepoints (p = .035, .048, and .032, respectively). The post-hoc analysis revealed that the QSM values increased at t1 in the caudate nucleus (66.8 ± 18.0 vs 76.4 ± 16.6, p = .037) and decreased from t1 to t2 in the putamen (69.4 ± 16.3 vs 62.5 ± 13.6, p = .025). Changes in the QSM values for the pulvinar and caudate nuclei at t1 were positively and negatively correlated with symptomatic improvement, respectively (r = 0.361 and -0.466, respectively). Intravenous iron treatment results in changes in brain iron content which correlate to reductions in RLS severity. This suggests a connection between symptom improvement and the associated specific brain regions constituting the sensorimotor network.

Direct localization and delineation of human pedunculopontine nucleus based on a self-supervised magnetic resonance image super-resolution method.

The pedunculopontine nucleus (PPN) is a small brainstem structure and has attracted attention as a potentially effective deep brain stimulation (DBS) target for the treatment of Parkinson's disease (PD). However, the in vivo location of PPN remains poorly described and barely visible on conventional structural magnetic resonance (MR) images due to a lack of high spatial resolution and tissue contrast. This study aims to delineate the PPN on a high-resolution (HR) atlas and investigate the visibility of the PPN in individual quantitative susceptibility mapping (QSM) images. We combine a recently constructed Montreal Neurological Institute (MNI) space unbiased QSM atlas (MuSus-100), with an implicit representation-based self-supervised image super-resolution (SR) technique to achieve an atlas with improved spatial resolution. Then guided by a myelin staining histology human brain atlas, we localize and delineate PPN on the atlas with improved resolution. Furthermore, we examine the feasibility of directly identifying the approximate PPN location on the 3.0-T individual QSM MR images. The proposed SR network produces atlas images with four times the higher spatial resolution (from 1 to 0.25 mm isotropic) without a training dataset. The SR process also reduces artifacts and keeps superb image contrast for further delineating small deep brain nuclei, such as PPN. Using the myelin staining histological atlas as guidance, we first identify and annotate the location of PPN on the T1-weighted (T1w)-QSM hybrid MR atlas with improved resolution in the MNI space. Then, we relocate and validate that the optimal targeting site for PPN-DBS is at the middle-to-caudal part of PPN on our atlas. Furthermore, we confirm that the PPN region can be identified in a set of individual QSM images of 10 patients with PD and 10 healthy young adults. The contrast ratios of the PPN to its adjacent structure, namely the medial lemniscus, on images of different modalities indicate that QSM substantially improves the visibility of the PPN both in the atlas and individual images. Our findings indicate that the proposed SR network is an efficient tool for small-size brain nucleus identification. HR QSM is promising for improving the visibility of the PPN. The PPN can be directly identified on the individual QSM images acquired at the 3.0-T MR scanners, facilitating a direct targeting of PPN for DBS surgery.

Frameless Robot-Assisted Asleep Centromedian Thalamic Nucleus Deep Brain Stimulation Surgery in Patients with Drug-Resistant Epilepsy: Technical Description and Short-Term Clinical Results.

This purpose of this work is to give a detailed description of a surgical technique for frameless robot-assisted asleep deep brain stimulation (DBS) of the centromedian thalamic nucleus (CMT) in drug-resistant epilepsy (DRE). Ten consecutively enrolled patients who underwent CMT-DBS were included in the study. The FreeSurfer "Thalamic Kernel Segmentation" module and experience target coordinates were used for locating the CMT, and quantitative susceptibility mapping (QSM) images were used to check the target. The patient's head was secured with a head clip, and electrode implantation was performed with the assistance of the neurosurgical robot Sinovation®. After opening the dura, the burr hole was continuously flushed with physiological saline to stop air from entering the skull. All procedures were performed under general anesthesia without intraoperative microelectrode recording (MER). The mean age of the patients at surgery and onset of seizures was 22years (range 11-41years) and 11years (range 1-21years), respectively. The median duration of seizures before CMT-DBS surgery was 10years (2-26years). CMT was successfully segmented, and its position was verified by experience target coordinates and QSM images in all ten patients. The mean surgical time for bilateral CMT-DBS in this cohort was 165 ± 18min. The mean pneumocephalus volume was 2 cm3. The median absolute errors in the x-, y-, and z-axes were 0.7mm, 0.5mm, and 0.9mm, respectively. The median Euclidean distance (ED) and radial error (RE) was 1.3 ± 0.5mm and 1.0 ± 0.3mm, respectively. No significant difference was found between right- and left-sided electrodes regarding the RE nor the ED. After a mean 12-month follow-up, the average reduction in seizures was 61%, and six patients experienced a ≥ 50% reduction in seizures, including one patient who had no seizures after the operation. All patients tolerated the anesthesia operation, and no permanent or serious complications were reported. Frameless robot-assisted asleep surgery is a precise and safe approach for placing CMT electrodes in patients with DRE, shortening the surgery time. The segmentation of the thalamic nuclei enables the precise location of the CMT, and the flow of physiological saline to seal the burr holes is a good way to reduce the influx of air. CMT-DBS is an effective method to reduce seizures.

Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping

BackgroundMicrostructural changes in deep gray matter (DGM) nuclei are related to physiological behavior, cognition, and memory. Therefore, it is critical to study age-dependent trajectories of biomarkers in DGM nuclei for understanding brain development and aging, as well as predicting cognitive or neurodegenerative diseases. ObjectivesWe aimed to (1) characterize age-dependent trajectories of mean susceptibility, adjusted volume, and total iron content simultaneously in DGM nuclei using quantitative susceptibility mapping (QSM); (2) examine potential contributions of sex related effects to the different age-dependence trajectories of volume and iron deposition; and (3) evaluate the ability of brain age prediction by combining mean magnetic susceptibility and volume of DGM nuclei. MethodsMagnetic susceptibilities and volumetric values of DGM nuclei were obtained from 220 healthy participants (aged 10–70 years) scanned on a 3T MRI system. Regions of interest (ROIs) were drawn manually on the QSM images. Univariate regression analysis between age and each of the MRI measurements in a single ROI was performed. Pearson correlation coefficients were calculated between magnetic susceptibility and adjusted volume in a single ROI. The statistical significance of sex differences in age-dependent trajectories of magnetic susceptibilities and adjusted volumes were determined using one-way ANCOVA. Multiple regression analysis was used to evaluate the ability to estimate brain age using a combination of the mean susceptibilities and adjusted volumes in multiple DGM nuclei. ResultsMean susceptibility and total iron content increased linearly, quadratically, or exponentially with age in all six DGM nuclei. Negative linear correlation was observed between adjusted volume and age in the head of the caudate nucleus (CN; R2 = 0.196, p < 0.001). Quadratic relationships were found between adjusted volume and age in the putamen (PUT; R2 = 0.335, p < 0.001), globus pallidus (GP; R2 = 0.062, p = 0.001), and dentate nucleus (DN; R2 = 0.077, p < 0.001). Males had higher mean magnetic susceptibility than females in the PUT (p = 0.001), red nucleus (RN; p = 0.002), and substantia nigra (SN; p < 0.001). Adjusted volumes of the CN (p < 0.001), PUT (p = 0.030), GP (p = 0.007), SN (p = 0.021), and DN (p < 0.001) were higher in females than those in males throughout the entire age range (10–70 years old). The total iron content of females was higher than that of males in the CN (p < 0.001), but lower than that of males in the PUT (p = 0.014) and RN (p = 0.043) throughout the entire age range (10–70 years old). Multiple regression analyses revealed that the combination of the mean susceptibility value of the PUT, and the volumes of the CN and PUT had the strongest associations with brain age (R2 = 0.586). ConclusionsQSM can be used to simultaneously investigate age- and sex- dependent changes in magnetic susceptibility and volume of DGM nuclei, thus enabling a comprehensive understanding of the developmental trajectories of iron accumulation and volume in DGM nuclei during brain development and aging.

Regional high iron deposition on quantitative susceptibility mapping correlates with cognitive decline in type 2 diabetes mellitus.

To quantitatively evaluate the iron deposition and volume changes in deep gray nuclei according to threshold-method of quantitative susceptibility mapping (QSM) acquired by strategically acquired gradient echo (STAGE) sequence, and to analyze the correlation between the magnetic susceptibility values (MSV) and cognitive scores in type 2 diabetes mellitus (T2DM) patients. Twenty-nine patients with T2DM and 24 healthy controls (HC) matched by age and gender were recruited in this prospective study. QSM images were used to evaluate whole-structural volumes (Vwh), regional magnetic susceptibility values (MSVRII), and volumes (VRII) in high-iron regions in nine gray nuclei. All QSM data were compared between groups. Receiver operating characteristic (ROC) analysis was used to assess the discriminating ability between groups. The predictive model from single and combined QSM parameters was also established using logistic regression analysis. The correlation between MSVRII and cognitive scores was further analyzed. Multiple comparisons of all statistical values were corrected by false discovery rate (FDR). A statistically significant P-value was set at 0.05. Compared with HC group, the MSVRII of all gray matter nuclei in T2DM were increased by 5.1-14.8%, with significant differences found in bilateral head of caudate nucleus (HCN), right putamen (PUT), right globus pallidus (GP), and left dentate nucleus (DN) (P < 0.05). The Vwh of most gray nucleus in T2DM group were decreased by 1.5-16.9% except bilateral subthalamic nucleus (STN). Significant differences were found in bilateral HCN, bilateral red nucleus (RN), and bilateral substantia nigra (SN) (P < 0.05). VRII was increased in bilateral GP, bilateral PUT (P < 0.05). VRII/Vwh was also increased in bilateral GP, bilateral PUT, bilateral SN, left HCN and right STN (P < 0.05). Compared with the single QSM parameter, the combined parameter showed the largest area under curve (AUC) of 0.86, with a sensitivity of 87.5% and specificity of 75.9%. The MSVRII in the right GP was strongly associated with List A Long-delay free recall (List A LDFR) scores (r = -0.590, P = 0.009). In T2DM patients, excessive and heterogeneous iron deposition as well as volume loss occurs in deep gray nuclei. The MSV in high iron regions can better evaluate the distribution of iron, which is related to the decline of cognitive function.

Combining in vivo proton exchange rate (k ex) MRI with quantitative susceptibility mapping to further stratify the gadolinium-negative multiple sclerosis lesions.

Conventional gadolinium (Gd)-enhanced MRI is currently used for stratifying the lesion activity of multiple sclerosis (MS) despite limited correlation with disability and disease activity. The stratification of MS lesion activity needs further improvement to better support clinics. To investigate if the novel proton exchange rate (k ex ) MRI combined with quantitative susceptibility mapping (QSM) may help to further stratify non-enhanced (Gd-negative) MS lesions. From December 2017 to December 2020, clinically diagnosed relapsing-remitting MS patients who underwent MRI were consecutively enrolled in this IRB-approved retrospective study. The customized MRI protocol covered conventional T2-weighted, T2-fluid-attenuated-inversion-recovery, pre- and post-contrast T1-weighted imaging, and quantitative sequences, including k ex MRI based on direct-saturation removed omega plots and QSM. Each MS lesion was evaluated based on its Gd-enhancement as well as its susceptibility and k ex elevation compared to the normal appearing white matter. The difference and correlation concerning lesion characteristics and imaging contrasts were analyzed using the Mann-Whitney U test or Kruskal-Wallis test, and Spearman rank analysis with p < 0.05 considered significant. A total of 322 MS lesions from 30 patients were identified with 153 Gd-enhanced and 169 non-enhanced lesions. We found that the k ex elevation of all lesions significantly correlated with their susceptibility elevation (r = 0.30, p < 0.001). Within the 153 MS lesions with Gd-enhancement, ring-enhanced lesions showed higher k ex elevation than the nodular-enhanced ones' (p < 0.001). Similarly, lesions with ring-hyperintensity in QSM also had higher k ex elevation than the lesions with nodular-QSM-hyperintensity (p < 0.001). Of the 169 Gd-negative lesions, three radiological patterns were recognized according to lesion manifestations on the k ex map and QSM images: Pattern I (k ex + and QSM+, n = 114, 67.5%), Pattern II (only k ex + or QSM+, n = 47, 27.8%) and Pattern III (k ex - and QSM-, n = 8, 4.7%). Compared to Pattern II and III, Pattern I had higher k ex (p < 0.001) and susceptibility (p < 0.05) elevation. The percentage of Pattern I of each subject was negatively correlated with the disease duration (r = -0.45, p = 0.015). As a potential imaging biomarker for inflammation due to oxidative stress, in vivo k ex MRI combined with QSM is promising in extending the clinical classification of MS lesions beyond conventional Gd-enhanced MRI.