Susceptibility Weighted Imaging Data Research Articles

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices. To address these challenges, we propose a Self-supervised Anatomical Continuity Enhancement (SACE) network to synthesize 7T SWI from 3T SWI using plentiful 3T SWI data and limited 3T–7T paired data. The SACE employs two specifically designed pretext tasks to utilize low-level representations from abundant 3T SWI data for assisting 7T SWI synthesis in a downstream task with limited paired data. One pretext task emphasizes input-specific morphology by balancing the elimination of redundant patterns with the preservation of essential morphology, preventing the blurring of synthetic 7T SWI images. The other task improves the synthesis of tissues that are invisible in a single 3T SWI slice by aligning adjacent slices with the current slice and predicting their difference fields. The downstream task innovatively combines clinical knowledge with brain substructure diagrams to selectively enhance clinically relevant features. When evaluated on a dataset comprising 97 cases (5495 slices), the proposed method achieved a Peak Signal-to-Noise Ratio (PSNR) of 23.05 dB and a Structural Similarity Index (SSIM) of 0.688. Due to the absence of specific methods for 7T SWI, our method was compared with existing enhancement techniques for general 7T MRI synthesis, outperforming these techniques in the context of 7T SWI synthesis. Clinical evaluations have shown that our synthetic 7T SWI is clinically effective, demonstrating its potential as a clinical tool.

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.

Automatic detection of cerebral microbleeds using susceptibility weighted imaging and artificial intelligence.

Efficiently and accurately detecting cerebral microbleeds (CMBs) is crucial for diagnosing dementia, stroke, and traumatic brain injury. Manual CMB detection, however, is time-consuming and error-prone. This study evaluates a novel artificial intelligence (AI) software designed for the automated detection of CMBs using susceptibility weighted imaging (SWI). The SWI data from 265 patients, 206 of whom had a history of stroke and others of whom presented a variety of other medical histories, including hypertension, diabetes, hyperlipidemia, cerebral hemorrhage, intracerebral vascular malformations, tumors, and inflammation, collected between January 2015 and December 2018, were analyzed. Two independent radiologists initially reviewed the images to identify and count the number of CMBs. Subsequently, the images were processed using an automatic CMB detection software. The generated reports were then reviewed by the radiologists. A final consensus between the two radiologists, obtained after a second review of the images, was used to compare results obtained from the initial manual detection and those of the automatic CMB detection software. The differences of detection sensitivity and precision for patients with or without CMBs and for individual CMBs between the radiologist and the automatic CMB detection software were compared using Pearson chi-squared tests. A total of 1,738 CMBs were detected among 148 patients (71.4±10.7 years, 100 males) from the analyzed SWI data. While the radiologists identified 139 cases with CMBs, the automatic CMB detection software detected 145 cases. Nevertheless, there was no statistical difference in the sensitivity and specificity of the automatic CMB detection software compared to manual detection in determining patients with CMBs (P=0.656 and P=0.212, chi-square test). However, the radiologist identified 93 patients without CMBs, while the automatic CMB detection software detected 121 patients without CMBs, exhibiting a statistically significant difference (P=0.016, chi-square test). In terms of individual CMBs, the radiologists found 1,284, whereas the automatic CMB detection software detected 1,677 CMBs. The detection sensitivity for human versus the automatic CMB detection software were 75.5% and 96.5% respectively (P<0.001, chi-square test), while the precision rates were 92.2% and 86.0% (P<0.001, chi-square test), respectively. Notably, the radiologists were more likely to overlook CMBs when the number of CMBs was high (above 30). The automatic CMB detection software proved to be an effective tool for the detection and quantification of CMBs. It demonstrated higher sensitivity than the radiologists, especially in detecting minuscule CMBs and in cases with high CMB prevalence.

Improvement grading brain glioma using T2 relaxation times and susceptibility-weighted images in MRI

Developing a new non-invasive, and accurate method for glioma grading for glioma treatment and management is very important. Magnetic resonance imaging (MRI) has many applications in the field of neuroimaging. The study aims to create a precise way to improve glioma grading using different MRI image weights. There were 47 participants in this study. A 1.5 T MRI scanner acquired T2 multi-echoes (T2 ME) with eight echoes for T2 maps reconstruction, Susceptibility-Weighted Imaging (SWI), and T1 post-contrast. T2 maps were reconstructed from T2 ME data using homemade MATLAB code. To compare T2 relaxation times and intratumoral susceptibility signal intensity (ITSS) between the high-grade gliomas (HGGs) and low-grade gliomas (LGGs) and other analyses, we used SPSS software. There was a significant difference in T2 relaxation times and ITSS between the HGGs and LGGs. The average T2 relaxation times in HGGs and LGGs were 0.12 and 0.16 s, respectively. Further analysis showed that the ITSS in HGGs is significantly greater than in LGGs. There was a significant negative correlation between T2 relaxation times and ITSS in gliomas. It is interesting to note that combining T2 relaxation times, ITSS, and tumor enhancement status data with a multivariate binary logistic regression model can increase the area under the curve (AUC) to 0.94 for glioma-grade classification. We recommended the clinical use of combined T2 maps, SWI, and T1 post-contrast image-weights data with a multivariate binary logistic regression model for improving glioma grading.

&lt;I&gt;In vivo&lt;/I&gt; mapping of hippocampal venous vasculature and oxygen saturation using dual-echo SWI/QSM at 7 T: a potential marker for neurodegeneration

Background: The current understanding of the venous system in the hippocampus is mostly based on histological and autopsy studies.1 However, the main disadvantage is that it only reveals the anatomy of the vascular system at the post-mortem stage and lacks physiological aspects associated with neuronal metabolism. In vivo characterization of the venous system using susceptibility weighted imaging (SWI) at 7 T could provide valuable information on both venous anatomy and blood oxygen saturation, through high-resolution SWI venography2 and quantitative susceptibility mapping (QSM).3 In this study, we aim to elucidate the hierarchical network of the hippocampal venous system and then test the feasibility of using venous susceptibility to characterize venous oxygenation level changes related to neurodegeneration.&#x0D; Methods: Seven healthy volunteers were recruited for this study. We used high in-plane resolution of flow-compensated dual-echo gradient echo sequence (TE1/TE2/TR=7.5/15/22 ms, voxel size: 0.25*0.25*1 mm). SWI and QSM were then reconstructed using the iterative SWI and mapping (iterative SWIM) algorithm,3 as shown in Figure 1. Hippocampus masks were extracted from the T1-MPRAGE image, which was transformed to SWI space afterwards. To reduce the partial volume effect from the tissue-vessel boundary, we extract the venous susceptibility value from each voxel along the centerline of the vessels.&#x0D; Results: High-resolution in vivo mapping of hippocampal venous vasculature exhibits a high analogy to Duvernoy’s reference4 for hippocampal vascularization. As shown in Figure 1, there is a shape of venous arch near the fimbria of the hippocampus, and small veins extending through the arch are possibly the intrahippocampal veins. The intrahippocampal veins will eventually reach the inferior ventricular vein (IVV) (anteriorly) and medial atrial vein (MAV) (posteriorly), before joining the basal vein of Rosenthal (BVR). For venous susceptibility quantification, Figure 1 shows the representative color-coded QSM for centerline extraction on BVR.&#x0D; Conclusions: Our results showed improved visualization of the micro venous system in the hippocampus using high-resolution 7 T SWI data without the contrast agent.5 In summary, the characterization of venous QSM in major tributaries related to the hippocampus offers a novel perspective on oxygen utilization in the hippocampus, which may be useful for studying age-related dementia. We delineated the hierarchical network of the hippocampus venous system using SWI/QSM at 7 T and extract the venous density and venous susceptibility value in hippocampus-related small veins and major venous tributaries, as an overall measure for venous oxygenation level related to the hippocampus, which may be used as an early marker for hippocampal atrophy in Alzheimer’s disease.

CMB-HUNT: Automatic detection of cerebral microbleeds using a deep neural network

Cerebral microbleeds (CMBs) are gaining increasing interest due to their importance in diagnosing cerebral small vessel diseases. However, manual inspection of CMBs is time-consuming and prone to human error. Existing automated or semi-automated solutions still have insufficient detection sensitivity and specificity. Furthermore, they frequently use more than one magnetic resonance imaging modality, but these are not always available. The majority of AI-based solutions use either numeric or image data, which may not provide sufficient information about the true nature of CMBs.This paper proposes a deep neural network with multi-type input data for automated CMB detection (CMB-HUNT) using only susceptibility-weighted imaging data (SWI). Combination of SWIs and radiomic-type numerical features allowed us to identify CMBs with high accuracy without the need for additional imaging modalities or complex predictive models. Two independent datasets were used: one with 304 patients (39 with CMBs) for training and internal system validation and one with 61 patients (21 with CMBs) for external validation.For the hold-out testing dataset, CMB-HUNT reached a sensitivity of 90.0%. As results of testing showed, CMB-HUNT outperforms existing methods in terms of the number of FPs per case, which is the lowest reported thus far (0.54 FPs/patient). The proposed system was successfully applied to the independent validation set, reaching a sensitivity of 91.5% with 1.9 false positives per patient and proving its generalization potential. The results were comparable to previous studies. Our research confirms the usefulness of deep learning solutions for CMB detection based only on one MRI modality.

S2Q-Net: Mining the High-Pass Filtered Phase Data in Susceptibility Weighted Imaging for Quantitative Susceptibility Mapping.

Susceptibility weighted imaging (SWI) is a routine magnetic resonance imaging (MRI) sequence that combines the magnitude and high-pass filtered phase images to qualitatively enhance the image contrasts related to tissue susceptibility. Tremendous amounts of the high-pass filtered phase data with low signal to noise ratio and incomplete background field removal have thus been collected under default clinical settings. Since SWI cannot quantitatively estimate the susceptibility, it is thus non-trivial to derive quantitative susceptibility mapping (QSM) directly from these redundant phase data, which effectively promotes the mining of the SWI data collected previously. To this end, a novel deep learning based SWI-to-QSM-Net (S2Q-Net) is proposed for QSM reconstruction from SWI high-pass filtered phase data. S2Q-Net firstly estimates the edge maps of QSM to integrate edge prior into features, which benefits the network to reconstruct QSM with realistic and clear tissue boundaries. Furthermore, a novel Second-order Cross Dense Block is proposed in S2Q-Net, which can capture rich inter-region interactions to provide more non-local phase information related to local tissue susceptibility. Experimental results on both simulated and in-vivo data indicate its superiority over all the compared deep learning based QSM reconstruction methods.

Quantitative susceptibility mapping of both ring and non-ring white matter lesions in relapsing remitting multiple sclerosis

BackgroundRecent evidence suggests that the presence of magnetic susceptibility changes in MS lesions correlates with patients' expanded disability status scale (EDSS). PurposeThis study evaluated the presence of ring lesions (RLs) and non-RLs, as well as changes in lesion susceptibility and lesion volume over a two-year time period in relapsing-remitting multiple sclerosis (RRMS) patients and compared these measures to the EDSS. Study typeLongitudinal cohort. Materials and methodsA total of forty-three (43) patients with RRMS were recruited for this study. All subjects underwent 3 T MRI at baseline and forty-one (41) subjects had follow-up scans over a two-year period. The protocol included T2 fluid attenuated inversion recovery (FLAIR), pre- and post-contrast 2D T1-weighted gradient echo imaging and susceptibility weighted imaging (SWI) sequences. All data were acquired prior to gadolinium-based contrast agent injection except for SWI and post-contrast T1-weighted data. Mean and peak susceptibilities of lesions were measured using quantitative susceptibility mapping (QSM). For the RLs, inner−/outer-diameters from SWI data and absolute intensity from the FLAIR data were measured. The susceptibility and volumes of RLs and non-RLs were correlated with EDSS. ResultsThe number of RLs correlated positively with EDSS (p-value = 0.04), but the RL volumes did not show a correlation with EDSS. Measurements of the annular ring and regions with high susceptibility remained constant over time (p = 0.2). For non-ring QSM-positive (QSM+) lesions, the average susceptibility was significantly correlated to EDSS (p < 0.01.) This was also the case for lesion volume, as well as the product of volume and susceptibility when compared to EDSS (p < 0.01 for both). ConclusionsThe susceptibility distribution and lesion volumes for the RLs remained almost constant over time, suggesting that the changes in pathophysiology of the RLs is a gradual process. On the other hand, the presence of more than one RL along with both volume and susceptibility of non-RL QSM+ lesions were significantly associated with increased disability as measured by EDSS. These findings may strengthen the role of QSM in assessing MS patients radiologically.

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and evaluate the clinical feasibility of this approach. A complex-valued convolutional neural network (ComplexNet) was developed to reconstruct high-quality SWI from highly accelerated k-space data. ComplexNet can leverage the inherently complex-valued nature of SWI data and learn richer representations by using complex-valued network. SWI data were acquired from 117 participants who underwent clinical brain MRI examination between 2019 and 2021, including patients with tumor, stroke, hemorrhage, traumatic brain injury, etc. Reconstruction quality was evaluated using quantitative image metrics and image quality scores, including overall image quality, signal-to-noise ratio, sharpness, and artifacts. The average reconstruction time of ComplexNet was 19 ms per section (1.33 s per participant). ComplexNet achieved significantly improved quantitative image metrics compared to a conventional compressed sensing method and a real-valued network with acceleration rates of 5 and 8 (p < 0.001). Meanwhile, there was no significant difference between fully sampled and ComplexNet approaches in terms of overall image quality and artifacts (p > 0.05) at both acceleration rates. Furthermore, ComplexNet showed comparable diagnostic performance to the fully sampled SWI for visualizing a wide range of pathology, including hemorrhage, cerebral microbleeds, and brain tumor. ComplexNet can effectively accelerate SWI while providing superior performance in terms of overall image quality and visualization of pathology for routine clinical brain imaging. • The complex-valued convolutional neural network (ComplexNet) allowed fast and high-quality reconstruction of highly accelerated SWI data, with an average reconstruction time of 19 ms per section. • ComplexNet achieved significantly improved quantitative image metrics compared to a conventional compressed sensing method and a real-valued network with acceleration rates of 5 and 8 (p < 0.001). • ComplexNet showed comparable diagnostic performance to the fully sampled SWI for visualizing a wide range of pathology, including hemorrhage, cerebral microbleeds, and brain tumor.

Neuroimaging correlates of brain injury in Wilson's disease: a multimodal, whole-brain MRI study.

Wilson’s disease is an autosomal-recessive disorder of copper metabolism with neurological and hepatic presentations. Chelation therapy is used to ‘de-copper’ patients but neurological outcomes remain unpredictable. A range of neuroimaging abnormalities have been described and may provide insights into disease mechanisms, in addition to prognostic and monitoring biomarkers. Previous quantitative MRI analyses have focused on specific sequences or regions of interest, often stratifying chronically treated patients according to persisting symptoms as opposed to initial presentation.In this cross-sectional study, we performed a combination of unbiased, whole-brain analyses on T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and susceptibility-weighted imaging data from 40 prospectively recruited patients with Wilson’s disease (age range 16–68). We compared patients with neurological (n = 23) and hepatic (n = 17) presentations to determine the neuroradiological sequelae of the initial brain injury. We also subcategorized patients according to recent neurological status, classifying those with neurological presentations or deterioration in the preceding 6 months as having ‘active’ disease. This allowed us to compare patients with active (n = 5) and stable (n = 35) disease and identify imaging correlates for persistent neurological deficits and copper indices in chronically treated, stable patients.Using a combination of voxel-based morphometry and region-of-interest volumetric analyses, we demonstrate that grey matter volumes are lower in the basal ganglia, thalamus, brainstem, cerebellum, anterior insula and orbitofrontal cortex when comparing patients with neurological and hepatic presentations. In chronically treated, stable patients, the severity of neurological deficits correlated with grey matter volumes in similar, predominantly subcortical regions. In contrast, the severity of neurological deficits did not correlate with the volume of white matter hyperintensities, calculated using an automated lesion segmentation algorithm. Using tract-based spatial statistics, increasing neurological severity in chronically treated patients was associated with decreasing axial diffusivity in white matter tracts whereas increasing serum non-caeruloplasmin-bound (‘free’) copper and active disease were associated with distinct patterns of increasing mean, axial and radial diffusivity. Whole-brain quantitative susceptibility mapping identified increased iron deposition in the putamen, cingulate and medial frontal cortices of patients with neurological presentations relative to those with hepatic presentations and neurological severity was associated with iron deposition in widespread cortical regions in chronically treated patients.Our data indicate that composite measures of subcortical atrophy provide useful prognostic biomarkers, whereas abnormal mean, axial and radial diffusivity are promising monitoring biomarkers. Finally, deposition of brain iron in response to copper accumulation may directly contribute to neurodegeneration in Wilson’s disease.

Revealing vascular abnormalities and measuring small vessel density in multiple sclerosis lesions using USPIO

Background and PurposeMultiple Sclerosis (MS) is a progressive, inflammatory, neuro-degenerative disease of the central nervous system (CNS) characterized by a wide range of histopathological features including vascular abnormalities. In this study, an ultra-small superparamagnetic iron oxide (USPIO) contrast agent, Ferumoxytol, was administered to induce an increase in susceptibility for both arteries and veins to help better reveal the cerebral microvasculature. The purpose of this work was to examine the presence of vascular abnormalities and vascular density in MS lesions using high-resolution susceptibility weighted imaging (SWI). MethodsSix subjects with relapsing remitting MS (RRMS, age = 47.3 ± 11.8 years with 3 females and 3 males) and fourteen age-matched healthy controls were scanned at 3 T with SWI acquired before and after the infusion of Ferumoxytol. Composite data was generated by registering the FLAIR data to the high resolution SWI data in order to highlight the vascular information in MS lesions. Both the central vein sign (CVS) and, a new measure, the multiple vessel sign (MVS) were identified, along with any vascular abnormalities, in the lesions on pre- and post-contrast SWI-FLAIR fusion data. The small vessel density within the periventricular normal-appearing white matter (NAWM) and the periventricular lesions were compared for all subjects. ResultsAveraged across two independent raters, a total of 530 lesions were identified across all patients. The total number of lesions with vascularity on pre- and post-contrast data were 287 and 488, respectively. The lesions with abnormal vascular behavior were broken up into following categories: small lesions appearing only at the vessel boundary; dilated vessels within the lesions; and developmental venous angiomas. These vessel abnormalities observed within lesions increased from 55 on pre-contrast data to 153 on post-contrast data. Finally, across all the patients, the periventricular lesional vessel density was significantly higher (p < 0.05) than that of the periventricular NAWM. ConclusionsBy inducing a super-paramagnetic susceptibility in the blood using Ferumoxytol, the vascular abnormalities in the RRMS patients were revealed and small vessel densities were obtained. This approach has the potential to monitor the venous vasculature present in MS lesions, catalogue their characteristics and compare the vascular structures spatially to the presence of lesions. These enhanced vascular features may provide new insight into the pathophysiology of MS.

Subvoxel vascular imaging of the midbrain using USPIO-Enhanced MRI

There is an urgent need for better detection and understanding of vascular abnormalities at the micro-level, where critical vascular nourishment and cellular metabolic changes occur. This is especially the case for structures such as the midbrain where both the feeding and draining vessels are quite small. Being able to monitor and diagnose vascular changes earlier will aid in better understanding the etiology of the disease and in the development of therapeutics. In this work, thirteen healthy volunteers were scanned with a dual echo susceptibility weighted imaging (SWI) sequence, with a resolution of 0.22 ​× ​0.44 ​× ​1 ​mm3 at 3T. Ultra-small superparamagnetic iron oxides (USPIO) were used to induce an increase in susceptibility in both arteries and veins. Although the increased vascular susceptibility enhances the visibility of small subvoxel vessels, the accompanying strong signal loss of the large vessels deteriorates the local tissue contrast. To overcome this problem, the SWI data were acquired at different time points during a gradual administration (final concentration ​= ​4 ​mg/kg) of the USPIO agent, Ferumoxytol, and the data was processed to combine the SWI data dynamically, in order to see through these blooming artifacts. The major vessels and their tributaries (such as the collicular artery, peduncular artery, peduncular vein and the lateral mesencephalic vein) were identified on the combined SWI data using arterio-venous maps. Dynamically combined SWI data was then compared with previous histological work to validate that this protocol was able to detect small vessels on the order of 50 ​μm–100 ​μm. A complex division-based phase unwrapping was also employed to improve the quality of quantitative susceptibility maps by reducing the artifacts due to aliased voxels at the vessel boundaries. The smallest detectable vessel size was then evaluated by revisiting numerical simulations, using estimated true susceptibilities for the basal vein and the posterior cerebral artery in the presence of Ferumoxytol. These simulations suggest that vessels as small as 50 ​μm should be visible with the maximum dose of 4 ​mg/kg.

Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T.

The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging. High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B0 perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases. Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization. Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.

Visualizing the lateral habenula using susceptibility weighted imaging and quantitative susceptibility mapping

The habenulae consist of a pair of small nuclei which bridge the limbic forebrain and midbrain monoaminergic centers. They are implicated in major depressive disorders due to abnormal phasic response when provoked by a conditioned stimulus. The lateral habenula (Lhb) is believed to be involved in dopamine metabolism and is now a target for deep brain stimulation, a treatment which has shown promising anti-depression effects. We imaged the habenulae with susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in order to localize the lateral habenula. Fifty-six healthy controls were recruited for this study. For the quantitative assessment, we traced the structure to compute volume from magnitude images and mean susceptibility bilaterally for the habenula on QSM. Thresholding methods were used to delineate the Lhb habenula on QSM. SWI, true SWI (tSWI), and QSM data were subjectively reviewed for increased Lhb contrast. SWI, QSM, and tSWI showed bilateral signal changes in the posterior location of the habenulae relative to the anterior location, which may indicate increased putative iron content within the Lhb. This signal behavior was shown in 41/44 (93%) subjects. In summary, it is possible to localize the lateral component of the habenula using SWI and QSM at 3 T.

Mapping of apparent susceptibility yields promising diagnostic separation of progressive supranuclear palsy from other causes of parkinsonism

There is a need for methods that distinguish Parkinson’s disease (PD) from progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), which have similar characteristics in the early stages of the disease. In this prospective study, we evaluate mapping of apparent susceptibility based on susceptibility weighted imaging (SWI) for differential diagnosis. We included 134 patients with PD, 11 with PSP, 10 with MSA and 44 healthy controls. SWI data were processed into maps of apparent susceptibility. In PSP, apparent susceptibility was increased in the red nucleus compared to all other groups, and in globus pallidus, putamen, substantia nigra and the dentate nucleus compared to PD and controls. In MSA, putaminal susceptibility was increased compared to PD and controls. Including all studied regions and using discriminant analysis between PSP and PD, 100% sensitivity and 97% specificity was achieved, and 91% sensitivity and 90% specificity in separating PSP from MSA. Correlations between putaminal susceptibility and disease severity in PD could warrant further research into using susceptibility mapping for monitoring disease progression and in clinical trials. Our study indicates that susceptibility in deep nuclei could play a role in the diagnosis of atypical parkinsonism, especially in PSP.

Asymmetric Cortical Vessel Sign Indicates Hemodynamic Deficits in Adult Patients with Moyamoya Disease

Asymmetric cortical vessel sign (ACVS) on susceptibility-weighted imaging (SWI) indicates elevated concentration of deoxyhemoglobin and elevated oxygen extraction fraction in patients with cerebral ischemia. This study aimed to clarify whether ACVS is associated with impaired hemodynamics and hyperperfusion syndrome in patients with moyamoya disease (MMD). Consecutive adult patients with MMD were enrolled. ACVS data on SWI and perfusion data using dynamic perfusion computed tomography were obtained and evaluated preoperatively and on postoperative days 2 and180. A total of 24 patients with MMD were enrolled. Of 11 (45.83%) patients showing positive ACVS before surgery, 8 turned negative on postoperative day 2 and 9 showed absence of ACVS 180 days after surgery. Regions of interest showing positive ACVS had lower cerebral blood flow (CBF, P<0.001), increased cerebral blood volume (P= 0.021), prolonged time to peak (P<0.001), and mean transit time (P= 0.009). No patients with hemorrhagic symptoms showed positive ACVS(P= 0.041) and patients with positive ACVS showed more increase in CBF (P<0.004). In patients with MMD, ACVS on SWI indicates severe impairment in hemodynamics and is associated with more increase in CBF after bypass surgery. Hence, ACVS on SWI might be considered as a neuroimaging marker for the evaluation of hemodynamics in patients with MMD.

SWI processing using a local phase difference modulated venous enhancement filter with noise compensation

Susceptibility weighted imaging (SWI) involves post-processing of gradient echo images which are sensitive to the spatial variations in magnetic susceptibility. The aim of this study is to develop an automated filtering scheme to enhance the contrast-to-noise ratio (CNR) and blooming on SWI. Here, the high-pass filtering for SWI processing is designed by applying a weighting function to the neighboring phase differences to enhance the susceptibility-related (SuR) contrast. This is accomplished by summing the neighboring phase differences, weighted with a scaled and shifted error function of the phase difference. Besides using the filter weights of this weighted high-pass (WHP) filter to minimize the filtering artefacts using a filter scale parameter, the CNR is further increased by introduction of the neighborhood-based noise compensation weights into the filtering process. These weights are deduced from the channel phase distribution, conditioned on the channel magnitude and noise variance. Using in vivo SWI data acquired at 1.5 T (16 nos.) and 3.0 T (30 nos.), the magnitude SWI processed using the noise compensated WHP (WHPC) filter is shown to provide an average CNR improvement of 68.40% over that of a homodyne high-pass (HHP) filter. Two tailed t-tests performed separately for different field strengths, show significant differences (p < 0.001) between mean separations of phase masks generated from the WHPC and HHP filtered phase images. In conclusion, the WHPC filter, tuned by the mean separation of the phase mask, enhances the SuR contrast of magnitude SWI for evaluation of mild cognitive impairments, brain tumor and hemorrhagic stroke.

Increased Visibility of Deep Medullary Veins in Transient Ischemic Attack Patients by Using Susceptibility Weighted Imaging

Objectives: To explore alteration of deep medullary veins (DMVs) in transient ischemic attack (TIA) patients by using susceptibility weightedimaging (SWI). Patients and Methods: Fifty-three TIA patients and 53 matched gender and age controls’ SWI data were studied by comparing their DMVs asymmetry/score between groups. The DMVs score based on the degree of visibility were assessed from 0 (not visible) to 3 (very prominent) for both hemispheres separately. A different score between the two hemispheres was defined as asymmetric DMVs (ADMVs), and an equal score was defined as symmetric DMVs. The higher score in the two hemispheres worked as its DMVs score for ADMVs. In ADMVs, based on whether the affected hemisphere, which was defined according to clinical symptoms by neurologists, gets a higher score or not, was defined as ipsilateral or contralateral asymmetric DMVs (iADMVs or cADMVs). Results: The agreement between neuroradiologists for both asymmetry and score of DMVs on TIA patients’ SWI were excellent. There were statistically significant differences for the score of DMVs between TIA and the control group (P < 0.05) even when they were analyzed by further stratification analysis according to the asymmetry of DMVs, as well as asymmetry of DMVs. Of fifty-three patients, 16 patients were present with ADMVs, including 10 iADMVs, and six cADMVs. Conclusion: The visibility of DMVs in patients with TIA were increased, with or without ADMVs, and these alterations may reflect hemodynamic information following TIA process, laying foundations for DMVs application in TIA.

An interleaved sequence for simultaneous magnetic resonance angiography (MRA), susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM)

PurposeTo image the entire vasculature of the brain with complete suppression of signal from background tissue using a single 3D excitation interleaved rephased/dephased multi-echo gradient echo sequence. This ensures no loss of signal from fast flow and provides co-registered susceptibility weighted images (SWI) and quantitative susceptibility maps (QSM) from the same scan. Materials and methodsThe suppression of background tissue was accomplished by subtracting the flow-dephased images from the flow-rephased images with the same echo time of 12.5ms to generate a magnetic resonance angiogram and venogram (MRAV). Further, a 2.5ms flow-compensated echo was added in the rephased portion to provide sufficient signal for major arteries with fast flow. The QSM data from the rephased 12.5ms echo was used to suppress veins on the MRAV to generate an artery-only MRA. The proposed approach was tested on five healthy volunteers at 3T. ResultsThis three-echo interleaved GRE sequence provided complete background suppression of stationary tissues, while the short echo data gave high signal in the internal carotid and middle cerebral arteries (MCA). The contrast-to-noise ratio (CNR) of the arteries was significantly improved in the M3 territory of the MCA compared to the non-linear subtraction MRA and TOF-MRA. Veins were suppressed successfully utilizing the QSM data. ConclusionThe background tissue can be properly suppressed using the proposed interleaved MRAV sequence. One can obtain whole brain MRAV, MRA, SWI, true-SWI (or tSWI) and QSM data simultaneously from a single scan.

Magnitude image-guided phase unwrapping algorithm of susceptibility weighted images

To better use the phase information to compensate the influence of blood flow, the phase unwrapping problem in susceptibility weighted imaging (SWI) is studied in this paper. In order to improve the accuracy of unwrapping, this paper proposes a magnitude image-guided phase unwrapping algorithm of SWI. The basic idea is as follows: (1) reduce the influence of noise by improving the rotational invariant non-local principal component analysis method (PRI-NL-PCA); (2) extract the corresponding solid region in the phase image to avoid the influence of the background noise on the phase unwrapping method; (3) use the phase compensation method to constrain the phase image reconstructed by the K-space. Finally, the reliability of the unwrapping method is evaluated by using four kinds of statistics as quantification index: the number, mean (M), variance (Var), and positive percentage (Pos) and negative percentage (Neg) of phasic error points. By comparing the simulated data with 226 sets of true head SWI data, the results show that the proposed algorithm has high accuracy compared with the classical branch cut method and the least squares method.