MRI index of glymphatic system mediates the influence of locus coeruleus on cognition in Parkinson's disease.

INTRODUCTION: Although locus coeruleus (LC) has been demonstrated to play a critical role in the cognitive function of Parkinson's disease (PD), the underlying mechanism has not been elucidated. The objective was to investigate the relationship among LC degeneration, cognitive performance, and the glymphatic function in PD. METHODS: In this retrospective study, 71 PD subjects (21 with normal cognition; 29 with cognitive impairment (PD-MCI); 21 with dementia (PDD)) and 26 healthy controls were included. All participants underwent neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and diffusion tensor image scanning on a 3.0 T scanner. The brain glymphatic function was measured using diffusion along the perivascular space (ALPS) index, while LC degeneration was estimated using the NM contrast-to-noise ratio of LC (CNRLC). RESULTS: The ALPS index was significantly lower in both the whole PD group (P = 0.04) and the PDD subgroup (P = 0.02) when compared to the controls. Similarly, the CNRLC was lower in the whole PD group (P < 0.001) compared to the controls. In the PD group, a positive correlation was found between the ALPS index and both the Montreal Cognitive Assessment (MoCA) score (r = 0.36; P = 0.002) and CNRLC (r = 0.26; P = 0.03). Mediation analysis demonstrated that the ALPS index acted as a significant mediator between CNRLC and the MoCA score in PD subjects. CONCLUSION: The ALPS index, a neuroimaging marker of glymphatic function, serves as a mediator between LC degeneration and cognitive function in PD.

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

Background: 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). Methods: 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. Results: 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). Conclusions: 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.

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.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Locus coeruleus and substantia nigra neuromelanin magnetic resonance imaging differentiates Parkinson's disease and essential tremor.

BACKGROUND: Differential diagnosis of essential tremor (ET) and Parkinson's disease (PD) can still be a challenge in clinical practice. These two tremor disorders may have different pathogenesis related to the substantia nigra (SN) and locus coeruleus (LC). Characterizing neuromelanin (NM) in these structures may help improve the differential diagnosis. METHODS: Forty-three subjects with tremor-dominant PD (PDTD), 31 subjects with ET, and 30 age- and sex-matched healthy controls were included. All subjects were scanned with NM magnetic resonance imaging (NM-MRI). NM volume and contrast measures for the SN and contrast for the LC were evaluated. Logistic regression was used to calculate predicted probabilities by using the combination of SN and LC NM measures. The discriminative power of the NM measures in detecting subjects with PDTD from ET was assessed with a receiver operative characteristic curve, and the area under the curve (AUC) was calculated. RESULTS: The NM contrast-to-noise ratio (CNR) of the LC, the NM volume, and CNR of the SN on the right and left sides were significantly lower in PDTD subjects than in ET subjects or healthy controls (all P < 0.05). Furthermore, when combining the best model constructed from the NM measures, the AUC reached 0.92 in differentiating PDTD from ET. CONCLUSION: The NM volume and contrast measures of the SN and contrast for the LC provided a new perspective on the differential diagnosis of PDTD and ET, and the investigation of the underlying pathophysiology.

Does Vitamin D Provide Added Benefit to Antihypertensive Therapy in Reducing Left Ventricular Hypertrophy Determined by Cardiac Magnetic Resonance?

BACKGROUND: Left ventricular hypertrophy (LVH) and vitamin D deficiency have been linked to hypertension (HTN) and cardiovascular disease, particularly in African Americans (AAs). Our objective was to determine if the addition of vitamin D to antihypertensive therapy would lead to greater regression of LV mass index (LVMI) as determined by cardiac magnetic resonance (CMR) after 1 year in vitamin D deficient AA patients with uncontrolled HTN and LVH. METHODS: This study was a randomized, double-blind, placebo-controlled, single-center study. AA patients with HTN (systolic blood pressure [BP] >160 mm Hg), increased LVMI, and vitamin D deficiency (<20 ng/ml) were randomized. All patients received antihypertensive therapy combined with biweekly 50,000 IU vitamin D3 (vitamin D group, n = 55) or placebo (placebo group, n = 58). RESULTS: At 1 year, there were no statistical differences between the vitamin D and placebo groups in LVMI (-14.1 ± 14.6 vs. -16.9 ± 13.1 g/m2; P = 0.34) or systolic BP (-25.6 ± 32.1 vs. -25.7 ± 25.6 mm Hg; P = 0.99) reduction, respectively. Serum vitamin D levels increased significantly in the vitamin D group compared with placebo (12.7 ± 2.0 vs. 1.8 ± 8.2 ng/ml; P < 0.001). CONCLUSIONS: In this high-risk cohort of AAs we did not find an association between vitamin D supplementation and differential regression of LVMI or reduction in systolic BP. However, our study suffered from a small sample size with low statistical power precluding a definitive conclusion on the therapeutic benefit of vitamin D in such patients. CLINICAL TRIALS REGISTRATION: Trial Number NCT01360476. Full trial protocol is available from corresponding author.

Neuroimaging Markers for Determining Former American Football Players at Risk for Alzheimer's Disease.

NFL players, by virtue of their exposure to traumatic brain injury (TBI), are at higher risk of developing dementia and Alzheimer's disease (AD) than the general population. Early recognition and intervention before the onset of clinical symptoms could potentially avert/delay the long-term consequences of these diseases. Given that AD is thought to have a long pre-clinical incubation period, the aim of the current research was to determine whether former NFL players show evidence of incipient dementia in their structural imaging before diagnosis of AD. To identify neuroimaging markers of AD, against which former NFL players would be compared, we conducted a whole-brain volumetric analysis using a cohort of AD patients (ADNI clinical database) to produce a set of brain regions demonstrating sensitivity to early AD pathology (i.e., the "AD fingerprint"). A group of 46 former NFL players' brain magnetic resonance images were then interrogated using the AD fingerprint, that is, the former NFL subjects were compared volumetrically to AD patients using a T1-weighted magnetization-prepared rapid gradient echo sequence. The FreeSurfer image analysis suite (version 6.0) was used to obtain volumetric and cortical thickness data. The Automated Neuropsychological Assessment Metric-Version 4 was used to assess current cognitive functioning. A total of 55 brain regions demonstrated significant atrophy or ex vacuo dilatation bilaterally in AD patients versus controls. Of the 46 former NFL players, 41% demonstrated a greater than expected number of atrophied/dilated AD regions compared with age-matched controls, presumably reflecting AD pathology.

Use of magnetic resonance imaging in the diagnosis of fetal vertebral abnormalities in utero: a single-center retrospective cohort study.

Background: Magnetic resonance imaging (MRI) has been used increasingly as an adjunct examination to ultrasound (US) for the evaluation of fetal anomalies. The purpose of this study was to determine whether the accuracy and confidence of diagnosing fetal vertebral anomalies are improved with MRI. We also assessed whether fetal MRI provides additional information when diagnosing fetal vertebral anomalies. Methods: We performed a single-center, retrospective study of 127 pregnant women with fetuses suspected of having vertebral anomalies on US examination; women also underwent fetal MRI scanning. Comparisons of diagnostic accuracy and confidence were made between MRI and US for the identification of fetal vertebral anomalies. We also assessed any additional information provided by MRI. McNemar's paired binomial test, chi-square test, or Fisher's exact test were used to compare the diagnostic ability between MRI and US. In all cases, postnatal or postmortem imaging findings were used as reference standards. Results: A total of 127 participants were recruited between December 2015 and January 2021. Fetal vertebral anomalies were detected in 63.8% (81/127) cases and found to be negative in 36.2% (46/127) of cases at follow up. The diagnostic accuracy of vertebral anomalies was 46.9% (38/81) for US and 84.0% (68/81) for MRI [difference, 37.1%; 95% confidence interval (CI): 27% to 48%; P<0.001]. Both MRI and US were concordant and correct in 36.2% (46/127) of fetuses; MRI provided additional information for 16.5% (21/127) of fetuses, and corrected US diagnoses of 36.2% (46/127) of fetuses; both MRI and US were not consistent with postnatal findings in 10.2% (13/127) of fetuses, and the remaining fetus (0.8%, 1/127) was diagnosed correctly using US but failed to be diagnosed by MRI. Diagnoses were reported with high confidence using MRI in 95.3% (121/127) of cases and 73.2% (93/127) using US. Conclusions: Fetal vertebral MRI improves the accuracy and confidence of diagnosing fetal vertebral anomalies. This finding indicates that fetal MRI supplements the information provided by US and that MRI may be a good complement in selected fetuses, when US can either not achieve a definite diagnosis or there is doubt regarding its reliability. Thus, MRI may be used to inform prenatal counseling and management decisions.

Mapping Motor Pathways in Parkinson's Disease Patients with Subthalamic Deep Brain Stimulator: A Diffusion MRI Tractography Study.

INTRODUCTION: This study assessed the safety of postoperative diffusion tensor imaging (DTI) with on-state deep brain stimulation (DBS) and the feasibility of reconstruction of the white matter tracts in the vicinity of the stimulation site of the subthalamic nucleus (STN). The association between the impact of DBS on the nigrostriatal pathway (NSP) and the treatment effect on motor symptoms in Parkinson's disease (PD) was then evaluated. METHODS: Thirty-one PD patients implanted with STN-DBS (mean age: 66 years; 25 male) were scanned on a 1.5-T magnetic resonance imaging (MRI) scanner using the DTI sequence with DBS on. Twenty-three of them were scanned a second time with DBS off. The NSP, dentato-rubro-thalamic tract (DRTT), and hyperdirect pathway (HDP) were generated using both deterministic and probabilistic tractography methods. The DBS-on-state and off-state tractography results were validated and compared. Afterward, the relationships between the characteristics of the reconstructed white matter tracts and the clinical assessment of PD symptoms and the DBS effect were further examined. RESULTS: No adverse events related to DTI were identified in either the DBS-on-state or off-state. Overall, the NSP was best reconstructed, followed by the DRTT and HDP, using the probabilistic tractography method. The connection probability of the left NSP was significantly lower than that of the right side (p < 0.05), and a negative correlation (r = -0.39, p = 0.042) was identified between the preoperative symptom severity in the medication-on state and the connection probability of the left NSP in the DBS-on-state images. Furthermore, the distance from the estimated left-side volume of tissue activated (VTA) by STN-DBS to the ipsilateral NSP was significantly shorter in the DBS-responsive group compared to the DBS-non-responsive group (p = 0.046). CONCLUSIONS: DTI scanning is safe and delineation of white matter pathway is feasible for PD patients implanted with the DBS device. Postoperative DTI is a useful technique to strengthen our current understanding of the therapeutic effect of DBS for PD and has the potential to refine target selection strategies for brain stimulation.

For some more seriously affected Parkinson's disease (PD) patients, drugs are no longer effective in treating their symptoms. An alternate treatment is to use deep brain stimulation (DBS), a commonly used neurosurgical therapy for PD patients. For those DBS treatments targeting the subthalamic nucleus (STN), the electrical stimulation used may impact nearby white matter tracts and alter the effectiveness of the DBS treatment. The nigrostriatal pathway (NSP), dentato-rubro-thalamic tract, and hyperdirect pathway are three white matter tracts near the STN. They are all relevant to motor symptoms in PD. This study examined whether imaging these tracts using magnetic resonance imaging (MRI) is safe and feasible in the presence of DBS leads. The relationships between the fiber-tracking characteristics and distance to the DBS leads were then evaluated. For this purpose, 31 PD patients with stimulation-on were scanned on a 1.5 T MRI scanner using a diffusion tensor imaging sequence. A total of 23 subjects underwent another scan using the same sequence with stimulation-off. No adverse events related to diffusion tensor imaging were found. Among the white matter tracts near the STN, the NSP was best delineated, followed by the dentato-rubro-thalamic tract and the hyperdirect pathway. The connection probability of the left NSP was significantly lower than that of the right side as were the subject's motor symptoms. The closer the distance between the NSP and the stimulation location, the better the DBS outcome. These findings indicate that imaging white matter tracts with DBS on is safe and useful in mapping DBS outcomes.

Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in-vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC-GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC-GRE sequence was used to find the NM-rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45-81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine-tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM-rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template-based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.

Stability of AI-Enabled Diagnosis of Parkinson's Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging.

Purpose: Parkinson's disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm. Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel "gated pooling" operation and integrated it with deep learning to attain a joint framework for image segmentation and classification. Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the "gated pooling" operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline. Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.

Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study.

BACKGROUND: Emerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson's Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD. OBJECTIVE: To investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD. METHODS: Thirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1-3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone. RESULTS: For the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42). CONCLUSION: Our findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.

Imaging patients pre and post deep brain stimulation: Localization of the electrodes and their targets.

PURPOSE: Deep brain stimulation (DBS) has become a widely performed surgical procedure for patients with medically refractory movement disorders and mental disorders. It is clinically important to set up a MRI protocol to map the brain targets and electrodes of the patients before and after DBS and to understand the imaging artifacts caused by the electrodes. METHODS: Five patients with DBS electrodes implanted in the habenula (Hb), fourteen patients with globus pallidus internus (GPi) targeted DBS, three pre-DBS patients and seven healthy controls were included in the study. The MRI protocol consisted of magnetization prepared rapid acquisition gradient echo T1 (MPRAGE T1W), 3D multi-echo gradient recalled echo (ME-GRE) and 2D fast spin echo T2 (FSE T2W) sequences to map the brain targets and electrodes of the patients. Phantom experiments were also run to determine both the artifacts and the susceptibility of the electrodes. Signal to noise ratio (SNR) on T1W, T2W and GRE datasets were measured. The visibility of the brain structures was scored according to the Rose criterion. A detailed analysis of the characteristics of the electrodes in all three sequence types was performed to confirm the reliability of the postoperative MRI approach. In order to understand the signal behavior, we also simulated the corresponding magnitude data using the same imaging parameters as in the phantom sequences. RESULTS: The mean ± inter-subject variability of the SNRs, across the subjects for T1W, T2W, and GRE datasets were 20.1 ± 8.1, 14.9 ± 3.2, and 43.0 ± 7.6, respectively. High resolution MPRAGE T1W and FSE T2W data both showed excellent contrast for the habenula and were complementary to each other. The mean visibility of the habenula in the 25 cases for the MPRAGE T1W data was 5.28 ± 1.11; and the mean visibility in the 20 cases for the FSE T2W data was 5.78 ± 1.30. Quantitative susceptibility mapping (QSM), reconstructed from the ME-GRE sequence, provided sufficient contrast to distinguish the substructures of the globus pallidus. The susceptibilities of the GPi and globus pallidus externa (GPe) were 0.087 ± 0.013 ppm and 0.115 ± 0.015 ppm, respectively. FSE T2W sequences provided the best image quality with smallest image blooming of stimulator leads compared to MPRAGE T1W images and GRE sequence images, the measured diameters of electrodes were 1.91 ± 0.22, 2.77 ± 0.22, and 2.72 ± 0.20 mm, respectively. High resolution, high bandwidth and short TE (TE = 2.6 ms) GRE helped constrain the artifacts to the area of the electrodes and the dipole effect seen in the GRE filtered phase data provided an effective mean to locate the end of the DBS lead. CONCLUSION: The imaging protocol consisting of MPRAGE T1W, FSE T2W and ME-GRE sequences provided excellent pre- and post-operative visualization of the brain targets and electrodes for patients undergoing DBS treatment. Although the artifacts around the electrodes can be severe, sometimes these same artifacts can be useful in identifying their location.

A Comparison of Magnetic Resonance Imaging Methods to Assess Multiple Sclerosis Lesions: Implications for Patient Characterization and Clinical Trial Design.

Magnetic resonance imaging (MRI) is a sensitive imaging modality for identifying inflammatory and/or demyelinating lesions, which is critical for a clinical diagnosis of MS and evaluating drug responses. There are many unique means of probing brain tissue status, including conventional T1 and T2 weighted imaging (T1WI, T2WI), T2 fluid attenuated inversion recovery (FLAIR), magnetization transfer, myelin water fraction, diffusion tensor imaging (DTI), phase-sensitive inversion recovery and susceptibility weighted imaging (SWI), but no study has combined all of these modalities into a single well-controlled investigation. The goals of this study were to: compare different MRI measures for lesion visualization and quantification; evaluate the repeatability of various imaging methods in healthy controls; compare quantitative susceptibility mapping (QSM) with myelin water fraction; measure short-term longitudinal changes in the white matter of MS patients and map out the tissue properties of the white matter hyperintensities using STAGE (strategically acquired gradient echo imaging). Additionally, the outcomes of this study were anticipated to aid in the choice of an efficient imaging protocol reducing redundancy of information and alleviating patient burden. Of all the sequences used, T2 FLAIR and T2WI showed the most lesions. To differentiate the putative demyelinating lesions from inflammatory lesions, the fusion of SWI and T2 FLAIR was used. Our study suggests that a practical and efficient imaging protocol combining T2 FLAIR, T1WI and STAGE (with SWI and QSM) can be used to rapidly image MS patients to both find lesions and study the demyelinating and inflammatory characteristics of the lesions.

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data.

The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microstructures in-vivo have been a long-standing challenging problem. However, due to the high sparseness and noisiness in cerebrovasculature data as well as highly complex geometry and topology variations of micro vessels, it is still extremely challenging to extract the complete 3D vessel structure and visualize it in 3D with high fidelity. In this paper, we present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvascular structure through embedding the image composition, generated by maximum intensity projection (MIP), into the 3D volumetric image learning process to enhance the overall performance. The core novelty is to automatically leverage the volume visualization technique (e.g., MIP - a volume rendering scheme for 3D volume images) to enhance the 3D data exploration at the deep learning level. The MIP embedding features can enhance the local vessel signal (through canceling out the noise) and adapt to the geometric variability and scalability of vessels, which is of great importance in microvascular tracking. A multi-stream convolutional neural network (CNN) framework is proposed to effectively learn the 3D volume and 2D MIP feature vectors, respectively, and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the 2D feature vectors into the 3D volume embedding space. It is noted that the proposed framework can better capture the small/micro vessels and improve the vessel connectivity. To our knowledge, this is the first time that a deep learning framework is proposed to construct a joint convolutional embedding space, where the computed vessel probabilities from volume rendering based 2D projection and 3D volume can be explored and integrated synergistically. Experimental results are evaluated and compared with the traditional 3D vessel segmentation methods and the state-of-the-art in deep learning, by using extensive public and real patient (micro- )cerebrovascular image datasets. The application of this accurate segmentation and visualization of sparse and complicated 3D microvascular structure facilitated by our method demonstrates the potential in a powerful MR arteriogram and venogram diagnosis of vascular disease.

Imaging of the Spinal Cord in Multiple Sclerosis: Past, Present, Future.

Spinal cord imaging in multiple sclerosis (MS) plays a significant role in diagnosing and tracking disease progression. The spinal cord is one of four key areas of the central nervous system where documenting the dissemination in space in the McDonald criteria for diagnosing MS. Spinal cord lesion load and the severity of cord atrophy are believed to be more relevant to disability than white matter lesions in the brain in different phenotypes of MS. Axonal loss contributes to spinal cord atrophy in MS and its degree correlates with disease severity and prognosis. Therefore, measures of axonal loss are often reliable biomarkers for monitoring disease progression. With recent technical advances, more and more qualitative and quantitative MRI techniques have been investigated in an attempt to provide objective and reliable diagnostic and monitoring biomarkers in MS. In this article, we discuss the role of spinal cord imaging in the diagnosis and prognosis of MS and, additionally, we review various techniques that may improve our understanding of the disease.

Longitudinal Magnetic Resonance Imaging of Cerebral Microbleeds in Multiple Sclerosis Patients.

We hypothesized that cerebral microbleeds (CMBs) in multiple sclerosis (MS) patients will be detected with higher prevalence compared to healthy controls (HC) and that quantitative susceptibility mapping (QSM) will help remove false positives seen in susceptibility weighted imaging (SWI). A cohort of 100 relapsing remitting MS subjects scanned at 3T were used to validate a set of CMB detection guidelines specifically using QSM. A second longitudinal cohort of 112 MS and 25 HCs, also acquired at 3T, was reviewed across two time points. Both cohorts were imaged with SWI and fluid attenuated inversion recovery. Fourteen subjects in the first cohort (14%, 95% CI 8-21%) and twenty-one subjects in the second cohort (18.7%, 95% CI 11-27%) had at least one CMB. The combined information from SWI and QSM allowed us to discern stable CMBs and new CMBs from potential mimics and evaluate changes over time. The longitudinal results demonstrated that longer disease duration increased the chance to develop new CMBs. Higher age was also associated with increased CMB prevalence for MS and HC. We observed that MS subjects developed new CMBs between time points, indicating the need for longitudinal quantitative imaging of CMBs.

Strategically acquired gradient echo (STAGE)-derived MR angiography might be a superior alternative method to time-of-flight MR angiography in visualization of leptomeningeal collaterals.

OBJECTIVES: This study aimed to compare the performance of strategically acquired gradient echo (STAGE)-derived MR angiography and time-of-flight MR angiography (TOF-MRA) in visualization of leptomeningeal collaterals (LMCs). METHODS: Between May 2018 and January 2020, 75 participants (47 healthy volunteers and 28 intracranial atherosclerotic disease [ICAD] patients) undergoing TOF-MRA and STAGE-MRA were prospectively included. Image quality was scored at the internal carotid artery (ICA) terminus, proximal middle cerebral artery (MCA), and LMCs. Quantitative analysis included calculation of contrast-to-noise ratios (CNRs) in the M1-4 segments and number of LMCs counted in the line signal intensity profiles. Comparisons of image qualitative scores, CNRs, and number of LMCs were calculated using the Wilcoxon rank-sum test. RESULTS: Image qualitative scores were significantly higher in STAGE-MRA than in TOF-MRA for the ICA terminus, proximal MCA, and LMCs (ps < 0.05) in 75 participants. When referred to digital subtraction angiography (DSA) in 25 ICAD patients, STAGE-MRA showed higher qualitative scores only at LMCs. CNRs in the M1-4 segments were significantly higher in STAGE-MRA than in TOF-MRA (218.7 ± 90.7 vs 176.2 ± 72.6, 195.7 ± 86.0 vs 146.6 ± 71.7, 176.4 ± 71.6 vs 125.8 ± 61.1, 126.2 ± 62.9 vs 78.8 ± 43.6; all ps < 0.001). STAGE-MRA showed more LMCs (11.4 ± 3.4) than TOF-MRA (8.4 ± 3.3) with p < 0.05. CONCLUSIONS: STAGE-MRA might be superior to TOF-MRA in qualitative and quantitative assessment of LMCs in both healthy volunteers and ICAD patients; thus, it may serve as an alternative method in evaluating LMC. KEY POINTS: • Strategically acquired gradient echo (STAGE)-derived magnetic resonance angiography is a newly developed sequence with a pair of rephasing/dephasing gradient echoes. • STAGE-MRA enables higher image qualitative score, improves contrast-to-noise ratio, and shows greater number of leptomeningeal collaterals (LMCs) in healthy volunteers and patients with intracranial atherosclerotic disease. • LMC visualization by STAGE-MRA shows good to excellent inter-observer agreement.

Iron Content in Deep Gray Matter as a Function of Age Using Quantitative Susceptibility Mapping: A Multicenter Study.

PURPOSE: To evaluate the effect of resolution on iron content using quantitative susceptibility mapping (QSM); to verify the consistency of QSM across field strengths and manufacturers in evaluating the iron content of deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a susceptibility baseline as a function of age for each DGM structure using both a global and regional iron analysis. METHODS: Data from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5 Tesla and two 3.0 Tesla MR scanners. Eight subcortical gray matter nuclei were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM data. Mean susceptibility, volume and total iron content with age correlations were evaluated for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied. RESULTS: The mean susceptibilities of the caudate nucleus (CN), globus pallidus (GP) and putamen (PUT) were not significantly affected by changing the slice thickness from 0.5 to 3 mm. But for small structures, the susceptibility was reduced by 10% for 2 mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN). There was a negative correlation with age in the thalamus (THA). The volumes of most nuclei were negatively correlated with age. Apart from the GP, THA, and pulvinar thalamus (PT), all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most of the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content. CONCLUSION: A reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data when high enough resolutions are used. These estimates can be used for correcting for age related iron changes when studying diseases like Parkinson's disease, Alzheimer's disease, and other iron related neurodegenerative diseases.