Automatically measuring brain ventricular volume within PACS using artificial intelligence

Fast Quantitative Magnetic Resonance Imaging Evaluation of Hydrocephalus Using 3-Dimensional Fluid-Attenuated Inversion Recovery: Initial Experience

OBJECTIVE

This study aimed to demonstrate the initial experience of using fast quantitative magnetic resonance imaging (MRI) to evaluate hydrocephalus.

METHODS

A total of 109 brain MRI volumetry examinations (acquisition time, 7 minutes 30 seconds) were performed in 72 patients with hydrocephalus. From the measured ventricular system and brain volumes, ventricle-brain volume percentage was calculated to standardize hydrocephalus severity (processing time, <5 minutes). The obtained values were categorized into no, mild, and severe based on the fronto-occipital horn ratio (FOHR) and the ventricle-brain volume percentages reported in the literature. The measured volumes and percentages were compared between patients with mild hydrocephalus and those with severe hydrocephalus. The diagnostic performance of brain hydrocephalus MRI volumetry was evaluated using receiver operating characteristic curve analysis.

RESULTS

Ventricular volumes and ventricle-brain volume percentages were significantly higher in in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 352.6 ± 165.6 cm 3 vs 149.1 ± 78.5 cm 3 , P < 0.001, and 26.8% [20.8%-33.1%] vs 12.1% ± 6.0%, P < 0.001; percentage-based severity: 359.5 ± 143.3 cm 3 vs 137.0 ± 62.9 cm 3 , P < 0.001, and 26.8% [21.8%-33.1%] vs 11.3% ± 4.2%, P < 0.001, respectively), whereas brain volumes were significantly lower in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 878.1 ± 363.5 cm 3 vs 1130.1 cm 3 [912.1-1244.2 cm 3 ], P = 0.006; percentage-based severity: 896.2 ± 324.6 cm 3 vs 1142.3 cm 3 [944.2-1246.6 cm 3 ], P = 0.005, respectively). The ventricle-brain volume percentage was a good diagnostic parameter for evaluating the degree of hydrocephalus (area under the curve, 0.855; 95% confidence interval, 0.719-0.990; P < 0.001).

CONCLUSIONS

Brain MRI volumetry can be used to evaluate hydrocephalus severity and may provide guide interpretation because of its rapid acquisition and postprocessing times.

Morphological evaluation of the normal and hydrocephalic third ventricle on cranial magnetic resonance imaging in children: a retrospective study

BACKGROUND

Third ventricle morphological changes reflect changes in the ventricular system in pediatric hydrocephalus, so visual inspection of the third ventricle shape is standard practice. However, normal pediatric reference data are not available.

OBJECTIVE

To investigate both the normal development of the third ventricle in the 0-18-year age group and changes in its biometry due to hydrocephalus.

MATERIALS AND METHODS

For this retrospective study, we selected individuals ages 0-18 years who had magnetic resonance imaging (MRI) from 2012 to 2020. We included 700 children (331 girls) who had three-dimensional (3-D) T1-weighted sequences without and 25 with hydrocephalus (11 girls). We measured the distances between the anatomical structures limiting the third ventricle by dividing the third ventricle into anterior and posterior regions. We made seven linear measurements and three index calculations using 3DSlicer and MRICloud pipeline, and we analyzed the results of 23 age groups in normal and hydrocephalic patients using SPSS (v. 23).

RESULTS

Salient findings are: (1) The posterior part of the third ventricle is more affected by both developmental and hydrocephalus-related changes. (2) For third ventricle measurements, gender was insignificant while age was significant. (3) Normal third ventricular volumetric development showed a segmental increase in the 0-18 age range. The hydrocephalic third ventricle volume cut-off value in this age group was 3 cm³.

CONCLUSION

This study describes third ventricle morphometry using a linear measurement method. The ratios defined in the midsagittal plane were clinically useful for diagnosing the hydrocephalic third ventricle. The linear and volumetric reference data and ratios are expected to help increase diagnostic accuracy in distinguishing normal and hydrocephalic third ventricles.

Accurate image-based CSF volume calculation of the lateral ventricles

The size/volume of the brain’s ventricles is essential in diagnosing and treating many neurological disorders, with various forms of hydrocephalus being some of the most common. Initial ventricular size and changes, if any, in response to disease progression or therapeutic intervention are monitored by serial imaging methods. Significant variance in ventricular size is readily noted, but small incremental changes can be challenging to appreciate. We have previously reported using artificial intelligence to determine ventricular volume. The values obtained were compared with those calculated using the inaccurate manual segmentation as the “gold standard”. This document introduces a strategy to measure ventricular volumes where manual segmentation is not employed to validate the estimations. Instead, we created 3D printed models that mimic the lateral ventricles and measured those 3D models’ volume with a tuned water displacement device. The 3D models are placed in a gel and taken to the magnetic resonance scanner. Images extracted from the phantoms are fed to an artificial intelligence-based algorithm. The volumes yielded by the automation must equal those yielded by water displacement to assert validation. Then, we provide certified volumes for subjects in the age range (1–114) months old and two hydrocephalus patients.

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

Objective and Impact Statement. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction. Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans' index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods. We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results. Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion. Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.

Evolution of MRI Findings in Patients with Idiopathic Intracranial Hypertension after Venous Sinus Stenting

BACKGROUND AND PURPOSE

The correlation between imaging findings and clinical status in patients with idiopathic intracranial hypertension is unclear. We aimed to examine the evolution of idiopathic intracranial hypertension-related MR imaging findings in patients treated with venous sinus stent placement.

MATERIALS AND METHODS

Thirteen patients with idiopathic intracranial hypertension (median age, 26.9 years) were assessed for changes in the CSF opening pressure, transstenotic pressure gradient, and symptoms after venous sinus stent placement. Optic nerve sheath diameter, posterior globe flattening and/or optic nerve protrusion, empty sella, the Meckel cave, tonsillar ectopia, the ventricles, the occipital emissary vein, and subcutaneous fat were evaluated on MR imaging before and 6 months after venous sinus stent placement. Data are expressed as percentages, medians, or correlation coefficients (r) with P values.

RESULTS

Although all patients showed significant reductions of the CSF opening pressure (31 versus 21 cm H₂O; P = .005) and transstenotic pressure gradient (22.5 versus 1.5 mm Hg; P = .002) and substantial improvement of clinical symptoms 6 months after venous sinus stent placement, a concomitant reduction was observed only for posterior globe involvement (61.5% versus 15.4%; P = .001), optic nerve sheath diameter (6.8  versus 6.1 mm; P < .001), and subcutaneous neck fat (8.9  versus 7.4 mm; P = .001). Strong correlations were observed between decreasing optic nerve sheath diameters and improving nausea/emesis (right optic nerve sheath diameter, r =  0.592, P = .033; left optic nerve sheath diameter, r = 0.718, P = .006), improvement of posterior globe involvement and decreasing papilledema (r = 0.775, P = .003), and decreasing occipital emissary vein diameter and decreasing headache frequency (r = 0.74, P = .035). Decreasing transstenotic pressure gradient at 6 months strongly correlated with decreasing empty sella (r = 0.625, P = .022) and regressing cerebellar ectopia (r = 0.662, P = .019).

CONCLUSIONS

Most imaging findings persist long after normalization of intracranial pressure and clinical improvement. However, MR imaging findings related to the optic nerve may reflect treatment success.

Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms

BACKGROUND

No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or craniofacial syndromes. In this work, we use deep learning algorithms to measure ventricular and cranial vault volumes in a large dataset of head computed tomography (CT) scans.

METHODS

A cross-sectional dataset comprising 13,851 CT scans was used to deploy U-Net deep learning networks to segment and quantify lateral cerebral ventricular and cranial vault volumes in relation to age and sex. The models were validated against manual segmentations. Corresponding radiologic reports were annotated using a rule-based natural language processing framework to identify normal scans, cerebral atrophy, or hydrocephalus.

RESULTS

U-Net models had high fidelity to manual segmentations for lateral ventricular and cranial vault volume measurements (Dice index, 0.878 and 0.983, respectively). The natural language processing identified 6239 (44.7%) normal radiologic reports, 1827 (13.1%) with cerebral atrophy, and 1185 (8.5%) with hydrocephalus. Age-based and sex-based reference tables with medians, 25th and 75th percentiles for scans classified as normal, atrophy, and hydrocephalus were constructed. The median lateral ventricular volume in normal scans was significantly smaller compared with hydrocephalus (15.7 vs. 82.0 mL; P < 0.001).

CONCLUSIONS

This is the first study to measure lateral ventricular and cranial vault volumes in a large dataset, made possible with artificial intelligence. We provide a robust method to establish normal values for these volumes and a tool to report these on CT scans when evaluating for hydrocephalus.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms

Background

For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure and whether medically meaningful segmentation is possible.

Methods

Ninety-five routinely performed true FISP MRI sequences were retrospectively analyzed in 43 patients with pediatric hydrocephalus. Using a freely available and clinically established segmentation algorithm based on a hidden Markov random field model, four classes of segmentation (brain, cerebrospinal fluid (CSF), background, and tissue) were generated. Fifty-nine randomly selected data sets (10,432 slices) were used as a training data set. Images were augmented for contrast, brightness, and random left/right and X/Y translation. A convolutional neural network (CNN) for semantic image segmentation composed of an encoder and corresponding decoder subnetwork was set up. The network was pre-initialized with layers and weights from a pre-trained VGG 16 model. Following the network was trained with the labeled image data set. A validation data set of 18 scans (3289 slices) was used to monitor the performance as the deep CNN trained. The classification results were tested on 18 randomly allocated labeled data sets (3319 slices) and on a T2-weighted BrainWeb data set with known ground truth.

Results

The segmentation of clinical test data provided reliable results (global accuracy 0.90, Dice coefficient 0.86), while the CNN segmentation of data from the BrainWeb data set showed comparable results (global accuracy 0.89, Dice coefficient 0.84). The segmentation of the BrainWeb data set with the classical FAST algorithm produced consistent findings (global accuracy 0.90, Dice coefficient 0.87). Likewise, the area development of brain and CSF in the long-term clinical course of three patients was presented.

Conclusion

Using the presented methods, we showed that conventional segmentation algorithms can be transferred to new advances in deep learning with comparable accuracy, generating a large number of training data sets with relatively little effort. A clinically meaningful segmentation possibility was demonstrated.

Normal cerebral ventricular volume growth in childhood

OBJECTIVE

Normal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.

METHODS

The authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.

RESULTS

Ventricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0-1 year, 6-month intervals for ages 1-3 years, and 12-month intervals for ages 3-18 years. Additional percentile values were calculated for boys only and girls only.

CONCLUSIONS

The authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.

Planar single plane area determination is a viable substitute for total volumetry of CSF and brain in childhood hydrocephalus

BACKGROUND

In the treatment of childhood hydrocephalus, 3D volumetry seems to have many advantages over classical planar index measurements for dedicated monitoring of changes in cerebrospinal fluid and brain volume. Nevertheless, this method requires extensive technical effort and access to the complete three-dimensional data set. Against this background, we evaluated the possibility of planar area determination in a single plane and the correlation to volumetry.

METHODS

138 routinely performed true FISP MRI sequences (1 mm isovoxel) were analyzed retrospectively in 68 patients with pediatric hydrocephalus. After preprocessing, the 3D-data sets were skull stripped to estimate the inner skull volume. A 2-class segmentation into different tissue types (brain matter and CSF) was performed, and the volumes of CSF (VCSF) and brain matter (VBrain) were calculated. A plane at the level of the foramina of Monro was manually identified in the ac-pc oriented data. In this plane, the areas of brain (ABrain) and CSF (ACSF) in cm² were calculated and used for further correlation analysis.

RESULTS

Mean VCSF was 340 ± 145 cm³ and VBrain 1173 ± 254 cm³. In the selected plane, ACSF was 26 ± 14 cm², and ABrain was 107 ± 25 cm². There was a very strong positive correlation between both ACSF and VCSF (r = 0.895) and between ABrain and VBrain (r = 0.846). The prediction equations for VBrain and VCSF were highly significant.

CONCLUSION

Planar area determination of brain and CSF correlates excellently with both VCSF and VBrain. Thus, areas can serve as a surrogate marker for total brain and CSF volumes for a quantitated objective tracking of changes during treatment of childhood hydrocephalus.

Reliability and Interobserver Variability of Evans' Index and Disproportionately Enlarged Subarachnoid Space Hydrocephalus as Diagnostic Criteria for Idiopathic Normal Pressure Hydrocephalus

Background:

The image diagnosis of idiopathic normal-pressure hydrocephalus (iNPH) is based on the ventriculomegaly, whose criterion is an Evans' Index (EI) >0.3. Recently, disproportionately enlarged subarachnoid space hydrocephalus (DESH) has been proposed as a morphological characteristic to iNPH. Several studies cast doubt on the reliability of these criteria in the diagnosis of iNPH. Furthermore, interobserver differences of these criteria have not yet been investigated. The objective of this study was to assess the diagnostic reliability and interobserver variability of EI and DESH.

Materials and Methods:

The preoperative magnetic resonance (MR) images of 84 definite iNPH patients were retrospectively evaluated by a neuroradiologist (NR) and physical therapist (PT). They independently assessed the EI and DESH. The MR images were evaluated preoperatively by a neurosurgeon (NS). The results were showed in mean (standard deviation).

Results:

The mean age was 78.4 (6.3) years (male:female = 49:35). The mean EI was 0.33 (0.04), 0.32 (0.04), and 0.31 (0.03) for NS, NR, and PT, respectively (P < 0.0001). The rate of accurate diagnosis of iNPH with EI >0.3 was 74%, 66%, and 61% for NS, NR, and PT, respectively, and there was a moderate level of agreement. By contrast, there was a substantial lower level of accuracy in assessment with DESH for all three evaluators as 50%, 44%, and 27% for NS, NR, and PT, respectively, again with a moderate level of agreement. However, the rates of patients fulfilling both EI >0.3 and DESH were remarkably lower than either of the two parameters individually at a mere 37%, 30%, and 16% for NS, NR, and PT, respectively, with a low level of agreement between the rates.

Conclusion:

This study suggests that DESH cannot be a diagnostic criterion for iNPH. If EI >0.3 and DESH were both necessary to diagnose iNPH, then more than 70% of patients would have been misdiagnosed and would have been deprived of the chance of treatment and its benefits. These results request a paradigm shift in the concepts of iNPH.

Automatic volumetry of cerebrospinal fluid and brain volume in severe paediatric hydrocephalus, implementation and clinical course after intervention

BACKGROUND

In childhood hydrocephalus, both the amount of cerebrospinal fluid and the brain volume are relevant for the prognosis of the development and for therapy monitoring. Since classical planar measurements of ventricular size are subject to strong limitations, imprecise and neglect brain volume, 3D volumetry is most desirable. We used and evaluated the robust segmentation algorithms of the freely available FSL-toolbox in paediatric hydrocephalus patients before and after specific therapy.

METHODS

Retrospectively 76 pre- and postoperative high-resolution T2-weighted MRI sequences (true FISP, 1 mm isovoxel) were analyzed in 38 patients with paediatric hydrocephalus (mean 4.4 ± 5.1 years) who underwent surgical treatment (ventriculo-peritoneal (VP) shunt n = 22, endoscopic third ventriculostomy (ETV) n = 16). After preprocessing, the 3D-datasets were skull stripped to estimate the inner skull surface. Following, a 2 class segmentation into different tissue types (brain matter and CSF) was performed. The volumes of CSF and brain were calculated.

RESULTS

The method could be implemented in an automated fashion in all 76 MRIs. In the VP shunt cohort, the amount of CSF (p < 0.001) decreased. Consecutively brain volume increased significantly (p < 0.001). Following ETV, CSF volume (p = 0.019) decreased significantly (p = 0.012) although the reduction was less pronounced than after shunt implantation. Brain volume expanded (p = 0.02).

CONCLUSION

A reliable automated segmentation of CSF and brain could be performed with the implemented algorithm. The method was able to track changes after therapy and detected significant differences in CSF and brain volumes after shunting and after ETV.

CT-Based Quantitative Analysis for Pathological Features Associated With Postoperative Recurrence and Potential Application Upon Artificial Intelligence: A Narrative Review With a Focus on Chronic Subdural Hematomas

Chronic subdural hematomas (CSDHs) frequently affect the elderly population. The postoperative recurrence rate of CSDHs is high, ranging from 3% to 20%. Both qualitative and quantitative analyses have been explored to investigate the mechanisms underlying postoperative recurrence. We surveyed the pathophysiology of CSDHs and analyzed the relative factors influencing postoperative recurrence. Here, we summarize various qualitative methods documented in the literature and present our unique computer-assisted quantitative method, published previously, to assess postoperative recurrence. Imaging features of CSDHs, based on qualitative analysis related to postoperative high recurrence rate, such as abundant vascularity, neomembrane formation, and patent subdural space, could be clearly observed using the proposed quantitative analysis methods in terms of mean hematoma density, brain re-expansion rate, hematoma volume, average distance of subdural space, and brain shifting. Finally, artificial intelligence (AI) device types and applications in current health care are briefly outlined. We conclude that the potential applications of AI techniques can be integrated to the proposed quantitative analysis method to accomplish speedy execution and accurate prediction for postoperative outcomes in the management of CSDHs.

Fetal aqueductal stenosis: Prenatal diagnosis and intervention

Fetal severe central nervous system ventriculomegaly is associated with poor neurologic outcomes, usually driven by a primary malformation, deformation, or disruption of brain parenchyma. In utero shunting of excess cerebrospinal fluid (CSF) in hopes of improving neurologic outcomes was attempted in the 1980s but was abandoned due to perceived lack of effect, likely due to technological limitations of the time that precluded proper patient selection. Little progress on the antenatal management of severe ventriculomegaly has been made in the intervening decades. A multidisciplinary, evidence-based reassessment of ventriculoamniotic shunting for isolated fetal aqueductal stenosis (FAS), a unique form of severe ventriculomegaly (supratentorial intracranial hypertension), is currently underway. An accurate diagnosis of FAS must precede in utero intervention. Magnetic resonance imaging (MRI) will be an excellent adjunct to high-resolution prenatal ultrasound and next-generation genetic testing to correctly diagnose FAS in a timely fashion while excluding other intracranial and extracranial anomalies. This manuscript will briefly discuss the history, current management, and future directions of the prenatal diagnosis and potential intervention for FAS.

Clinical feasibility of brain quantitative susceptibility mapping

PURPOSE

To evaluate the quality of brain quantitative susceptibility mapping (QSM) that is fully automatically reconstructed in clinical MRI of various neurological diseases.

METHODS

393 consecutive patients in one month were recruited for this evaluation study. QSM was reconstructed using Morphology Enabled Dipole Inversion without zero reference regularization (MEDI) and using MEDI with cerebrospinal fluid automatic zero-reference regularization to generate susceptibility values (MEDI+0). Two neuroradiologists independently assessed the image quality of MEDI+0 and MEDI and image concordance between them. Lesion susceptibility values were measured in 20 cases of glioma, 21 cases of ischemic stroke and 43 multiple sclerosis (MS) cases on both MEDI+0 and MEDI images.

RESULTS

The two neuroradiologists rated the MEDI+0 image qualities of the 393 cases as 351 (89.3%) and 362 (92.1%) excellent, 29 (7.4%) and 24 (6.1%) diagnostic, and 13 (3.3%) and 7 (1.8%) poor, and scored the concordances between MEDI+0 and MEDI as 364 (92.6%) and 351 (89.3%) excellent, 13 (3.3%) and 31 (7.9%) good, 14 (3.6%) and 9 (2.3%) intermediate, 2 (0.5%) and 2 (0.5%) poor, and 0 (0%) and 0 (0%) none. There was good correlation between MEDI+0 and MEDI in lesion susceptibility contrast of glioma, ischemic stroke, and MS cases (all p < 0.05). The MS lesion susceptibility time course from this patient cohort was found to be similar to the reported pattern: isointense initially for acute enhancing lesions, and hyperintense over the following years for active chronic lesions.

CONCLUSION

Brain QSM images of various neurological diseases have reliable diagnostic quality in clinical MRI, with MEDI+0 providing susceptibility values automatically referenced to CSF in longitudinal and cross-center studies.