Learning to see the invisible: A data-driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy

Objective

To find the covert patterns of abnormality in patients with unilateral temporal lobe epilepsy (TLE) and visually normal brain magnetic resonance images (MRI‐negative), comparing them to those with visible abnormalities (MRI‐positive).

Methods

We used multimodal brain MRI from patients with unilateral TLE and employed contemporary machine learning methods to predict the known laterality of seizure onset in 104 subjects (82 MRI‐positive, 22 MRI‐negative). A visualization approach entitled "Importance Maps" was developed to highlight image features predictive of seizure laterality in both the MRI‐positive and MRI‐negative cases.

Results

Seizure laterality could be predicted with an area under the receiver operating characteristic curve of 0.981 (95% confidence interval [CI] =0.974‐0.989) in MRI‐positive and 0.842 (95% CI = 0.736‐0.949) in MRI‐negative cases. The known image features arising from the hippocampus were the leading predictors of seizure laterality in the MRI‐positive cases, whereas widespread temporal lobe abnormalities were revealed in the MRI‐negative cases.

Significance

Covert abnormalities not discerned on visual reading were detected in MRI‐negative TLE, with a spatial pattern involving the whole temporal lobe, rather than just the hippocampus. This suggests that MRI‐negative TLE may be associated with subtle but widespread temporal lobe abnormalities. These abnormalities merit close inspection and postacquisition processing if there is no overt lesion.

Age and hippocampal volume predict distinct parts of default mode network activity

Group comparison studies have established that activity in the posterior part of the default-mode network (DMN) is down-regulated by both normal ageing and Alzheimer’s disease (AD). In this study linear regression models were used to disentangle distinctive DMN activity patterns that are more profoundly associated with either normal ageing or a structural marker of neurodegeneration. 312 datasets inclusive of healthy adults and patients were analysed. Days of life at scan (DOL) and hippocampal volume were used as predictors. Group comparisons confirmed a significant association between functional connectivity in the posterior cingulate/retrosplenial cortex and precuneus and both ageing and AD. Fully-corrected regression models revealed that DOL significantly predicted DMN strength in these regions. No such effect, however, was predicted by hippocampal volume. A significant positive association was found between hippocampal volumes and DMN connectivity in the right temporo-parietal junction (TPJ). These results indicate that postero-medial DMN down-regulation may not be specific to neurodegenerative processes but may be more an indication of brain vulnerability to degeneration. The DMN-TPJ disconnection is instead linked to the volumetric properties of the hippocampus, may reflect early-stage regional accumulation of pathology and might be of aid in the clinical detection of abnormal ageing.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

Computer-Assisted Planning for Stereoelectroencephalography (SEEG)

Stereoelectroencephalography (SEEG) is a diagnostic procedure in which multiple electrodes are stereotactically implanted within predefined areas of the brain to identify the seizure onset zone, which needs to be removed to achieve remission of focal epilepsy. Computer-assisted planning (CAP) has been shown to improve trajectory safety metrics and generate clinically feasible trajectories in a fraction of the time needed for manual planning. We report a prospective validation study of the use of EpiNav (UCL, London, UK) as a clinical decision support software for SEEG. Thirteen consecutive patients (125 electrodes) undergoing SEEG were prospectively recruited. EpiNav was used to generate 3D models of critical structures (including vasculature) and other important regions of interest. Manual planning utilizing the same 3D models was performed in advance of CAP. CAP was subsequently employed to automatically generate a plan for each patient. The treating neurosurgeon was able to modify CAP generated plans based on their preference. The plan with the lowest risk score metric was stereotactically implanted. In all cases (13/13), the final CAP generated plan returned a lower mean risk score and was stereotactically implanted. No complication or adverse event occurred. CAP trajectories were generated in 30% of the time with significantly lower risk scores compared to manually generated. EpiNav has successfully been integrated as a clinical decision support software (CDSS) into the clinical pathway for SEEG implantations at our institution. To our knowledge, this is the first prospective study of a complex CDSS in stereotactic neurosurgery and provides the highest level of evidence to date.

Handheld Active Add-On Control Unit for a Cable-Driven Flexible Endoscope

The instruments currently used by surgeons for in utero treatment of the twin-to-twin transfusion syndrome (TTTS) are rigid or semi-rigid. Their poor dexterity makes this surgical intervention risky and the surgeon's work very complex. This paper proposes the design, assembly and quantitative evaluation of an add-on system intended to be placed on a commercialized cable-driven flexible endoscope. The add-on system is lightweight and easily exchangeable thanks to the McKibben muscle actuators embedded in its system. The combination of the flexible endoscope and the new add-on unit results in an easy controllable flexible instrument with great potential use in TTTS treatment, and especially for regions that are hard to reach with conventional instruments. The fetoscope has a precision of 7.4% over its entire bending range and allows to decrease the maximum planar force on the body wall of 6.15% compared to the original endoscope. The add-on control system also allows a more stable and precise actuation of the endoscope flexible tip.

Intraoperative multispectral and hyperspectral label-free imaging: A systematic review of in vivo clinical studies

Multispectral and hyperspectral imaging (HSI) are emerging optical imaging techniques with the potential to transform the way surgery is performed but it is not clear whether current systems are capable of delivering real-time tissue characterization and surgical guidance. We conducted a systematic review of surgical in vivo label-free multispectral and HSI systems that have been assessed intraoperatively in adult patients, published over a 10-year period to May 2018. We analysed 14 studies including 8 different HSI systems. Current in-vivo HSI systems generate an intraoperative tissue oxygenation map or enable tumour detection. Intraoperative tissue oxygenation measurements may help to predict those patients at risk of postoperative complications and in-vivo intraoperative tissue characterization may be performed with high specificity and sensitivity. All systems utilized a line-scanning or wavelength-scanning method but the spectral range and number of spectral bands employed varied significantly between studies and according to the system's clinical aim. The time to acquire a hyperspectral cube dataset ranged between 5 and 30 seconds. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of intraoperative in-vivo label-free HSI but further work is needed to fully integrate it into the current surgical workflow.

Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning

Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.

Stereoelectroencephalography electrode placement: Detection of blood vessel conflicts

OBJECTIVE

Various forms of vascular imaging are performed to identify vessels that should be avoided during stereoelectroencephalography (SEEG) planning. Digital subtraction angiography (DSA) is the gold standard for intracranial vascular imaging. DSA is an invasive investigation, and a balance is necessary to identify all clinically relevant vessels and not to visualize irrelevant vessels that may unnecessarily restrict electrode placement. We sought to estimate the size of vessels that are clinically significant for SEEG planning.

METHODS

Thirty-three consecutive patients who underwent 354 SEEG electrode implantations planned with computer-assisted planning and DSA segmentation between 2016 and 2018 were identified from a prospectively maintained database. Intracranial positions of electrodes were segmented from postimplantation computed tomography scans. Each electrode was manually reviewed using "probe-eye view" with the raw preoperative DSA images for vascular conflicts. The diameter of vessels and the location of conflicts were noted. Vessel conflicts identified on raw DSA images were cross-referenced against other modalities to determine whether the conflict could have been detected.

RESULTS

One hundred sixty-six vessel conflicts were identified between electrodes and DSA-identified vessels, with 0-3 conflicts per electrode and a median of four conflicts per patient. The median diameter of conflicting vessels was 1.3 mm (interquartile range [IQR] = 1.0-1.5 mm). The median depth of conflict was 31.0 mm (IQR = 14.3-45.0 mm) from the cortical surface. The addition of sulcal models to DSA, magnetic resonance venography (MRV), and T1 + gadolinium images, as an exclusion zone during computer-assisted planning, would have prevented the majority of vessel conflicts. We were unable to determine whether vessels were displaced or transected by the electrodes.

SIGNIFICANCE

Vascular segmentation from DSA images was significantly more sensitive than T1 + gadolinium or MRV images. Electrode conflicts with vessels 1-1.5 mm in size did not result in a radiologically detectable or clinically significant hemorrhage and could potentially be excluded from consideration during SEEG planning.

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

OBJECTIVE

The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists.

METHODS

A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists.

RESULTS

The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82).

CONCLUSIONS

Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists.

KEY POINTS

• Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Pruning strategies for efficient online globally consistent mosaicking in fetoscopy

Twin-to-twin transfusion syndrome is a condition in which identical twins share a certain pattern of vascular connections in the placenta. This leads to an imbalance in the blood flow that, if not treated, may result in a fatal outcome for both twins. To treat this condition, a surgeon explores the placenta with a fetoscope to find and photocoagulate all intertwin vascular connections. However, the reduced field of view of the fetoscope complicates their localization and general overview. A much more effective exploration could be achieved with an online mosaic created at exploration time. Currently, accurate, globally consistent algorithms such as bundle adjustment cannot be used due to their offline nature, while online algorithms lack sufficient accuracy. We introduce two pruning strategies facilitating the use of bundle adjustment in a sequential fashion: (1) a technique that efficiently exploits the potential of using an electromagnetic tracking system to avoid unnecessary matching attempts between spatially inconsistent image pairs, and (2) an aggregated representation of images, which we refer to as superframes, that allows decreasing the computational complexity of a globally consistent approach. Quantitative and qualitative results on synthetic and phantom-based datasets demonstrate a better trade-off between efficiency and accuracy.

Multicenter validation of automated trajectories for selective laser amygdalohippocampectomy

Objective

Laser interstitial thermal therapy (LITT) is a novel minimally invasive alternative to open mesial temporal resection in drug‐resistant mesial temporal lobe epilepsy (MTLE). The safety and efficacy of the procedure are dependent on the preplanned trajectory and the extent of the planned ablation achieved. Ablation of the mesial hippocampal head has been suggested to be an independent predictor of seizure freedom, whereas sparing of collateral structures is thought to result in improved neuropsychological outcomes. We aim to validate an automated trajectory planning platform against manually planned trajectories to objectively standardize the process.

Methods

Using the EpiNav platform, we compare automated trajectory planning parameters derived from expert opinion and machine learning to undertake a multicenter validation against manually planned and implemented trajectories in 95 patients with MTLE. We estimate ablation volumes of regions of interest and quantify the size of the avascular corridor through the use of a risk score as a marker of safety. We also undertake blinded external expert feasibility and preference ratings.

Results

Automated trajectory planning employs complex algorithms to maximize ablation of the mesial hippocampal head and amygdala, while sparing the parahippocampal gyrus. Automated trajectories resulted in significantly lower calculated risk scores and greater amygdala ablation percentage, whereas overall hippocampal ablation percentage did not differ significantly. In addition, estimated damage to collateral structures was reduced. Blinded external expert raters were significantly more likely to prefer automated to manually planned trajectories.

Significance

Retrospective studies of automated trajectory planning show much promise in improving safety parameters and ablation volumes during LITT for MTLE. Multicenter validation provides evidence that the algorithm is robust, and blinded external expert ratings indicate that the trajectories are clinically feasible. Prospective validation studies are now required to determine if automated trajectories translate into improved seizure freedom rates and reduced neuropsychological deficits.

The quantitative neuroradiology initiative framework: application to dementia

There are numerous challenges to identifying, developing and implementing quantitative techniques for use in clinical radiology, suggesting the need for a common translational pathway. We developed the quantitative neuroradiology initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We hypothesize that quantification will support reporters with clinically relevant measures contextualized with normative data, increase the precision of longitudinal comparisons, and generate more consistent reporting across levels of radiologists' experience. The QNI framework comprises the following steps: (1) establishing an area of clinical need and identifying the appropriate proven imaging biomarker(s) for the disease in question; (2) developing a method for automated analysis of these biomarkers, by designing an algorithm and compiling reference data; (3) communicating the results via an intuitive and accessible quantitative report; (4) technically and clinically validating the proposed tool pre-use; (5) integrating the developed analysis pipeline into the clinical reporting workflow; and (6) performing in-use evaluation. We will use current radiology practice in dementia as an example, where radiologists have established visual rating scales to describe the degree and pattern of atrophy they detect. These can be helpful, but are somewhat subjective and coarse classifiers, suffering from floor and ceiling limitations. Meanwhile, several imaging biomarkers relevant to dementia diagnosis and management have been proposed in the literature; some clinically approved radiology software tools exist but in general, these have not undergone rigorous clinical validation in high volume or in tertiary dementia centres. The QNI framework aims to address this need. Quantitative image analysis is developing apace within the research domain. Translating quantitative techniques into the clinical setting presents significant challenges, which must be addressed to meet the increasing demand for accurate, timely and impactful clinical imaging services.

Erratum: Author Correction: High-dimensional detection of imaging response to treatment in multiple sclerosis

[This corrects the article DOI: 10.1038/s41746-019-0127-8.].

Hippocampal Subfield Volumetry: Differential Pattern of Atrophy in Different Forms of Genetic Frontotemporal Dementia

Background:

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder, with a strong genetic component. Previous research has shown that medial temporal lobe atrophy is a common feature of FTD. However, no study has so far investigated the differential vulnerability of the hippocampal subfields in FTD.

Objectives:

We aimed to investigate hippocampal subfield volumes in genetic FTD.

Methods:

We in6/2/2018vestigated hippocampal subfield volumes in a cohort of 75 patients with genetic FTD (age: mean (standard deviation) 59.3 (7.7) years; disease duration: 5.1 (3.4) years; 29 with MAPT, 28 with C9orf72, and 18 with GRN mutations) compared with 97 age-matched controls (age: 62.1 (11.1) years). We performed a segmentation of their volumetric T1-weighted MRI scans to extract hippocampal subfields volumes. Left and right volumes were summed and corrected for total intracranial volumes.

Results:

All three groups had smaller hippocampi than controls. The MAPT group had the most atrophic hippocampi, with the subfields showing the largest difference from controls being CA1-4 (24–27%, p < 0.0005). For C9orf72, the CA4, CA1, and dentate gyrus regions (8–11%, p < 0.0005), and for GRN the presubiculum and subiculum (10–14%, p < 0.0005) showed the largest differences from controls.

Conclusions:

The hippocampus was affected in all mutation types but a different pattern of subfield involvement was found in the three genetic groups, consistent with differential cortical-subcortical network vulnerability.

Progression of regional grey matter atrophy in multiple sclerosis

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article.

Grey matter atrophy is present from the earliest stages of multiple sclerosis, but its temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in multiple sclerosis and its association with disability accumulation. In this longitudinal study, we included 1417 subjects: 253 with clinically isolated syndrome, 708 with relapsing-remitting multiple sclerosis, 128 with secondary-progressive multiple sclerosis, 125 with primary-progressive multiple sclerosis, and 203 healthy control subjects from seven European centres. Subjects underwent repeated MRI (total number of scans 3604); the mean follow-up for patients was 2.41 years (standard deviation = 1.97). Disability was scored using the Expanded Disability Status Scale. We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template and used an established data-driven event-based model to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific event-based model stage, based on the number of their atrophic regions. Linear mixed-effects models were used to explore associations between the rate of increase in event-based model stages, and T₂ lesion load, disease-modifying treatments, comorbidity, disease duration and disability accumulation. The first regions to become atrophic in patients with clinically isolated syndrome and relapse-onset multiple sclerosis were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. A similar sequence of atrophy was detected in primary-progressive multiple sclerosis with the involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset multiple sclerosis and late atrophy in primary-progressive multiple sclerosis. Patients with secondary-progressive multiple sclerosis showed the highest event-based model stage (the highest number of atrophic regions, P < 0.001) at the study entry. All multiple sclerosis phenotypes, but clinically isolated syndrome, showed a faster rate of increase in the event-based model stage than healthy controls. T₂ lesion load and disease duration in all patients were associated with increased event-based model stage, but no effects of disease-modifying treatments and comorbidity on event-based model stage were observed. The annualized rate of event-based model stage was associated with the disability accumulation in relapsing-remitting multiple sclerosis, independent of disease duration (P < 0.0001). The data-driven staging of atrophy progression in a large multiple sclerosis sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across multiple sclerosis phenotypes. The spread of atrophy was associated with disease duration and with disability accumulation over time in relapsing-remitting multiple sclerosis.

Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy

Posterior cortical atrophy is a clinico-radiological syndrome characterized by progressive decline in visual processing and atrophy of posterior brain regions. With the majority of cases attributable to Alzheimer's disease and recent evidence for genetic risk factors specifically related to posterior cortical atrophy, the syndrome can provide important insights into selective vulnerability and phenotypic diversity. The present study describes the first major longitudinal investigation of posterior cortical atrophy disease progression. Three hundred and sixty-one individuals (117 posterior cortical atrophy, 106 typical Alzheimer's disease, 138 controls) fulfilling consensus criteria for posterior cortical atrophy-pure and typical Alzheimer's disease were recruited from three centres in the UK, Spain and USA. Participants underwent up to six annual assessments involving MRI scans and neuropsychological testing. We constructed longitudinal trajectories of regional brain volumes within posterior cortical atrophy and typical Alzheimer's disease using differential equation models. We compared and contrasted the order in which regional brain volumes become abnormal within posterior cortical atrophy and typical Alzheimer's disease using event-based models. We also examined trajectories of cognitive decline and the order in which different cognitive tests show abnormality using the same models. Temporally aligned trajectories for eight regions of interest revealed distinct (P < 0.002) patterns of progression in posterior cortical atrophy and typical Alzheimer's disease. Patients with posterior cortical atrophy showed early occipital and parietal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion leading to tissue loss of comparable extent later. Hippocampal, entorhinal and frontal regions underwent a lower rate of change and never approached the extent of posterior cortical involvement. Patients with typical Alzheimer's disease showed early hippocampal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion. Cognitive models showed tests sensitive to visuospatial dysfunction declined earlier in posterior cortical atrophy than typical Alzheimer's disease whilst tests sensitive to working memory impairment declined earlier in typical Alzheimer's disease than posterior cortical atrophy. These findings indicate that posterior cortical atrophy and typical Alzheimer's disease have distinct sites of onset and different profiles of spatial and temporal progression. The ordering of disease events both motivates investigation of biological factors underpinning phenotypic heterogeneity, and informs the selection of measures for clinical trials in posterior cortical atrophy.

Periventricular magnetisation transfer ratio abnormalities in multiple sclerosis improve after alemtuzumab

BACKGROUND

In multiple sclerosis (MS), disease effects on magnetisation transfer ratio (MTR) increase towards the ventricles. This periventricular gradient is evident shortly after first symptoms and is independent of white matter lesions.

OBJECTIVE

To explore if alemtuzumab, a peripherally acting disease-modifying treatment, modifies the gradient's evolution, and whether baseline gradients predict on-treatment relapses.

METHODS

Thirty-four people with relapsing-remitting MS underwent annual magnetic resonance imaging (MRI) scanning (19 receiving alemtuzumab (four scans each), 15 untreated (three scans each)). The normal-appearing white matter was segmented into concentric bands. Gradients were measured over the three bands nearest the ventricles. Mixed-effects models adjusted for age, gender, relapse rate, lesion number and brain parenchymal fraction compared the groups' baseline gradients and evolution.

RESULTS

Untreated, the mean MTR gradient increased (+0.030 pu/band/year) but decreased following alemtuzumab (-0.045 pu/band/year, p = 0.037). Within the alemtuzumab group, there were no significant differences in baseline lesion number (p = 0.568) nor brain parenchymal fraction (p = 0.187) between those who relapsed within 4 years (n = 4) and those who did not (n = 15). However, the baseline gradient was significantly different (p = 0.020).

CONCLUSION

Untreated, abnormal periventricular gradients worsen with time, but appear reversible with peripheral immunotherapy. Baseline gradients - but not lesion loads or brain volumes - may predict on-treatment relapses. Larger confirmatory studies are required.

Association of Piriform Cortex Resection With Surgical Outcomes in Patients With Temporal Lobe Epilepsy

Importance

A functional area associated with the piriform cortex, termed area tempestas, has been implicated in animal studies as having a crucial role in modulating seizures, but similar evidence is limited in humans.

Objective

To assess whether removal of the piriform cortex is associated with postoperative seizure freedom in patients with temporal lobe epilepsy (TLE) as a proof-of-concept for the relevance of this area in human TLE.

Design, Setting, and Participants

This cohort study used voxel-based morphometry and volumetry to assess differences in structural magnetic resonance imaging (MRI) scans in consecutive patients with TLE who underwent epilepsy surgery in a single center from January 1, 2005, through December 31, 2013. Participants underwent presurgical and postsurgical structural MRI and had at least 2 years of postoperative follow-up (median, 5 years; range, 2-11 years). Patients with MRI of insufficient quality were excluded. Findings were validated in 2 independent cohorts from tertiary epilepsy surgery centers. Study follow-up was completed on September 23, 2016, and data were analyzed from September 24, 2016, through April 24, 2018.

Exposures

Standard anterior temporal lobe resection.

Main Outcomes and Measures

Long-term postoperative seizure freedom.

Results

In total, 107 patients with unilateral TLE (left-sided in 68; 63.6% women; median age, 37 years [interquartile range {IQR}, 30-45 years]) were included in the derivation cohort. Reduced postsurgical gray matter volumes were found in the ipsilateral piriform cortex in the postoperative seizure-free group (n = 46) compared with the non-seizure-free group (n = 61). A larger proportion of the piriform cortex was resected in the seizure-free compared with the non-seizure-free groups (median, 83% [IQR, 64%-91%] vs 52% [IQR, 32%-70%]; P < .001). The results were seen in left- and right-sided TLE and after adjusting for clinical variables, presurgical gray matter alterations, presurgical hippocampal volumes, and the proportion of white matter tract disconnection. Findings were externally validated in 2 independent cohorts (31 patients; left-sided TLE in 14; 54.8% women; median age, 41 years [IQR, 31-46 years]). The resected proportion of the piriform cortex was individually associated with seizure outcome after surgery (derivation cohort area under the curve, 0.80 [P < .001]; external validation cohorts area under the curve, 0.89 [P < .001]). Removal of at least half of the piriform cortex increased the odds of becoming seizure free by a factor of 16 (95% CI, 5-47; P < .001). Other mesiotemporal structures (ie, hippocampus, amygdala, and entorhinal cortex) and the overall resection volume were not associated with outcomes.

Conclusions and Relevance

These results support the importance of resecting the piriform cortex in neurosurgical treatment of TLE and suggest that this area has a key role in seizure generation.

Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study

Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen’s d = −0.24 to −0.73; P < 1.49 × 10⁻⁴), and lower thickness in the precentral gyri bilaterally (d = −0.34 to −0.52; P < 4.31 × 10⁻⁶). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = −1.73 to −1.91, P < 1.4 × 10⁻¹⁹), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = −0.36 to −0.52; P < 1.49 × 10⁻⁴). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = −0.29 to −0.54; P < 1.49 × 10⁻⁴). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = −0.27 to −0.51; P < 1.49 × 10⁻⁴). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < −0.0018; P < 1.49 × 10⁻⁴). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts

Diffusion MRI and tractography hold great potential for surgery planning, especially to preserve eloquent white matter during resections. However, fiber tract reconstruction requires an expert with detailed understanding of neuroanatomy. Several automated approaches have been proposed, using different strategies to reconstruct the white matter tracts in a supervised fashion. However, validation is often limited to comparison with manual delineation by overlap-based measures, which is limited in characterizing morphological and topological differences. In this work, we set up a fully automated pipeline based on anatomical criteria that does not require manual intervention, taking advantage of atlas-based criteria and advanced acquisition protocols available on clinical-grade MRI scanners. Then, we extensively validated it on epilepsy patients with specific focus on language-related bundles. The validation procedure encompasses different approaches, including simple overlap with manual segmentations from two experts, feasibility ratings from external multiple clinical raters and relation with task-based functional MRI. Overall, our results demonstrate good quantitative agreement between automated and manual segmentation, in most cases better performances of the proposed method in qualitative terms, and meaningful relationships with task-based fMRI. In addition, we observed significant differences between experts in terms of both manual segmentation and external ratings. These results offer important insights on how different levels of validation complement each other, supporting the idea that overlap-based measures, although quantitative, do not offer a full perspective on the similarities and differences between automated and manual methods.

Applying causal models to explore the mechanism of action of simvastatin in progressive multiple sclerosis

Understanding the mode of action of drugs is a challenge with conventional methods in clinical trials. Here, we aimed to explore whether simvastatin effects on brain atrophy and disability in secondary progressive multiple sclerosis (SPMS) are mediated by reducing cholesterol or are independent of cholesterol. We applied structural equation models to the MS-STAT trial in which 140 patients with SPMS were randomized to receive placebo or simvastatin. At baseline, after 1 and 2 years, patients underwent brain magnetic resonance imaging; their cognitive and physical disability were assessed on the block design test and Expanded Disability Status Scale (EDSS), and serum total cholesterol levels were measured. We calculated the percentage brain volume change (brain atrophy). We compared two models to select the most likely one: a cholesterol-dependent model with a cholesterol-independent model. The cholesterol-independent model was the most likely option. When we deconstructed the total treatment effect into indirect effects, which were mediated by brain atrophy, and direct effects, simvastatin had a direct effect (independent of serum cholesterol) on both the EDSS, which explained 69% of the overall treatment effect on EDSS, and brain atrophy, which, in turn, was responsible for 31% of the total treatment effect on EDSS [β = -0.037; 95% credible interval (CI) = -0.075, -0.010]. This suggests that simvastatin's beneficial effects in MS are independent of its effect on lowering peripheral cholesterol levels, implicating a role for upstream intermediate metabolites of the cholesterol synthesis pathway. Importantly, it demonstrates that computational models can elucidate the causal architecture underlying treatment effects in clinical trials of progressive MS.

Increasing the accuracy of 3D EEG implantations

OBJECTIVEThe accuracy of stereoelectroencephalography (SEEG) electrode implantation is an important factor in maximizing its safety. The authors established a quality assurance (QA) process to aid advances in implantation accuracy.METHODSThe accuracy of three consecutive modifications of a frameless implantation technique was quantified in three cohorts comprising 22, 8, and 23 consecutive patients. The modifications of the technique aimed to increase accuracy of the bolt placement.RESULTSThe lateral shift of the axis of the implanted bolt at the level of the planned entry point was reduced from a mean of 3.0 ± 1.6 mm to 1.4 ± 0.8 mm. The lateral shift of the axis of the implanted bolt at the level of the planned target point was reduced from a mean of 3.8 ± 2.5 mm to 1.6 ± 0.9 mm.CONCLUSIONSThis QA framework helped to isolate and quantify the factors introducing inaccuracy in SEEG implantation, and to monitor ongoing accuracy and the effect of technique modifications.

Segmentation of medial temporal subregions reveals early right-sided involvement in semantic variant PPA

Background

Semantic variant of primary progressive aphasia (svPPA) is a subtype of frontotemporal dementia characterized by asymmetric temporal atrophy.

Methods

We investigated the pattern of medial temporal lobe atrophy in 24 svPPA patients compared to 72 controls using novel approaches to segment the hippocampal and amygdalar subregions on MRIs. Based on semantic knowledge scores, we split the svPPA group into 3 subgroups of early, middle and late disease stage.

Results

Early stage: all left amygdalar and hippocampal subregions (except the tail) were affected in svPPA (21–35% smaller than controls), together with the following amygdalar nuclei in the right hemisphere: lateral, accessory basal and superficial (15–23%). On the right, only the temporal pole was affected among the cortical regions. Middle stage: the left hippocampal tail became affected (28%), together with the other amygdalar nuclei (22–26%), and CA4 (15%) on the right, with orbitofrontal cortex and subcortical structures involvement on the left, and more posterior temporal lobe on the right. Late stage: the remaining right hippocampal regions (except the tail) (19–24%) became affected, with more posterior left cortical and right extra-temporal anterior cortical involvement.

Conclusions

With advanced subregions segmentation, it is possible to detect early involvement of the right medial temporal lobe in svPPA that is not detectable by measuring the amygdala or hippocampus as a whole.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0489-9) contains supplementary material, which is available to authorized users.

Magnetic Resonance Imaging of Cerebral Small Vessel Disease in Men Living with HIV and HIV-Negative Men Aged 50 and Above

We assessed whether HIV status was associated with white matter hyperintensities (WMH), a neuroimaging correlate of cerebral small vessel disease (CSVD), in men aged ≥50 years. A cross-sectional substudy was nested within a larger cohort study. Virologically suppressed men living with HIV (MLWH) and demographically matched HIV-negative men aged ≥50 underwent magnetic resonance imaging (MRI) at 3 Tesla. Sequences included volumetric three-dimensional (3D) T1-weighted, fluid-attenuated inversion recovery and pseudocontinuous arterial spin labeling. Regional segmentation by automated image processing algorithms was used to extract WMH volume (WMHV) and resting cerebral blood flow (CBF). The association between HIV status and WMHV as a proportion of intracranial volume (ICV; log-transformed) was estimated using a multivariable linear regression model. Thirty-eight MLWH [median age 59 years (interquartile range, IQR 55-64)] and 37 HIV-negative [median 58 years (54-63)] men were analyzed. MLWH had median CD4⁺ count 570 (470-700) cells/μL and a median time since diagnosis of 20 (14-24) years. Framingham 10-year risk of cardiovascular disease was 6.5% in MLWH and 7.4% in controls. Two (5%) MLWH reported a history of stroke or transient ischemic attack and five (13%) reported coronary heart disease compared with none of the controls. The total WMHV in MLWH was 1,696 μL (IQR 1,229-3,268 μL) or 0.10% of ICV compared with 1,627 μL (IQR 1,032-3,077 μL), also 0.10% of ICV in the HIV-negative group (p = .43). In the multivariable model, WMHV/ICV was not associated with HIV status (p = .86). There was an age-dependent decline in cortical CBF [-3.9 mL/100 mL/min per decade of life (95% confidence interval 1.1-6.7 mL)] but no association between CBF and HIV status (p > .2 in all brain regions analyzed). In conclusion, we found no quantitative MRI evidence of an increased burden of CSVD in MLWH aged 50 years and older.

VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient

Background Biologic specificity of diffusion MRI in relation to prostate cancer aggressiveness may improve by examining separate components of the diffusion MRI signal. The Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) model estimates three distinct signal components and associates them to (a) intracellular water, (b) water in the extracellular extravascular space, and (c) water in the microvasculature. Purpose To evaluate the repeatability, image quality, and diagnostic utility of intracellular volume fraction (FIC) maps obtained with VERDICT prostate MRI and to compare those maps with apparent diffusion coefficient (ADC) maps for Gleason grade differentiation. Materials and Methods Seventy men (median age, 62.2 years; range, 49.5-82.0 years) suspected of having prostate cancer or undergoing active surveillance were recruited to a prospective study between April 2016 and October 2017. All men underwent multiparametric prostate and VERDICT MRI. Forty-two of the 70 men (median age, 67.7 years; range, 50.0-82.0 years) underwent two VERDICT MRI acquisitions to assess repeatability of FIC measurements obtained with VERDICT MRI. Repeatability was measured with use of intraclass correlation coefficients (ICCs). The image quality of FIC and ADC maps was independently evaluated by two board-certified radiologists. Forty-two men (median age, 64.8 years; range, 49.5-79.6 years) underwent targeted biopsy, which enabled comparison of FIC and ADC metrics in the differentiation between Gleason grades. Results VERDICT MRI FIC demonstrated ICCs of 0.87-0.95. There was no significant difference between image quality of ADC and FIC maps (score, 3.1 vs 3.3, respectively; P = .90). FIC was higher in lesions with a Gleason grade of at least 3+4 compared with benign and/or Gleason grade 3+3 lesions (mean, 0.49 ± 0.17 vs 0.31 ± 0.12, respectively; P = .002). The difference in ADC between these groups did not reach statistical significance (mean, 1.42 vs 1.16 × 10-3 mm2/sec; P = .26). Conclusion Fractional intracellular volume demonstrates high repeatability and image quality and enables better differentiation of a Gleason 4 component cancer from benign and/or Gleason 3+3 histology than apparent diffusion coefficient. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Sigmund and Rosenkrantz in this issue.

Cortical cerebral blood flow in ageing: effects of haematocrit, sex, ethnicity and diabetes

OBJECTIVES

Cerebral blood flow (CBF) estimates from arterial spin labelling (ASL) show unexplained variability in older populations. We studied the impact of variation of haematocrit (Hct) on CBF estimates in a tri-ethnic elderly population.

MATERIALS AND METHODS

Approval for the study was obtained from the Fulham Research Ethics Committee and participants gave written informed consent. Pseudo-continuous arterial spin labelling was performed on 493 subjects (age 55-90) from a tri-ethnic community-based cohort recruited in London. CBF was estimated using a simplified Buxton equation, with and without correction for Hct measured from blood samples. Differences in perfusion were compared, stratified by sex, ethnicity and diabetes. Results of Student's t tests were reported with effect size.

RESULTS

Hct adjustment decreased CBF estimates in all categories except white European men. The decrease for women was 2.7 (3.0, 2.4) mL/100 g/min) (mean (95% confidence interval (CI)), p < 0.001 d = 0.38. The effect size differed by ethnicity with estimated mean perfusion in South Asian and African Caribbean women found to be lower by 3.0 (3.6, 2.5) mL/100 g/min, p < 0.001 d = 0.56 and 3.1 (3.6, 2.5) mL/100 g/min), p < 0.001 d = 0.48, respectively. Estimates of perfusion in subjects with diabetes decreased by 1.8 (2.3, 1.4) mL/100 g/min, p < 0.001 d = 0.23) following Hct correction. Correction for individual Hct altered sample frequency distributions of CBF values, especially in women of non-European ethnicity.

CONCLUSION

ASL-derived CBF values in women, non-European ethnicities and individuals with diabetes are overestimated if calculations are not appropriately adjusted for individual Hct.

KEY POINTS

• CBF quantification from ASL using a fixed Hct of 43.5%, as recommended in the ISMRM white paper, may lead to erroneous CBF estimations particularly in non-European and female subjects. • Individually measured Hct values improve the accuracy of CBF estimation and, if these are not available, an adjusted value according to gender, ethnicity or diabetes status should be considered. • Hct-corrected ASL could be potentially important for CBF threshold decision making in the fields of neurodegenerative disease and neuro-oncology.

TP3-5 Structural connectivity driven stereoelectroencephalography (SEEG) electrode targeting in suspected pseudotemporal and temporal plus epilepsy

Objectives

One third of patients with drug resistant focal mesial temporal lobe epilepsy (MTLE) fail to achieve long-term seizure freedom following temporal lobe resections. Reasons for failure may include ictal onset outside the temporal lobe (TL), termed ‘pseudotemporal lobe epilepsy’ (pTLE), with propagation from strongly connected neighboring areas or temporal plus (TL+) epilepsy, when the epileptogenic zone primarily involves the temporal lobe and also extends to neighboring regions. In such cases the perisylvian and orbito-frontal (OF) cortices, cingulum and temporo-parieto-occipital junction may be implicated. Stereoelectroencephalography (SEEG) is a procedure in which electrodes are stereotactically placed within predefined brain regions to delineate the SOZ and allows evaluation of deep anatomical structures adjacent to the TL. SEEG electrode contacts sample from a core radius of 3–5 mm. It is unclear which sub-regions of target structures should be preferentially implanted to optimally detect the network involved in seizure onset and rapid propagation. Using normalized average group templates of structural connectivity from patients with hippocampal sclerosis (HS), we determine the greatest connectivity to critical sub-regions and based upon this propose optimal locations for SEEG targeting.

Design

Observational cross-sectional study.

Subjects

Twelve patients with HS (6 right) that had undergone SEEG and pre-operative diffusion imaging were identified from a prospectively maintained database.

Methods

Whole brain connectomes with 10 million tracts were generated using cortical seed regions derived from whole brain GIF parcellations. Normalized group templates were generated separately for right and left HS patients. Orbitofrontal cortex (OF), insula (INS), cingulum (Cing) and temporo-parietal-occipital junction (supramarginal gyrus, angular gyrus, precuneus, fusiform gyrus and lingual gyrus) were segmented into surgically targetable subregions. All subregions had similar volumes. Connectivity of the amygdalohippocampal complex (AHC) was defined based on the number of streamlines terminating in the subregions of interest.

Results

Left HS showed preferential connections to the ipsilateral: posterior part of lateral OF cortex, posterior short gyrus of anterior INS, posterior part of the posterior Cing, middle part of lingual gyrus, posterior part of precuneus and middle part of fusiform gyrus. Right HS showed preferential connections to the ipsilateral: posterior part of the lateral OF cortex, anterior long gyrus of posterior INS, posterior part of posterior Cing, anterior part of lingual gyrus and posterior part of precuneus.

Conclusions

Using whole brain connectomes we determine surgically feasible targets in sub-regions based on greatest connectivity to the AHC. We propose that SEEG targeting utilizing computer-assisted planning may improve the understanding of the overall network connectivity in order to enhance the diagnostic utility of the SEEG implantation. SEEG electrode placement within structures associated with pTLE and TL +may aid in delineating the SOZ if the correct sub-regions are targeted. This should be evaluated prospectively.

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

P30 A PRISMA systematic review and meta-analysis of open and novel ‘minimally invasive’ techniques for mesial temporal lobe epilepsy (MTLE)

Objectives

One third of patients with focal epilepsy fail to achieve seizure freedom despite best medical therapy. Surgery may provide seizure freedom if the epileptogenic zone can be safely remove. We compare the outcomes following open surgery, laser interstitial thermal therapy (LITT), radiofrequency ablation (RFA) and radiosurgery (RS).

Design

PRISMA systematic review and meta-analysis.

Subjects

MTLE

Methods

Structured searchs of PubMed, Embase and Cochrane databases. Random effects meta-analysis to calculate effects sizes and a pooled estimate of the probability of remaining seizure free at one year following intervention.

Results

From 1212 screened publications, 57 articles were included in the quantitative analysis. Open surgery included anterior temporal lobectomy as well as transcortical, subtemporal and transsylvian selective amygdalohippocampectomy. The probability of remaining seizure free at one year was 0.89 (95% CI 0.83–0.93) with open surgery based on Level 1 and 2 evidence. RS resulted in 0.88 (95% CI 0.84–0.90) probability and a single RCT revealed RS was less efficacious than open surgery. Follow up duration and study sizes were limited with LITT and RFA providing a probability of remaining seizure free at one year of 0.71 (95% CI 0.65–0.76) and 0.86 (95% CI 0.76–0.92) respectively.

Conclusions

There is no evidence supporting novel ‘minimally invasive’ approaches as being as efficacious as open surgery. Secondary outcome measures such as neuropsychological outcome and intervention morbidity are poorly reported.

Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study

Background

Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth - an important factor in the success of curative endoscopic therapy. IPCLs visualised on magnification endoscopy with Narrow Band Imaging (ME-NBI) can be used to train convolutional neural networks (CNNs) to detect the presence and classify staging of ESCN lesions.

Methods

A total of 7046 sequential high-definition ME-NBI images from 17 patients (10 ESCN, 7 normal) were used to train a CNN. IPCL patterns were classified by three expert endoscopists according to the Japanese Endoscopic Society classification. Normal IPCLs were defined as type A, abnormal as B1-3. Matched histology was obtained for all imaged areas.

Results

This CNN differentiates abnormal from normal IPCL patterns with 93.7% accuracy (86.2% to 98.3%) and sensitivity and specificity for classifying abnormal IPCL patterns of 89.3% (78.1% to 100%) and 98% (92% to 99.7%), respectively. Our CNN operates in real time with diagnostic prediction times between 26.17 ms and 37.48 ms.

Conclusion

Our novel and proof-of-concept application of computer-aided endoscopic diagnosis shows that a CNN can accurately classify IPCL patterns as normal or abnormal. This system could be used as an in vivo, real-time clinical decision support tool for endoscopists assessing and directing local therapy of ESCN.

TP3-4 Changes in whole brain connectomes with simulated laser interstitial thermal therapy (LITT) using seizure free and non-seizure free ablation cavities in mesial temporal sclerosis: a graph theory approach

Objectives

LITT is a novel means of focal lesioning. Improved seizure free outcome has been associated with the extent to which the mesial hippocampal head is ablated, but not overall ablation volume. We question whether specific changes in structural network connectivity exist in patients that achieve seizure freedom.

Design

Retrospective

Subjects

25 MTS patients after LITT with 2 year outcome.

Methods

Ablation cavities from 11 seizure free and 14 non-seizure free patients were combined to generate group masks. In 12 separate pre-operative patients with MTS (6 right), weighted normalized connectomes were generated with 1 × 10^7 streamlines. To simulate ablations the group cavity masks were excluded from the connectomes prior to normalization. Differences in connectomes were assessed by graph theory metrics.

Results

Greater node strength (str) in non-seizure free patients were present in the ipsilateral basal temporo-occipital cortices in both right and left MTS. Str and local efficiency were relatively maintained in the ipsilateral thalamus of seizure free cavities. Betweenness centrality in non-seizure free cavities were greater in ipsilateral temporal poles in right and left MTS.

Conclusions

Differences in network connectivity are present following simulated LITT for MTS between seizure free and non-seizure free ablation cavities. LiTT ablation cavities may be pre-operatively modelled to ensure the ablation cavity includes important structures and non-essential or inhibitory connectivity is spared. Prospective validation is required.

P35 Ability to quantify stereoelectroencephalography (SEEG) electrode trajectory proximity to vessels across imaging protocols

Objectives

Automated planning of stereoelectroencephalography (SEEG) electrode trajectories is dependent on vessel segmentation.1 We quantify imaging protocols ability to measure trajectory-to-vessel distance.

Design

Retrospective analysis.

Subjects

Ten consecutive patients were selected whom had SEEG implantation (95 electrodes) and Digital Catheter Subtraction Angiography (DSA) with catheterization of carotid or vertebral arteries, post-gadolinium T1-weighted (GAD), phase-contrast MR angiography and MR venography (MR) acquired.

Methods

SEEG trajectories were planned manually with DSA. Minimum distance to vessels and risk1 were computed for each trajectory using vessel segmentation from GAD, MR, or DSA. Vessel size was considered by including DSA vessels diameters above 1, 2, 3, or 4 mm.

Results

Minimum distance to a vessel was 6.2±3.9 mm (GAD), 2.5±1.6 mm (MR), and 1.5±1.2 mm (DSA). Based on DSA vessel size minimum distances were 2.0±1.5 mm (DSA >1 mm), 3.4±2.6 (DSA >2 mm), 6.6±4.6 mm (DSA >3 mm), and 11.8±7.9 mm (DSA >4 mm). Risk was 0.4±0.4 (GAD), 0.8±0.4 (MR), and 1.1±0.2 (all DSA), 1.0±0.2 (DSA >1 mm), 0.7±0.4 (DSA >2 mm), 0.4±0.5 (DSA >3 mm), and 0.2±0.3 (DSA >4 mm).

Conclusions

DSA is best able to segment vessels. MR has metrics similar to DSA vessels above 2 mm. GAD has metrics similar to DSA vessels above 3 mm.

WP1-15 Computer-assisted versus manual planning for stereotactic brain biopsy: retrospective comparative pilot study

Objectives

Stereotactic brain biopsy is among the most common neurosurgical procedures. Planning a safe surgical trajectory requires careful attention to a number of features including:

traversing the skull perpendicularly;

avoiding critical neurovascular structures; and

minimising trajectory length.

The aim of this study was to develop a platform, SurgiNav, for automated trajectory planning in stereotactic brain biopsy.

Methods

A prospectively maintained database was searched between February and August 2017 to identify all adult patients that underwent stereotactic brain biopsy in whom post-operative imaging was available. In each case, the standard pre-operative T1-weighted gadolinium-enhanced MRI was used to generate models of the cortex and vasculature. A surgical trajectory was then generated using automated computer-assisted planning (CAP) and metrics compared to the trajectory of the implemented manual plan (MP) using the paired T-test.

Results

15 consecutive patients were identified; who had a diagnostic biopsy and there were no immediate complications. Feasible trajectories were generated using CAP in 12 patients, and in these the mean trajectory length using CAP was comparable to MP (31.7 mm vs. 37.1 mm; p=0.3), and mean angle was similarly perpendicular from orthogonal (9.3° vs. 15.3° p=0.1), but the risk-metric was significantly lower (0.16 vs. 0.48; p=0.03).

Conclusions

Computer-assisted planning for stereotactic brain biopsy appears feasible in most cases and may be safer in selected cases.

P31 Optimising trajectories in computer assisted planning for cranial laser interstitial thermal therapy: a machine learning approach

Objectives

Optimal trajectory planning for cranial laser interstitial thermal therapy (cLITT) in drug resistant focal mesial temporal lobe epilepsy (MTLE).

Design

A composite ablation score of ablated AHC minus ablated PHG volumes were calculated and normalised. Random forest and linear regression were implemented to predict composite ablation scores and determine the optimal entry and target point combinations to maximize this.

Subjects

Ten patients with hippocampal sclerosis were included.

Methods

Computer Assisted Planning (CAP) cLITT trajectories were generated using entry regions that include the inferior occipital gyri (IOG), middle occipital gyri (MOG), inferior temporal gyri (ITG) and middle temporal gyri (MTG). Target points were varied by sequential erosions and transformations of the centroid of the amygdala. In total 760 trajectory combinations were generated per patient and ablation volumes were calculated based on a conservative 15 mm maximum ablation diameter.

Results

Linear regression was superior to random forest predictions. Linear regression indicated that maximal composite ablation scores were associated with entry points that clustered around the junction of the IOG, MOG and MTG. The optimal target point was a translation of the centroid of the amygdala anteriorly and medially.

Conclusions

Machine learning techniques accurately predict composite ablation scores with linear regression outperforming the random forest approach. Optimal CAP entry points for cLITT maximize ablation of the AHC and spare the PHG.

Clinical Applications for Diffusion MRI and Tractography of Cranial Nerves Within the Posterior Fossa: A Systematic Review

Objective: This paper presents a systematic review of diffusion MRI (dMRI) and tractography of cranial nerves within the posterior fossa. We assess the effectiveness of the diffusion imaging methods used and examine their clinical applications.

Methods: The Pubmed, Web of Science and EMBASE databases were searched from January 1st 1997 to December 11th 2017 to identify relevant publications. Any study reporting the use of diffusion imaging and/or tractography in patients with confirmed cranial nerve pathology was eligible for selection. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) tool.

Results: We included 41 studies comprising 16 studies of patients with trigeminal neuralgia (TN), 22 studies of patients with a posterior fossa tumor and three studies of patients with other pathologies. Most acquisition protocols used single-shot echo planar imaging (88%) with a single b-value of 1,000 s/mm² (78%) but there was significant variation in the number of gradient directions, in-plane resolution, and slice thickness between studies. dMRI of the trigeminal nerve generated interpretable data in all cases. Analysis of diffusivity measurements found significantly lower fractional anisotropy (FA) values within the root entry zone of nerves affected by TN and FA values were significantly lower in patients with multiple sclerosis. Diffusivity values within the trigeminal nerve correlate with the effectiveness of surgical treatment and there is some evidence that pre-operative measurements may be predictive of treatment outcome. Fiber tractography was performed in 30 studies (73%). Most studies evaluating fiber tractography involved patients with a vestibular schwannoma (82%) and focused on generating tractography of the facial nerve to assist with surgical planning. Deterministic tractography using diffusion tensor imaging was performed in 93% of cases but the reported success rate and accuracy of generating fiber tracts from the acquired diffusion data varied considerably.

Conclusions: dMRI has the potential to inform our understanding of the microstructural changes that occur within the cranial nerves in various pathologies. Cranial nerve tractography is a promising technique but new avenues of using dMRI should be explored to optimize and improve its reliability.

GAS: A genetic atlas selection strategy in multi-atlas segmentation framework

Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap. More precisely, the key idea of GAS is that if two images are similar, the performances of an atlas for segmenting each image are similar. Since the ground truth of each atlas is known, GAS first selects a predefined number of similar images to the target, then, for each one of them, finds a near-optimal subset of atlases by means of a genetic algorithm. All these near-optimal subsets are then combined and used to segment the target image. GAS was tested on single-label and multi-label segmentation problems. In the first case, we considered the segmentation of both the whole prostate and of the left ventricle of the heart from magnetic resonance images. Regarding multi-label problems, the zonal segmentation of the prostate into peripheral and transition zone was considered. The results showed that the performance of MAS algorithms statistically improved when GAS is used.

Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume-rendered images

Purpose

To determine whether the interactive visualisation of patient-specific virtual 3D models of the renal anatomy influences the pre-operative decision-making process of urological surgeons for complex renal cancer operations.

Methods

Five historic renal cancer patient pre-operative computed tomography (CT) datasets were retrospectively selected based on RENAL nephrectomy score and variety of anatomy. Interactive virtual 3D models were generated for each dataset using image segmentation software and were made available for online visualisation and manipulation. Consultant urologists were invited to participate in the survey which consisted of CT and volume-rendered images (VRI) for the control arm, and CT with segmentation overlay and the virtual 3D model for the intervention arm. A questionnaire regarding anatomical structures, surgical approach, and confidence was administered.

Results

Twenty-five participants were recruited (54% response rate), with 19/25 having > 5 years of renal surgery experience. The median anatomical clarity score increased from 3 for the control to 5 for the intervention arm. A change in planned surgical approach was reported in 19% of cases. Virtual 3D models increased surgeon confidence in the surgical decisions in 4/5 patient datasets. There was a statistically significant improvement in surgeon opinion of the potential utility for decision-making purposes of virtual 3D models as compared to VRI at the multidisciplinary team meeting, theatre planning, and intra-operative stages.

Conclusion

The use of pre-operative interactive virtual 3D models for surgery planning influences surgical decision-making. Further studies are needed to investigate if the use of these models changes renal cancer surgery outcomes.

Electronic supplementary material

The online version of this article (10.1007/s11548-019-01913-5) contains supplementary material, which is available to authorized users.

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach

Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE.

A Framework For Memory Performance Prediction From Brain Volume In Preterm-Born Adolescents

With advances in medical care, higher numbers of extremely preterm-born babies are now surviving, however the rate of neurodevelopmental and neurological complications has not improved at the same rate and the relative rate of disabilities and health problems is increasing, with associated high costs for health care systems and education. Understanding brain development after early birth is of great importance to be able to make informed decisions. Many studies have associated different areas of the preterm brain with poor cognitive performance, however it is less clear whether these associations persist into adult life. In this study, we investigate how well cortical volumes describe memory performance in 133 19 year-old adolescents, 61% of whom were born extremely preterm. We employ LASSO to identify brain regions that better explain memory performance. The brain regions identified by LASSO explained 27% and 32% of the variance in the visual working memory scores and the visual short term memory respectively. Furthermore, the correlation between the predicted scores and validation scores is statistically significant and it is 58% for the visual working memory task and 56% for the visual short term memory task.

Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

A vectorial representation of the vascular network that embodies quantitative features-location, direction, scale, and bifurcations-has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimized anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically aware framework, by comparing the proposed method against popular vascular segmentation tool kits on clinical angiographies. VTrails potentials are discussed towards integrating groupwise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery.

Evaluation of prospective motion correction of high-resolution 3D-T2-FLAIR acquisitions in epilepsy patients

T2-FLAIR is the single most sensitive MRI contrast to detect lesions underlying focal epilepsies but 3D sequences used to obtain isotropic high-resolution images are susceptible to motion artefacts. Prospective motion correction (PMC) - demonstrated to improve 3D-T1 image quality in a pediatric population - was applied to high-resolution 3D-T2-FLAIR scans in adult epilepsy patients to evaluate its clinical benefit. Coronal 3D-T2-FLAIR scans were acquired with a 1mm isotropic resolution on a 3T MRI scanner. Two expert neuroradiologists reviewed 40 scans without PMC and 40 with navigator-based PMC. Visual assessment addressed six criteria of image quality (resolution, SNR, WM-GM contrast, intensity homogeneity, lesion conspicuity, diagnostic confidence) on a seven-point Likert scale (from non-diagnostic to outstanding). SNR was also objectively quantified within the white matter. PMC scans had near-identical scores on the criteria of image quality to non-PMC scans, with the notable exception that intensity homogeneity was generally worse. Using PMC, the percentage of scans with bad image quality was substantially lower than without PMC (3.25% vs. 12.5%) on the other five criteria. Quantitative SNR estimates revealed that PMC and non-PMC had no significant difference in SNR (P=0.07). Application of prospective motion correction to 3D-T2-FLAIR sequences decreased the percentage of low-quality scans, reducing the number of scans that need to be repeated to obtain clinically useful data.

Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections

Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction algorithms. This registration is difficult due to the large differences in image contrast and resolution, as well as the complex nonrigid deformations and artefacts produced when sectioning the sample and mounting it on the glass slide. It has been shown in brain MRI registration that better spatial alignment across modalities can be obtained by synthesising one modality from the other and then using intra-modality registration metrics, rather than by using information theory based metrics to solve the problem directly. However, such an approach typically requires a database of aligned images from the two modalities, which is very difficult to obtain for histology and MRI.

Here, we overcome this limitation with a probabilistic method that simultaneously solves for deformable registration and synthesis directly on the target images, without requiring any training data. The method is based on a probabilistic model in which the MRI slice is assumed to be a contrast-warped, spatially deformed version of the histological section. We use approximate Bayesian inference to iteratively refine the probabilistic estimate of the synthesis and the registration, while accounting for each other’s uncertainty. Moreover, manually placed landmarks can be seamlessly integrated in the framework for increased performance and robustness.

Experiments on a synthetic dataset of MRI slices show that, compared with mutual information based registration, the proposed method makes it possible to use a much more flexible deformation model in the registration to improve its accuracy, without compromising robustness. Moreover, our framework also exploits information in manually placed landmarks more efficiently than mutual information: landmarks constrain the deformation field in both methods, but in our algorithm, it also has a positive effect on the synthesis – which further improves the registration. We also show results on two real, publicly available datasets: the Allen and BigBrain atlases. In both of them, the proposed method provides a clear improvement over mutual information based registration, both qualitatively (visual inspection) and quantitatively (registration error measured with pairs of manually annotated landmarks).

Separating fetal and maternal placenta circulations using multiparametric MRI

PURPOSE

The placenta is a vital organ for the exchange of oxygen, nutrients, and waste products between fetus and mother. The placenta may suffer from several pathologies, which affect this fetal-maternal exchange, thus the flow properties of the placenta are of interest in determining the course of pregnancy. In this work, we propose a new multiparametric model for placental tissue signal in MRI.

METHODS

We describe a method that separates fetal and maternal flow characteristics of the placenta using a 3-compartment model comprising fast and slowly circulating fluid pools, and a tissue pool is fitted to overlapping multiecho T2 relaxometry and diffusion MRI with low b-values. We implemented the combined model and acquisition on a standard 1.5 Tesla clinical system with acquisition taking less than 20 minutes.

RESULTS

We apply this combined acquisition in 6 control singleton placentas. Mean myometrial T2 relaxation time was 123.63 (±6.71) ms. Mean T2 relaxation time of maternal blood was 202.17 (±92.98) ms. In the placenta, mean T2 relaxation time of the fetal blood component was 144.89 (±54.42) ms. Mean ratio of maternal to fetal blood volume was 1.16 (±0.6), and mean fetal blood saturation was 72.93 (±20.11)% across all 6 cases.

CONCLUSION

The novel acquisition in this work allows the measurement of histologically relevant physical parameters, such as the relative proportions of vascular spaces. In the placenta, this may help us to better understand the physiological properties of the tissue in disease.