The contribution of TMS-EEG coregistration in the exploration of the human cortical connectome

Recent developments in neuroscience have emphasised the importance of integrated distributed networks of brain areas for successful cognitive functioning. Our current understanding is that the brain has a modular organisation in which segregated networks supporting specialised processing are linked through a few long-range connections, ensuring processing integration. Although such architecture is structurally stable, it appears to be flexible in its functioning, enabling long-range connections to regulate the information flow and facilitate communication among the relevant modules, depending on the contingent cognitive demands. Here we show how insights brought by the coregistration of transcranial magnetic stimulation and electroencephalography (TMS-EEG) integrate and support recent models of functional brain architecture. Moreover, we will highlight the types of data that can be obtained through TMS-EEG, such as the timing of signal propagation, the excitatory/inhibitory nature of connections and causality. Last, we will discuss recent emerging applications of TMS-EEG in the study of brain disorders.

High precision anatomy for MEG

Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1 mm. Estimates of relative co-registration error were < 1.5 mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6 month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5 mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.

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We introduce MEG coregistration scheme using 3D printed subject specific head casts.

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Using this scheme reduces relative/absolute coregistration errors to 1–2 mm levels.

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The ability to reposition between sessions results in high SNR data sets.

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At this level of coregistration error, we see a clear benefit for using individual cortical meshes.

Requirements for Coregistration Accuracy in On-Scalp MEG

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be [Formula: see text] and [Formula: see text], respectively.

Intraoperative coregistration of magnetic resonance imaging, positron emission tomography, and electrocorticographic data for neocortical lesional epilepsies may improve the localization of the epileptogenic focus: a pilot study

OBJECTIVE

To objectively mark out abnormal areas of magnetic resonance imaging (MRI), positron emission tomography (PET), and electrocorticography (ECoG) using neuronavigation so as to 1) enhance the accuracy of margins of the epileptogenic zone and 2) understand the relationships of all the three modalities with each other.

METHODS

A prospective study was conducted of 37 patients with intractable epilepsy due to lesional, neocortical pathologies from noneloquent areas. Prior to surgery, fusion and transfer of MRI and PET images onto a neuronavigation system was performed. At surgery, this was correlated to intraoperative ECoG using the electrode as referential points. An objective score was created for every electrode point that was correlated with MRI and PET abnormality at the point. The extent of surgical resection was mapped out using these data.

RESULTS

From a total of the data recorded from 1280 electrode points, 23.5% were located over the lesion. In addition, over the lesions, 93% of PET and 66% of ECoG points were abnormal. Over the perilesional areas, 43% of PET and 45% of ECoG points were abnormal. Using these data for surgery, both lesional and perileisonal areas were resected; 33/37 patients had good outcome (25 Engel I, 8 Engel II) (mean follow-up: 23.6 ± 3.2 months; range 18-31 months).

CONCLUSION

Multimodal imaging and ECoG using this method seems to provide a better objective localization of the epileptogenic foci.

Utility of angiography-physiology coregistration maps during percutaneous coronary intervention in clinical practice

This study aimed to evaluate the utility and feasibility of physiological maps coregistered with angiograms using the pullback of a pressure guidewire with continuous instantaneous wave-free ratio (iFR) measurements. iFR pullback was obtained for 70 lesions from 70 patients with stable angina pectoris using SyncVision (Philips Corp.). Physiological maps were created, whereby the post-intervention iFR (post-iFR) was predicted as iFRpred. The iFR gap was defined if the difference between the iFRpred and post-iFR was ≥ 0.3. The lesion morphology changed from that during the physiological assessment to that during the angiographic assessment in 26 lesions (37.1%). In particular, 22.6% of angiographic tandem lesions changed to physiological focal lesions. The mean pre-intervention iFR, post-iFR, and iFRpred were 0.73 ± 0.17, 0.90 ± 0.06, and 0.93 ± 0.05, respectively. The mean difference between the iFRpred and post-iFR was 0.029 ± 0.099, with 95% limits of agreement of -0.070-0.128. iFR gaps occurred in 28 patients (40%). Notably, a new iFR gradient causing a ≥ 0.03 iFR drop after stenting occurred in 11 (15.7%) cases. The study patients were divided into two groups according to biases between post-iFR and iFRpred < 0.03 (good concordance group) or ≥ 0.03 (poor concordance group). The pre-intervention heart rate was the only independent predictor of poor concordance (odds ratio, 0.936; 95% confidence interval 0.883-0.992; p = 0.027). Physiological maps under resting conditions may contribute to a reduction in unnecessary stent placements without missing lesions requiring treatment. However, the predictive accuracy of post-iFR performance in the present study was slightly lower than that in the previous reports.

Automated Voxel Placement: A Linux-based Suite of Tools for Accurate and Reliable Single Voxel Coregistration

Background

Single-voxel proton magnetic resonance spectroscopy (¹H MRS) is a powerful technique for studying in vivo neurochemistry, but has an often-overlooked source of error variance: inconsistent voxel placement between scans. We developed and evaluated an Automated Voxel Placement (AVP) procedure for accurate and reliable ¹H MRS voxel prescription. AVP is a suite of Linux-based programs that facilitate automated template-driven single-voxel coregistration.

Methods

Three studies were conducted to evaluate AVP for prescription of one voxel: left dorsolateral prefrontal cortex. First, we evaluated how robust AVP was to ‘extreme’ subject head positions/angulations within the scanner head coil. Second, subjects (N = 13) were recruited and underwent MR scans. Manual voxel prescription (n = 5) was contrasted with AVP (n = 8). A subset of AVP subjects (n = 4) completed a second scan. Third, ongoing data collection (n = 16; recruited for a separate study) helped evaluate AVP. Voxel placement accuracy was quantified as 3D geometric voxel overlap percentage between each subject’s voxel and the template voxel. Reliability was quantified as 3D geometric voxel overlap percentage across subjects at each time point and within subjects who completed two scans.

Results

Results demonstrated that AVP was robust to ‘extreme’ head positions (97.5% - 97.9% overlap with the template voxel). AVP was significantly more accurate (baseline and follow-up: 96.2% ± 3.0% and 97.6% ± 1.4% overlap) than manual voxel placement (67.7% ± 22.8% overlap; ps<.05). AVP was reliable within- (97.9%) and between-subjects (94.2% and 97.2% overlap; baseline and follow-up; respectively). Finally, ongoing data collection indicates AVP is accurate (96.0%).

Conclusion

These pilot studies demonstrated that AVP was feasible, accurate, and reliable method for automated single voxel coregistration.

Comparison of PET/CT and whole-mount histopathology sections of the human prostate: a new strategy for voxel-wise evaluation

Background

Implementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation. Direct visual comparison of PET/CT slices to whole prostate sections is hampered by considerable uncertainties from imperfect coregistration and fundamentally different image modalities. In the current study, we present a novel method for advanced voxel-wise comparison of histopathology from excised prostates to pre-surgical PET. Resected prostates from eight patients who underwent PSMA-PET/CT were scanned (ex vivo CT) and thoroughly pathologically prepared. In vivo and ex vivo CT including histopathology were coregistered with three different methods (manual, semi−/automatic). Spatial overlap after CT-based registration was evaluated with dice similarity (DSC). Furthermore, we constructed 3D cancer distribution models from histopathologic information in various slices. Subsequent smoothing reflected the intrinsically limited spatial resolution of PSMA-PET. The resulting histoPET models were used for quantitative analysis of spatial histopathology-PET pattern agreement focusing on p values and coefficients of determination (R²). We examined additional rigid mutual information (MI) coregistration directly based on PSMA-PET and histoPET.

Results

Mean DSC for the three different methods (ManReg, ScalFactReg, and DefReg) were 0.79 ± 0.06, 0.82 ± 0.04, and 0.90 ± 0.02, respectively, while quantification of PET-histopathology pattern agreement after CT-based registration revealed R² 45.7, 43.2, and 41.3% on average with p < 10⁻⁵. Subsequent PET-based MI coregistration yielded R² 61.3, 55.9, and 55.6%, respectively, while implying anatomically plausible transformations.

Conclusions

Creating 3D histoPET models based on thorough histopathological preparation allowed sophisticated quantitative analyses showing highly significant correlations between histopathology and (PSMA-)PET. We recommend manual CT-based coregistration followed by a PET-based MI algorithm to overcome limitations of purely CT-based coregistrations for meaningful voxel-wise comparisons between PET and histopathology.

Automatic coregistration of MRI and on-scalp MEG

BACKGROUND

Recent progress in optically pumped magnetometers (OPMs) and high-temperature superconducting quantum interference devices (SQUIDs) has facilitated the development of an on-scalp magnetoencephalography (MEG) system that offers high signal intensity and flexibility at a lower cost. While the on-scalp sensor array has high flexibility, it brings new challenges to accurate sensor-to-brain coregistration, which is essential for MEG source localization.

NEW METHOD

A novel automatic filtering algorithm based on plane segmentation was proposed to locate on-scalp MEG sensors in 3D images reconstructed from optical scanning. Global image registration was employed for the automatic alignment of anatomical images and sensor positions.

RESULTS

Seventy-one sensor dummies on the scalp were located and registered to brain anatomical images. The deviations of the sensor location and orientation from the averaged result of 10 measurements were less than 1 mm and 0.6°, respectively. The entire process could be completed in less than 4 min.

COMPARISON WITH EXISTING METHODS

Compared with existing methods that involve various manual procedures, such as moving digitizers to fiducials and repeatedly pulling out sensors, our proposed coregistration method is more efficient and accurate.

CONCLUSION

An automatic method for the coregistration of anatomical structure and on-scalp sensors that will have a large impact on the practical use of on-scalp MEG is developed.

MarLe: Markerless estimation of head pose for navigated transcranial magnetic stimulation

Navigated transcranial magnetic stimulation (nTMS) is a valuable tool for non-invasive brain stimulation. Currently, nTMS requires fixing of markers on the patient's head. Head marker displacements lead to changes in coil placement and brain stimulation inaccuracy. A markerless neuronavigation method is needed to increase the reliability of nTMS and simplify the nTMS protocol. In this study, we introduce and release MarLe, a Python markerless head tracker neuronavigation software for TMS. This novel software uses computer-vision techniques combined with low-cost cameras to estimate the head pose for neuronavigation. A coregistration algorithm, based on a closed-form solution, was designed to track the patient's head and the TMS coil referenced to the individual's brain image. We show that MarLe can estimate head pose based on real-time video processing. An intuitive pipeline was developed to connect the MarLe and nTMS neuronavigation software. MarLe achieved acceptable accuracy and stability in a mockup nTMS experiment. MarLe allows real-time tracking of the patient's head without any markers. The combination of face detection and a coregistration algorithm can overcome nTMS head marker displacement concerns. MarLe can improve reliability, simplify, and reduce the protocol time of brain intervention techniques such as nTMS.

Use of Stereovision for Intraoperative Coregistration of a Spinal Surgical Field: A Human Feasibility Study

BACKGROUND

The use of image guidance during spinal surgery has been limited by several anatomic factors such as intervertebral segment motion and ineffective spine immobilization. In its current form, the surgical field is coregistered with a preoperative computed tomography (CT), often obtained in a different spinal confirmation, or with intraoperative cross-sectional imaging. Stereovision offers an alternative method of registration.

OBJECTIVE

To demonstrate the feasibility of stereovision-mediated coregistration of a human spinal surgical field using a proof-of-principle study, and to provide preliminary assessments of the technique's accuracy.

METHODS

A total of 9 subjects undergoing image-guided pedicle screw placement also underwent stereovision-mediated coregistration with preoperative CT imaging. Stereoscopic images were acquired using a tracked, calibrated stereoscopic camera system mounted on an operating microscope. Images were processed, reconstructed, and segmented in a semi-automated manner. A multistart registration of the reconstructed spinal surface with preoperative CT was performed. Registration accuracy, measured as surface-to-surface distance error, was compared between stereovision registration and a standard registration.

RESULTS

The mean surface reconstruction error of the stereovision-acquired surface was 2.20 ± 0.89 mm. Intraoperative coregistration with stereovision was performed with a mean error of 1.48 ± 0.35 mm compared to 2.03 ± 0.28 mm using a standard point-based registration method. The average computational time for registration with stereovision was 95 ± 46 s (range 33-184 s) vs 10to 20 min for standard point-based registration.

CONCLUSION

Semi-automated registration of a spinal surgical field using stereovision is possible with accuracy that is at least comparable to current landmark-based techniques.

It is the locus coeruleus! Or… is it?: a proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic locus coeruleus

The noradrenergic locus coeruleus (LC) is one of the protein pathology epicenters in neurodegenerative diseases. In contrast to PET (positron emission tomography), MRI (magnetic resonance imaging) offers the spatial resolution necessary to investigate the 3-4 mm wide and 1.5 cm long LC. However, standard data postprocessing is often too spatially imprecise to allow investigating the structure and function of the LC at the group level. Our analysis pipeline uses a combination of existing toolboxes (SPM12, ANTs, FSL, FreeSurfer), and is tailored towards achieving suitable spatial precision in the brainstem area. Its effectiveness is demonstrated using 2 datasets comprising both younger and older adults. We also suggest quality assessment procedures which allow to quantify the spatial precision obtained. Spatial deviations below 2.5 mm in the LC area are achieved, which is superior to current standard approaches. Relevant for ageing and clinical researchers interested in brainstem imaging, we provide a tool for more reliable analyses of structural and functional LC imaging data which can be also adapted for investigating other nuclei of the brainstem.

Large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data

Cropland mapping in smallholder landscapes is challenged by complex and fragmented landscapes, labor-intensive and unmechanized land management causing high within-field variability, rapid dynamics in shifting cultivation systems, and substantial proportions of short-term fallows. To overcome these challenges, we here present a large-area mapping framework to identify active cropland and short-term fallows in smallholder landscapes for the 2020/2021 growing season at 4.77 m spatial resolution. Our study focuses on Northern Mozambique, an area comprising 381,698 km2. The approach is based on Google Earth Engine and time series of PlanetScope mosaics made openly available through Norwaýs International Climate and Forest Initiative (NICFI) data program. We conducted multi-temporal coregistration of the PlanetScope data using seasonal Sentinel-2 base images and derived consistent and gap-free seasonal time series metrics to classify active cropland and short-term fallows. An iterative active learning framework based on Random Forest class probabilities was used for training rare classes and uncertain regions. The map was accurate (area-adjusted overall accuracy 88.6% ± 1.5%), with the main error type being the commission of active cropland. Error-adjusted area estimates of active cropland extent (61,799.5 km2 ± 4,252.5 km2) revealed that existing global and regional land cover products tend to under-, or over-estimate active cropland extent, respectively. Short-term fallows occupied 28.9% of the cropland in our reference sample (13% of the mapped cropland), with consolidated agricultural regions showing the highest shares of short-term fallows. Our approach relies on openly available PlanetScope data and cloud-based processing in Google Earth Engine, which minimizes financial constraints and maximizes replicability of the methods. All code and maps were made available for further use.

Mark3D - A semi-automated open-source toolbox for 3D head- surface reconstruction and electrode position registration using a smartphone camera video

Source localization in EEG necessitates co-registering the EEG sensor locations with the subject's MRI, where EEG sensor locations are typically captured using electromagnetic tracking or 3D scanning of the subject's head with EEG cap, using commercially available 3D scanners. Both methods have drawbacks, where, electromagnetic tracking is slow and immobile, while 3D scanners are expensive. Photogrammetry offers a cost-effective alternative but requires multiple photos to sample the head, with good spatial sampling to adequately reconstruct the head surface. Post-reconstruction, the existing tools for electrode position labelling on the 3D head-surface have limited visual feedback and do not easily accommodate customized montages, which are typical in multi-modal measurements. We introduce Mark3D, an open-source, integrated tool for 3D head-surface reconstruction from phone camera video. It eliminates the need for keeping track of spatial sampling during image capture for video-based photogrammetry reconstruction. It also includes blur detection algorithms, a user-friendly interface for electrode and tracking, and integrates with popular toolboxes such as FieldTrip and MNE Python. The accuracy of the proposed method was benchmarked with the head-surface derived from a commercially available handheld 3D scanner Einscan-Pro + (Shining 3D Inc.,) which we treat as the "ground truth". We used reconstructed head-surfaces of ground truth (G1) and phone camera video (M1080) to mark the EEG electrode locations in 3D space using a dedicated UI provided in the tool. The electrode locations were then used to form pseudo-specific MRI templates for individual subjects to reconstruct source information. Somatosensory source activations in response to vibrotactile stimuli were estimated and compared between G1 and M1080. The mean positional errors of the EEG electrodes between G1 and M1080 in 3D space were found to be 0.09 ± 0.01 mm across different cortical areas, with temporal and occipital areas registering a relatively higher error than other regions such as frontal, central or parietal areas. The error in source reconstruction was found to be 0.033 ± 0.016 mm and 0.037 ± 0.017 mm in the left and right cortical hemispheres respectively.

Improving 3D edge detection for visual inspection of MRI coregistration and alignment

BACKGROUND

Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another.

NEW METHOD

We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative).

RESULTS

We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity.

COMPARISON WITH EXISTING METHODS

Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image.

CONCLUSION

The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

Coregistered histology sections with diffusion tensor imaging data at 200 µm resolution in meningioma tumors

A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA. To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S₀) as well as DTI parameters: MD, FA, in-plane FA (FA_IP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach. Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.

Physiology and Intravascular Imaging Coregistration-Best of all Worlds?

Percutaneous coronary intervention is increasingly guided by coronary physiology and optimized using intravascular imaging. Pressure-based measurements determine the significance of a stenosis using hyperemic or nonhyperemic pressure ratios (eg, the instantaneous wave-free ratio). Intravascular ultrasound and optical coherence tomography provide cross-sectional and longitudinal detail regarding plaque composition and vessel characteristics. These facilitate lesion preparation and optimization of stent sizing and positioning. This review explores the evidence-base and practical aspects of coregistering pressure gradient assessment and intravascular imaging with angiography. We then discuss gaps in the evidence and what is needed to help integrate these techniques into clinical practice.

Physiology and Intravascular Imaging Coregistration-Best of all Worlds?

Percutaneous coronary intervention is increasingly guided by coronary physiology and optimized using intravascular imaging. Pressure-based measurements determine the significance of a stenosis using hyperemic or nonhyperemic pressure ratios (eg, the instantaneous wave-free ratio). Intravascular ultrasound and optical coherence tomography provide cross-sectional and longitudinal detail regarding plaque composition and vessel characteristics. These facilitate lesion preparation and optimization of stent sizing and positioning. This review explores the evidence-base and practical aspects of coregistering pressure gradient assessment and intravascular imaging with angiography. We then discuss gaps in the evidence and what is needed to help integrate these techniques into clinical practice.

Is covert attention necessary for programming accurate saccades? Evidence from saccade-locked event-related potentials

For decades, researchers have assumed that shifts of covert attention mandatorily occur prior to eye movements to improve perceptual processing of objects before they are fixated. However, recent research suggests that the N2pc component-a neural measure of covert attentional allocation-does not always precede eye movements. The current study investigated whether the N2pc component mandatorily precedes eye movements and assessed its role in the accuracy of gaze control. In three experiments, participants searched for a letter of a specific color (e.g., red) and directed gaze to it as a response. Electroencephalograms and eye movements were coregistered to determine whether neural markers of covert attention preceded the initial shift of gaze. The results showed that the presaccadic N2pc only occurred under limited conditions: when there were many potential target locations and distractors. Crucially, there was no evidence that the presence or magnitude of the presaccadic N2pc was associated with improved eye movement accuracy in any of the experiments. Interestingly, ERP decoding analyses were able to classify the target location well before the eyes started to move, which likely reflects a presaccadic cognitive process that is distinct from the attentional process measured by the N2pc. Ultimately, we conclude that the covert attentional mechanism indexed by the N2pc is not necessary for precise gaze control.

A semi-automatic registration protocol to match ex-vivo high-field 7T MR images and histological slices in surgical samples from patients with drug-resistant epilepsy

BACKGROUND

MRI is a fundamental tool to detect brain structural anomalies and improvement in this technique has the potential to visualize subtle abnormalities currently undetected. Correlation between pre-operative MRI and histopathology is required to validate the neurobiological basis of MRI abnormalities. However, precise MRI-histology matching is very challenging with the surgical samples. We previously developed a coregistration protocol to match the in-vivo MRI with ex-vivo MRI obtained from surgical specimens. Now, we complete the process to successfully align ex-vivo MRI data with the proper digitalized histological sections in an automatic way.

NEW METHOD

The implemented pipeline is composed by the following steps: a) image pre-processing made of MRI and histology volumes conversion and masking; b) gross rigid body alignment between MRI volume and histology virtual slides; c) rigid alignment between each MRI section and histology slice and estimate of the correlation coefficient for each step to select the MRI slice that best matches histology; d) final linear registration of the selected slices.

RESULTS

This method is fully automatic, except for the first masking step, fast and reliable in comparison to the manual one, as assessed using a Bland-Altman plot.

COMPARISON WITH EXISTING METHODS

The visual assessment usually employed for choosing the best fitting ex-vivo MRI slice for each stained section takes hours and requires practice. Goubran et al. (2015) proposed an iterative registration protocol but its aim and methods were different from ours. No others similar methods are reported in the literature.

CONCLUSIONS

This protocol completes our previous pipeline. The ultimate goal will be to apply the entire process to finely investigate the relationship between clinical MRI data and histopathological features in patients with drug-resistant epilepsy.