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Constructing a Human Atrial Fibre Atlas.

ABSTRACT: Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.

SUBMITTER: Roney CH

PROVIDER: S-EPMC7773625 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

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Constructing a Human Atrial Fibre Atlas.

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