Mechanistic Inquiry into the Role of Tissue Remodeling in Fibrotic Lesions in Human Atrial Fibrillation

AbstractAtrial fibrillation (AF), the most common arrhythmia in humans, is initiated when triggered activity from the pulmonary veins propagates into atrial tissue and degrades into reentrant activity. Although experimental and clinical findings show a correlation between atrial fibrosis and AF, the causal relationship between the two remains elusive. This study used an array of 3D computational models with different representations of fibrosis based on a patient-specific atrial geometry with accurate fibrotic distribution to determine the mechanisms by which fibrosis underlies the degradation of a pulmonary vein ectopic beat into AF. Fibrotic lesions in models were represented with combinations of: gap junction remodeling; collagen deposition; and myofibroblast proliferation with electrotonic or paracrine effects on neighboring myocytes. The study found that the occurrence of gap junction remodeling and the subsequent conduction slowing in the fibrotic lesions was a necessary but not sufficient condition for AF development, whereas myofibroblast proliferation and the subsequent electrophysiological effect on neighboring myocytes within the fibrotic lesions was the sufficient condition necessary for reentry formation. Collagen did not alter the arrhythmogenic outcome resulting from the other fibrosis components. Reentrant circuits formed throughout the noncontiguous fibrotic lesions, without anchoring to a specific fibrotic lesion

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

Background: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods: In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. Results: In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95–0.96) and 0.80 (IQR 0.67–0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29–0.76) for tumor segmentation. Conclusions: Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. Relevance statement: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist’s workload and increasing accuracy and consistency. Key points: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations. Graphical Abstract: [Figure not available: see fulltext.]

Computational methods for mapping the human cerebral cortex

Human brain, the most complex living structure ever known, has been a subject of research for centuries. With the advent of revolutionary imaging techniques, neuroscientists can now acquire high-resolution structural and functional brain data in vivo. Integration of the acquired data using computational techniques to form maps of the human brain has extensive scientific and clinical applications. In this thesis, we develop novel computational methods for human brain mapping. Our focus is on the cerebral cortex, the most important component of the human brain. It is widely accepted that factors such as genetics, experience, and disease influence the shape of the cortical surface. These influencing factors can be indirectly studied by detecting and mapping the shape changes that they induce. The first major contribution of this thesis is a study which presents an effective yet simple method for detecting systematic cortical surface shape differences between different subject groups. This study has detected differences between Chinese and Caucasians, and the differences might be related to the genetic factor or the experiential factor, namely the language development. The second contribution is a new computational framework which unifies three computational procedures in cerebral cortex mapping, including cortical surface parameterization, cortical surface registration, and electromagnetic neural source imaging of the cortex. The parameterization represents the cortical surface in a form that facilitates the use of sophisticated mapping techniques. The registration integrates different cortical surfaces in a common coordinate space in which anatomically corresponding points occupy the same spatial coordinates. The source imaging identifies the electric currents in the cortex from electromagnetic measurements in vivo. Notably, the framework is based on the knowledge that the anatomy and physiology of the cortex is locally coherent. To make use of this local coherency, the framework adopts a representation scheme in which the cortical surface data are expressed using a linear combination of the low-frequency eigenvectors of a Laplacian of the underlying triangular mesh. Due to the conciseness and the faithfulness of this representation scheme, the computations are efficient as well as accurate

Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling.

Research has indicated that atrial fibrillation (AF) ablation failure is related to the presence of atrial fibrosis. However it remains unclear whether this information can be successfully used in predicting the optimal ablation targets for AF termination. We aimed to provide a proof-of-concept that patient-specific virtual electrophysiological study that combines i) atrial structure and fibrosis distribution from clinical MRI and ii) modeling of atrial electrophysiology, could be used to predict: (1) how fibrosis distribution determines the locations from which paced beats degrade into AF; (2) the dynamic behavior of persistent AF rotors; and (3) the optimal ablation targets in each patient. Four MRI-based patient-specific models of fibrotic left atria were generated, ranging in fibrosis amount. Virtual electrophysiological studies were performed in these models, and where AF was inducible, the dynamics of AF were used to determine the ablation locations that render AF non-inducible. In 2 of the 4 models patient-specific models AF was induced; in these models the distance between a given pacing location and the closest fibrotic region determined whether AF was inducible from that particular location, with only the mid-range distances resulting in arrhythmia. Phase singularities of persistent rotors were found to move within restricted regions of tissue, which were independent of the pacing location from which AF was induced. Electrophysiological sensitivity analysis demonstrated that these regions changed little with variations in electrophysiological parameters. Patient-specific distribution of fibrosis was thus found to be a critical component of AF initiation and maintenance. When the restricted regions encompassing the meander of the persistent phase singularities were modeled as ablation lesions, AF could no longer be induced. The study demonstrates that a patient-specific modeling approach to identify non-invasively AF ablation targets prior to the clinical procedure is feasible

Correction: Virtual Electrophysiological Study of Atrial Fibrillation in Fibrotic Remodeling.

[This corrects the article DOI: 10.1371/journal.pone.0117110.]

Transmembrane potential maps at four time instants in substrates Utah III (top row) and Utah IV (bottom row) following pacing from a “prime” region outside the PVs (pacing sites marked by red arrows).

<p>Transmembrane potential maps at four time instants in substrates Utah III (top row) and Utah IV (bottom row) following pacing from a “prime” region outside the PVs (pacing sites marked by red arrows).</p

Transmembrane potential maps at five time instants, documenting AF initiation in substrates Utah III (top two rows) and IV (bottom three rows) from different PV pacing locations (as noted at left).

<p>Lines of block are marked in red. White arrows indicate direction of propagation. Red dots (left-most column) indicate pacing location.</p

Patient-specific distributions of LA fibrosis for substrates Utah I—IV.

<p>Pacing locations in the left PVs (L1–L5) and right PVs (R1–R5) are indicated for each substrate. Anatomical locations for the left superior PV (LSPV), left inferior PV (LIPV), right superior PV (RSPV), right inferior PV (RIPV), and LA appendage (LAA) are indicated in the left-most substrate; all substrates are presented in identical orientations.</p