The technique at the center of the controversy is called phase mapping. The idea is elegant in principle. At every point on the heart, an electrode or optical sensor records how the local electrical activity changes over time. Researchers convert each raw signal into a phase value, a number that cycles between zero and two pi and indicates where in the excitation-recovery cycle that patch of tissue currently sits, much like the position of a hand sweeping around a clock face. When a spiral wave of electrical activity rotates through the tissue, the phase values around its core wind through a full turn, and the point at the very center, where all phases meet, is called a phase singularity. Finding these singularities has long been considered the gold standard for locating the engines that sustain fibrillation and tachycardia.

The trouble, as the team led by Bjorn Verstraeten and Nele Vandersickel demonstrates, is that conventional phase mapping quietly assumes the phase field is continuous everywhere across the heart. That assumption breaks down in the real organ. Scar tissue, fibrotic patches, anatomical obstacles such as valves and blood vessels, and regions of functional conduction block all interrupt the smooth flow of electrical waves. In these places, the phase value becomes discontinuous or simply undefined, because the signal there does not follow the normal cycle. The researchers call these troubled regions phase defects, and they are not rare curiosities. In fibrillating hearts, and especially in patients with structural heart disease, phase defects are common and often sit exactly where clinicians most need reliable information.

When a phase defect interrupts the phase landscape, the standard algorithms for computing the phase index, the mathematical test used to detect a rotating core, begin to misbehave. The phase index is normally calculated by summing the phase changes around a small closed loop of measurement points. If the loop encircles a genuine rotating spiral core, the phases wind through a full cycle and the index flags a rotor. But if the loop crosses a phase defect, where the phase jumps abruptly or is undefined, the calculation can produce a spurious winding number. The result is a false positive: the algorithm reports a rotor where no rotation exists. Conversely, genuine rotations that pass near or around a defect can evade detection entirely, producing false negatives. In a clinical setting, both errors matter enormously, because ablation catheters may be guided to the wrong spots while true drivers are left untouched.

The solution proposed by the Belgian-led team, which included collaborators from Ghent University, OLV Hospital Aalst, Imperial College London, and clinical electrophysiology groups, is a reimplementation they call extended phase mapping. Rather than pretending the phase field is continuous, the new approach explicitly detects where phase defects lie and then works around them. When the algorithm needs to evaluate the phase index in a region, it constructs paths that detour around any detected defects, much as a hiker routes around an impassable ravine. Only when a complete, defect-free closed loop can be formed is the winding calculation performed. This seemingly simple change restores the mathematical validity of the phase index in exactly the regions where it was previously meaningless.

To test the approach rigorously, the researchers applied it to three very different datasets, spanning the full pipeline from theory to bedside. First, they used simulated cardiac excitation patterns generated with the Fenton-Karma model, a well-established mathematical description of cardiac electrical propagation that allows ground truth to be known exactly. In these simulations, the team could compare what extended phase mapping reported against what was truly happening in the tissue. Second, they analyzed experimental optical mapping data from rat ventricular fibrillation, where high-speed cameras record the fluorescence of voltage-sensitive dyes to capture electrical activity across the heart surface with exquisite spatial detail. Third, and perhaps most importantly for patients, they processed a clinical CARTO activation map of atrial tachycardia, the kind of electroanatomical map acquired in the electrophysiology laboratory during a real ablation procedure.

The results were strikingly consistent across all three settings. Erroneous detections, the false positives that plague conventional phase mapping near lines of block and anatomical boundaries, were eliminated. At the same time, rotations that the standard method had missed, including reentrant circuits that travel around near-complete conduction blocks and around anatomical obstacles, were correctly resolved. The unified treatment revealed something conceptually important: whether a reentry circles a functional block, a scar, or a valve annulus, the underlying dynamics look the same once phase defects are properly accounted for. The phase index, computed around defects rather than through them, provides a single physiologically consistent framework for characterizing every type of rotational driver in cardiac tissue.

This finding carries real weight for the ongoing debate in cardiac electrophysiology about what actually sustains atrial fibrillation and ventricular fibrillation. For years, researchers have reported conflicting results about whether stable rotors exist in fibrillating hearts and whether targeting them improves ablation outcomes. Some of that disagreement may now be attributable to measurement artifacts. If conventional phase mapping generates phantom rotors near conduction block and fibrosis, then clinical trials built on those detections may have been targeting artifacts in some patients and missing true drivers in others. Extended phase mapping offers a way to separate genuine rotational dynamics from defects in the measurement itself, potentially sharpening both the science and the therapy.

The clinical implications extend naturally to the tools already in the catheterization laboratory. Electroanatomical mapping systems like CARTO reconstruct activation patterns from sparse catheter sampling, and their interpolation across unmapped regions creates exactly the kinds of discontinuities that corrupt phase-based analysis. An implementation that explicitly identifies and routes around phase defects could make rotor detection on clinical maps substantially more trustworthy, guiding ablation lesions to circuits that genuinely drive the arrhythmia. For patients with persistent atrial fibrillation, where ablation success rates remain frustratingly modest, a more reliable map of the arrhythmia’s engines could translate directly into better outcomes.

There is also a broader lesson here about the quiet power of assumptions in computational science. Phase mapping has been used for decades, in simulations, in animal experiments, and in patients, and its core continuity assumption went largely unexamined because it fails only in specific, structured situations rather than randomly. By naming the problem, phase defects, and building the workaround directly into the algorithm, the researchers have turned a hidden source of error into an explicit, testable component of the method. The work suggests that the next generation of arrhythmia mapping should track not just the singular points where phases collide, but the entire topology of the phase landscape, defects included. In doing so, it moves the field closer to a complete and honest picture of how rotational waves organize, and sometimes devastate, the beating heart.

Subject of Research: An improved phase mapping algorithm that corrects phase singularity detection for phase defects in cardiac arrhythmia mapping

Article Title: Going around phase defects: Addressing a fundamental flaw of phase singularity detection

Article References: Verstraeten, B., Lootens, S., Van Den Abeele, R., Van Nieuwenhuize, V., Okenov, A., Hendrickx, S., Santos Bezerra, A., Nezlobinskii, T., Kappadan, V., Handa, B. S., Ng, F. S., Duytschaever, M., & Vandersickel, N. (2026). Going around phase defects: Addressing a fundamental flaw of phase singularity detection. PLOS Computational Biology, 22(10), e1013963. https://doi.org/10.1371/journal.pcbi.1013963

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1013963

Keywords: cardiac arrhythmia, phase mapping, phase singularity, rotor detection, atrial fibrillation, ventricular fibrillation, cardiac electrophysiology, reentry, ablation, CARTO mapping, Fenton-Karma model, computational biology