Decoding the Spatiotemporal Patterns of Acute Myocardial Ischemia and Coronary Artery Interaction

Saturday, October 11, 2025

9:00 AM - 9:05 AM PDT

Location: Room 30B

Presenting Author(s)

Connor Browning

Student / Manufacturing Intern
University of Utah
Morgan, Utah, United States

Session Chair(s)

Bryan C. Good, PhD

Assistant Professor
University of Tennessee
Knoxville, Tennessee, United States
Ellie Rahbar

Associate Professor
Wake Forest School of Medicine, United States

Introduction: : Cardiovascular disease is the leading cause of mortality, accounting for 20.5 million deaths worldwide in 2021 [1]. The most common form of cardiovascular disease is coronary artery disease (CAD), with 98% of the world's population having at least one CAD risk factor [2]. CAD results in a limited flow of blood and oxygen to the myocardium and ultimately can lead to myocardial ischemia [3]. The standard clinical method for diagnosing myocardial ischemia is through the examination of a 12-lead ECG. However, ECG diagnosis has a sensitivity of 50%-70% and a specificity of 70%-90% [4]. This poor performance by the ECG can lead to potential misdiagnoses with severe consequences, underscoring the need for improved diagnostic methods.

Despite extensive research, the spatiotemporal progression of myocardial ischemia remains a topic of debate [5]. To improve the diagnosis of myocardial ischemia, it is crucial to understand how ischemic regions develop throughout the ventricular walls over time. Additionally, exploring the relationship between this progression and patient-specific coronary vasculature could provide deeper insights into the disease state. To address these gaps in our understanding, we have utilized large animal experimental models to acquire patient-specific anatomical structures and ischemic zone locations and severities [4]. These experimental datasets offer a unique opportunity to decode the spatiotemporal and electroanatomical development of myocardial ischemia. Ultimately, our findings aim to inform more effective diagnostic techniques and improve patient outcomes for myocardial ischemia and CAD.

Materials and

Methods: : To investigate the spatiotemporal development of myocardial ischemia, we utilized a large animal experimental model [4]. Four episodes of acute myocardial ischemia were induced using a hydraulic occluder placed around the left anterior descending (LAD) artery. During these episodes, electrical potentials were recorded both on the heart's surface and intramurally, utilizing a 247-electrode epicardial sock array and 137 specialized needle electrodes. Cardiac anatomy was captured using magnetic resonance imaging (MRI) and computed tomography (CT) imaging [4].

From the MRI and CT images, we created three-dimensional segmentations of the ventricles and coronary vasculature and transformed them into tetrahedral meshes. We quantified the ischemic regions as ST-segment elevations greater than 1 mV for each time instant [6]. These elevations were quantified using the point 40% of the time between the S and T wave (ST40) [7]. By mapping ST40 data onto the ventricular meshes, ischemic zones within the ventricular wall were identified and extracted using the open-source software SCIRun.

We then calculated the ischemic zones’ volume and surface area. Furthermore, we quantified ischemic zones’ location using the American Heart Association’s standard 17-ventricular-segment nomenclature [8]. Ultimately, these methods allowed for further analysis of the spatiotemporal development of acute myocardial ischemia and its relationship to the coronary vasculature.

Results, Conclusions, and Discussions::

Results: Using the methods described above, we generated detailed visualizations of ischemic zones’ surface area and volume. An example from one experimental procedure is shown in Figure 1, illustrating a clear increase in ischemic zone size as the duration of occlusion progresses. Additionally, the percentage of the ischemic zones located within each of the 17 standardized ventricular segments was quantified to assess spatial distribution over time (Figure 2). Early in the occlusion, the ischemic region was highly localized within segment 13, the apical septal region of the left ventricle, but it became progressively more widespread as the episode continued.

The spatial relationship between the ischemic zones and coronary vasculature was also examined using the standard 17 segments (Figure 3). While the coronary arteries provided broad overall coverage, certain ventricular segments—specifically segment 5 (basal inferolateral), segment 8 (mid anteroseptal), segment 9 (mid anterior), and segment 11 (mid inferolateral)—were found to have little or no visible vasculature.

Discussion: The results of this study provide further insights into the complex spatial distribution of myocardial ischemia and its relationship with the coronary vasculature. By utilizing advanced imaging techniques and high-resolution three-dimensional electrograms, we were able to identify and quantify ischemic zones within the ventricular walls. The detailed visualizations and quantification of these zones, aligned with patient-specific anatomy, indicate that the ischemic regions developed in segment 13, which encompasses a major portion of the experimentally occluded vessel (LAD). Expanding on this finding could ultimately allow physicians to predict areas of damaged myocardium due to CAD.

These findings highlight the importance of validating and understanding the convoluted spatiotemporal development of myocardial ischemia. By gaining a better understanding of the physiological mechanisms of the disease, we can inform more effective diagnostic techniques and patient-specific treatments, ultimately leading to the more effective treatment of myocardial ischemia and CAD.

Acknowledgements and/or References (Optional): : [1] M. D. Cesare et al., “The Heart of the World,” Glob. Heart, vol. 19, no. 1, Jan. 2024, doi: 10.5334/gh.1288.
[2] M. A. Khan et al., “Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study,” Cureus, vol. 12, no. 7, p. e9349, doi: 10.7759/cureus.9349.
[3] D. C. Crossman, “The pathophysiology of myocardial ischaemia,” May 2004, doi: 10.1136/hrt.2003.029017.
[4] B. Zenger et al., “Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform,” Physiol. Meas., vol. 41, no. 1, p. 015002, Feb. 2020, doi: 10.1088/1361-6579/ab64b9.
[5] K. Aras, B. Burton, D. Swenson, and R. MacLeod, “Spatial organization of acute myocardial ischemia,” J. Electrocardiol., vol. 49, no. 3, pp. 323–336, May 2016, doi: 10.1016/j.jelectrocard.2016.02.014.
[6] B. Zenger et al., “Transient recovery of epicardial and torso ST-segment ischemic signals during cardiac stress tests: A possible physiological mechanism,” J. Electrocardiol., vol. 69S, pp. 38–44, 2021, doi: 10.1016/j.jelectrocard.2021.07.007.
[7] B. M. Burton, K. K. Aras, W. W. Good, J. D. Tate, B. Zenger, and R. S. MacLeod, “Image-based modeling of acute myocardial ischemia using experimentally derived ischemic zone source representations,” J. Electrocardiol., vol. 51, no. 4, pp. 725–733, Jul. 2018, doi: 10.1016/j.jelectrocard.2018.05.005.
[8] American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging: et al., “Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart,” Circulation, vol. 105, no. 4, pp. 539–542, Jan. 2002, doi: 10.1161/hc0402.102975.