Effect of Wall Motion Sampling on CFD-Derived Left Atrial Flow Metrics

Effect of Wall Motion Sampling on CFD-Derived Left Atrial Flow Metrics | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Effect of Wall Motion Sampling on CFD-Derived Left Atrial Flow Metrics View ORCID Profile Yvonne Stöcker , View ORCID Profile Manuel Guerrero-Hurtado , Eduardo Durán , Alejandro Gonzalo , View ORCID Profile Zorica Ristić , View ORCID Profile Åshild Telle , View ORCID Profile Ahmad Kassar , View ORCID Profile Romanos Haykal , View ORCID Profile Nazem Akoum , View ORCID Profile Patrick M. Boyle , View ORCID Profile Oscar Flores , View ORCID Profile Christoph M. Augustin , View ORCID Profile Juan Carlos del Alamo , Manuel García-Villalba doi: https://doi.org/10.1101/2025.10.24.684343 Yvonne Stöcker 1 Institute of Fluid Mechanics and Heat Transfer, TU Wien , Vienna, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yvonne Stöcker For correspondence: yvonne.stoecker{at}tuwien.ac.at Manuel Guerrero-Hurtado 2 Hospital General Universitario Gregorio Marañón , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuel Guerrero-Hurtado Eduardo Durán 3 Departamento de Ingeniería Mecánica, Térmica y de Fluidos, Universidad de Málaga , Málaga, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alejandro Gonzalo 4 Department of Mechanical Engineering, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zorica Ristić 5 Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz , Graz, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zorica Ristić Åshild Telle 6 Department of Biongineering, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Åshild Telle Ahmad Kassar 7 Division of Cardiology, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ahmad Kassar Romanos Haykal 7 Division of Cardiology, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Romanos Haykal Nazem Akoum 7 Division of Cardiology, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nazem Akoum Patrick M. Boyle 6 Department of Biongineering, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrick M. Boyle Oscar Flores 8 Department of Aerospace Engineering, Universidad Carlos III de Madrid , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oscar Flores Christoph M. Augustin 5 Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz , Graz, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christoph M. Augustin Juan Carlos del Alamo 4 Department of Mechanical Engineering, University of Washington , Seattle, USA 7 Division of Cardiology, University of Washington , Seattle, USA 9 Center for Cardiovascular Biology, University of Washington , Seattle, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Juan Carlos del Alamo Manuel García-Villalba 1 Institute of Fluid Mechanics and Heat Transfer, TU Wien , Vienna, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF Abstract The temporal resolution of medical imaging sequences used to drive patient-specific computational fluid dynamics (CFD) simulations remains limited, typically providing 10–20 frames per cardiac cycle. Therefore, temporal interpolation to reconstruct left atrial (LA) wall motion is required, but its impact on hemodynamic predictions has not been systematically characterized. To investigate this, we constructed high-temporal-resolution reference wall motion data using electromechanical (EM) simulations on five patient-specific atrial geometries with a history of atrial fibrillation. We then generated temporally downsampled datasets to emulate clinical frame rates (10, 20, and 40 frames per cycle) and performed CFD simulations to isolate the effects of temporal undersampling on hemodynamic metrics. The focus was placed on kinetic energy, KE, and residence time, T R , particularly in the left atrial appendage (LAA), where thrombosis is most likely to occur. We employed an immersed boundary method to prescribe the wall motion and computed blood T R through a passive scalar transport equation. Results indicate that while global LA hemodynamic indices showed relatively modest sensitivity to frame rate (errors < 15%), LAA T R was substantially affected, with 10-frame reconstructions overestimating T R by up to 43% compared to reference values. CFD simulations based on 20 and 40 frames per cycle yielded favorable agreement with reference results (errors ≤ 9%), whereas 10-frame reconstructions consistently produced elevated stasis metrics and failed to capture physiological features like atrial contraction peaks. Our analysis suggests that anatomical reconstructions with ≥ 20 frames per cardiac cycle provide sufficiently reliable LAA blood-stasis indices, while lower temporal resolutions should be interpreted cautiously, particularly for patient stratification purposes where accurate ranking is essential. 1 Introduction Atrial fibrillation (AF) affects over 50 million people worldwide [ 1 ] and is characterized by weak, erratic contractions of the left atrium (LA). In AF, weakened LA motion promotes blood stasis, particularly in the left atrial appendage (LAA)—a small, narrow-necked chamber whose emptying is especially compromised under reduced contractility. This predisposes to thrombus formation in the LAA, which can embolize and cause ischemic stroke [ 2 ]. Anticoagulation therapy reduces stroke risk but increases bleeding risk, making risk stratification essential to ensure a net clinical benefit [ 3 , 4 ]. Commonly, stroke risk stratification relies on scores such as the CHADS 2 score and its variations, which are purely based on patient demographics and medical history factors derived from large population-based studies [ 5 ]. Notably, they lack information about patient-specific hemodynamics [ 6 , 7 ], and their predictive accuracy is moderate [ 8 ]. Because blood stasis is a major thrombogenic factor [ 9 ], patient-specific computational fluid dynamics (CFD) analyses hold promise for improving individualized stroke risk estimation [ 10 – 13 ]. Accurate modeling of LA hemodynamics is essential to predict thrombus formation, and recent years have seen substantial progress in this area [ 14 ]. However, CFD results can be sensitive to modeling assumptions and numerical methods. Some studies have analyzed the effects of spatiotemporal resolution and numerical order of discretization [ 15 ], or the potential use of turbulence models in LA simulations [ 16 ], where flow remains predominantly laminar during most of the cardiac cycle. Others have assessed the effects of non-Newtonian rheology of slow-flowing blood [ 12 , 17 ], inflow boundary condition choices [ 18 ], including the pulmonary vein (PV) flow split [ 16 , 19 – 21 ], and presence of the mitral valve (MV) [ 22 ]. Constructing patient-specific CFD models requires segmenting the interface between atrial tissue and blood—hereafter, the LA wall —from 3D medical images and generating computational meshes. Capturing wall motion further requires time-resolved imaging (i.e., 4D), frame-by-frame segmentation, and registration across frames over the cardiac cycle. However, 4D cardiac imaging is not routinely performed at many centers. Moreover, in patients with paroxysmal AF, the rhythm present at the scan cannot be controlled; patients may be imaged in AF or in sinus rhythm, which further complicates model construction. These constraints have led many investigators to use rigid-wall simulations to approximate impaired wall motion during AF [ 10 , 12 , 23 – 29 ], or even sinus rhythm [ 10 , 15 , 16 , 25 , 30 ]. Several studies have evaluated this approximation [ 16 , 18 , 31 , 32 ], concluding that it yields unphysiological flow in sinus rhythm whereas it tends to overestimate thrombosis predictors in AF. Motivated by these limitations, researchers have utilized simplified motion laws [ 6 , 33 – 38 ], fluid– structure-interaction approaches [ 39 – 45 ], prescribed wall motion obtained from prior electro– mechanics (EM) simulations [ 46 , 47 ], or applied deep learning approaches to recreate wall motion from still images [ 48 ]. Although more sophisticated than assuming rigid LA walls, these strategies still depend on modeling choices and require careful parameterization or training. Consequently, when available, CFD driven by 4D medical images (typically magnetic resonance imaging (MRI) or computed tomography (CT)) is commonly adopted [ 17 , 20 , 22 , 30 , 31 , 49 – 59 ]. However, these modalities provide limited temporal resolution (∼ 10 frames per cycle), necessitating temporal interpolation, and the impact of this undersampling on simulation-derived metrics has not been systematically characterized. In this work, we quantify how time resolution of wall motion affects atrial hemodynamics in CFD simulations. To address the lack of high-frame-rate image sequences to build ground-truth LA models, we construct a high-temporal-resolution reference of wall motion utilizing EM simulations on five patient-specific atrial geometries. Subsequently, we generate temporally downsampled datasets that emulate clinical frame rates and perform new CFD simulations. In this way, we isolate the effect of temporal undersampling on hemodynamic metrics, with particular emphasis on the LAA, the most common site of atrial thrombosis in AF. Our results suggest that comparisons between CFD models with differing temporal resolutions should be interpreted cautiously, particularly when the underlying 4D image sequences contain fewer than 20 frames per cardiac cycle. 2 Methodology 2.1 Ethics statement The study (STUDY00015081) was approved by the Institutional Review Board of the University of Washington. All patients provided their written consent. 2.2 Patients & Imaging The five patients considered here were recruited from the University of Washington Medical Center, three of whom (B, D, and E) are the same as in Telle et al . [ 60 ]. They all had a history of AF, were scheduled for ablation. MRI scans were taken prior to ablation at the end of atrial diastole and then segmented, processed, and analyzed by Merisight (Marrek Inc., Salt Lake City, UT) as described in Marrouche et al . [ 61 ]. 2.3 Mesh generation To perform EM simulations, the LA myocardium was meshed using meshtool [ 62 ], an open-source software for mesh generation and manipulation tailored to cardiac modeling applications. First, the interface between blood pool and tissue was extracted from the image segmentations and discretized with triangular elements, yielding the surface meshes displayed in Figure 1 . For finer spatial assessment of the CFD results, each LAA will be partitioned into proximal and distal subregions of similar volumes by the cutting planes as shown in the bottom row of Figure 1 . Download figure Open in new tab Figure 1: Left atrial anatomies of the five study subjects, showing individual color assignments and delineation of the LAA in each case, as well as the separation of the appendages into the proximal and distal part of similar volumes. For subject B, the locations of the PVs are indicated by an asterisk, and the MV by a circle. Subsequently, a volumetric, tetrahedral mesh of the myocardial tissue was generated by extruding the blood pool interface by 2 mm [ 63 ]. Atrial myocyte orientations and wall thickness were assigned based on automatically identified anatomical landmark regions, following previous works [ 64 , 65 ]. The average tetrahedral edge length was approximately 500 µm. Additionally, auxiliary volumetric patches were introduced to close the MV and PV orifices. These caps facilitate the definition of boundary conditions in both the EM and CFD simulations, and help to preserve the anatomical shape of the orifices [ 60 ]. 2.4 EM simulations Based on the patient-specific anatomies, myocardial EM simulations were performed using the in-house code CARPentry [ 66 , 67 ]. The human atrial action potential was represented by the Courtemanche et al . [ 68 ] model of cellular electrophysiology, with modifications as described in Bayer et al . [ 69 ]. Excitation of the LA was initiated through transmembrane current stimulation in three selected points around the right superior PV, anatomically representing approximate activation sites as observed for patients B, D, and E in a previous study [ 60 ]. The propagation of electrical activation in the myocardium was simulated using the reaction– eikonal equation [ 70 ], which enables consistent and physiological activation patterns regardless of mesh discretization. Longitudinal and transverse conduction velocity was set to 1.308 m/s and 0.585 m/s, respectively, based on average values reported for patients B, D, and E [ 60 ]. Active stress generation was represented by the model of human atrial contraction dynamics described in Land and Niederer [ 71 ]. Passive tension in the LA was described using a reduced Holzapfel–Ogden formulation with fiber dispersion [ 72 ], while the auxiliary PV and MV caps were represented by a stiff Demiray material [ 60 ]. Both materials were treated as nearly incompressible. Robin-type boundary conditions were used to constrain atrial motion: normal springs on the epicardium and omnidirectional springs at the PV inlets. For more information on the EM model, including model parameters, see [ 46 , 60 , 72 ]. Since the exact pressure at the time of image acquisition was unknown, an unloaded reference configuration was estimated using a backward displacement algorithm [ 73 ] with a predefined pressure of 10 mmHg. This procedure ensures that the pressurized anatomical model matches the geometry obtained from imaging data. To account for the hemodynamic interactions with the remaining parts of the circulatory system, the 3D EM model of the LA was strongly coupled to CircAdapt [ 74 , 75 ], a non-linear 0D lumped parameter model that efficiently simulates the dynamics of the other heart chambers as well as the systemic and pulmonary circulation. CircAdapt simulated hydrostatic pressures and blood flow across the PV inlets and MV outlet, which were used to impose physiological pressure boundary conditions and volume constraints on the solid LA structure [ 67 ]. Additionally, the ventricular contraction in the 0D model was used to approximate atrioventricular plane displacement, which was applied as a traction boundary condition on the MV annulus following Telle et al . [ 60 ]. Electrical activation was initialized at 0 ms of each heartbeat in the sinoatrial node within the 0D framework. An inter-atrial conduction delay of 30 ms was applied to account for a physiological propagation time from the right atrium to the LA. The EM simulations were performed at a heart rate of 60 bpm. To ensure periodicity, 25 beats were first simulated using a coarser mechanical time step of Δ t mech = 1 ms, followed by 5 additional beats with a refined time step of Δ t mech = 50 µs, consistent with the requirements of the CFD simulations described below. For the electrophysiology components, a uniform time step size of Δ t EP = 25 µs was used throughout all beats. The final state from the 30 th beat was then provided to the CFD pipeline. 2.5 CFD simulations Assuming incompressibility and Newtonian blood rheology, the Navier–Stokes equations that govern the dynamics of the blood flow were solved with a fractional step method using the in-house code TUCANGPU [ 76 ], which is the GPU-accelerated successor of the CPU-based flow solver TUCAN [ 77 ]. The same heart rate (1 s −1 ) and blood viscosity (0.04 cm 2 /s) were applied across all subjects to standardize comparisons. Discretization in space was performed with second-order central finite differences in a staggered, uniform Cartesian grid. For integration in time, a low-storage, semi-implicit Runge–Kutta scheme with three stages was employed. Simultaneously, the blood residence time T R was computed from the forced transport equation for a passive scalar where is the fluid velocity vector. This equation was spatially discretized with a third-order WENO scheme [ 78 ] to ensure accuracy and avoid spurious oscillations near sharp gradients. Each simulation was performed in a triply-periodic cubic computational domain with an edge length of 13 cm and 256 uniformly distributed points per direction, resulting in a grid step size of approximately Δ x = 0.51 mm (16.8 million grid cells). The time step size Δ t = 50 µs was kept constant and chosen to achieve a Courant number CFL < 0.3 throughout the simulations. To couple the flow to the motion of the LA wall, an immersed boundary method (IBM) [ 79 ] was used. In this approach, the moving LA wall is placed in the fixed computational domain and the no-slip condition at the LA surface is imposed by adding appropriate localized volumetric forces as a source term in the momentum equations of the fluid. These IBM forces are evaluated at the location of specific marker points, which are distributed over the LA surface. They correspond to the center points of the triangular surface elements of the inner myocardial wall (also including the PV and MV caps), whose motion is imposed as presented in the next section. To reduce leakage across the immersed boundary, which has also been observed by other groups [ 80 , 81 ], a second layer of marker points was added by shifting each original marker by one grid step size Δ x along the outward normal direction of its corresponding triangular element. The flow rate through the PVs was enforced via a prescribed surface-normal velocity in a non-circular buffer region upstream of each PV orifice cap. It is computed based on mass conservation where V LA is the LA volume and Q MV the known outflow rate obtained from the EM simulations. This Q PV is then split between all PVs such that equal velocity is obtained in each vein. The buffer region at the PV inlets is also used to set the residence time T R to zero. When outflow is desired, i.e. Q MV > 0, no IBM force is imposed at the marker points that belong to the MV orifice cap. Otherwise, a no-slip condition is imposed. No specific model is applied for the MV leaflets, since preliminary studies showed no significant differences in terms of the results presented in section 3 . All simulations were initialized with zero velocity and residence time, and run for 20 cardiac cycles to ensure convergence to a quasi-periodic state. This long convergence time corresponds to the washout time of the fluid particle with the highest residence time, which, especially in the LAA, can be very high. Results were collected from the subsequent eight cycles. 2.6 Wall motion In previous atrial flow studies of our group, the LA geometry and its motion were obtained from patient-specific time-resolved computed tomography (4D–CT) images [ 17 , 20 , 31 ], which had a temporal resolution in the order of 10 to 20 images per cardiac cycle. However, with the duration of a cardiac cycle set to T = 1 s in the CFD simulations, and as they are carried out with a time step of 50 µs, motion information at 20 000 time instants per cycle is required. To achieve this, Fourier interpolation in time was applied, with the number of Fourier modes depending on the number of available CT frames. In contrast to the 4D–CT scans, EM simulations can supply such high temporal resolution that no interpolation would be needed. This provided the novel opportunity to assess how sensitive the CFD results are to the motion interpolation by comparing CFD results based on low-resolution, interpolated motion with results from fully resolved motion. First, a reference simulation was performed for each subject by prescribing the LA wall motion from the full-resolution motion data from the EM simulations, i.e. without applying any interpolation. Then, the temporal resolution of the motion data was artificially decreased by retaining only a predefined number n f of equidistant time frames of one cycle, thereby mimicking the data available from 4D–CT scans. Subsequently, the motion was reconstructed using Fourier interpolation. The number of retained frames was chosen to be equal ( n f = 10 and 20) and higher ( n f = 40) than in previous studies [ 17 , 20 , 31 , 49 , 50 , 52 , 58 ]. In the following, these simulated cases will be referred to by a letter identifying the subject, followed by a case identifier that denotes the number of retained frames n f . E.g., the cases of subject A are named Aref, A10, A20, and A40. 3 Results This study utilized patient-specific EM and CFD LA models from N = 5 subjects obtained from MRI. Table 1 summarizes relevant model-predicted anatomical and global functional chamber indices for each subject, as well as time-averaged LA residence time . The table includes a queue-model estimate for based on global indices previously shown to approximate reference values from patient-specific CFD simulations based 4D–CT segmentations [ 17 , 20 ] (see Martínez-Legazpi et al . [ 82 ] for further details about cardiac chamber queue models). The estimated residence times are slightly higher than the values reported in previous studies [ 17 , 20 , 31 , 32 ], which is induced by the low left ventricular stroke volume (LVSV) of the subjects. View this table: View inline View popup Download powerpoint Table 1: Model-predicted anatomical and functional parameters of the LA and LAA. Mean values represent time-averaged quantities. EF: emptying fraction, LVSV: left ventricular stroke volume. represents residence time in the LA. A conduit-dominated queue model estimate (top) is , where T is the cardiac cycle and the parameter α is fixed fitting previous data [ 17 , 20 ]. The colors are used to identify the subjects throughout the manuscript figures. Although the mean volumes vary significantly across the subjects, the functional parameters are similar. The LA and LAA emptying fractions are on the high end of normality, helping to set a more stringent benchmark for wall-motion models than previous studies that considered weakly beating atria [ 32 ]. The LVSV values are rather small and very similar, allowing us to focus on the effect of the motion reconstruction procedure. The remainder of this section reports detailed results for subjects A and D, followed by statistical quantities for all five subjects. Particular attention is given to flow kinetic energy and residence time (see Equation 1 ) as hemodynamic metrics, and on the LAA, the most likely thrombosis region. Results are collected at 40 equidistant time instants per cardiac cycle in the 21 st to 28 th cycle, and are obtained by considering only the Eulerian voxels that are inside the respective regions of interest. 3.1 Electro–mechanical simulations results Representative results of the electro–mechanical simulations for subjects A and D are shown in Figure 2 , taken from the final, 30 th , simulated cycle, after the solution had converged. Due to the imposed inter-atrial conduction delay of 30 ms, the earliest activation in the LA occurs at 30 ms in the 3D simulations ( Fig. 2a ). These earliest activation sites, represented in dark blue, correspond to those identified in the electro–anatomical maps. Electrical activation then propagates throughout the remaining tissue, triggering active tension generation ( Fig. 2b ). This leads to active contraction in the cells and thus to deformation of the tissue, which in turn produces both passive and active mechanical stress ( Fig. 2c ). Finally, the interaction with the 0D model of the circulatory system yields the physiological pressure–volume relationship depicted in Figure 2d . Download figure Open in new tab Figure 2: (a) Electrical activation sequence, with earliest activation sites shown in dark blue. (b, c) Instantaneous spatial distribution of model outputs at 90 ms after atrial excitation: (b) active cellular tension and (c) first principal component of the total mechanical stress (sum of active and passive stress). (d) Left atrial pressure–volume curve, with markers indicating the • MV opening, ▪ MV closure, and ▴ point corresponding to panels (b, c). In (c), the PV caps are shown in gray to indicate that stress distribution there is not relevant for further analysis. Since they are modeled as non-conductive passive tissue, they are omitted in (a, b). 3.2 Impact of LA Wall-Motion Temporal Resolution on Global Blood Mass Conservation We first assess how the temporal resolution of the LA wall reconstruction and its interpolation affect global mass conservation for the fluid inside the chamber, since mass balance ( Eq. 2 ) is used to prescribe PV inflow boundary conditions. Figure 3a,b displays the temporal change of LA volume d V LA / d t , the MV outflow rate Q MV , and the PV flow rate Q PV over time for one cycle of the reference cases Aref and Dref. Atrial contraction (A-wave) occurs at t/T ≈ 0.125, followed by atrial expansion and the left ventricular filling (E-wave), which starts at approximately t/T ≈ 0.65. Download figure Open in new tab Figure 3: Temporal evolution of flow quantities during one cycle for subjects (left) A and (right) D. — Q MV , ‥‥‥ Q PV , and --- d V LA / d t in (a) Aref and (b) Dref; (c, d) Time derivative of LA volume and (e, f) PV flow rate. In (c–f), the 10-, 20-, and 40-frame reconstructions are plotted in color scheme with an increasing number of frames from lighter to darker color, while the reference 4D LA reconstruction is plotted in black. In Figure 3c,d , the temporal change of LA volume is shown for all four LA reconstructions of subjects A and D. In the interpolated reconstructions, the volume curves exhibit Fourier-induced spurious oscillations, whose amplitude grows and frequency falls as the number of retained 4D frames decreases. Notably, key physiological landmarks, e.g., the E- and A-wave peaks, are smeared and attenuated in the interpolated reconstructions with n f = 10 retained frames. As n f increases, the curves approach their reference graph, with the 40-frame reconstruction producing almost a perfect reconstruction. As anticipated, these differences in d V LA / d t could impact the CFD simulations because the outflow rate through the MV is prescribed identically for all cases of the same subject, regardless of the temporal resolution of its 4D LA wall-motion model. Thus, since the flow through the PVs is prescribed to preserve mass while enforcing the target outflow ( Eq. 2 ), reconstruction-dependent differences in d V LA / d t can produce significantly different inflow boundary conditions Q PV within the same subject, as shown in Figure 3e,f . The mean absolute errors made in Q PV of the reconstructions with respect to the reference flow rate are summarized in Table 2 , showing that this error systematically increased as n f was lowered. The next section evaluates how these differences affect main hemodynamic metrics such as KE and T R . View this table: View inline View popup Download powerpoint Table 2: Mean absolute error [mL/s] of the PV flow rate in the motion-reconstruction cases with respect to the reference flow rate , where n t is the number of time steps. 3.3 Impact of LA Wall-Motion Temporal Resolution on the Dynamics of Hemodynamic Indices For subjects A and D, Figure 4 shows the time evolution of the spatially-averaged blood kinetic energy. Since the cycle-to-cycle variability is low, as can be seen in the insets of Figure 4 , we focus only on the last simulated cycle. Download figure Open in new tab Figure 4: Spatially-averaged kinetic energy: Temporal evolution during one cycle in the LA of subjects (a) A and (b) D, and in the LAA of 1subjects (c) A and (d) D. The 10-, 20-, and 40-frame reconstructions are plotted in color scheme with an increasing number of frames from lighter to darker color, while the reference 4D LA reconstruction is plotted in black. The insets show the same quantity during 8 cycles. Figure 5 presents the corresponding plots for the blood residence time. From the evolution of T R it is verified that the simulations indeed converged to a quasi-periodic state. However, cycle-to-cycle variability of T R is appreciable in the LAA. Together with the values being quite high, this justifies the decision to collect results over eight cycles after an initialization phase of 20 cycles. In the LAA, the KE is systematically lower and T R consistently higher than in the LA, consistent with previous studies [ 16 , 17 , 20 , 21 , 31 ]. Download figure Open in new tab Figure 5: Spatially-averaged residence time: Temporal evolution in the LA of subjects (a) A and (b) D, and in the LAA of subjects (c) A and (d) D. Colors as in Figure 4 . Regarding the impact of LA wall-motion reconstruction, we observe that CFD analysis based on the 10-frame reconstruction fails to reproduce the KE peak associated to the A-wave ( t/T ≈ 0.125). Moreover, KE and T R also display the spurious oscillations observed in the flow-rate signals from the interpolated 10-, 20-, and 40-frame reconstructions in Figure 3 . In both subjects, there is a trend for to increase as the number of retained frames decreases, an effect not observed in the kinetic energy. These differences in behavior between T R and KE are to be expected since T R reflects overall flow transport over multiple cardiac cycles whereas KE reflects the instantaneous velocity field. 3.4 Impact of LA Wall-Motion Temporal Resolution on Flow and Residence Time Fields To gain further insight about the effect of LA wall motion, Figure 6 shows velocity vectors in a planar section and the 3D distribution of T R throughout the LA for subject D. Three instants are considered: peak A-wave, an instant during the atrial expansion phase, and peak E-wave. Download figure Open in new tab Figure 6: Visualization of the instantaneous residence time in the entire left atrium and velocity vectors in one planar section through the LA and LAA of subject D at three time instants in the final simulated cycle, as indicated in the volume curves in the last row. During atrial expansion and E-wave, the LAA velocity vectors are elongated fivefold. For the time instant of atrial expansion, T R is omitted to facilitate visualization of the velocity vectors. For clarity, only regions with T R ≥ 3 s are shown and this quantity is entirely omitted for the snapshots of the atrial expansion phase. At peak A-wave, all simulations produce vigorous transmitral jets, with the 10-frame reconstruction CFD model (D10) yielding slightly weaker flow. In addition, the simulations based on 20- and 40-frame 4D LA wall-motion reconstructions (D20 and D40) reproduce the LAA emptying flow observed in the reference simulation (Dref), whereas D10 exhibits only a weak outflow directed towards the MV, suggesting LAA emptying is degraded in this simulation. Consequently, T R in D10 is significantly elevated compared to the other cases. During the atrial expansion phase between the A- and E-waves, a pronounced flow is consistently observed near the LA roof, driven by PV inflow and largely independent of n f . The D20 and D40 simulations successfully reproduce the LAA filling jet evident in the reference case (green vectors). By contrast, the D10 simulation exhibits a markedly weaker and incoherent flow structure in the vicinity of the LAA ostium, suggesting impaired LAA filling. Consequently, distal regions of the LAA remain predominantly stagnant in D10. At the E-wave peak, all cases appear similar because the LA functions primarily as a conduit, channeling freshly incoming blood from the PVs into the left ventricle (LV). The LAA residence time in D10 is still notably elevated, as fluid entering the LA does not reach the appendage. 3.5 Impact of LA Wall-Motion Temporal Resolution on Flow Statistics To quantify the effects described above while accounting for inter-subject variability, we analyze statistics for KE and T R inside each of the five subject’s LA and LAA. Additionally, each LAA will be divided into the proximal and distal subregions shown in Figure 1 . Kinetic Energy Statistics Figure 7 shows half-violin plots of the probability distributions of KE inside the LA, the entire LAA, and the proximal and distal LAA regions for all subjects and wall-motion reconstruction qualities. As expected, the kinetic energy decreases from the LA to the LAA, and within the LAA from the proximal to the distal region. Wall-motion resolution has only a minor effect on LA kinetic energy, with probability distributions that are nearly indistinguishable ( Fig. 7a ). The KE medians range between 12–32 cm 2 / s 2 in the reference CFD simulations, and the simulations based on interpolated wall reconstructions slightly underestimate these values. On average over all five subjects, the medians of LA KE in the 10-, 20-, and 40-frame models deviate from their reference by 14%, 5%, and 2%, respectively. Download figure Open in new tab Figure 7: Kinetic energy: Probability distribution in the (a) LA, (b) entire LAA, (c) proximal LAA part, and (d) distal LAA part. The 10-, 20-, and 40-frame reconstructions of each subject are plotted in color scheme, with increasing number of frames from lighter to darker color, while the reference case is plotted in black for each subject. The horizontal lines indicate the medians. The fidelity of wall-motion reconstruction affects the LAA KE more noticeably ( Fig. 7b ). For some subjects (A, D, and E), the mode of the KE distribution shifts to lower values in the interpolated-wall simulations. However, in some subjects (A, B, and C), wall interpolation also widens the high-KE tails of the distributions relative to the reference. The LAA KE medians range from 1.6 cm 2 / s 2 to 5.6 cm 2 / s 2 , deviating by approximately 15% from the reference with no systematic dependence on the number of retained frames. This absence of a consistent pattern also holds for proximal–distal comparisons within the LAA. Residence Time Statistics Compared to flow kinetic energy, the half-violin plots of T R ( Fig. 8 ) suggest that LA wall-motion reconstruction fidelity has a larger, more systematic effect on stasis metrics derived from CFD simulations. For the LA as a whole, wall-motion reconstruction has a modest effect (median range 1.1–2.6 s). Across patients, the T R distribution is consistently bimodal ( Fig. 8a ), with a T R ≈ 0 s mode reflecting the LA’s reservoir role as fresh fluid enters during the atrial expansion, and a primary mode at nonzero times. Simulations based on 10-frame reconstructions slightly overestimate T R , whereas 20- and 40-frame reconstructions yield distributions very similar to the reference ones. The medians deviate by 11%, 2%, and 2% from their reference, respectively. Download figure Open in new tab Figure 8: Residence time: Probability distribution in the (a) LA, (b) entire LAA, (c) proximal LAA part, and (d) distal LAA part. Colors as in Figure 7 . The horizontal lines indicate the medians. However, within the LAA the differences are substantial: the T R medians span 3.0 s to 6.8 s, deviating by 26%, 9%, and 9% from the reference values. Compared to the LA, the LAA experiences higher T R with greater inter-patient and proximal–distal variability, consistent with its complex morphology. The distribution of T R in the entire LAA ( Fig. 8b ) is bimodal for most subjects (B, C, D, and E), an effect also observed by Dueñas-Pamplona et al . [ 16 ]. While the mode at a lower value mainly corresponds to fluid close to the LAA orifice ( Fig. 8c ), the second mode reflects fluid in its apex ( Fig. 8d ). Across all patients, 10-frame wall-motion reconstruction produces a pronounced upward shift in the T R distributions, especially in the distal LAA ( Fig. 8d ). The 20- and 40-frame reconstructions also shift T R upward, though less markedly and with the exception of subject B. Does Under-Resolved LA Motion Undermine CFD Stratification? It may be acceptable to use patient-specific CFD based on standardized, under-resolved LA wall reconstructions for clinical decision support—even if the hemodynamic metrics are biased— provided the approximations preserve patient ranking (i.e., serve as surrogate indices for risk stratification). In that setting, the key requirement is to reproduce the ordering of patients by the metric, not its absolute values, which is a less stringent criterion than numerical accuracy. To investigate this question, we present the median of KE and the 75 th percentile of T R inside the LAA in Figure 9 , plotted vs. the number of frames retained in the LA wall reconstruction. Consistent with prior sections, the median LAA KE shows irregular sensitivity to the number of retained frames, yielding a broad spread across reconstructions ( Fig. 9a ). Even so, the rank ordering is largely preserved: subject A always exhibits the lowest value, C and D are located at the highest values, while B and E fall in between. Also consistent with our prior results, the 75 th percentile of LAA T R generally increases as the number of reconstruction frames decreases. In the reference case, subjects A, B, and D have fairly similar values, so their ranking is easily shuffled in the interpolated-wall simulations even though subject A stays near its reference and the three subjects remain close to one another. Subject E consistently exhibits the lowest residence time and subject C has the highest value except in the 10-frame reconstruction. Download figure Open in new tab Figure 9: Hemodynamic metrics within the LAA of subjects A, B, C, D, and E. corresponds to results presented in § 3.6. (a) Median kinetic energy and (b) 75 th percentile of the residence time. In summary, LA-wall reconstructions with coarse temporal resolution may be adequate for ranking patients according to KE and T R when n f ≳ 20. However, caution is warranted near decision boundaries and with respect to standardization, as the ranking correspondence is not exact and CFD-predicted values of these metrics vary with n f . Consequently, mixing simulation results obtained with different n f values may confound patient stratification. 3.6 Impact of Transmitral Flow Profile Temporal Resolution Thus far, we have deliberately isolated the influence of LA wall-motion resolution. In every CFD run, the mitral outflow Q MV was taken from the temporally fully-resolved reference EM simulation, ensuring that any differences arise from how the wall motion is sampled and interpolated rather than from changes in transmitral flow. This analysis is directly relevant to CFD work-flows that use patient-specific or generic Doppler measurements to prescribe Q MV [ 16 , 27 , 50 , 52 , 83 ]. However, many pipelines derive patient-specific Q MV profiles from the time derivative of LV volume obtained from medical images [ 6 , 17 , 20 , 31 , 32 , 49 , 55 , 56 , 59 ]. In those work-flows, the temporal resolution of the 4D imaging sequences affects not only the LA wall motion but also the estimated Q MV . This sensitivity carries to the PV inflow profiles via Equation 2 , making both the boundary conditions and the CFD outputs sensitive to undersampling. To investigate the impact of LV wall-motion resolution on the CFD results, we sampled the ventricular volume V LV provided by the EM simulations at the same time instants retained for reconstructing the LA motion, and subsequently interpolated and differentiated it in time using Fourier series. The mitral outflow rate was then obtained as , and new simulations were run for subject D. These additional simulations are based on the same LA wall motions as D10, D20, and D40, respectively. However, to indicate the different treatment of Q MV , the cases are named with an asterisk, i.e. D10*, D20*, and D40*. The reference case Dref remains the same. Figure 10 compares the transmitral flow rate profiles of cases D10*, D20*, and D40* with the reference profile. The E-wave is captured well by cases D20* and D40*, whereas it is attenuated and delayed in case D10*. The A-wave is weakened and advanced in cases D10* and D20*, but it is accurately reproduced in case D40*. In all motion reconstructions, the Fourier interpolation leads to non-zero Q MV values during ventricular systole (0.15 ≲ t/T ≲ 0.6). This causes the mitral valve in these simulations to repeatedly switch between the opened and closed state, while it remains closed in the reference case. Download figure Open in new tab Figure 10: Outflow rate through the mitral valve for case — Dref, and the interpolated cases D10*, D20*, and D40*, where the flow rate is obtained from the time rate of change of LV volume. Figure 11a displays the half-violin plots for the results of KE in the LA and LAA. The KE distributions of cases D10*, D20*, and D40* are similar to those from their corresponding cases D10, D20, and D40, shown above ( Fig. 7 ), implying that the temporal resolution of transmitral flow profiles has a negligible incremental effect on the flow KE. On the other hand, the T R distributions are altered substantially in the LAA, and the discrepancies with the reference distribution are amplified when coarsening the time resolution of Q MV , particularly in the LAA, where T R reaches even higher values in case D10* than in case D10. Similar trends can be observed in the median KE and 75 th percentile of T R in the LAA, which are displayed in gray markers in Figure 9 . Download figure Open in new tab Figure 11: Probability distribution of (a) KE and (b) T R for the cases of subject D, in which the outflow through the MV was obtained from interpolation as described above. The respective regions of interest are indicated on the x-axis. Colors as in Figure 10 . The horizontal lines indicate the medians. 4 Discussion Left atrial (LA) wall motion changes rapidly as the chamber cycles through reservoir, conduit, and booster-pump phases each heartbeat. This imprints short-timescale features on filling and emptying flows via atrial volume changes and coupling with pulmonary venous (PV) inflow and left ventricle (LV) suction [ 84 ]. In patient-specific computational fluid dynamics (CFD), LA wall motion is reconstructed from 4D images with limited temporal sampling, which can blur these dynamics and introduce interpolation artifacts that propagate through mass balance into the boundary conditions and, ultimately, the simulated flow [ 32 ]. Critically, such errors persist even with fine spatial meshes and small solver time steps, because a numerically well-resolved CFD solver will faithfully propagate inaccuracies in the prescribed boundary conditions [ 85 ]. For robust clinical decision support, one would need to quantify how the temporal resolution of LA motion reconstruction influences metrics such as kinetic energy (KE) and residence time ( T R ), and whether coarser reconstructions still preserve patient ordering for risk stratification [ 86 ]. However, high-frame-rate imaging is often unavailable for technical, comfort, or safety reasons, limiting the accessibility to ground-truth LA wall reconstructions [ 87 , 88 ]. Consequently, it remains an open question to which extent temporal undersampling distorts clinically relevant metrics—and when coarse reconstructions are still adequate for ranking. In atrial fibrillation (AF), the loss of coordinated atrial systole and remodeling of reservoir and conduit dynamics alter the timing and amplitude of atrial function in complex, patient-specific ways [ 84 ], underscoring the importance of how LA wall motion is represented. Moreover, cardiogenic embolic events—including those strongly suspected to originate from the atrium— can occur even during sinus rhythm for large groups of patients [ 89 – 92 ]. Notably, embolic strokes of undetermined source (ESUS) occurring in the absence of AF may be mediated by LA cardiopathy. Although the underlying mechanisms are not entirely clear, these events are more prevalent among patients with substantial LA fibrosis, which is associated with disturbances in atrial electrophysiology, biomechanics, and flow dynamics [ 46 , 90 , 93 , 94 ]. Therefore, evaluating the impact of temporal undersampling in sinus rhythm, where wall motion is more vigorous than in AF and exerts a strong influence on LA flow [ 31 , 32 ], is also essential. This manuscript presents a new approach to analyze the impact of LA wall-motion resolution on CFD-predicted hemodynamic metrics, where patient-specific atrial electro–mechanical (EM) simulations [ 46 , 60 ] are used to generate highly resolved data that is used as surrogate ground truth. For each patient, reference CFD simulations are performed using LA wall motion directly obtained from the EM simulations. The corresponding moving LA anatomies are then temporally undersampled to mimic the typical resolution of 4D medical imaging, and additional CFD simulations are conducted using the interpolated LA models. The interpolated-wall simulation results are then compared to the reference results, with emphasis on KE and T R in the left atrial appendage (LAA), the most common site of LA thrombosis. A substantial part of our effort involved prescribing the reference transmitral outflow rate Q MV from the EM simulations across all CFD runs. This strategy ensured that differences in the results arose solely to the temporal sampling and interpolation of LA wall motion. Such an approach assumes that Q MV is independent of the anatomical medical images, reflecting CFD workflows where the inflow is prescribed from Doppler, 4D flow MRI measurements, or mathematical models [ 16 , 27 , 50 , 52 , 83 ]. From the temporal evolution of LA volume, we observed that atrial contraction is poorly reproduced when using a low number of frames ( n f ) for motion interpolation, as the contraction occurs over a short timescale that is not resolved by the frames. This limitation also affects the temporal profile of KE, which lacks the expected peak during the A-wave. In addition, under-sampling introduces Fourier oscillations in the atrial motion that propagate into the temporal evolution of all investigated flow quantities. Global hemodynamic indices for the entire LA chamber, such as median KE and T R , were relatively insensitive to n f , with errors of less than 15% with respect to the reference simulations when varying n f between 10 and 40. However, CFD simulations based on LA wall-motion reconstructions with n f = 20 and 40 yielded favorable agreement, with errors averaging ≤ 5% across our N = 5 patients. The observed range of variation was consistent with these quantities being mostly determined by the heart rate, mean LA volume, and LV stroke volume [ 17 ], which are relatively well captured even with n f = 10, although this reconstruction performed notably worse. Focusing on LAA stasis, we found that deviations attributable to n f were only moderate compared to other effects. Similar or larger variations in LAA stasis indicators arise from treating blood rheology as Newtonian versus non-Newtonian [ 17 , 21 ], or from plausible physiological or anatomical changes, such as varying the PV inflow split ratios [ 20 , 21 ] or PV orientation [ 27 ]. The n f = 10 cases, however, consistently lay at the upper end of these physiological variability ranges and occasionally exceeded them. For all n f , the resulting variations in hemodynamic metrics remained smaller than those typically observed between moving-wall and fixed-wall simulations [ 21 , 30 – 32 ]. Kjeldsberg et al . [ 32 ] further reported that the parametrized LA wall-motion model proposed by Corti et al . [ 37 ] improves the accuracy of hemodynamic metrics only when it is driven by patient-specific LA volume profiles with n f = 20, without examining other values of n f . This suggests that a coarse patient-derived LA wall-motion model may still be preferable to rigid walls, but the influence of temporal resolution in the LA volume profile remains unresolved. An alternative, self-consistent method to reconstruct the LA wall geometry and prescribe Q MV from a unique anatomical image acquisition is to segment time-resolved 4D sequences, and use atrial surfaces for wall motion, while the time derivative of LV volume is used for transmitral flow [ 6 , 17 , 20 , 31 , 32 , 49 ]. In these workflows, self-consistency implies that Q MV is sensitive to image resolution as well. To assess how much this affects the CFD results, additional simulations were run, where the outflow rate was derived from undersampled LV volumes. Results showed that flow KE was largely unaffected, while LAA residence time increased modestly for n f ≥ 20 but rose sharply when n f was reduced to 10. Notably, this occurred even though the underresolved Q MV profile produced a cardiac output 7% higher than the reference simulation, highlighting that LAA transport arises from multiple interacting factors. Taken together, these data suggest that anatomical reconstructions with n f ≥ 20 appear sufficiently resolved to provide reliable, patient-specific LAA blood-stasis indices commonly used as surrogate markers of coagulation risk. This frame rate is achievable with most contemporary medical imaging modalities. When imaging data with n f < 20 must be used, the results can still provide approximate insights into LAA blood-stasis indices and related hemodynamic metrics, but their interpretation requires caution. As n f varies, the patient ordering by KE or T R may not be preserved exactly, introducing potential inaccuracies near decision boundaries. Moreover, CFD-predicted values of these metrics vary with n f , so aggregating simulations based on different temporal resolutions may therefore confound patient stratification. In such settings, standardized reconstruction protocols and explicit reporting of temporal resolution become critical to ensure that comparisons across patients or cohorts remain meaningful. Without such standardization, differences may reflect methodological inconsistencies rather than physiological variability. Limitations This study is subject to several limitations that should be addressed in future work. First, atrial EM simulations were used as surrogate ground truth for wall motion and transmitral flow. Although these models have been validated [ 95 ], checked for physiological behavior [ 60 , 67 ], offer high spatiotemporal resolution [ 66 ], and in our case generated physiologically realistic results (see §3.1), they still rely on modeling assumptions, parameter tuning, and simplified tissue properties that may not fully capture patient-specific atrial mechanics. We only considered Fourier interpolation for LA wall-motion reconstruction. Alternative methods, such as spline interpolation, have been applied by other groups in this field [ 16 , 32 , 33 ]. Although splines result in a less compact representation that imposes a larger memory footprint in the CFD solver, they help reduce the spurious oscillations that arise in underresolved Fourier interpolants. The size of the cohort is small ( N = 5), which is partly attributed to the computational and storage requirements of the fully-resolved reference simulations. Although the cohort is diverse in anatomical shape and volume (see Figure 1 and Table 1 ), all subjects were modeled in sinus rhythm with homogeneous electro–mechanical myocardial properties (i.e., without fibrotic regions) and identical heart rates. The risk of embolic stroke in AF patients has been shown to be sensitive to intricate details of LAA shape [ 96 , 97 ]. However, the baseline LA anatomical segmentations used to drive the EM simulations were derived from MRI, yielding spatially smoother LAA representations than those typically obtained from modalities such as 4D–CT. The interaction between temporal and spatial resolution in anatomical modeling should be explored in future work. Finally, all simulations were performed in sinus rhythm with preserved atrial contraction. As discussed above, this rhythm is clinically relevant, as embolic events can occur during sinus rhythm, even in subjects with subclinical intermittent AF [ 89 ]. Nonetheless, our findings may not fully extrapolate to AF, where atrial systole is absent or severely blunted. Prior work suggests that capturing wall motion may be less critical for flow predictions in AF due to reduced atrial dynamics [ 31 ], so the impact of temporal undersampling on stasis metrics may differ, warranting future investigation. 5 Conclusion This study evaluated how CFD results are affected by the time resolution of patient-specific LA geometry data and the consequent interpolation of wall motion, which is necessary when the LA motion is derived from medical images. The findings indicate that the results based on 20 and 40 images per cardiac cycle exhibit no significant differences and closely align with the reference results. While lower temporal resolution can serve as reasonable estimate, it should be handled carefully, especially when analyzing values within the LAA. 6 Acknowledgments This study was supported by the US National Institutes of Health (NIH) under award numbers R01HL158667 (NA, PMB, CMA, JCA) and R01HL160024 (JCA), and by the Austrian Science Fund (FWF) [10.55776/P37063] (CMA). The computational results of the electro–mechanical simulations have been achieved using the Austrian Scientific Computing (ASC) infrastructure. 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