Mechanics informed fluoroscopy of esophageal transport

Halder, Sourav; Acharya, Shashank; Kou, Wenjun; Kahrilas, Peter J.; Pandolfino, John E.; Patankar, Neelesh A.

doi:10.1007/s10237-021-01420-0

Cited by 18 publications

(24 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 2a presents a plot of the pressure at the EGJ as a function of EGJ cross-sectional area during a single contractile cycle. The pressure at the EGJ is obtained by using a constitutive equation for pressure (discussed in section 2.1), as proposed by [11]. As figure 2a shows, the cross-sectional area at the EGJ increases with pressure increase, and decreases with pressure decrease.…”

Section: Pressure-area Loops At Egjmentioning

confidence: 99%

See 1 more Smart Citation

Pressure -- area loop based phenotypic classification and mechanics of esophagogastric junction physiology

Elisha¹,

Halder²,

Acharya³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The esophagogastric junction (EGJ) is located at the distal end of the esophagus and acts as a valve allowing swallowed materials to enter the stomach and preventing acid reflux. Irregular weakening or stiffening of the EGJ muscles result in changes to its opening and closing patterns which can progress into esophageal disorders. Therefore, understanding the physics behind the opening and closing cycle of the EGJ provides a mechanistic insight into its function and can help identify the underlying conditions that cause its degradation. Using clinical FLIP data, we plotted the pressure-area hysteresis at the EGJ location and distinguished two major loop types, a pressure dominant loop (PDL) and a tone dominant loop (TDL). In this study, we aimed to identify the key characteristics that define each loop type and find what causes the inversion from one loop to another. To do so, the clinical observations were reproduced using 1D simulations of flow inside a FLIP device located in the esophagus, and the work done by the EGJ wall over time was calculated. This work was decomposed into active and passive components, which revealed the competing mechanisms that dictate the loop type. These mechanisms are esophagus stiffness, fluid viscosity, and the EGJ relaxation pattern. In PDL, the leading source of energy in the cycle is coming from the fluid pressure increase from the peristaltic contraction wave, and in TDL the leading source of energy in the cycle is coming from the contraction and relaxation of the EGJ tone.

show abstract

Section: Pressure-area Loops At Egjmentioning

confidence: 99%

“…For this calculation, the cross-sectional area values are direct FLIP readings. The activation function (θ(x, t)), P o , and the ratio K e /A o are calculated as proposed by [11] using the tube law relation in equation ( 4). The fluid properties are density ρ = 1000 kg/m 3 and viscosity µ = 0.001 Pa • s [28].…”

Section: Application To Clinical Datamentioning

confidence: 99%

Pressure -- area loop based phenotypic classification and mechanics of esophagogastric junction physiology

Elisha¹,

Halder²,

Acharya³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Eqns. 4 and 5 were solved using the numerical approach described in Halder et al [19]. The details of the numerical solution are provided in the Supplementary.…”

Section: Calculation Of the Primary Mechanics-based Parametersmentioning

confidence: 99%

“…We used the pressure and diameter data obtained from EndoFLIP to calculate mechanics-based parameters such as esophageal wall properties, muscle contraction strength, effective EGJ tone, and active relaxation of the esophageal muscles. Esophageal biomechanics have been extensively studied using both experimental [10,11,12,13,14,15] and computational [16,17,18,19,20,21,22,23,24] approaches. For our analysis, we used the mathematical framework as described in Halder et al [19] to calculate the mechanics-based parameters since it works on clinical diagnosis data from flexible tubular organs and makes fast predictions with limited computational resources.…”

Section: Introductionmentioning

confidence: 99%

“…Esophageal biomechanics have been extensively studied using both experimental [10,11,12,13,14,15] and computational [16,17,18,19,20,21,22,23,24] approaches. For our analysis, we used the mathematical framework as described in Halder et al [19] to calculate the mechanics-based parameters since it works on clinical diagnosis data from flexible tubular organs and makes fast predictions with limited computational resources. The mechanics-based parameters quantitatively estimate the mechanical "health" of the esophagus in a patient-specific manner but identifying patterns in these parameters that are unique to various esophageal disorders is a challenging task, especially with the parameters that are functions of both time and location along the esophageal length.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Esophageal virtual disease landscape using mechanics-informed machine learning

Halder¹,

Yamasaki²,

Acharya³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The pathogenesis of esophageal disorders is related to the esophageal wall mechanics. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map the esophageal wall mechanics-based parameters onto physiological and pathophysiological conditions corresponding to altered bolus transit and supraphysiologic IBP. In this work, we present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of the various esophageal disorders and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called endoscopic functional lumen imaging probe (EndoFLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal walls. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder (VAE) that generates a latent space and a side network that predicts mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and form clusters corresponding to the various esophageal disorders. The VDL not only distinguishes different disorders but can also be used to predict disease progression in time. Finally, we also demonstrate the clinical applicability of this framework for estimating the effectiveness of a treatment and track patient condition after a treatment.

show abstract

A mechanics-based perspective on the function of the esophagogastric junction during functional luminal imaging probe manometry

Elisha

Halder

Acharya

et al. 2023

Biomech Model Mechanobiol

Self Cite

View full text Add to dashboard Cite

The esophagogastric junction (EGJ) is located at the distal end of the esophagus and acts as a valve allowing swallowed food to enter the stomach and preventing acid reflux. Irregular weakening or stiffening of the EGJ muscles results in changes to its opening and closing patterns which can progress into esophageal disorders. Therefore, understanding the physics of the opening and closing cycle of the EGJ can provide mechanistic insights into its function and can help identify the underlying conditions that cause its dysfunction. Using clinical functional lumen imaging probe (FLIP) data, we plotted the pressure-cross-sectional area loops at the EGJ location and distinguished two major loop types -a pressure dominant loop (PDL) and a tone dominant loop (TDL). In this study, we aimed to identify the key characteristics that define each loop type and determine what causes the inversion from one loop to another. To do so, the clinical observations are reproduced using 1D simulations of flow inside a FLIP device located in the esophagus, and the work done by the EGJ wall over time is calculated. This work is decomposed into active and passive components, which reveal the competing mechanisms that dictate the loop type. These mechanisms are esophagus stiffness, fluid viscosity, and the EGJ relaxation pattern.

show abstract

Mechanics informed fluoroscopy of esophageal transport

Cited by 18 publications

References 26 publications

Pressure -- area loop based phenotypic classification and mechanics of esophagogastric junction physiology

Pressure -- area loop based phenotypic classification and mechanics of esophagogastric junction physiology

Esophageal virtual disease landscape using mechanics-informed machine learning

A mechanics-based perspective on the function of the esophagogastric junction during functional luminal imaging probe manometry

Contact Info

Product

Resources

About