Dr. Rahim R. Rizi Profile

Dr. Rahim R. Rizi

at Univ of Pennsylvania

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Publications (2)

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Proceedings Article | 29 March 2024 Poster + Paper

In-silico CT lung phantom generated from finite-element mesh

Sunder Neelakantan, Tanmay Mukherjee, Bradford Smith, Kyle Myers, Rahim Rizi, Reza Avazmohammadi

Proceedings Volume 12928, 1292829 (2024) https://doi.org/10.1117/12.3006973

KEYWORDS: Lung, Voxels, Image registration, Computed tomography, Deformation, Image resolution, Kinematics

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Several lung diseases lead to alterations in regional lung mechanics, including ventilator- and radiation-induced lung injuries. Such alterations can lead to localized underventilation of the affected areas, resulting in the overdistension of the surrounding healthy regions. Thus, there has been growing interest in quantifying the dynamics of the lung parenchyma using regional biomechanical markers. Image registration through dynamic imaging has emerged as a powerful tool to assess lung parenchyma’s kinematic and deformation behaviors during respiration. However, the difficulty in validating the image registration estimation of lung deformation, primarily due to the lack of ground-truth deformation data, has limited its use in clinical settings. To address this barrier, we developed a method to convert a finite-element (FE) mesh of the lung into a phantom computed tomography (CT) image, advantageously possessing ground-truth information included in the FE model. The phantom CT images generated from the FE mesh replicated the geometry of the lung and large airways that were included in the FE model. Using spatial frequency response, we investigated the effect of “imaging parameters” such as voxel size (resolution) and proximity threshold values on image quality. A series of high-quality phantom images generated from the FE model simulating the respiratory cycle will allow for the validation and evaluation of image registration-based estimations of lung deformation. In addition, the present method could be used to generate synthetic data needed to train machine-learning models to estimate kinematic biomarkers from medical images that could serve as important diagnostic tools to assess heterogeneous lung injuries.

Proceedings Article | 3 April 2023 Poster + Paper

Volumetric versus distortional deformation in rat lungs

Sunder Neelakantan, Mostafa Ismail, Tanmay Mukherjee, Bradford Smith, Rahim Rizi, Reza Avazmohammadi

Proceedings Volume 12466, 124661R (2023) https://doi.org/10.1117/12.2653648

KEYWORDS: Lung, Deformation, Distortion, Image registration, Injuries, Image segmentation, Voxels, Kinematics, Computed tomography, Pulmonary disorders

Read Abstract +

There are several lung diseases that lead to alterations in regional lung mechanics, including acute respiratory distress syndrome. Such alterations can lead to localized underventilation of the affected areas resulting in the overdistension of the surrounding healthy regions. They can also lead to the surrounding alveoli expanding unevenly or distorting. Therefore, the quantification of the regional deformation in the lungs offers insights into identifying the regions at risk of lung injury. Although few recent studies have developed image processing techniques to quantify the regional volumetric deformation in the lung from dynamic imaging, the presence and extent of distortional deformation in the lung, and its correlation with volumetric deformation, remain poorly understood. In this study, we present a method that uses the four-dimensional displacement field obtained from image registration to quantify both regional volumetric and distortional deformation in the lung. We used dynamic computed tomography scans in a healthy rat over the course of one respiratory cycle in free breathing. Non-rigid image registration was performed to quantify voxel displacement during respiration. The deformation gradient was calculated using the displacement field and its determinant was used to quantify regional volumetric deformation. Regional distortion was calculated as the ratio of maximum to minimum principal stretches using the isochoric part of the Cauchy green tensor. We found an inverse correlation between volumetric strains and distortion indicating that poorly expanding alveoli tend to experience larger distortion. The combination of regional volumetric strains and distortion may serve as high-fidelity biomarkers to identify the regions at risk of most adverse lung injuries.

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