A.S. Naini

Publications (2)4.56 Total impact

• Article: Measurement of Lung Hyperelastic Properties Using Inverse Finite Element Approach

  A.S. Naini, R.V. Patel, A. Samani
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  ABSTRACT: Hyperelastic properties of deflated lung tissue have been characterized via an inverse finite element approach. Such properties are useful in many medical diagnosis and treatment applications where tissue deformation can be modeled to account for during the procedure. Several indentation experiments were conducted on various porcine lungs' tissue specimens resected immediately from different regions and lobes after the animals were sacrificed. Three different strain energy models, namely Ogden, Yeoh, and Polynomial, were used and respective hyperelastic parameters were obtained. The parameters for each model were estimated through an optimization process where the experimental force-displacement profiles of indentation were fitted to those obtained from finite element simulations performed specifically for the samples' geometries. Results obtained in this investigation for all the three models indicate convergence with reasonably low average fitting errors ranging from 2.3% to 6.2%. Independent tests were also performed to assess the effects of samples' heterogeneities on the obtained parameters. The outcome of these tests was encouraging and confirmed small impact of tissue inhomogeneities on the estimated parameters. The reported hyperelastic properties can, accordingly, pave the way for more accurate biomechanical modeling of the lung's soft tissue in the emerging applications of minimally invasive medical intervention for lung cancer diagnosis and treatment.
  IEEE Transactions on Biomedical Engineering 11/2011; · 2.28 Impact Factor
• Article: Estimation of Lung's Air Volume and Its Variations Throughout Respiratory CT Image Sequences

  A.S. Naini, Ting-Yim Lee, R.V. Patel, A. Samani
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  ABSTRACT: A respiratory image-sequence-segmentation technique is introduced based on a novel image-sequence analysis. The proposed technique is capable of segmenting the lung's air and its soft tissues followed by estimating the lung's air volume and its variations throughout the image sequence. Accurate estimation of these two parameters is very important in many applications related to lung disease diagnosis and treatment systems (e.g., brachytherapy), where the parameters are either the variables of interest themselves or are dependent/independent variables. The concept of the proposed technique involves using the image sequence's combined histogram to obtain a reasonable initial guess for the lung's air segmentation thresholds. This is followed by an optimization process to find the optimum threshold values that best satisfy the lung's air mass conservation and tissue incompressibility principles. These threshold values are consequently applied to estimate the lung's air volume and its variations throughout respiratory Computed Tomography (CT) image sequences. Ex vivo experiments were conducted on porcine left lungs in order to demonstrate the performance of the proposed technique. The proposed method was initially validated using a breath-hold CT image sequence with known air volumes inside the lung, where results show that the proposed technique outperforms single-histogram-based methods. This was followed by demonstrating the proposed technique's application in a 4-D-CT respiratory sequence, where the air volume inside the lung was unknown. Consistency of the obtained results in the latter experiment with tissue near incompressibility principle was validated. The results indicate a very good ability of the proposed method for estimating the lung's air volume and its variations in a respiratory image sequence.
  IEEE Transactions on Biomedical Engineering 02/2011; · 2.28 Impact Factor