Identification of tumor nodule in soft tissue: An inverse finite‐element framework based on mechanical characterization

Abstract

Identification and characterization of nodules in soft tissue, including their size, shape, and location, provide a basis for tumor identification. This study proposes an inverse finite‐element (FE) based computational framework, for characterizing the size of examined tissue sample and detecting the presence of embedded tumor nodules using instrumented palpation, without a priori anatomical knowledge. The inverse analysis was applied to a model system, the human prostate, and was based on the reaction forces which can be obtained by trans‐rectal mechanical probing and those from an equivalent FE model, which was optimized iteratively, by minimizing an error function between the two cases, toward the target solution. The tumor nodule can be identified through its influence on the stress state of the prostate. The effectiveness of the proposed method was further verified using a realistic prostate model reconstructed from magnetic resonance (MR) images. The results show the proposed framework to be capable of characterizing the key geometrical indices of the prostate and identifying the presence of cancerous nodules. Therefore, it has potential, when combined with instrumented palpation, for primary diagnosis of prostate cancer, and, potentially, solid tumors in other types of soft tissue.

Mechanical mapping of the prostate in vivo using Dynamic Instrumented Palpation; towards an in vivo strategy for cancer assessment

“…In parallel work, 28 an inverse finite element approach has been developed to improve the precision of interpretation of the point probing results. Assuming the C and NC areas to be homogeneous with static moduli E C and E NC , the reaction force profile can be simulated using inverse FEA methods so that the difference between reaction force profiles from the FE model and the experimental measurements is minimised.…”

Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy

“…The definition of such targets, however, requires a priori knowledge of the boundary loads, and resulting stress distribution within the deformed domain of study. In practice, some controlled in vitro experiments have successfully applied inverse models with stress-based targets by having accurate measurements of forces and deformations in three orthogonal directions on samples of reduced size [18,116,117]. The definition of stress-based targets for patient-specific in vivo applications could be extremely beneficial to improve the accuracy and uniqueness of the solution.…”

The Use of Contravariant Tensors to Model Anisotropic Soft Tissues

“…Another instance of the isotropic phenomenological model can be due to Ogden [6] which is a polynomial of the principal stretches. These isotropic models with polynomial forms have numerous applications in computational biomechanical studies like predicting the mechanical response of the brain during neurosurgery [7], identifying nodules in soft tissues to predict tumour [8], biomechanical behaviour of temporomandibular joints [9], wound rupture of skin etc [10]. Putra et al [11] studied the performance of various hyperelastic material models for wearable biomedical devices and determined the suitability of the Yeoh model under biaxial extension tests.…”