Biomechanical model-based 4DCT simulation

Abstract

Dear Fatih Porikli,This a request for information about any 2014 publications of yours that we can claim for the HERDC (Higher Education Research Data Collection). HERDC points bring us lots of money, and they're a significant indicator of our performance, so it's very important that we get a good return.All 2014 publications for which you carried out some research at the College of Engineering and Computer Sciences (CECS) at the ANU count: books, book chapters, journal articles, and refereed conference papers.For books we would like a pdf copy if possible (otherwise we will have to find a hard copy to scan). For journal articles, we need the title of the article, the journal, the volume, issue, and if possible a link. (A pdf would be helpful, but not needed if we can get it online.)And could I ask you to fill in the template below, please? Many thanks, Rebecca Potter Publication Collections Officer School of Engineering and Computer Sciences I certify that I carried out some aspects of the research leading to the following publication whilst I

Towards Non-Invasive Lung Tumor Tracking Based on Patient Specific Model of Respiratory System

“…In our simulation, we have obtained an average mean error less than 2.0 ± 1.3mm. These results show that the developed physiological model coupled with the personalized lung-pressure/diaphragm-force optimization algorithm of the respiratory system is in a good agreement with the experimental data, produces more accurate predictions with lower errors compared to other works ( [22], [21]) that used the same datasets.We have also evaluated the influence of the variation of lung tissue Young's modulus and Poisson's ratio commonly used in the literature [42], [25], [41], [43]. Young modulus can vary between 0.1kPa to 10kPa and Poisson's ratio between 0. ratio=0.49, is three times larger than the lung landmarks error obtained by Poisson's ratio=0.33.…”

“…Furthermore, lung tumors can even present hysteresis in their trajectories [17], making them more difficult to locate with precision. Methods to estimate respiratory organ motions can be divided into 4 major categories; deformable image registration [18], [19], biomechanical models [20], [21], [22], [23], [24], [19], hybrid models with biomechanics and deformable image registration [25], [26] and statistical models including or not the machine learning techniques [27], [28], [29]. Currently, the existing methods do not explicitly take into account the information related to breathing physiology and physical properties of organ tissues, and all those methods assume a reproducible motion of the respiratory system and cannot fully take into account the variability of the respiratory motion.…”

FEMOSSA: Patient‐specific finite element simulation of the prostate–rectum spacer placement, a predictive model for prostate cancer radiotherapy

“…To provide a realistic simulation of hydrogel injection, we used the finite element (FE) method, a powerful numerical tool for modeling and simulating physical phenomenon. Owing to its accuracy and versatility, the FE method has been demonstrated as an effective approach for simulation of soft tissue deformation during organ's normal activity, such as lungs during breathing, 21,22 intestine natural movements, 23,24 and deformations as a result of medical procedures like needle insertion during prostate brachytherapy 25–27 . Additionally, it has been used for model‐based deformable intraoperative image registration for RT planning 28,29 .…”