Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy

A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems

Biomechanical Modeling and Inverse Problem Based Elasticity Imaging for Prostate Cancer Diagnosis

Early detection of prostate cancer plays an important role in successful prostate cancer treatment. This requires screening the prostate periodically after the age of 50. If screening tests lead to prostate cancer suspicion, prostate needle biopsy is administered which is still considered as the clinical gold standard for prostate cancer diagnosis. Given that needle biopsy is invasive and is associated with issues including discomfort and infection, it is desirable to develop a prostate cancer diagnosis system that has high sensitivity and specificity for early detection with a potential to improve needle biopsy outcome. Given the complexity and variability of prostate cancer pathologies, many research groups have been pursuing multi-parametric imaging approach as no single modality imaging technique has proven to be adequate. While imaging additional tissue properties increases the chance of reliable prostate cancer detection and diagnosis, selecting an additional property needs to be done carefully by considering clinical acceptability and cost. Clinical acceptability entails ease with respect to both operating by the radiologist and patient comfort. In this work, effective tissue biomechanics based diagnostic techniques are proposed for prostate cancer assessment with the aim of early detection and minimizing the numbers of prostate biopsies. The techniques take advantage of the low cost, widely available and well established TRUS imaging method. The proposed techniques include novel elastography methods which were formulated based on an inverse finite element frame work. Conventional finite element analysis is known to have high computational complexity, hence computation time demanding. This renders the proposed elastography methods not suitable for real-time applications. To address this issue, an accelerated finite element method was proposed which proved to be suitable for prostate elasticity reconstruction. In this method, accurate finite element analysis of a large number of prostates undergoing TRUS probe loadings was performed. Geometry input and displacement and stress fields output obtained from the analysis were used to train a neural network mapping function to be used for elastopgraphy imaging of prostate cancer patients. The last part of the research presented in this thesis tackles an issue with the current 3D TRUS prostate needle biopsy. Current 3D TRUS prostate needle biopsy systems require registering preoperative 3D TRUS to intra-operative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, concept of a robotic system was proposed which can reliably determine the preoperative TRUS probe position relative to the prostate to place at the same position relative to the prostate intra-operatively. For this purpose, a contact pressure feedback system is proposed to ensure similar prostate deformation during 3D and 2D image acquisition in order to bypass the registration step

Novel Ultrasound Elastography Imaging System for Breast Cancer Assessment

Abstract Most conventional methods of breast cancer screening such as X-ray, Ultrasound (US) and MRI have some issues ranging from weaknesses associated with tumour detection or classification to high cost or excessive time of image acquisition and reconstruction. Elastography is a non- invasive technique to visualize suspicious areas in soft tissues such as the breast, prostate and myocardium using tissue stiffness as image contrast mechanism. In this study, a breast Elastography system based on US imaging is proposed. This technique is fast, expected to be cost effective and more sensitive and specific compared to conventional US imaging. Unlike current Elastography techniques that image relative elastic modulus, this technique is capable of imaging absolute Young\u27s modulus (YM). In this technique, tissue displacements and surface forces used to mechanically stimulate the tissue are acquired and used as input to reconstruct the tissue YM distribution. For displacements acquisition, two techniques were used in this research: 1) a modified optical flow technique, which estimates the displacement of each node from US pre- and post-compression images and 2) Radio Frequency (RF) signal cross-correlation technique. In the former, displacements are calculated in 2 dimensions whereas in the latter, displacements are calculated in the US axial direction only. For improving the quality of elastography images, surface force data was used to calculate the stress distribution throughout the organ of interest by using an analytical model and a statistical numerical model. For force data acquisition, a system was developed in which load cells are used to measure forces on the surface of the breast. These forces are input into the stress distribution models to estimate the tissue stress distribution. By combining the stress field with the strain field calculated from the acquired displacements using Hooke\u27s law, the YM can be reconstructed efficiently. To validate the proposed technique, numerical and tissue mimicking phantom studies were conducted. For the numerical phantom study, a 3D breast-shape phantom was created with synthetic US pre- and post-compression images where the results showed the feasibility of reconstructing the absolute value of YM of tumour and background. In the tissue mimicking study, a block shape gelatine- agar phantom was constructed with a cylindrical inclusion. Results obtained from this study also indicated reasonably accurate reconstruction of the YM. The quality of the obtained elasticity images shows that image quality is improved by incorporating the adapted stress calculation techniques. Furthermore, the proposed elastography system is reasonably fast and can be potentially used in real-time clinical applications

Применение метода конечных элементов в процессе математического моделирования в урологии

The article presents data on possibility of the application of the method of finite elements (FEM) in the mathematical modeling of various diseases of the organs of the urogenital system, their diagnostics and treatment. Special attention was paid to the prospects of application of FEM for modelling methods of surgical treatment of diseases of the kidneys and urinary tract.В статье представлены данные о возможностях применения метода конечных элементов (МКЭ) в математическом моделировании различных заболеваний органов мочеполовой системы, их диагностики и лечения. Особое внимание уделено перспективам применения МКЭ для моделирования методов оперативного лечения заболеваний почек и мочевыводящих путей

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation

MAGNETIC RESONANCE ELASTOGRAPHY FOR APPLICATIONS IN RADIATION THERAPY

Magnetic resonance elastography (MRE) is an imaging technique that combines mechanical waves and magnetic resonance imaging (MRI) to determine the elastic properties of tissue. Because MRE is non-invasive, there is great potential and interest for its use in the detection of cancer. The first part of this thesis concentrates on parameter optimization and imaging quality of an MRE system. To do this, we developed a customized quality assurance phantom, and a series of quality control tests to characterize the MRE system. Our results demonstrated that through optimizing scan parameters, such as frequency and amplitude, MRE could provide a good qualitative elastogram for targets with different elasticity values and dimensions. The second part investigated the feasibility of integrating MRE into radiation therapy (RT) workflow. With the aid of a tissue-equivalent prostate phantom (embedded with three dominant intraprostatic lesions (DILs)), an MRE-integrated RT framework was developed. This framework contains a comprehensive scan protocol including Computed Tomography (CT) scan, combined MRI/MRE scans and a Volumetric Modulated Arc Therapy (VMAT) technique for treatment delivery. The results showed that using the comprehensive information could boost the MRE defined DILs to 84 Gy while keeping the remainder of the prostate to 78 Gy. Using a VMAT based technique allowed us to achieve a highly conformal plan (conformity index for the prostate and combined DILs was 0.98 and 0.91). Based on our feasibility study, we concluded that MRE data can be used for targeted radiation dose escalation. In summary, this thesis demonstrates that MRE is feasible for applications in radiation oncology

Nonrigid Registration of 3-Dimensional Images of the Carotid Arteries

Atherosclerosis at the carotid bifurcation can result in cerebral emboli, which in turn can block the blood supply to the brain causing ischemic strokes. Non-invasive imaging tools that characterize arterial wall, and atherosclerotic plaque structure and composition may help to determine the factors, which lead to the development of unstable lesions, and identify patients at risk of plaque disruption. Registration of 3D ultrasound (US) images of carotid plaque obtained at different time points, and with Magnetic Resonance (MR) images are required for monitoring of plaque changes in volume and surface morphology, and combining the complementary information of the two modalities for better understanding of factors that define plaque vulnerability. These registration techniques should be nonrigid, to remove deformations caused by bending and torsion in the neck during image acquisition sessions. The high degrees of freedom and large number of parameters associated with nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, high computational complexity, and low reliability. Thus, we used a “twisting and bending” model with only six parameters to model the natural movement of the neck for nonrigid registration. We calculated the Mean Registration Error (MRE) between the segmented vessel surfaces in the target and the registered images using the distance between “matched points” to evaluate registration results. We registered 3D US carotid images acquired at different head positions to simulate images acquired at different times, and obtained an average MRE of 0.8±0.3mm for nonrigid registration. We registered 3D US and MR carotid images at field strengths, 1.5T and 3.0T, of the same subject acquired on the same day, and obtained an average MRE of 1.4±0.3mm for 1.5T and 1.5±0.4mm for 3.0T, using nonrigid registration. Furthermore, we showed that the error metric used here was not significantly different from the widely accepted Target Registration Error (TRE)

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Elasticity parameter estimation is essential for generating accurate and controlled simulation results for computer animation and medical image analysis. However, finding the optimal parameters for a particular simulation often requires iterations of simulation, assessment, and adjustment and can become a tedious process. Elasticity values are especially important in medical image analysis, since cancerous tissues tend to be stiffer. Elastography is a popular type of method for finding stiffness values by reconstructing a dense displacement field from medical images taken during the application of forces or vibrations. These methods, however, are limited by the imaging modality and the force exertion or vibration actuation mechanisms, which can be complicated for deep-seated organs. In this thesis, I present a novel method for reconstructing elasticity parameters without requiring a dense displacement field or a force exertion device. The method makes use of natural deformations within the patient and relies on surface information from segmented images taken on different days. The elasticity value of the target organ and boundary forces acting on surrounding organs are optimized with an iterative optimizer, within which the deformation is always generated by a physically-based simulator. Experimental results on real patient data are presented to show the positive correlation between recovered elasticity values and clinical prostate cancer stages. Furthermore, to resolve the performance issue arising from the high dimensionality of boundary forces, I propose to use a reduced finite element model to improve the convergence of the optimizer. To find the set of bases to represent the dimensions for forces, a statistical training based on real patient data is performed. I demonstrate the trade-off between accuracy and performance by using different numbers of bases in the optimization using synthetic data. A speedup of more than an order of magnitude is observed without sacrificing too much accuracy in recovered elasticity.Doctor of Philosoph

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland