Feasibility of magnetic resonance imaging-based radiation therapy for brain tumour treatment

Purpose : The increasing use of MRI alongside CT images has brought about growing interest in trying to determine radiation attenuation information based on MR images only. The primary aim of this thesis is, therefore, to determine what head tissue compartments need to have separate HU values in order to obtain sufficient RT planning accuracy. This can serve as input for an MR-based classification thus enabling pseudo-CT generation in an MR-only RT workflow. Methods: To achieve this target, flattened (stratified) CT images (fCT) were generated and compared to the original CT images. Mean (ME) and mean absolute (MAE) errors were used for the fCT quality assessment, as was dose comparisons. 70 CT-based RT plans were generated and the dose distributions compared to those obtained when using the different fCT versions in place of the original CT images. The dose agreement was assessed using DVH and 1%/1mm gamma analysis. Results: The lowest MAE of 59.63 HU was calculated for an fCT8 version. DVH analysis showed low differences in the range between 3% (water-filled fCT) and 0.05% depending on the tissue stratification of the fCT version. 1%/1mm gamma analysis correctly identified plans where insufficiently fine-grained tissue classification was the main reason for dose discrepancy. The best RT planning accuracy was obtained for the fCT5 with segmented air cavities, fat, water-rich tissue, spongy, and compact bone, and for the fCT8 where also the brain tissue was stratified. Conclusions: The small differences in dose accuracy between CT and fCT images shows the feasibility of using MR-only RT planning for the brain. Nonetheless, other aspects of the MR-only workflow, such as patient positioning, as well as the impact of e.g. the surgical incisions in the skull should be subject to further research

Development of the VHP-Female Full-Body Computational Model and Its Applications for Biomedical Electromagnetic Modeling

Computational modeling offers better insight into a wide range of bioelectrical and biomechanical problems with improved tools for the design of medical devices and the diagnosis of pathologies. Electromagnetic modeling at low and high frequencies is particularly necessary. Modeling electromagnetic, structural, thermal, and acoustic response of the human body to different internal and external stimuli is limited by the availability of numerically efficient computational human models. This study describes the development to date of a computational full-body human model - Visible Human Project (VHP) - Female Model. Its unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D. This study also describes progress made to date in using the newly developed tools for segmentation. A visualization tool is implemented within MATLAB and is based on customized version of the constrained 2D Delaunay triangulation method for intersecting objects. This thesis applies a VHP - Female Model to a specific application, transcranial Direct Current Stimulation (tDCS). Transcranial Direct Current Stimulation has been beneficial in the stimulation of cortical activity and treatment of neurological disorders in humans. The placement of electrodes, which is cephalic versus extracephalic montages, is studied for optimal targeting of currents for a given functional area. Given the difficulty of obtaining in vivo measurements of current density, modeling of conventional and alternative electrode montages via the FEM has been utilized to provide insight into the tDCS montage performance. An insight into future work and potential areas of research, such as study of bone quality have been presented too

Validation of Subject Specific Computed Tomography-based Finite Element Models of the Human Proximal Tibia using Full-field Experimental Displacement Measurements from Digital Volume Correlation

Quantitative computed tomography-based finite element (QCT-FE) modeling is a computational tool for predicting bone’s response to applied load, and is used by musculoskeletal researchers to better understand bone mechanics and their role in joint health. Decisions made at the modeling stage, such as the method for assigning material properties, can dictate model accuracy. Predictions of surface strains/stiffness from QCT-FE models of the proximal tibia have been validated against experiment, yet it is unclear whether these models accurately predict internal bone mechanics (displacement). Digital volume correlation (DVC) can measure internal bone displacements and has been used to validate FE models of bone; though, its use has been limited to small specimens. The objectives of this study were to 1) establish a methodology for high-resolution peripheral QCT (HR-pQCT) scan acquisition and image processing resulting in low DVC displacement measurement error in long human bones, and 2) apply different density-modulus relationships and material models from the literature to QCT-FE models of the proximal tibia and identify those approaches which best predicted experimentally measured internal bone displacements and related external reaction forces, with highest explained variance and least error. Using a modified protocol for HR-pQCT, DVC displacement errors for large scan volumes were less than 19μm (0.5 voxels). Specific trabecular and cortical models from the literature were identified which resulted in the most accurate QCT-FE predictions of internal displacements (RMSE%=3.9%, R2>0.98) and reaction forces (RMSE%=12.2%, R2=0.78). This study is the first study to quantify experimental displacements inside a long human bone using DVC. It is also the first study to assess the accuracy of QCT-FE predicted internal displacements in the tibia. Our results indicate that QCT-FE models of the tibia offer reasonably accurate predictions of internal bone displacements and reaction forces for use in studying bone mechanics and joint health

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Facial soft tissue segmentation

The importance of the face for socio-ecological interaction is the cause for a high demand on any surgical intervention on the facial musculo-skeletal system. Bones and soft-tissues are of major importance for any facial surgical treatment to guarantee an optimal, functional and aesthetical result. For this reason, surgeons want to pre-operatively plan, simulate and predict the outcome of the surgery allowing for shorter operation times and improved quality. Accurate simulation requires exact segmentation knowledge of the facial tissues. Thus semi-automatic segmentation techniques are required. This thesis proposes semi-automatic methods for segmentation of the facial soft-tissues, such as muscles, skin and fat, from CT and MRI datasets, using a Markov Random Fields (MRF) framework. Due to image noise, artifacts, weak edges and multiple objects of similar appearance in close proximity, it is difficult to segment the object of interest by using image information alone. Segmentations would leak at weak edges into neighboring structures that have a similar intensity profile. To overcome this problem, additional shape knowledge is incorporated in the energy function which can then be minimized using Graph-Cuts (GC). Incremental approaches by incorporating additional prior shape knowledge are presented. The proposed approaches are not object specific and can be applied to segment any class of objects be that anatomical or non-anatomical from medical or non-medical image datasets, whenever a statistical model is present. In the first approach a 3D mean shape template is used as shape prior, which is integrated into the MRF based energy function. Here, the shape knowledge is encoded into the data and the smoothness terms of the energy function that constrains the segmented parts to a reasonable shape. In the second approach, to improve handling of shape variations naturally found in the population, the fixed shape template is replaced by a more robust 3D statistical shape model based on Probabilistic Principal Component Analysis (PPCA). The advantages of using the Probabilistic PCA are that it allows reconstructing the optimal shape and computing the remaining variance of the statistical model from partial information. By using an iterative method, the statistical shape model is then refined using image based cues to get a better fitting of the statistical model to the patient's muscle anatomy. These image cues are based on the segmented muscle, edge information and intensity likelihood of the muscle. Here, a linear shape update mechanism is used to fit the statistical model to the image based cues. In the third approach, the shape refinement step is further improved by using a non-linear shape update mechanism where vertices of the 3D mesh of the statistical model incur the non-linear penalty depending on the remaining variability of the vertex. The non-linear shape update mechanism provides a more accurate shape update and helps in a finer shape fitting of the statistical model to the image based cues in areas where the shape variability is high. Finally, a unified approach is presented to segment the relevant facial muscles and the remaining facial soft-tissues (skin and fat). One soft-tissue layer is removed at a time such as the head and non-head regions followed by the skin. In the next step, bones are removed from the dataset, followed by the separation of the brain and non-brain regions as well as the removal of air cavities. Afterwards, facial fat is segmented using the standard Graph-Cuts approach. After separating the important anatomical structures, finally, a 3D fixed shape template mesh of the facial muscles is used to segment the relevant facial muscles. The proposed methods are tested on the challenging example of segmenting the masseter muscle. The datasets were noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. dental fillings and dental implants. Qualitative and quantitative experimental results show that by incorporating prior shape knowledge leaking can be effectively constrained to obtain better segmentation results

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields