Generating Synthetic Computed Tomography and Synthetic Magnetic Resonance (sMR: sT1w/sT2w) Images of the Brain Using Atlas-Based Method

Introduction: Nowadays, magnetic resonance imaging (MRI) in combination with computed-tomography (CT) is increasingly being used in radiation therapy planning. MR and CT images are applied to determine the target volume and calculate dose distribution, respectively. Since the use of these two imaging modalities causes registration uncertainty and increases department workload and costs, in this study, brain synthetic CT (sCT) and synthetic MR (sMR: sT1w/sT2w) images were generated using Atlas-based method; consequently, just one type of image (CT or MR) is taken from the patient. Material and Methods: The dataset included MR and CT paired images from 10 brain radiotherapy (RT) patients. To generate sCT/sMR images, first each MR/CT Atlas was registered to the MR/CT target image, the resulting transformation was applied to the corresponding CT/MR Atlas, which created the set of deformed images. Then, the deformed images were fused to generate a single sCT/sMR image, and finally, the sCT/sMR images were compared to the real CT/MR images using the mean absolute error (MAE). Results: The results showed that the MAE of sMR (sT1w/sT2w) was less than that of sCT images. Moreover, sCT images based on T1w were in better agreement with real CT than sCT-based T2w. In addition, sT1w images represented a lower MAE relative to sT2w. Conclusion: The CT target image was more successful in transferring the geometry of the brain tissues to the synthetic image than MR target

Development of a statistical shape and appearance model of the skull from a South African population

Statistical shape models (SSMs) and statistical appearance models (SAMs) have been applied in medical analysis such as in surgical planning, finite element analysis, model-based segmentation, and in the fields of anthropometry and forensics. Similar applications can make use of SSMs and SAMs of the skull. A combination of the SSM and SAM of the skull can also be used in model-based segmentation. This document presents the development of a SSM and a SAM of the human skull from a South African population, using the Scalismo software package. The SSM development pipeline was composed of three steps: 1) Image data segmentation and processing; 2) Development of a free-form deformation (FFD) model for establishing correspondence across the training dataset; and 3) Development and validation of a SSM from the corresponding dataset. The SSM was validated using the leave one-out cross-validation method. The first eight principal components of the SSM represented 92.13% of the variation in the model. The generality of the model in terms of the Hausdorff distance between a new shape generated by the SSM and instances of the SSM had a steady state value of 1.48mm. The specificity of the model (in terms of Hausdorff distance) had a steady state value of 2.04mm. The SAM development pipeline involved four steps: 1) Volumetric mesh generation of the reference mesh to be used in establishing volumetric correspondence; 2) Sampling of intensity values from original computed tomography (CT) images using the in-correspondence volumetric meshes; and 3) Development of a SAM from the in-correspondence intensity values. A complete validation of the SAM was not possible due to limitations of the Scalismo software. As a result, only the shapes of the incomplete skulls were reconstructed and thereby validated. The amount of missing detail, as represented by absent landmarks, affected the registration results. Complete validation of the SAM is recommended as future work, via the use of a combined shape and intensity model (SSIM)

A Demons algorithm for image registration with locally adaptive regularization.

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules

A Demons algorithm for image registration with locally adaptive regularization.

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules

Development of acquisition system and algorithms for registration towards modeling displacement and deformation of the contour on the digital image

Centralna tema ovog rada je primena sistema za akviziciju slike u cilju procene i modelovanja deformacija i pomeranja objekata koji su snimljeni. Glavna metoda koja je pri tom korišćena je metoda registracije slika. Sam postupak registracije podrazumeva skup algoritama i metoda kojim se vrši pronalaženje transformacije koja preslikava prostor jedne slike u prostor druge. Ukoliko se radi o slikama istog objekta u različitim položajima ili konfiguracijama moguće je odrediti pomeranja i deformacije željene tačke poznavanjem ove transformacije. U radu su opisani već postojeći algoritmi, sa svojim najznačajnijim svojstvima. Na bazi ovih osobina razvijen je metod registracije baziran na rešavanju Laplasove jednačine za elektrostatičko polje. Ovakav pristup je moguć zahvaljujući činjenici da gradijent deformacija odgovara linijama elektrostatičkog polja, koje je dobijeno rešavanjem Laplasove jednačine i zadovoljava sva bitna svojstva koja treba da ima registraciona transformacija. Ove osobine se odnose na glatkost polja deformacije, postojanje inverzne funkcije i zabranu ukrštanja linija polja. Sam postupak rešavanja navedene jednačine i određivanje tražene transformaicje sproveden je primenom metode konačnih elemenata pri čemu je korišćena formulacija minimuma energija sistema. Jedna od inspiracija za rad na metodama registracije slike bio je i problem procene mehaničkih karakteristika tkiva aorte sa aneurizmom. U radu je opisana realizacija i način rada sistema koji je iskorišćen za karakterizaciju mehaničkih svojstava aorte, koji kao izlazne podatke daje informaciju o pomeranjima skupa tačaka tkiva kao i o vrednostima pritiska fluida koji izaziva ta pomeranja. Deformacije su procenjene primenom metoda segmentacije slike i izdvajanja ivica nakon čega je primenjen metod registracije slike kojom je određena deformacija tačaka tkiva u određenim vremenskim trenucima. Na osnovu ovih vrednosti primenom genetskog algoritma određena je vrednost Jangovog modula tkiva pri čemu je korišćen mehanički model deformacije tkiva. Analiza hoda upotrebom slika hoda je takođe jedan od izazova kada je u pitanju neinvazivna dijagnostika i praćenje stanja dijagnostifiko- vanih kao i zdravih subjekata. U ovom radu je prikazan postupak određivanja mehaničkog naprezanja hrskavice primenom slika snimljenih kamerom i vrednostima sile normalne reakcije podloge koja nastaje tokom hoda. Za procenu deformacija hrskavice korišćeni su algoritmi registracije slike između slika dobijenih sa kamere i slika dobijenih kompjuterizovanom tomografijom. Postupkom optimizacije procenjeni su i mehanički parametri hrskavice (Jangov modul i Poasonov koeficijent).The main aim of this thesis is the application of image acquisition system for the purpose of assessing and modeling the deformation and displacement of the objects acquired in digital images. The technique used in the study is method of image registration. The procedure of the registration includes a set of algorithms and methods which performs the assessment of transformation that maps the space of one image to another one. If there are images of the same object in different positions or configurations it is possible to determine the displacement and deformation of the desired point of understanding this transformation. The thesis describes the existing algorithms, along with their most important properties. The novel algorithms for image registration is developed based of solving the Laplace equation for electrostatic field. This approach is possible due to the fact that the transformation which corresponds to the deformation gradient field lines of the electrostatic field, which is obtained by solving the Laplace equation satisfies all essential features that should have the registration transformation. These properties are related to the smoothness of the deformation field, the existence of an inverse function of the prohibition of crossing the line field. The procedure for solving the above equation and determining the required transformation was conducted using finite element method with use of a formulation of minimum energy of the system. The motivation for this thesis was consideration problem of evaluation mechanical properties of tissues affected aortic aneurysm. The paper describes the implementation and operation of the system that was used to characterize the mechanical properties of the aorta, which as output data provides information about a set of deformation points on the tissue surface as well as the values of applied fluid pressure. Strains at the certain moment of time were estimated using the image segmentation method and edges extraction, and finally image registration is applied. Using strain values in the mechanical model of tissue, and genetic algorithm as optimization technique, the Young's modulus is assessment. Gait analysis based on the images data is also one of the challenges in non-invasive diagnosis and monitoring of both diagnosed patients and healthy subjects.. This thesis presents a method for determining the mechanical stress of the cartilage using the camera image, and the values of the normal ground reaction force, which is generated during the walk, for assessment of cartilage deformation algorithms were used image registration of images obtained from the camera and the images obtained by computed tomography. Mechanical parameters of cartilage (Young's modulus and Poisson's ratio) are evaluated in the optimization process

Registration of magnetic resonance and ultrasound images for guiding prostate cancer interventions

Prostate cancer is a major international health problem with a large and rising incidence in many parts of the world. Transrectal ultrasound (TRUS) imaging is used routinely to guide surgical procedures, such as needle biopsy and a number of minimally-invasive therapies, but its limited ability to visualise prostate cancer is widely recognised. Magnetic resonance (MR) imaging techniques, on the other hand, have recently been developed that can provide clinically useful diagnostic information. Registration (or alignment) of MR and TRUS images during TRUS-guided surgical interventions potentially provides a cost-effective approach to augment TRUS images with clinically useful, MR-derived information (for example, tumour location, shape and size). This thesis describes a deformable image registration framework that enables automatic and/or semi-automatic alignment of MR and 3D TRUS images of the prostate gland. The method combines two technical developments in the field: First, a method for constructing patient-specific statistical shape models of prostate motion/deformation, based on learning from finite element simulations of gland motion using geometric data from a preoperative MR image, is proposed. Second, a novel “model-to-image” registration framework is developed to register this statistical shape model automatically to an intraoperative TRUS image. This registration approach is implemented using a novel model-to-image vector alignment (MIVA) algorithm, which maximises the likelihood of a particular instance of a statistical shape model given a voxel-intensity-based feature vector that represents an estimate of the surface normal vectors at the boundary of the organ in question. Using real patient data, the MR-TRUS registration accuracy of the new algorithm is validated using intra-prostatic anatomical landmarks. A rigorous and extensive validation analysis is also provided for assessing the image registration experiments. The final target registration error after performing 100 MR–TRUS registrations for each patient have a median of 2.40 mm, meaning that over 93% registrations may successfully hit the target representing a clinically significant lesion. The implemented registration algorithms took less than 30 seconds and 2 minutes for manually defined point- and normal vector features, respectively. The thesis concludes with a summary of potential applications and future research directions