Bootstrap Optical Flow Confidence and Uncertainty Measure

We address the problem of estimating the uncertainty of optical flow algorithm results. Our method estimates the error magnitude at all points in the image. It can be used as a confidence measure. It is based on bootstrap resampling, which is a computational statistical inference technique based on repeating the optical flow calculation several times for different randomly chosen subsets of pixel contributions. As few as ten repetitions are enough to obtain useful estimates of geometrical and angular errors. For demonstration, we use the combined local-global optical flow method (CLG) which generalizes both Lucas-Kanade and Horn-Schunck type methods. However, the bootstrap method is very general and can be applied to almost any optical flow algorithm that can be formulated as a pixel-based minimization problem. We show experimentally on synthetic as well as real video sequences with known ground truth that the bootstrap method performs better than all other confidence measures tested

Fast no ground truth image registration accuracy evaluation: Comparison of bootstrap and Hessian approaches

Image registration algorithms provide a displacement field between two images. We consider the problem of estimating accuracy of the calculated displacement field from the input images only and without assuming any specific model for the deformation. We compare two algorithms: the first is based on bootstrap resampling, the second, new method, uses an estimate of the criterion Hessian matrix. We also present a block matching strategy using multiple window sizes where the final result is obtained by fusion of partial results controlled by the accuracy estimates for the blocks involved. Both accuracy estimation methods and the new registration strategy are experimentally compared on synthetic as well as real medical ultrasound data

Realistic Total-Body J-PET Geometry Optimization -- Monte Carlo Study

Total-Body PET is one of the most promising medical diagnostics modalities. The high sensitivity provided by Total-Body technology can be advantageous for novel tomography methods like positronium imaging. Several efforts are ongoing to lower the price of the TB-PET systems. Among the alternatives, the Jagiellonian PET (J-PET) technology, based on plastic scintillator strips, offers a low-cost alternative. The work aimed to compare five Total-Body J-PET geometries as a possible next generation J-PET scanner design. We present comparative studies of performance characteristics of the cost-effective Total-Body PET scanners using J-PET technology. We investigated in silico five Total-Body scanner geometries. Monte Carlo simulations of the XCAT phantom, the 2-meter sensitivity line source and positronium sensitivity phantoms were performed. We compared the sensitivity profiles for 2-gamma and 3-gamma tomography, relative cost of the setups and performed quantitative analysis of the reconstructed images. The analysis of the reconstructed XCAT images reveals the superiority of the seven-ring scanners over the three-ring setups. However, the three-ring scanners would be approximately 2-3 times cheaper. The peak sensitivity values for two-gamma vary from 20 to 34 cps/kBq. The sensitivity curves for the positronium tomography have a similar shape to the two-gamma sensitivity profiles. The peak values are lower compared to the two-gamma cases, from about 20-28 times, with a maximum of 1.66 cps/kBq. The results show the feasibility of multi-organ imaging of all the systems to be considered for the next generation of TB J-PET designs. The relative cost for all the scanners is about 10-4 times lower compared to the cost of the uExplorer. These properties coupled together with J-PET cost-effectiveness, make the J-PET technology an attractive solution for broad application in clinics

Deformation Estimation and Assessment of Its Accuracy in Ultrasound Images

This thesis aims to address two problems; one in ultrasound elastography and one in image registration. The first problem entails estimation of tissue displacement in Ultrasound Elastography (UE). UE is an emerging technique used to estimate mechanical properties of tissue. It involves calculating the displacement field between two ultrasound Radio Frequency (RF) frames taken before and after a tissue deformation. A common way to calculate the displacement is to use correlation based approaches. However, these approaches fail in the presence of signal decorrelation. To address this issue, Dynamic Programming was used to find the optimum displacement using all the information on the RF-line. Although taking this approach improved the results, some failures persisted. In this thesis, we have formulated the DP method on a tree. Doing so allows for more information to be used for estimating the displacement and therefore reducing the error. We evaluated our method on simulation, phantom and real patient data. Our results shows that the proposed method outperforms the previous method in terms of accuracy with small added computational cost. In this work, we also address a problem in image registration. Although there is a vast literature in image registration, quality evaluation of registration is a field that has not received as much attention. This evaluation becomes even more crucial in medical imaging due to the sensitive nature of the field. We have addressed the said problem in the context of ultrasound guided radiotherapy. Image guidance has become an important part of radiotherapy wherein image registration is a critical step. Therefore, an evaluation of this registration can play an important role in the outcome of the therapy. In this work, we propose using both bootstrapping and supervised learning methods to evaluate the registration. We test our methods on 2D and 3D data acquired from phantom and patients. According to our results, both methods perform well while having advantages and disadvantages over one another. Supervised learning methods offer more accuracy and less computation time. On the other hand, for bootstrapping, no training data is required and also offers more sensitivity