Registration of brain tumor images using hyper-elastic regularization

In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean strain energy density model as a regularizer in deformable registration framework. Implementations on synthetic and patient data provide promising results, which might have relevant use in clinical problems

Cellular automata segmentation of brain tumors on post contrast MR images

In this paper, we re-examine the cellular automata(CA) al- gorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmenta- tion method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Val- idation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type

Cellular automata segmentation of brain tumors on post contrast MR images

Abstract. In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type

A three-dimensional lead(II) polymer with bridging saccharinate and unusually coordinated acetate ligands - Synthesis, IR spectra, and crystal structure of [Pb(H2O)(mu-OAc)(mu-sac)](n)

Andac, Omer/0000-0003-3641-9690; Yilmaz, Veysel/0000-0002-2849-3332;WOS: 000182160800025The complex [Pb(H2O)(mu-OAc)(mu-sac)]n with acetate (OAc) and saccharinate (sac) ligands was characterized by IR, elemental analysis and X-ray crystallography The mixed-anion lead(II) complex crystallizes in the triclinic crystal system with the space group of P1. The single crystal X-ray analysis shows that the complex is a coordination polymer in which the lead(II) ions have a highly distorted pentagonal bipyramidal coordination geometry. Lead(II) ions are bridged by carboxylate groups in a zigzag arrangement forming one-dimensional infinite chains, which are also linked by sac bridges and aromatic pi-pi contacts between the adjacent phenyl rings of sac ligands, resulting in a three-dimensional network. One water molecule coordinates the lead(II) ion and also forms weak hydrogen bonds with the sulfonyl oxygen atoms of the neighboring sac ligands. The sac ligand acts as a bridging ligand through the nitrogen and carbonyl oxygen atoms, while the carboxylate moiety of the acetate ligand shows an unusual (bidentate, and bridging) coordination behaviour, which was observed for the first time in the structure

Functionally weighted track density imaging (İşlevsel ağırlıklı yolak yoğunluğu görüntüleme)

In this study, a method, which fuses white matter fiber anatomy determined by Diffusion Tensor MRI with cortical activities measured by Resting State fMRI, is proposed. Preliminary results obtained on a sample dataset showed that the produced maps are consistent with that reported in the literature, even for noisy data

Working memory dysfunction in delusional disorders: An fMRI investigation

Background: Delusional disorder (DD) is a rare and understudied psychiatric disorder. There is limited number of studies concerning cognitive characteristics in DD. Using an established working memory paradigm with variable levels of memory load, we investigated alterations in functional magnetic resonance imaging (fMRI) of brain regions in patients with DD

Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more timeconsuming, Cartesian-grid scheme. Importantly, we show that simple pre-and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods