Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields.

BACKGROUND AND OBJECTIVE: We focus on three-dimensional higher-order tensorial (HOT) images using Finsler geometry. In biomedical image analysis, these images are widely used, and they are based on the diffusion profiles inside the voxels. The diffusion information is stored in the so-called diffusion tensor D. Our objective is to present new methods revealing the architecture of neural fibers in presence of crossings and high curvatures. After tracking the fibers, we achieve direct 3D image segmentation to analyse the brain's white matter structures. METHODS: To deal with the construction of the underlying fibers, the inverse of the second-order diffusion tensor D, understood as the metric tensor D-1, is commonly used in DTI modality. For crossing and highly curved fibers, higher order tensors are more relevant, but it is challenging to find an analogue of such an inverse in the HOT case. We employ an innovative approach to metrics based on higher order tensors to track the fibers properly. We propose to feed the tracked fibers as the internal initial contours in an efficient version of 3D segmentation. RESULTS: We propose a brand-new approach to the inversion of a diffusion HOT, and an effective way of fiber tracking in the Finsler setting, based on innovative classification of the individual voxels. Thus, we can handle complex structures with high curvatures and crossings, even in the presence of noise. Based on our novel tractography approach, we also introduce a new segmentation method. We feed the detected fibers as the initial position of the contour surfaces to segment the image using a relevant active contour method (i.e., initiating the segmentation from inside the structures). CONCLUSIONS: This is a pilot work, enhancing methods for fiber tracking and segmentation. The implemented algorithms were successfully tested on both synthetic and real data. The new features make our algorithms robust and fast, and they allow distinguishing individual objects in complex structures, even under noise.

Role of Neuroimaging in COVID 19 Infection-A Retrospective Study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the family of coronaviruses, induces COVID-19 disease which is predominantly a respiratory illness. Several authors have reported neurological manifestations such as acute venous and arterial strokes and rarely acute disseminated encephalomyelitis, hemorrhagic posterior reversible encephalopathy syndrome, and acute inflammatory demyelinating polyneuropathy by studying magnetic resonance imaging of brain parenchymal signal abnormalities in patients with COVID-19. Owing to sparsity of literature on extrapulmonary manifestation of COVID, it is imperative to study the mechanism of neuronal invasion and manifestations of COVID-19. This study aimed to assess and correlate neurological manifestations in patients with COVID using imaging findings on computed tomography and magnetic resonance images. A retrospective observational study was conducted among 80 patients who tested positive for COVID-19 disease via a reverse transcription-polymerase chain reaction (RT-PCR) test in a tertiary care teaching institute in Pimpri, India. Most patients presented with acute arterial and venous strokes. Arterial stroke was the most prevalent finding; nearly 42.50% showed imaging findings consistent with arterial stroke; 25% patients had findings of venous stroke. Meningitis and spontaneous hemorrhage were found in 10% of cases. Rare complications such as acute disseminated encephalomyelitis comprised 7.50%, and encephalitis was found in 5%. This study highlights the need for increased neuroimaging in suspected patients to alert the treating physicians about the neurological complications. As the conventional RT-PCR and serum tests are unremarkable in the early onset of COVID-19, it is important to have a multidisciplinary approach and more neurology consultations in COVID-19 suspected patients with an emphasis on prompt neuroimaging to prevent potential life-threatening complications. To alert the attending physician about neurological involvement in patients with COVID-19, it is vital that nursing staff is aware about the neurological manifestations of COVID-19 so that they can recognize change in patients' neurological status without any undue time delay.

Geodesic fiber tracking in white matter using activation function.

BACKGROUND AND OBJECTIVE: The geodesic ray-tracing method has shown its effectiveness for the reconstruction of fibers in white matter structure. Based on reasonable metrics on the spaces of the diffusion tensors, it can provide multiple solutions and get robust to noise and curvatures of fibers. The choice of the metric on the spaces of diffusion tensors has a significant impact on the outcome of this method. Our objective is to suggest metrics and modifications of the algorithms leading to more satisfactory results in the construction of white matter tracts as geodesics. METHODS: Starting with the DTI modality, we propose to rescale the initially chosen metric on the space of diffusion tensors to increase the geodetic cost in the isotropic regions. This change should be conformal in order to preserve the angles between crossing fibers. We also suggest to enhance the methods to be more robust to noise and to employ the fourth order tensor data in order to handle the fiber crossings properly. RESULTS: We propose a way to choose the appropriate conformal class of metrics where the metric gets scaled according to tensor anisotropy. We use the logistic functions, which are commonly used in statistics as cumulative distribution functions. To prevent deviation of geodesics from the actual paths, we propose a hybrid ray-tracing approach. Furthermore, we suggest how to employ diagonal projections of 4th order tensors to perform fiber tracking in crossing regions. CONCLUSIONS: The algorithms based on the newly suggested methods were succesfuly implemented, their performance was tested on both synthetic and real data, and compared to some of the previously known approaches.