Efficient automatic correction and segmentation based 3D visualization of magnetic resonance images

In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs. The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm (atd), and a new fast technique for interactive 3D visualization of segmented volumes called gravitational shading (gs). These newly developed algorithms provided a foundation for the automated MR processing pipeline incorporated into the UniViewer medical imaging software developed in our group and available to the public. This allowed the extensive testing and evaluation of the proposed techniques. Dsf was compared with two previously published methods on 17 digital image volumes. Dsf demonstrated faster correction speeds and uniform image quality improvement in this comparison. Dsf was the only algorithm that did not remove anatomic detail. Gs was compared with the previously published algorithm fsvr and produced rendering quality improvement while preserving real-time frame-rates. These results show that the automated pipeline design principles used in this dissertation provide necessary tools for development of a fast and effective system for the automated correction and visualization of digital MR image volumes

Clinical and morphological characteristics of myocardial infarction, developed after operations on the heart and lurge vessels

The aim of this study was to investigate the leading causes of the development of postoperative acute myocardial infarction and assess their significance in tanatogeneze. The foundation works as a clinical pathoanatomical comparisons carried out in 23 cases of death after adult cardiac operations in the offices and Vascular Surgery of Chelyabinsk Oblast Hospital for the period 2005-2007 d. It was found that the development of intra- and postoperative myocardial infarction in cardiosurgery practice is the result of a complex set of causal factors leading to the absolute coronary insufficiency. Lead importance here is the nature of the surgical pathology of the heart and large vessels, the actual surgery, the presence of risk factors such as angina, hypertension, diabetes mellitus.Целью исследования являлось изучение ведущих причин развития острых послеоперационных инфарктов миокарда и оценка их значимости в танатогенезе. Основой работы послужили клинико- атологоанатомические сопоставления, проведенные в 23 случаях смерти взрослых после операций в отделениях кардиохирургии и сосудистой хирургии Челябинской областной клинической больницы за период 2005-2007 гг. Установлено, что развитие интра- и послеоперационных инфарктов миокарда в кардиохирургической практике является следствием сложного комплекса причинных факторов, приводящих к абсолютной коронарной недостаточности. Ведущее значение здесь имеет характер хирургической патологии сердца и крупных сосудов, собственно оперативное вмешательство, наличие таких факторов риска как стенокардия, гипертоническая болезнь, сахарный диабет

A feasibility study to evaluate early treatment response of brain metastases one week after stereotactic radiosurgery using perfusion weighted imaging

BACKGROUND: To explore if early perfusion-weighted magnetic resonance imaging (PWI) may be a promising imaging biomarker to predict local recurrence (LR) of brain metastases after stereotactic radiosurgery (SRS). METHODS: This is a prospective pilot study of adult brain metastasis patients who were treated with SRS and imaged with PWI before and 1 week later. Relative cerebral blood volume (rCBV) parameter maps were calculated by normalizing to the mean value of the contralateral white matter on PWI. Cox regression was conducted to explore factors associated with time to LR, with Bonferroni adjusted p\u3c0.0006 for multiple testing correction. LR rates were estimated with the Kaplan-Meier method and compared using the log-rank test. RESULTS: Twenty-three patients were enrolled from 2013 through 2016, with 22 evaluable lesions from 16 patients. After a median follow-up of 13.1 months (range: 3.0-53.7), 5 lesions (21%) developed LR after a median of 3.4 months (range: 2.3-5.7). On univariable analysis, larger tumor volume (HR 1.48, 95% CI 1.02-2.15, p = 0.04), lower SRS dose (HR 0.45, 95% CI 0.21-0.97, p = 0.04), and higher rCBV at week 1 (HR 1.07, 95% CI 1.003-1.14, p = 0.04) had borderline association with shorter time to LR. Tumors \u3e2.0cm3 had significantly higher LR than if ≤2.0cm3: 54% vs 0% at 1 year, respectively, p = 0.008. A future study to confirm the association of early PWI and LR of the high-risk cohort of lesions \u3e2.0cm3 is estimated to require 258 patients. CONCLUSIONS: PWI at week 1 after SRS may have borderline association with LR. Tumors \u3c2.0cm3 have low risk of LR after SRS and may be low-yield for predictive biomarker studies. Information regarding sample size and potential challenges for future imaging biomarker studies may be gleaned from this pilot study

Ragweed pollen rain impact on allergy rate and severity in Krasnodar: a three-year non-randomised controlled study

Background. The main hay fever agent in Krasnodar Krai is ragweed pollen (Ambrosia gen.). An important alerting guide for medical practitioners and allergic citizens is the seasonal anthetic calendar and pollen peak times.Objectives. Obtaining of relevant data on ragweed pollen air contamination rate in Krasnodar in a three-year-dynamics (2018–2020) to estimate the anthetic activity correlation with abiotic and anthropogenic factors and the role of pollen indicators in allergic morbidity.Methods. We surveyed the daily average ragweed pollen values in Krasnodar air. Allergic medical visits were analysed in terms of the ragweed anthetic activity and pollen air contamination of the city.Results. A maximal daily average ragweed pollen peak occurs in August: 663.35 p.g./m3 in 2018, 209.89 p.g./m3 in 2019, 80.62 p.g./m3 in 2020. Numbers of medical visits for pollinosis per a selected municipal medical facility: 314 in 2018, 335 in 2019 and 146 in 2020, with a peak period in September--October. Analyses of the air pollution impact on ragweed pollen production revealed a correlation between the pollen rate and values of CO (correlation coefficient r-0.356), NH3 (r-0.198) and dust pollution (r-0.361) in July, August, September and October 2018–2020.Conclusion. Analyses of climatic factors uncovered clear patterns: strongest anthesis corresponds to minimal humidity (<60%), the pollen grain content diminishes with lower humidities dropping to minimal with precipitations and increases at temperatures 20 °C and above. No significant dependency was observed between air pollution and the allergic pollen content. Anthesis in ragweed moderately correlates with the rate of medical visits

Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation

OBJECTIVES: Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method-Heterogeneity Diffusion Imaging (HDI)-to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan. METHODS: Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction. RESULTS: The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV. CONCLUSIONS: Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI\u27s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors

Cognition based bTBI mechanistic criteria; a tool for preventive and therapeutic innovations

Blast-induced traumatic brain injury has been associated with neurodegenerative and neuropsychiatric disorders. To date, although damage due to oxidative stress appears to be important, the specific mechanistic causes of such disorders remain elusive. Here, to determine the mechanical variables governing the tissue damage eventually cascading into cognitive deficits, we performed a study on the mechanics of rat brain under blast conditions. To this end, experiments were carried out to analyse and correlate post-injury oxidative stress distribution with cognitive deficits on a live rat exposed to blast. A computational model of the rat head was developed from imaging data and validated against in vivo brain displacement measurements. The blast event was reconstructed in silico to provide mechanistic thresholds that best correlate with cognitive damage at the regional neuronal tissue level, irrespectively of the shape or size of the brain tissue types. This approach was leveraged on a human head model where the prediction of cognitive deficits was shown to correlate with literature findings. The mechanistic insights from this work were finally used to propose a novel helmet design roadmap and potential avenues for therapeutic innovations against blast traumatic brain injury